Designing neural networks that process mean values of random variables

International Nuclear Information System (INIS)

Barber, Michael J.; Clark, John W.

2014-01-01

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

Designing neural networks that process mean values of random variables

Energy Technology Data Exchange (ETDEWEB)

Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

2014-06-13

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

The guitar chord-generating algorithm based on complex network

Science.gov (United States)

Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

2016-02-01

This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

Unraveling spurious properties of interaction networks with tailored random networks.

Directory of Open Access Journals (Sweden)

Stephan Bialonski

Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Solution-Processed Carbon Nanotube True Random Number Generator.

Science.gov (United States)

Gaviria Rojas, William A; McMorrow, Julian J; Geier, Michael L; Tang, Qianying; Kim, Chris H; Marks, Tobin J; Hersam, Mark C

2017-08-09

With the growing adoption of interconnected electronic devices in consumer and industrial applications, there is an increasing demand for robust security protocols when transmitting and receiving sensitive data. Toward this end, hardware true random number generators (TRNGs), commonly used to create encryption keys, offer significant advantages over software pseudorandom number generators. However, the vast network of devices and sensors envisioned for the "Internet of Things" will require small, low-cost, and mechanically flexible TRNGs with low computational complexity. These rigorous constraints position solution-processed semiconducting single-walled carbon nanotubes (SWCNTs) as leading candidates for next-generation security devices. Here, we demonstrate the first TRNG using static random access memory (SRAM) cells based on solution-processed SWCNTs that digitize thermal noise to generate random bits. This bit generation strategy can be readily implemented in hardware with minimal transistor and computational overhead, resulting in an output stream that passes standardized statistical tests for randomness. By using solution-processed semiconducting SWCNTs in a low-power, complementary architecture to achieve TRNG, we demonstrate a promising approach for improving the security of printable and flexible electronics.

Gradient networks on uncorrelated random scale-free networks

International Nuclear Information System (INIS)

Pan Guijun; Yan Xiaoqing; Huang Zhongbing; Ma Weichuan

2011-01-01

Uncorrelated random scale-free (URSF) networks are useful null models for checking the effects of scale-free topology on network-based dynamical processes. Here, we present a comparative study of the jamming level of gradient networks based on URSF networks and Erdos-Renyi (ER) random networks. We find that the URSF networks are less congested than ER random networks for the average degree (k)>k c (k c ∼ 2 denotes a critical connectivity). In addition, by investigating the topological properties of the two kinds of gradient networks, we discuss the relations between the topological structure and the transport efficiency of the gradient networks. These findings show that the uncorrelated scale-free structure might allow more efficient transport than the random structure.

Generative adversarial networks for brain lesion detection

Science.gov (United States)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Unified Model for Generation Complex Networks with Utility Preferential Attachment

International Nuclear Information System (INIS)

Wu Jianjun; Gao Ziyou; Sun Huijun

2006-01-01

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.

Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

CERN Document Server

Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

2016-01-01

This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

Very late stent thrombosis with second generation drug eluting stents compared to bare metal stents: Network meta-analysis of randomized primary percutaneous coronary intervention trials.

Science.gov (United States)

Philip, Femi; Stewart, Susan; Southard, Jeffrey A

2016-07-01

The relative safety of drug-eluting stents (DES) and bare-metal stents (BMS) in primary percutaneous coronary intervention (PPCI) in ST elevation myocardial infarction (STEMI) continues to be debated. The long-term clinical outcomes between second generation DES and BMS for primary percutaneous coronary intervention (PCI) using network meta-analysis were compared. Randomized controlled trials comparing stent types (first generation DES, second generation DES, or BMS) were considered for inclusion. A search strategy used Medline, Embase, Cochrane databases, and proceedings of international meetings. Information about study design, inclusion criteria, and sample characteristics were extracted. Network meta-analysis was used to pool direct (comparison of second generation DES to BMS) and indirect evidence (first generation DES with BMS and second generation DES) from the randomized trials. Twelve trials comparing all stents types including 9,673 patients randomly assigned to treatment groups were analyzed. Second generation DES was associated with significantly lower incidence of definite or probable ST (OR 0.59, 95% CI 0.39-0.89), MI (OR 0.59, 95% CI 0.39-0.89), and TVR at 3 years (OR 0.50: 95% CI 0.31-0.81) compared with BMS. In addition, there was a significantly lower incidence of MACE with second generation DES versus BMS (OR 0.54, 95% CI 0.34-0.74) at 3 years. These were driven by a higher rate of TVR, MI and stent thrombosis in the BMS group at 3 years. There was a non-significant reduction in the overall and cardiac mortality [OR 0.83, 95% CI (0.60-1.14), OR 0.88, 95% CI (0.6-1.28)] with the use of second generation DES versus BMS at 3 years. Network meta-analysis of randomized trials of primary PCI demonstrated lower incidence of MACE, MI, TVR, and stent thrombosis with second generation DES compared with BMS. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Effects of traffic generation patterns on the robustness of complex networks

Science.gov (United States)

Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

2018-02-01

Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

Quantum random number generator

Science.gov (United States)

Pooser, Raphael C.

2016-05-10

A quantum random number generator (QRNG) and a photon generator for a QRNG are provided. The photon generator may be operated in a spontaneous mode below a lasing threshold to emit photons. Photons emitted from the photon generator may have at least one random characteristic, which may be monitored by the QRNG to generate a random number. In one embodiment, the photon generator may include a photon emitter and an amplifier coupled to the photon emitter. The amplifier may enable the photon generator to be used in the QRNG without introducing significant bias in the random number and may enable multiplexing of multiple random numbers. The amplifier may also desensitize the photon generator to fluctuations in power supplied thereto while operating in the spontaneous mode. In one embodiment, the photon emitter and amplifier may be a tapered diode amplifier.

Quantum random number generator

Science.gov (United States)

Soubusta, Jan; Haderka, Ondrej; Hendrych, Martin

2001-03-01

Since reflection or transmission of a quantum particle on a beamsplitter is inherently random quantum process, a device built on this principle does not suffer from drawbacks of neither pseudo-random computer generators or classical noise sources. Nevertheless, a number of physical conditions necessary for high quality random numbers generation must be satisfied. Luckily, in quantum optics realization they can be well controlled. We present an easy random number generator based on the division of weak light pulses on a beamsplitter. The randomness of the generated bit stream is supported by passing the data through series of 15 statistical test. The device generates at a rate of 109.7 kbit/s.

Intrinsically-generated fluctuating activity in excitatory-inhibitory networks

Science.gov (United States)

Mastrogiuseppe, Francesca; Ostojic, Srdjan

2017-01-01

Recurrent networks of non-linear units display a variety of dynamical regimes depending on the structure of their synaptic connectivity. A particularly remarkable phenomenon is the appearance of strongly fluctuating, chaotic activity in networks of deterministic, but randomly connected rate units. How this type of intrinsically generated fluctuations appears in more realistic networks of spiking neurons has been a long standing question. To ease the comparison between rate and spiking networks, recent works investigated the dynamical regimes of randomly-connected rate networks with segregated excitatory and inhibitory populations, and firing rates constrained to be positive. These works derived general dynamical mean field (DMF) equations describing the fluctuating dynamics, but solved these equations only in the case of purely inhibitory networks. Using a simplified excitatory-inhibitory architecture in which DMF equations are more easily tractable, here we show that the presence of excitation qualitatively modifies the fluctuating activity compared to purely inhibitory networks. In presence of excitation, intrinsically generated fluctuations induce a strong increase in mean firing rates, a phenomenon that is much weaker in purely inhibitory networks. Excitation moreover induces two different fluctuating regimes: for moderate overall coupling, recurrent inhibition is sufficient to stabilize fluctuations; for strong coupling, firing rates are stabilized solely by the upper bound imposed on activity, even if inhibition is stronger than excitation. These results extend to more general network architectures, and to rate networks receiving noisy inputs mimicking spiking activity. Finally, we show that signatures of the second dynamical regime appear in networks of integrate-and-fire neurons. PMID:28437436

Accelerating Pseudo-Random Number Generator for MCNP on GPU

Science.gov (United States)

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Deng, Li; Gong, Zhenghu

2010-09-01

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

Random sampling of elementary flux modes in large-scale metabolic networks.

Science.gov (United States)

Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugénio C; Rocha, Isabel

2012-09-15

The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks. Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler. dmachado@deb.uminho.pt Supplementary data are available at Bioinformatics online.

Creating Turbulent Flow Realizations with Generative Adversarial Networks

Science.gov (United States)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

Humans can consciously generate random number sequences: a possible test for artificial intelligence.

Science.gov (United States)

Persaud, Navindra

2005-01-01

Computer algorithms can only produce seemingly random or pseudorandom numbers whereas certain natural phenomena, such as the decay of radioactive particles, can be utilized to produce truly random numbers. In this study, the ability of humans to generate random numbers was tested in healthy adults. Subjects were simply asked to generate and dictate random numbers. Generated numbers were tested for uniformity, independence and information density. The results suggest that humans can generate random numbers that are uniformly distributed, independent of one another and unpredictable. If humans can generate sequences of random numbers then neural networks or forms of artificial intelligence, which are purported to function in ways essentially the same as the human brain, should also be able to generate sequences of random numbers. Elucidating the precise mechanism by which humans generate random number sequences and the underlying neural substrates may have implications in the cognitive science of decision-making. It is possible that humans use their random-generating neural machinery to make difficult decisions in which all expected outcomes are similar. It is also possible that certain people, perhaps those with neurological or psychiatric impairments, are less able or unable to generate random numbers. If the random-generating neural machinery is employed in decision making its impairment would have profound implications in matters of agency and free will.

Embedded generation and network management issues

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-07-01

This report focuses on the characteristics of power generators that are important to accommodation in a distribution system. Part 1 examines the differences between transmission and distribution systems, and issues such as randomness, diversity, predictability, and controllability associated with accommodation in a distribution system. Part 2 concentrates on technical and operational issues relating to embedded generation, and the possible impact of the New Electricity Trading Arrangements. Commercial issues, contractual relationships for network charging and provision of services, and possible ways forward are examined in the last three parts of the report.

Random number generation

International Nuclear Information System (INIS)

Coveyou, R.R.

1974-01-01

The subject of random number generation is currently controversial. Differing opinions on this subject seem to stem from implicit or explicit differences in philosophy; in particular, from differing ideas concerning the role of probability in the real world of physical processes, electronic computers, and Monte Carlo calculations. An attempt is made here to reconcile these views. The role of stochastic ideas in mathematical models is discussed. In illustration of these ideas, a mathematical model of the use of random number generators in Monte Carlo calculations is constructed. This model is used to set up criteria for the comparison and evaluation of random number generators. (U.S.)

Self-correcting random number generator

Science.gov (United States)

Humble, Travis S.; Pooser, Raphael C.

2016-09-06

A system and method for generating random numbers. The system may include a random number generator (RNG), such as a quantum random number generator (QRNG) configured to self-correct or adapt in order to substantially achieve randomness from the output of the RNG. By adapting, the RNG may generate a random number that may be considered random regardless of whether the random number itself is tested as such. As an example, the RNG may include components to monitor one or more characteristics of the RNG during operation, and may use the monitored characteristics as a basis for adapting, or self-correcting, to provide a random number according to one or more performance criteria.

Employing online quantum random number generators for generating truly random quantum states in Mathematica

Science.gov (United States)

Miszczak, Jarosław Adam

2013-01-01

The presented package for the Mathematica computing system allows the harnessing of quantum random number generators (QRNG) for investigating the statistical properties of quantum states. The described package implements a number of functions for generating random states. The new version of the package adds the ability to use the on-line quantum random number generator service and implements new functions for retrieving lists of random numbers. Thanks to the introduced improvements, the new version provides faster access to high-quality sources of random numbers and can be used in simulations requiring large amount of random data. New version program summaryProgram title: TRQS Catalogue identifier: AEKA_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKA_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 18 134 No. of bytes in distributed program, including test data, etc.: 2 520 49 Distribution format: tar.gz Programming language: Mathematica, C. Computer: Any supporting Mathematica in version 7 or higher. Operating system: Any platform supporting Mathematica; tested with GNU/Linux (32 and 64 bit). RAM: Case-dependent Supplementary material: Fig. 1 mentioned below can be downloaded. Classification: 4.15. External routines: Quantis software library (http://www.idquantique.com/support/quantis-trng.html) Catalogue identifier of previous version: AEKA_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183(2012)118 Does the new version supersede the previous version?: Yes Nature of problem: Generation of random density matrices and utilization of high-quality random numbers for the purpose of computer simulation. Solution method: Use of a physical quantum random number generator and an on-line service providing access to the source of true random

Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network

Science.gov (United States)

Kuhn, D. Richard; Kacker, Raghu; Lei, Yu

2010-01-01

This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.

Random walks and diffusion on networks

Science.gov (United States)

Masuda, Naoki; Porter, Mason A.; Lambiotte, Renaud

2017-11-01

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

How random are random numbers generated using photons?

International Nuclear Information System (INIS)

Solis, Aldo; Angulo Martínez, Alí M; Ramírez Alarcón, Roberto; Cruz Ramírez, Hector; U’Ren, Alfred B; Hirsch, Jorge G

2015-01-01

Randomness is fundamental in quantum theory, with many philosophical and practical implications. In this paper we discuss the concept of algorithmic randomness, which provides a quantitative method to assess the Borel normality of a given sequence of numbers, a necessary condition for it to be considered random. We use Borel normality as a tool to investigate the randomness of ten sequences of bits generated from the differences between detection times of photon pairs generated by spontaneous parametric downconversion. These sequences are shown to fulfil the randomness criteria without difficulties. As deviations from Borel normality for photon-generated random number sequences have been reported in previous work, a strategy to understand these diverging findings is outlined. (paper)

Complex network structure of musical compositions: Algorithmic generation of appealing music

Science.gov (United States)

Liu, Xiao Fan; Tse, Chi K.; Small, Michael

2010-01-01

In this paper we construct networks for music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurring connections. We analyze classical music from Bach, Mozart, Chopin, as well as other types of music such as Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. We conjecture that preserving the universal network properties is a necessary step in artificial composition of music. Power-law exponents of node degree, node strength and/or edge weight distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be composed artificially using a controlled random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. By generating a large number of compositions, we find that this algorithm generates music which has the necessary qualities to be subjectively judged as appealing.

Digital random-number generator

Science.gov (United States)

Brocker, D. H.

1973-01-01

For binary digit array of N bits, use N noise sources to feed N nonlinear operators; each flip-flop in digit array is set by nonlinear operator to reflect whether amplitude of generator which feeds it is above or below mean value of generated noise. Fixed-point uniform distribution random number generation method can also be used to generate random numbers with other than uniform distribution.

Random walk centrality for temporal networks

International Nuclear Information System (INIS)

Rocha, Luis E C; Masuda, Naoki

2014-01-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included. (paper)

Random walk centrality for temporal networks

Science.gov (United States)

Rocha, Luis E. C.; Masuda, Naoki

2014-06-01

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

Statistical properties of random clique networks

Science.gov (United States)

Ding, Yi-Min; Meng, Jun; Fan, Jing-Fang; Ye, Fang-Fu; Chen, Xiao-Song

2017-10-01

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erdös and Rényi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.

Spectra of random networks in the weak clustering regime

Science.gov (United States)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

Random linear network coding for streams with unequally sized packets

DEFF Research Database (Denmark)

Taghouti, Maroua; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk

2016-01-01

State of the art Random Linear Network Coding (RLNC) schemes assume that data streams generate packets with equal sizes. This is an assumption that results in the highest efficiency gains for RLNC. A typical solution for managing unequal packet sizes is to zero-pad the smallest packets. However, ...

Generation of clusters in complex dynamical networks via pinning control

International Nuclear Information System (INIS)

Li Kezan; Fu Xinchu; Small, Michael

2008-01-01

Many real-world networks show community structure, i.e., groups (or clusters) of nodes that have a high density of links within them but with a lower density of links between them. In this paper, by applying feedback injections to a fraction of network nodes, various clusters are synchronized independently according to the community structure generated by the group partition of the network (cluster synchronization). This control is achieved by pinning (i.e. applying linear feedback control) to a subset of the network nodes. Those pinned nodes are selected not randomly but according to the topological structure of communities of a given network. Specifically, for a given group partition of a network, those nodes with direct connections between groups must be pinned in order to achieve cluster synchronization. Both the local stability and global stability of cluster synchronization are investigated. Taking the tree-shaped network and the most modular network as two particular examples, we illustrate in detail how the pinning strategy influences the generation of clusters. The simulations verify the efficiency of the pinning schemes used in this paper

Random number generation and creativity.

Science.gov (United States)

Bains, William

2008-01-01

A previous paper suggested that humans can generate genuinely random numbers. I tested this hypothesis by repeating the experiment with a larger number of highly numerate subjects, asking them to call out a sequence of digits selected from 0 through 9. The resulting sequences were substantially non-random, with an excess of sequential pairs of numbers and a deficit of repeats of the same number, in line with previous literature. However, the previous literature suggests that humans generate random numbers with substantial conscious effort, and distractions which reduce that effort reduce the randomness of the numbers. I reduced my subjects' concentration by asking them to call out in another language, and with alcohol - neither affected the randomness of their responses. This suggests that the ability to generate random numbers is a 'basic' function of the human mind, even if those numbers are not mathematically 'random'. I hypothesise that there is a 'creativity' mechanism, while not truly random, provides novelty as part of the mind's defence against closed programming loops, and that testing for the effects seen here in people more or less familiar with numbers or with spontaneous creativity could identify more features of this process. It is possible that training to perform better at simple random generation tasks could help to increase creativity, through training people to reduce the conscious mind's suppression of the 'spontaneous', creative response to new questions.

Analysis of using interpulse intervals to generate 128-bit biometric random binary sequences for securing wireless body sensor networks.

Science.gov (United States)

Zhang, Guang-He; Poon, Carmen C Y; Zhang, Yuan-Ting

2012-01-01

Wireless body sensor network (WBSN), a key building block for m-Health, demands extremely stringent resource constraints and thus lightweight security methods are preferred. To minimize resource consumption, utilizing information already available to a WBSN, particularly common to different sensor nodes of a WBSN, for security purposes becomes an attractive solution. In this paper, we tested the randomness and distinctiveness of the 128-bit biometric binary sequences (BSs) generated from interpulse intervals (IPIs) of 20 healthy subjects as well as 30 patients suffered from myocardial infarction and 34 subjects with other cardiovascular diseases. The encoding time of a biometric BS on a WBSN node is on average 23 ms and memory occupation is 204 bytes for any given IPI sequence. The results from five U.S. National Institute of Standards and Technology statistical tests suggest that random biometric BSs can be generated from both healthy subjects and cardiovascular patients and can potentially be used as authentication identifiers for securing WBSNs. Ultimately, it is preferred that these biometric BSs can be used as encryption keys such that key distribution over the WBSN can be avoided.

Random walks on generalized Koch networks

International Nuclear Information System (INIS)

Sun, Weigang

2013-01-01

For deterministically growing networks, it is a theoretical challenge to determine the topological properties and dynamical processes. In this paper, we study random walks on generalized Koch networks with features that include an initial state that is a globally connected network to r nodes. In each step, every existing node produces m complete graphs. We then obtain the analytical expressions for first passage time (FPT), average return time (ART), i.e. the average of FPTs for random walks from node i to return to the starting point i for the first time, and average sending time (AST), defined as the average of FPTs from a hub node to all other nodes, excluding the hub itself with regard to network parameters m and r. For this family of Koch networks, the ART of the new emerging nodes is identical and increases with the parameters m or r. In addition, the AST of our networks grows with network size N as N ln N and also increases with parameter m. The results obtained in this paper are the generalizations of random walks for the original Koch network. (paper)

Search for Directed Networks by Different Random Walk Strategies

Science.gov (United States)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

The RANDOM computer program: A linear congruential random number generator

Science.gov (United States)

Miles, R. F., Jr.

1986-01-01

The RANDOM Computer Program is a FORTRAN program for generating random number sequences and testing linear congruential random number generators (LCGs). The linear congruential form of random number generator is discussed, and the selection of parameters of an LCG for a microcomputer described. This document describes the following: (1) The RANDOM Computer Program; (2) RANDOM.MOD, the computer code needed to implement an LCG in a FORTRAN program; and (3) The RANCYCLE and the ARITH Computer Programs that provide computational assistance in the selection of parameters for an LCG. The RANDOM, RANCYCLE, and ARITH Computer Programs are written in Microsoft FORTRAN for the IBM PC microcomputer and its compatibles. With only minor modifications, the RANDOM Computer Program and its LCG can be run on most micromputers or mainframe computers.

Percolation and epidemics in random clustered networks

Science.gov (United States)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

Unbiased All-Optical Random-Number Generator

Science.gov (United States)

Steinle, Tobias; Greiner, Johannes N.; Wrachtrup, Jörg; Giessen, Harald; Gerhardt, Ilja

2017-10-01

The generation of random bits is of enormous importance in modern information science. Cryptographic security is based on random numbers which require a physical process for their generation. This is commonly performed by hardware random-number generators. These often exhibit a number of problems, namely experimental bias, memory in the system, and other technical subtleties, which reduce the reliability in the entropy estimation. Further, the generated outcome has to be postprocessed to "iron out" such spurious effects. Here, we present a purely optical randomness generator, based on the bistable output of an optical parametric oscillator. Detector noise plays no role and postprocessing is reduced to a minimum. Upon entering the bistable regime, initially the resulting output phase depends on vacuum fluctuations. Later, the phase is rigidly locked and can be well determined versus a pulse train, which is derived from the pump laser. This delivers an ambiguity-free output, which is reliably detected and associated with a binary outcome. The resulting random bit stream resembles a perfect coin toss and passes all relevant randomness measures. The random nature of the generated binary outcome is furthermore confirmed by an analysis of resulting conditional entropies.

Robust network topologies for generating switch-like cellular responses.

Directory of Open Access Journals (Sweden)

Najaf A Shah

2011-06-01

Full Text Available Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity and extent of memory (bistability. Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability. Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28% and bistability (up to 18%; strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3% or bistable (up to 1% responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

The MIXMAX random number generator

Science.gov (United States)

Savvidy, Konstantin G.

2015-11-01

In this paper, we study the randomness properties of unimodular matrix random number generators. Under well-known conditions, these discrete-time dynamical systems have the highly desirable K-mixing properties which guarantee high quality random numbers. It is found that some widely used random number generators have poor Kolmogorov entropy and consequently fail in empirical tests of randomness. These tests show that the lowest acceptable value of the Kolmogorov entropy is around 50. Next, we provide a solution to the problem of determining the maximal period of unimodular matrix generators of pseudo-random numbers. We formulate the necessary and sufficient condition to attain the maximum period and present a family of specific generators in the MIXMAX family with superior performance and excellent statistical properties. Finally, we construct three efficient algorithms for operations with the MIXMAX matrix which is a multi-dimensional generalization of the famous cat-map. First, allowing to compute the multiplication by the MIXMAX matrix with O(N) operations. Second, to recursively compute its characteristic polynomial with O(N2) operations, and third, to apply skips of large number of steps S to the sequence in O(N2 log(S)) operations.

Source-Independent Quantum Random Number Generation

Science.gov (United States)

Cao, Zhu; Zhou, Hongyi; Yuan, Xiao; Ma, Xiongfeng

2016-01-01

Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics. In general, a physical generator contains two parts—a randomness source and its readout. The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness. However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output. To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted. In our randomness analysis, we make no assumptions about the dimension of the source. For instance, multiphoton emissions are allowed in optical implementations. Our analysis takes into account the finite-key effect with the composable security definition. In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit. In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over 5 ×103 bit /s .

Topological properties of random wireless networks

Indian Academy of Sciences (India)

Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs find many new applications and are being studied in their own right. In this paper we first provide a brief introduction to the ...

Source-Independent Quantum Random Number Generation

Directory of Open Access Journals (Sweden)

Zhu Cao

2016-02-01

Full Text Available Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics. In general, a physical generator contains two parts—a randomness source and its readout. The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness. However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output. To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted. In our randomness analysis, we make no assumptions about the dimension of the source. For instance, multiphoton emissions are allowed in optical implementations. Our analysis takes into account the finite-key effect with the composable security definition. In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit. In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over 5×10^{3}  bit/s.

Quality pseudo-random number generator

International Nuclear Information System (INIS)

Tarasiuk, J.

1996-01-01

The pseudo-random number generator (RNG) was written to match needs of nuclear and high-energy physics computation which in some cases require very long and independent random number sequences. In this random number generator the repetition period is about 10 36 what should be sufficient for all computers in the world. In this article the test results of RNG correlation, speed and identity of computations for PC, Sun4 and VAX computer tests are presented

Analysis of android random number generator

OpenAIRE

Sarıtaş, Serkan

2013-01-01

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013. Thesis (Master's) -- Bilkent University, 2013. Includes bibliographical references leaves 61-65. Randomness is a crucial resource for cryptography, and random number generators are critical building blocks of almost all cryptographic systems. Therefore, random number generation is one of the key parts of secure communication. Random number generatio...

Recurrence of random walks with long-range steps generated by fractional Laplacian matrices on regular networks and simple cubic lattices

Science.gov (United States)

Michelitsch, T. M.; Collet, B. A.; Riascos, A. P.; Nowakowski, A. F.; Nicolleau, F. C. G. A.

2017-12-01

We analyze a Markovian random walk strategy on undirected regular networks involving power matrix functions of the type L\\frac{α{2}} where L indicates a ‘simple’ Laplacian matrix. We refer to such walks as ‘fractional random walks’ with admissible interval 0walk. From these analytical results we establish a generalization of Polya’s recurrence theorem for fractional random walks on d-dimensional infinite lattices: The fractional random walk is transient for dimensions d > α (recurrent for d≤slantα ) of the lattice. As a consequence, for 0walk is transient for all lattice dimensions d=1, 2, .. and in the range 1≤slantα walk is transient only for lattice dimensions d≥slant 3 . The generalization of Polya’s recurrence theorem remains valid for the class of random walks with Lévy flight asymptotics for long-range steps. We also analyze the mean first passage probabilities, mean residence times, mean first passage times and global mean first passage times (Kemeny constant) for the fractional random walk. For an infinite 1D lattice (infinite ring) we obtain for the transient regime 0walk is generated by the non-diagonality of the fractional Laplacian matrix with Lévy-type heavy tailed inverse power law decay for the probability of long-range moves. This non-local and asymptotic behavior of the fractional random walk introduces small-world properties with the emergence of Lévy flights on large (infinite) lattices.

An integrable low-cost hardware random number generator

Science.gov (United States)

Ranasinghe, Damith C.; Lim, Daihyun; Devadas, Srinivas; Jamali, Behnam; Zhu, Zheng; Cole, Peter H.

2005-02-01

A hardware random number generator is different from a pseudo-random number generator; a pseudo-random number generator approximates the assumed behavior of a real hardware random number generator. Simple pseudo random number generators suffices for most applications, however for demanding situations such as the generation of cryptographic keys, requires an efficient and a cost effective source of random numbers. Arbiter-based Physical Unclonable Functions (PUFs) proposed for physical authentication of ICs exploits statistical delay variation of wires and transistors across integrated circuits, as a result of process variations, to build a secret key unique to each IC. Experimental results and theoretical studies show that a sufficient amount of variation exits across IC"s. This variation enables each IC to be identified securely. It is possible to exploit the unreliability of these PUF responses to build a physical random number generator. There exists measurement noise, which comes from the instability of an arbiter when it is in a racing condition. There exist challenges whose responses are unpredictable. Without environmental variations, the responses of these challenges are random in repeated measurements. Compared to other physical random number generators, the PUF-based random number generators can be a compact and a low-power solution since the generator need only be turned on when required. A 64-stage PUF circuit costs less than 1000 gates and the circuit can be implemented using a standard IC manufacturing processes. In this paper we have presented a fast and an efficient random number generator, and analysed the quality of random numbers produced using an array of tests used by the National Institute of Standards and Technology to evaluate the randomness of random number generators designed for cryptographic applications.

Object grammars and random generation

Directory of Open Access Journals (Sweden)

I. Dutour

1998-12-01

Full Text Available This paper presents a new systematic approach for the uniform random generation of combinatorial objects. The method is based on the notion of object grammars which give recursive descriptions of objects and generalize context-freegrammars. The application of particular valuations to these grammars leads to enumeration and random generation of objects according to non algebraic parameters.

BWIP-RANDOM-SAMPLING, Random Sample Generation for Nuclear Waste Disposal

International Nuclear Information System (INIS)

Sagar, B.

1989-01-01

1 - Description of program or function: Random samples for different distribution types are generated. Distribution types as required for performance assessment modeling of geologic nuclear waste disposal are provided. These are: - Uniform, - Log-uniform (base 10 or natural), - Normal, - Lognormal (base 10 or natural), - Exponential, - Bernoulli, - User defined continuous distribution. 2 - Method of solution: A linear congruential generator is used for uniform random numbers. A set of functions is used to transform the uniform distribution to the other distributions. Stratified, rather than random, sampling can be chosen. Truncated limits can be specified on many distributions, whose usual definition has an infinite support. 3 - Restrictions on the complexity of the problem: Generation of correlated random variables is not included

Sequentially switching cell assemblies in random inhibitory networks of spiking neurons in the striatum.

Science.gov (United States)

Ponzi, Adam; Wickens, Jeff

2010-04-28

The striatum is composed of GABAergic medium spiny neurons with inhibitory collaterals forming a sparse random asymmetric network and receiving an excitatory glutamatergic cortical projection. Because the inhibitory collaterals are sparse and weak, their role in striatal network dynamics is puzzling. However, here we show by simulation of a striatal inhibitory network model composed of spiking neurons that cells form assemblies that fire in sequential coherent episodes and display complex identity-temporal spiking patterns even when cortical excitation is simply constant or fluctuating noisily. Strongly correlated large-scale firing rate fluctuations on slow behaviorally relevant timescales of hundreds of milliseconds are shown by members of the same assembly whereas members of different assemblies show strong negative correlation, and we show how randomly connected spiking networks can generate this activity. Cells display highly irregular spiking with high coefficients of variation, broadly distributed low firing rates, and interspike interval distributions that are consistent with exponentially tailed power laws. Although firing rates vary coherently on slow timescales, precise spiking synchronization is absent in general. Our model only requires the minimal but striatally realistic assumptions of sparse to intermediate random connectivity, weak inhibitory synapses, and sufficient cortical excitation so that some cells are depolarized above the firing threshold during up states. Our results are in good qualitative agreement with experimental studies, consistent with recently determined striatal anatomy and physiology, and support a new view of endogenously generated metastable state switching dynamics of the striatal network underlying its information processing operations.

A random number generator for continuous random variables

Science.gov (United States)

Guerra, V. M.; Tapia, R. A.; Thompson, J. R.

1972-01-01

A FORTRAN 4 routine is given which may be used to generate random observations of a continuous real valued random variable. Normal distribution of F(x), X, E(akimas), and E(linear) is presented in tabular form.

Uniform random number generators

Science.gov (United States)

Farr, W. R.

1971-01-01

Methods are presented for the generation of random numbers with uniform and normal distributions. Subprogram listings of Fortran generators for the Univac 1108, SDS 930, and CDC 3200 digital computers are also included. The generators are of the mixed multiplicative type, and the mathematical method employed is that of Marsaglia and Bray.

Cross over of recurrence networks to random graphs and random ...

Indian Academy of Sciences (India)

2017-01-27

Jan 27, 2017 ... that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to .... municative [19] or social [20], deviate from the random ..... He has shown that the spatial effects become.

Graphical analysis of some pseudo-random number generators

OpenAIRE

Lewis, Peter A. W.

1986-01-01

There exist today many 'good' pseudo-random number generators; the problem is to retrieve them. This document discusses three commonly used pseudo- random number generators, the first being RANDU, a notoriously bad generator, but one which is still occasionally used. The next is the widely used prime modulus, multiplicative congruential generator used in LL-RANDOMII, the Naval Postgraduate School random number package, and the last is the random number generator provided for microcomputers wi...

Computer generation of random deviates

International Nuclear Information System (INIS)

Cormack, John

1991-01-01

The need for random deviates arises in many scientific applications. In medical physics, Monte Carlo simulations have been used in radiology, radiation therapy and nuclear medicine. Specific instances include the modelling of x-ray scattering processes and the addition of random noise to images or curves in order to assess the effects of various processing procedures. Reliable sources of random deviates with statistical properties indistinguishable from true random deviates are a fundamental necessity for such tasks. This paper provides a review of computer algorithms which can be used to generate uniform random deviates and other distributions of interest to medical physicists, along with a few caveats relating to various problems and pitfalls which can occur. Source code listings for the generators discussed (in FORTRAN, Turbo-PASCAL and Data General ASSEMBLER) are available on request from the authors. 27 refs., 3 tabs., 5 figs

All-optical fast random number generator.

Science.gov (United States)

Li, Pu; Wang, Yun-Cai; Zhang, Jian-Zhong

2010-09-13

We propose a scheme of all-optical random number generator (RNG), which consists of an ultra-wide bandwidth (UWB) chaotic laser, an all-optical sampler and an all-optical comparator. Free from the electric-device bandwidth, it can generate 10Gbit/s random numbers in our simulation. The high-speed bit sequences can pass standard statistical tests for randomness after all-optical exclusive-or (XOR) operation.

Generating Seismograms with Deep Neural Networks

Science.gov (United States)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

The effects of transcranial magnetic stimulation over the dorsolateral prefrontal cortex on suppression of habitual counting during random number generation.

Science.gov (United States)

Jahanshahi, M; Profice, P; Brown, R G; Ridding, M C; Dirnberger, G; Rothwell, J C

1998-08-01

Random number generation is an attention-demanding task that engages working memory and executive processes. Random number generation requires holding information 'on line', suppression of habitual counting, internally driven response generation and monitoring of responses. Evidence from PET studies suggests that the dorsolateral prefrontal cortex (DLPFC) is involved in the generation of random responses. We examined the effects of short trains of transcranial magnetic stimulation (TMS) over the left or right DLPFC or medial frontal cortex on random number generation in healthy normal participants. As in previous evidence, in control trials without stimulation participants performed poorly on the random number generation task, showing repetition avoidance and a tendency to count. Brief disruption of processing with TMS over the left DLPFC changed the balance of the individuals' counting bias, increasing the most habitual counting in ones and reducing the lower probability response of counting in twos. This differential effect of TMS over the left DLPFC on the balance of the subject's counting bias was not obtained with TMS over the right DLPFC or the medial frontal cortex. The results suggest that, with disruption of the left DLPFC with TMS, habitual counting in ones that has previously been suppressed is released from inhibition. From these findings a network modulation model of random number generation is proposed, whereby suppression of habitual responses is achieved through the modulatory influence of the left DLPFC over a number-associative network in the superior temporal cortex. To allow emergence of appropriate random responses, the left DLPFC inhibits the superior temporal cortex to prevent spreading activation and habitual counting in ones.

Damage Spreading in Spatial and Small-world Random Boolean Networks

Energy Technology Data Exchange (ETDEWEB)

Lu, Qiming [Fermilab; Teuscher, Christof [Portland State U.

2014-02-18

The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

Science.gov (United States)

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

Anomalous Anticipatory Responses in Networked Random Data

International Nuclear Information System (INIS)

Nelson, Roger D.; Bancel, Peter A.

2006-01-01

We examine an 8-year archive of synchronized, parallel time series of random data from a world spanning network of physical random event generators (REGs). The archive is a publicly accessible matrix of normally distributed 200-bit sums recorded at 1 Hz which extends from August 1998 to the present. The primary question is whether these data show non-random structure associated with major events such as natural or man-made disasters, terrible accidents, or grand celebrations. Secondarily, we examine the time course of apparently correlated responses. Statistical analyses of the data reveal consistent evidence that events which strongly affect people engender small but significant effects. These include suggestions of anticipatory responses in some cases, leading to a series of specialized analyses to assess possible non-random structure preceding precisely timed events. A focused examination of data collected around the time of earthquakes with Richter magnitude 6 and greater reveals non-random structure with a number of intriguing, potentially important features. Anomalous effects in the REG data are seen only when the corresponding earthquakes occur in populated areas. No structure is found if they occur in the oceans. We infer that an important contributor to the effect is the relevance of the earthquake to humans. Epoch averaging reveals evidence for changes in the data some hours prior to the main temblor, suggestive of reverse causation

A random walk evolution model of wireless sensor networks and virus spreading

International Nuclear Information System (INIS)

Wang Ya-Qi; Yang Xiao-Yuan

2013-01-01

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks (WSNs) due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations. (general)

Fault-tolerant topology in the wireless sensor networks for energy depletion and random failure

International Nuclear Information System (INIS)

Liu Bin; Dong Ming-Ru; Yin Rong-Rong; Yin Wen-Xiao

2014-01-01

Nodes in the wireless sensor networks (WSNs) are prone to failure due to energy depletion and poor environment, which could have a negative impact on the normal operation of the network. In order to solve this problem, in this paper, we build a fault-tolerant topology which can effectively tolerate energy depletion and random failure. Firstly, a comprehensive failure model about energy depletion and random failure is established. Then an improved evolution model is presented to generate a fault-tolerant topology, and the degree distribution of the topology can be adjusted. Finally, the relation between the degree distribution and the topological fault tolerance is analyzed, and the optimal value of evolution model parameter is obtained. Then the target fault-tolerant topology which can effectively tolerate energy depletion and random failure is obtained. The performances of the new fault tolerant topology are verified by simulation experiments. The results show that the new fault tolerant topology effectively prolongs the network lifetime and has strong fault tolerance. (general)

Next Generation Social Networks

DEFF Research Database (Denmark)

Sørensen, Lene Tolstrup; Skouby, Knud Erik

2008-01-01

different online networks for communities of people who share interests or individuals who presents themselves through user produced content is what makes up the social networking of today. The purpose of this paper is to discuss perceived user requirements to the next generation social networks. The paper...

Generation of pseudo-random numbers

Science.gov (United States)

Howell, L. W.; Rheinfurth, M. H.

1982-01-01

Practical methods for generating acceptable random numbers from a variety of probability distributions which are frequently encountered in engineering applications are described. The speed, accuracy, and guarantee of statistical randomness of the various methods are discussed.

Multi-agent coordination in directed moving neighbourhood random networks

International Nuclear Information System (INIS)

Yi-Lun, Shang

2010-01-01

This paper considers the consensus problem of dynamical multiple agents that communicate via a directed moving neighbourhood random network. Each agent performs random walk on a weighted directed network. Agents interact with each other through random unidirectional information flow when they coincide in the underlying network at a given instant. For such a framework, we present sufficient conditions for almost sure asymptotic consensus. Numerical examples are taken to show the effectiveness of the obtained results. (general)

Softening in Random Networks of Non-Identical Beams.

Science.gov (United States)

Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R

2016-02-01

Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.

LPTAU, Quasi Random Sequence Generator

International Nuclear Information System (INIS)

Sobol, Ilya M.

1993-01-01

1 - Description of program or function: LPTAU generates quasi random sequences. These are uniformly distributed sets of L=M N points in the N-dimensional unit cube: I N =[0,1]x...x[0,1]. These sequences are used as nodes for multidimensional integration; as searching points in global optimization; as trial points in multi-criteria decision making; as quasi-random points for quasi Monte Carlo algorithms. 2 - Method of solution: Uses LP-TAU sequence generation (see references). 3 - Restrictions on the complexity of the problem: The number of points that can be generated is L 30 . The dimension of the space cannot exceed 51

On grey levels in random CAPTCHA generation

Science.gov (United States)

Newton, Fraser; Kouritzin, Michael A.

2011-06-01

A CAPTCHA is an automatically generated test designed to distinguish between humans and computer programs; specifically, they are designed to be easy for humans but difficult for computer programs to pass in order to prevent the abuse of resources by automated bots. They are commonly seen guarding webmail registration forms, online auction sites, and preventing brute force attacks on passwords. In the following, we address the question: How does adding a grey level to random CAPTCHA generation affect the utility of the CAPTCHA? We treat the problem of generating the random CAPTCHA as one of random field simulation: An initial state of background noise is evolved over time using Gibbs sampling and an efficient algorithm for generating correlated random variables. This approach has already been found to yield highly-readable yet difficult-to-crack CAPTCHAs. We detail how the requisite parameters for introducing grey levels are estimated and how we generate the random CAPTCHA. The resulting CAPTCHA will be evaluated in terms of human readability as well as its resistance to automated attacks in the forms of character segmentation and optical character recognition.

Gossip in Random Networks

Science.gov (United States)

Malarz, K.; Szvetelszky, Z.; Szekf, B.; Kulakowski, K.

2006-11-01

We consider the average probability X of being informed on a gossip in a given social network. The network is modeled within the random graph theory of Erd{õ}s and Rényi. In this theory, a network is characterized by two parameters: the size N and the link probability p. Our experimental data suggest three levels of social inclusion of friendship. The critical value pc, for which half of agents are informed, scales with the system size as N-gamma with gamma approx 0.68. Computer simulations show that the probability X varies with p as a sigmoidal curve. Influence of the correlations between neighbors is also evaluated: with increasing clustering coefficient C, X decreases.

Generation and Analysis of Constrained Random Sampling Patterns

DEFF Research Database (Denmark)

Pierzchlewski, Jacek; Arildsen, Thomas

2016-01-01

Random sampling is a technique for signal acquisition which is gaining popularity in practical signal processing systems. Nowadays, event-driven analog-to-digital converters make random sampling feasible in practical applications. A process of random sampling is defined by a sampling pattern, which...... indicates signal sampling points in time. Practical random sampling patterns are constrained by ADC characteristics and application requirements. In this paper, we introduce statistical methods which evaluate random sampling pattern generators with emphasis on practical applications. Furthermore, we propose...... algorithm generates random sampling patterns dedicated for event-driven-ADCs better than existed sampling pattern generators. Finally, implementation issues of random sampling patterns are discussed....

Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding

OpenAIRE

Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank

2014-01-01

This work proposes a new protocol applying on–the–fly random linear network coding in wireless mesh net-works. The protocol provides increased reliability, low delay,and high throughput to the upper layers, while being obliviousto their specific requirements. This seemingly conflicting goalsare achieved by design, using an on–the–fly network codingstrategy. Our protocol also exploits relay nodes to increasethe overall performance of individual links. Since our protocolnaturally masks random p...

Program pseudo-random number generator for microcomputers

International Nuclear Information System (INIS)

Ososkov, G.A.

1980-01-01

Program pseudo-random number generators (PNG) intended for the test of control equipment and communication channels are considered. In the case of 8-bit microcomputers it is necessary to assign 4 words of storage to allocate one random number. The proposed economical algorithms of the random number generation are based on the idea of the ''mixing'' of such quarters of the preceeding random number to obtain the next one. Test results of the PNG are displayed for two such generators. A FORTRAN variant of the PNG is presented along with a program realizing the PNG made on the base of the INTEL-8080 autocode

MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.

Science.gov (United States)

Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño

2013-01-01

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.

Generating equilateral random polygons in confinement III

International Nuclear Information System (INIS)

Diao, Y; Ernst, C; Montemayor, A; Ziegler, U

2012-01-01

In this paper we continue our earlier studies (Diao et al 2011 J. Phys. A: Math. Theor. 44 405202, Diao et al J. Phys. A: Math. Theor. 45 275203) on the generation methods of random equilateral polygons confined in a sphere. The first half of this paper is concerned with the generation of confined equilateral random walks. We show that if the selection of a vertex is uniform subject to the position of its previous vertex and the confining condition, then the distributions of the vertices are not uniform, although there exists a distribution such that if the initial vertex is selected following this distribution, then all vertices of the random walk follow this same distribution. Thus in order to generate a confined equilateral random walk, the selection of a vertex cannot be uniform subject to the position of its previous vertex and the confining condition. We provide a simple algorithm capable of generating confined equilateral random walks whose vertex distribution is almost uniform in the confinement sphere. In the second half of this paper we show that any process generating confined equilateral random walks can be turned into a process generating confined equilateral random polygons with the property that the vertex distribution of the polygons approaches the vertex distribution of the walks as the polygons get longer and longer. In our earlier studies, the starting point of the confined polygon is fixed at the center of the sphere. The new approach here allows us to move the starting point of the confined polygon off the center of the sphere. (paper)

Random matrix approach to plasmon resonances in the random impedance network model of disordered nanocomposites

Science.gov (United States)

Olekhno, N. A.; Beltukov, Y. M.

2018-05-01

Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric and other two-component nanocomposites. In the present work, the spectral properties of resonances in random networks are studied within the framework of the random matrix theory. We have shown that the appropriate ensemble of random matrices for the considered problem is the Jacobi ensemble (the MANOVA ensemble). The obtained analytical expressions for the density of states in such resonant networks show a good agreement with the results of numerical simulations in a wide range of metal filling fractions 0

Weighted Scaling in Non-growth Random Networks

International Nuclear Information System (INIS)

Chen Guang; Yang Xuhua; Xu Xinli

2012-01-01

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

Fast physical random bit generation with chaotic semiconductor lasers

Science.gov (United States)

Uchida, Atsushi; Amano, Kazuya; Inoue, Masaki; Hirano, Kunihito; Naito, Sunao; Someya, Hiroyuki; Oowada, Isao; Kurashige, Takayuki; Shiki, Masaru; Yoshimori, Shigeru; Yoshimura, Kazuyuki; Davis, Peter

2008-12-01

Random number generators in digital information systems make use of physical entropy sources such as electronic and photonic noise to add unpredictability to deterministically generated pseudo-random sequences. However, there is a large gap between the generation rates achieved with existing physical sources and the high data rates of many computation and communication systems; this is a fundamental weakness of these systems. Here we show that good quality random bit sequences can be generated at very fast bit rates using physical chaos in semiconductor lasers. Streams of bits that pass standard statistical tests for randomness have been generated at rates of up to 1.7 Gbps by sampling the fluctuating optical output of two chaotic lasers. This rate is an order of magnitude faster than that of previously reported devices for physical random bit generators with verified randomness. This means that the performance of random number generators can be greatly improved by using chaotic laser devices as physical entropy sources.

Thermodynamics of random reaction networks.

Science.gov (United States)

Fischer, Jakob; Kleidon, Axel; Dittrich, Peter

2015-01-01

Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

Network Restoration for Next-Generation Communication and Computing Networks

Directory of Open Access Journals (Sweden)

B. S. Awoyemi

2018-01-01

Full Text Available Network failures are undesirable but inevitable occurrences for most modern communication and computing networks. A good network design must be robust enough to handle sudden failures, maintain traffic flow, and restore failed parts of the network within a permissible time frame, at the lowest cost achievable and with as little extra complexity in the network as possible. Emerging next-generation (xG communication and computing networks such as fifth-generation networks, software-defined networks, and internet-of-things networks have promises of fast speeds, impressive data rates, and remarkable reliability. To achieve these promises, these complex and dynamic xG networks must be built with low failure possibilities, high network restoration capacity, and quick failure recovery capabilities. Hence, improved network restoration models have to be developed and incorporated in their design. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and emerging xG networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving xG communication and computing paradigms are discussed.

Do-it-yourself networks: a novel method of generating weighted networks.

Science.gov (United States)

Shanafelt, D W; Salau, K R; Baggio, J A

2017-11-01

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

Quantifiers for randomness of chaotic pseudo-random number generators.

Science.gov (United States)

De Micco, L; Larrondo, H A; Plastino, A; Rosso, O A

2009-08-28

We deal with randomness quantifiers and concentrate on their ability to discern the hallmark of chaos in time series used in connection with pseudo-random number generators (PRNGs). Workers in the field are motivated to use chaotic maps for generating PRNGs because of the simplicity of their implementation. Although there exist very efficient general-purpose benchmarks for testing PRNGs, we feel that the analysis provided here sheds additional didactic light on the importance of the main statistical characteristics of a chaotic map, namely (i) its invariant measure and (ii) the mixing constant. This is of help in answering two questions that arise in applications: (i) which is the best PRNG among the available ones? and (ii) if a given PRNG turns out not to be good enough and a randomization procedure must still be applied to it, which is the best applicable randomization procedure? Our answer provides a comparative analysis of several quantifiers advanced in the extant literature.

Small-world effect induced by weight randomization on regular networks

International Nuclear Information System (INIS)

Li, Menghui; Fan, Ying; Wang, Dahui; Li, Daqing; Wu, Jinshan; Di, Zengru

2007-01-01

The concept of edge weight provides additional depth for describing and adjusting the properties of networks. Redistribution of edge weight can effectively change the properties of networks even though the corresponding binary topology remains unchanged. Based on regular networks with initially homogeneous dissimilarity weights, random redistribution of edge weight can be enough to induce small world phenomena. The effects of random weight redistribution on both static properties and dynamical models of networks are investigated. The results reveal that randomization of weight can enhance the ability of synchronization of chaotic systems dramatically

Spectral dimensionality of random superconducting networks

International Nuclear Information System (INIS)

Day, A.R.; Xia, W.; Thorpe, M.F.

1988-01-01

We compute the spectral dimensionality d of random superconducting-normal networks by directly examining the low-frequency density of states at the percolation threshold. We find that d = 4.1 +- 0.2 and 5.8 +- 0.3 in two and three dimensions, respectively, which confirms the scaling relation d = 2d/(2-s/ν), where s is the superconducting exponent and ν the correlation-length exponent for percolation. We also consider the one-dimensional problem where scaling arguments predict, and our numerical simulations confirm, that d = 0. A simple argument provides an expression for the density of states of the localized high-frequency modes in this special case. We comment on the connection between our calculations and the ''termite'' problem of a random walker on a random superconducting-normal network and point out difficulties in inferring d from simulations of the termite problem

A scaling law for random walks on networks

Science.gov (United States)

Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick

2014-10-01

The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.

RANDNA: a random DNA sequence generator.

Science.gov (United States)

Piva, Francesco; Principato, Giovanni

2006-01-01

Monte Carlo simulations are useful to verify the significance of data. Genomic regularities, such as the nucleotide correlations or the not uniform distribution of the motifs throughout genomic or mature mRNA sequences, exist and their significance can be checked by means of the Monte Carlo test. The test needs good quality random sequences in order to work, moreover they should have the same nucleotide distribution as the sequences in which the regularities have been found. Random DNA sequences are also useful to estimate the background score of an alignment, that is a threshold below which the resulting score is merely due to chance. We have developed RANDNA, a free software which allows to produce random DNA or RNA sequences setting both their length and the percentage of nucleotide composition. Sequences having the same nucleotide distribution of exonic, intronic or intergenic sequences can be generated. Its graphic interface makes it possible to easily set the parameters that characterize the sequences being produced and saved in a text format file. The pseudo-random number generator function of Borland Delphi 6 is used, since it guarantees a good randomness, a long cycle length and a high speed. We have checked the quality of sequences generated by the software, by means of well-known tests, both by themselves and versus genuine random sequences. We show the good quality of the generated sequences. The software, complete with examples and documentation, is freely available to users from: http://www.introni.it/en/software.

Generating and using truly random quantum states in Mathematica

Science.gov (United States)

Miszczak, Jarosław Adam

2012-01-01

The problem of generating random quantum states is of a great interest from the quantum information theory point of view. In this paper we present a package for Mathematica computing system harnessing a specific piece of hardware, namely Quantis quantum random number generator (QRNG), for investigating statistical properties of quantum states. The described package implements a number of functions for generating random states, which use Quantis QRNG as a source of randomness. It also provides procedures which can be used in simulations not related directly to quantum information processing. Program summaryProgram title: TRQS Catalogue identifier: AEKA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 7924 No. of bytes in distributed program, including test data, etc.: 88 651 Distribution format: tar.gz Programming language: Mathematica, C Computer: Requires a Quantis quantum random number generator (QRNG, http://www.idquantique.com/true-random-number-generator/products-overview.html) and supporting a recent version of Mathematica Operating system: Any platform supporting Mathematica; tested with GNU/Linux (32 and 64 bit) RAM: Case dependent Classification: 4.15 Nature of problem: Generation of random density matrices. Solution method: Use of a physical quantum random number generator. Running time: Generating 100 random numbers takes about 1 second, generating 1000 random density matrices takes more than a minute.

Routing in Networks with Random Topologies

Science.gov (United States)

Bambos, Nicholas

1997-01-01

We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.

Thermodynamics of random reaction networks.

Directory of Open Access Journals (Sweden)

Jakob Fischer

Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

Bipartite quantum states and random complex networks

International Nuclear Information System (INIS)

Garnerone, Silvano; Zanardi, Paolo; Giorda, Paolo

2012-01-01

We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of random graphs known as complex networks. In the case of classical random graphs, we derive an analytic expression for the averaged entanglement entropy S-bar while for general complex networks we rely on numerics. For a large number of nodes n we find a scaling S-bar ∼c log n +g e where both the prefactor c and the sub-leading O(1) term g e are characteristic of the different classes of complex networks. In particular, g e encodes topological features of the graphs and is named network topological entropy. Our results suggest that quantum entanglement may provide a powerful tool for the analysis of large complex networks with non-trivial topological properties. (paper)

New Trends in Pseudo-Random Number Generation

Science.gov (United States)

Gutbrod, F.

Properties of pseudo-random number generators are reviewed. The emphasis is on correlations between successive random numbers and their suppression by improvement steps. The generators under discussion are the linear congruential generators, lagged Fibonacci generators with various operations, and the improvement techniques combination, shuffling and decimation. The properties of the RANSHI generator are reviewed somewhat more extensively. The transition to 64-bit technology is discussed in several cases. The generators are subject to several tests, which look both for short range and for long range correlations. Some performance figures are given for a Pentium Pro PC. Recommendations are presented in the final chapter.

MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.

Directory of Open Access Journals (Sweden)

Behrang Barekatain

Full Text Available In recent years, Random Network Coding (RNC has emerged as a promising solution for efficient Peer-to-Peer (P2P video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.

Evidence of significant bias in an elementary random number generator

International Nuclear Information System (INIS)

Borgwaldt, H.; Brandl, V.

1981-03-01

An elementary pseudo random number generator for isotropically distributed unit vectors in 3-dimensional space has ben tested for bias. This generator uses the IBM-suplied routine RANDU and a transparent rejection technique. The tests show clearly that non-randomness in the pseudo random numbers generated by the primary IBM generator leads to bias in the order of 1 percent in estimates obtained from the secondary random number generator. FORTRAN listings of 4 variants of the random number generator called by a simple test programme and output listings are included for direct reference. (orig.) [de

Generating equilateral random polygons in confinement II

International Nuclear Information System (INIS)

Diao, Y; Ernst, C; Montemayor, A; Ziegler, U

2012-01-01

In this paper we continue an earlier study (Diao et al 2011 J. Phys. A: Math. Theor. 44 405202) on the generation algorithms of random equilateral polygons confined in a sphere. Here, the equilateral random polygons are rooted at the center of the confining sphere and the confining sphere behaves like an absorbing boundary. One way to generate such a random polygon is the accept/reject method in which an unconditioned equilateral random polygon rooted at origin is generated. The polygon is accepted if it is within the confining sphere, otherwise it is rejected and the process is repeated. The algorithm proposed in this paper offers an alternative to the accept/reject method, yielding a faster generation process when the confining sphere is small. In order to use this algorithm effectively, a large, reusable data set needs to be pre-computed only once. We derive the theoretical distribution of the given random polygon model and demonstrate, with strong numerical evidence, that our implementation of the algorithm follows this distribution. A run time analysis and a numerical error estimate are given at the end of the paper. (paper)

Quantum random number generation for loophole-free Bell tests

Science.gov (United States)

Mitchell, Morgan; Abellan, Carlos; Amaya, Waldimar

2015-05-01

We describe the generation of quantum random numbers at multi-Gbps rates, combined with real-time randomness extraction, to give very high purity random numbers based on quantum events at most tens of ns in the past. The system satisfies the stringent requirements of quantum non-locality tests that aim to close the timing loophole. We describe the generation mechanism using spontaneous-emission-driven phase diffusion in a semiconductor laser, digitization, and extraction by parity calculation using multi-GHz logic chips. We pay special attention to experimental proof of the quality of the random numbers and analysis of the randomness extraction. In contrast to widely-used models of randomness generators in the computer science literature, we argue that randomness generation by spontaneous emission can be extracted from a single source.

Generating random numbers by means of nonlinear dynamic systems

Science.gov (United States)

Zang, Jiaqi; Hu, Haojie; Zhong, Juhua; Luo, Duanbin; Fang, Yi

2018-07-01

To introduce the randomness of a physical process to students, a chaotic pendulum experiment was opened in East China University of Science and Technology (ECUST) on the undergraduate level in the physics department. It was shown chaotic motion could be initiated through adjusting the operation of a chaotic pendulum. By using the data of the angular displacements of chaotic motion, random binary numerical arrays can be generated. To check the randomness of generated numerical arrays, the NIST Special Publication 800-20 method was adopted. As a result, it was found that all the random arrays which were generated by the chaotic motion could pass the validity criteria and some of them were even better than the quality of pseudo-random numbers generated by a computer. Through the experiments, it is demonstrated that chaotic pendulum can be used as an efficient mechanical facility in generating random numbers, and can be applied in teaching random motion to the students.

The intermittency of vector fields and random-number generators

Science.gov (United States)

Kalinin, A. O.; Sokoloff, D. D.; Tutubalin, V. N.

2017-09-01

We examine how well natural random-number generators can reproduce the intermittency phenomena that arise in the transfer of vector fields in random media. A generator based on the analysis of financial indices is suggested as the most promising random-number generator. Is it shown that even this generator, however, fails to reproduce the phenomenon long enough to confidently detect intermittency, while the C++ generator successfully solves this problem. We discuss the prospects of using shell models of turbulence as the desired generator.

Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

CERN Document Server

Santi, Paolo

2012-01-01

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

Optimal Quantum Spatial Search on Random Temporal Networks

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Optimal Quantum Spatial Search on Random Temporal Networks.

Science.gov (United States)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G(n,p), where p is the probability that any two given nodes are connected: After every time interval τ, a new graph G(n,p) replaces the previous one. We prove analytically that, for any given p, there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O(sqrt[n]), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

Pseudo-Random Number Generator Based on Coupled Map Lattices

Science.gov (United States)

Lü, Huaping; Wang, Shihong; Hu, Gang

A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

Synchronization of random bit generators based on coupled chaotic lasers and application to cryptography.

Science.gov (United States)

Kanter, Ido; Butkovski, Maria; Peleg, Yitzhak; Zigzag, Meital; Aviad, Yaara; Reidler, Igor; Rosenbluh, Michael; Kinzel, Wolfgang

2010-08-16

Random bit generators (RBGs) constitute an important tool in cryptography, stochastic simulations and secure communications. The later in particular has some difficult requirements: high generation rate of unpredictable bit strings and secure key-exchange protocols over public channels. Deterministic algorithms generate pseudo-random number sequences at high rates, however, their unpredictability is limited by the very nature of their deterministic origin. Recently, physical RBGs based on chaotic semiconductor lasers were shown to exceed Gbit/s rates. Whether secure synchronization of two high rate physical RBGs is possible remains an open question. Here we propose a method, whereby two fast RBGs based on mutually coupled chaotic lasers, are synchronized. Using information theoretic analysis we demonstrate security against a powerful computational eavesdropper, capable of noiseless amplification, where all parameters are publicly known. The method is also extended to secure synchronization of a small network of three RBGs.

Generating random walks and polygons with stiffness in confinement

International Nuclear Information System (INIS)

Diao, Y; Ernst, C; Saarinen, S; Ziegler, U

2015-01-01

The purpose of this paper is to explore ways to generate random walks and polygons in confinement with a bias toward stiffness. Here the stiffness refers to the curvature angle between two consecutive edges along the random walk or polygon. The stiffer the walk (polygon), the smaller this angle on average. Thus random walks and polygons with an elevated stiffness have lower than expected curvatures. The authors introduced and studied several generation algorithms with a stiffness parameter s>0 that regulates the expected curvature angle at a given vertex in which the random walks and polygons are generated one edge at a time using conditional probability density functions. Our generating algorithms also allow the generation of unconfined random walks and polygons with any desired mean curvature angle. In the case of random walks and polygons confined in a sphere of fixed radius, we observe that, as expected, stiff random walks or polygons are more likely to be close to the confinement boundary. The methods developed here require that the random walks and random polygons be rooted at the center of the confinement sphere. (paper)

Generic Properties of Random Gene Regulatory Networks.

Science.gov (United States)

Li, Zhiyuan; Bianco, Simone; Zhang, Zhaoyang; Tang, Chao

2013-12-01

Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

Navigation by anomalous random walks on complex networks.

Science.gov (United States)

Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

2016-11-23

Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.

Navigation by anomalous random walks on complex networks

Science.gov (United States)

Weng, Tongfeng; Zhang, Jie; Khajehnejad, Moein; Small, Michael; Zheng, Rui; Hui, Pan

2016-11-01

Anomalous random walks having long-range jumps are a critical branch of dynamical processes on networks, which can model a number of search and transport processes. However, traditional measurements based on mean first passage time are not useful as they fail to characterize the cost associated with each jump. Here we introduce a new concept of mean first traverse distance (MFTD) to characterize anomalous random walks that represents the expected traverse distance taken by walkers searching from source node to target node, and we provide a procedure for calculating the MFTD between two nodes. We use Lévy walks on networks as an example, and demonstrate that the proposed approach can unravel the interplay between diffusion dynamics of Lévy walks and the underlying network structure. Moreover, applying our framework to the famous PageRank search, we show how to inform the optimality of the PageRank search. The framework for analyzing anomalous random walks on complex networks offers a useful new paradigm to understand the dynamics of anomalous diffusion processes, and provides a unified scheme to characterize search and transport processes on networks.

Robustness of Dengue Complex Network under Targeted versus Random Attack

Directory of Open Access Journals (Sweden)

Hafiz Abid Mahmood Malik

2017-01-01

Full Text Available Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO, 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5% targeted attack is equivalent to the result of 65% random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia.

Testing, Selection, and Implementation of Random Number Generators

National Research Council Canada - National Science Library

Collins, Joseph C

2008-01-01

An exhaustive evaluation of state-of-the-art random number generators with several well-known suites of tests provides the basis for selection of suitable random number generators for use in stochastic simulations...

Network information provision to potential generators: Appendices

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-07-01

This Code of Practice (CoP) has been prepared to outline the standard of information that Distribution Network Operators (DNOs) should be required to produce in relation to the provision of network maps, schematic diagrams and specific network data. Network information from DNOs may be required by generators (and other customers) in order to assess the potential opportunities available for the connection of new generation plant. Seven Year Statements are published annually by the Transmission Licensees operating in Great Britain, i.e. The National Grid Company, Scottish Power and Scottish Hydro Electric, and contain all the network information relating to each transmission system, e.g. Generation Capacities, System Parameters and Plant Fault Levels. A similar arrangement for DNOs has been outlined in the Electricity Distribution Licence published by Ofgem. Under Condition 25 of the licence, 'The Long Term Development Statement', distribution licence holders are required to make available historic and planned network data. By providing sufficient network information, competition in generation will be improved. At the time of writing, any party interested in assessing distribution network information needs to make contact with the appropriate DNO, identifying the correct department and person. Written applications are then sent to that person, describing the type of network information that is required. Information required from embedded generators by DNOs is specified in detail in both of The Distribution Codes of England and Wales, and Scotland. However, there are no guidelines or details of network information to be provided by DNOs. This Code of Practise is designed to balance this situation and help DNOs, prospective generators and other applicants for information to achieve satisfaction by clarifying expectations. (Author)

Structure of a randomly grown 2-d network

DEFF Research Database (Denmark)

Ajazi, Fioralba; Napolitano, George M.; Turova, Tatyana

2015-01-01

We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in...... in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons.......We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes...

Pseudo-random number generator based on asymptotic deterministic randomness

Science.gov (United States)

Wang, Kai; Pei, Wenjiang; Xia, Haishan; Cheung, Yiu-ming

2008-06-01

A novel approach to generate the pseudorandom-bit sequence from the asymptotic deterministic randomness system is proposed in this Letter. We study the characteristic of multi-value correspondence of the asymptotic deterministic randomness constructed by the piecewise linear map and the noninvertible nonlinearity transform, and then give the discretized systems in the finite digitized state space. The statistic characteristics of the asymptotic deterministic randomness are investigated numerically, such as stationary probability density function and random-like behavior. Furthermore, we analyze the dynamics of the symbolic sequence. Both theoretical and experimental results show that the symbolic sequence of the asymptotic deterministic randomness possesses very good cryptographic properties, which improve the security of chaos based PRBGs and increase the resistance against entropy attacks and symbolic dynamics attacks.

Pseudo-random number generator based on asymptotic deterministic randomness

International Nuclear Information System (INIS)

Wang Kai; Pei Wenjiang; Xia Haishan; Cheung Yiuming

2008-01-01

A novel approach to generate the pseudorandom-bit sequence from the asymptotic deterministic randomness system is proposed in this Letter. We study the characteristic of multi-value correspondence of the asymptotic deterministic randomness constructed by the piecewise linear map and the noninvertible nonlinearity transform, and then give the discretized systems in the finite digitized state space. The statistic characteristics of the asymptotic deterministic randomness are investigated numerically, such as stationary probability density function and random-like behavior. Furthermore, we analyze the dynamics of the symbolic sequence. Both theoretical and experimental results show that the symbolic sequence of the asymptotic deterministic randomness possesses very good cryptographic properties, which improve the security of chaos based PRBGs and increase the resistance against entropy attacks and symbolic dynamics attacks

The investigation of social networks based on multi-component random graphs

Science.gov (United States)

Zadorozhnyi, V. N.; Yudin, E. B.

2018-01-01

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdôs-Rényi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.

Towards a high-speed quantum random number generator

Science.gov (United States)

Stucki, Damien; Burri, Samuel; Charbon, Edoardo; Chunnilall, Christopher; Meneghetti, Alessio; Regazzoni, Francesco

2013-10-01

Randomness is of fundamental importance in various fields, such as cryptography, numerical simulations, or the gaming industry. Quantum physics, which is fundamentally probabilistic, is the best option for a physical random number generator. In this article, we will present the work carried out in various projects in the context of the development of a commercial and certified high speed random number generator.

Chaos-based Pseudo-random Number Generation

KAUST Repository

Barakat, Mohamed L.

2014-04-10

Various methods and systems related to chaos-based pseudo-random number generation are presented. In one example, among others, a system includes a pseudo-random number generator (PRNG) to generate a series of digital outputs and a nonlinear post processing circuit to perform an exclusive OR (XOR) operation on a first portion of a current digital output of the PRNG and a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output. In another example, a method includes receiving at least a first portion of a current output from a PRNG and performing an XOR operation on the first portion of the current PRNG output with a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output.

Chaos-based Pseudo-random Number Generation

KAUST Repository

Barakat, Mohamed L.; Mansingka, Abhinav S.; Radwan, Ahmed Gomaa Ahmed; Salama, Khaled N.

2014-01-01

Various methods and systems related to chaos-based pseudo-random number generation are presented. In one example, among others, a system includes a pseudo-random number generator (PRNG) to generate a series of digital outputs and a nonlinear post processing circuit to perform an exclusive OR (XOR) operation on a first portion of a current digital output of the PRNG and a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output. In another example, a method includes receiving at least a first portion of a current output from a PRNG and performing an XOR operation on the first portion of the current PRNG output with a permutated version of a corresponding first portion of a previous post processed output to generate a corresponding first portion of a current post processed output.

A Method of Erasing Data Using Random Number Generators

OpenAIRE

井上,正人

2012-01-01

Erasing data is an indispensable step for disposal of computers or external storage media. Except physical destruction, erasing data means writing random information on entire disk drives or media. We propose a method which erases data safely using random number generators. These random number generators create true random numbers based on quantum processes.

Efficient sampling of complex network with modified random walk strategies

Science.gov (United States)

Xie, Yunya; Chang, Shuhua; Zhang, Zhipeng; Zhang, Mi; Yang, Lei

2018-02-01

We present two novel random walk strategies, choosing seed node (CSN) random walk and no-retracing (NR) random walk. Different from the classical random walk sampling, the CSN and NR strategies focus on the influences of the seed node choice and path overlap, respectively. Three random walk samplings are applied in the Erdös-Rényi (ER), Barabási-Albert (BA), Watts-Strogatz (WS), and the weighted USAir networks, respectively. Then, the major properties of sampled subnets, such as sampling efficiency, degree distributions, average degree and average clustering coefficient, are studied. The similar conclusions can be reached with these three random walk strategies. Firstly, the networks with small scales and simple structures are conducive to the sampling. Secondly, the average degree and the average clustering coefficient of the sampled subnet tend to the corresponding values of original networks with limited steps. And thirdly, all the degree distributions of the subnets are slightly biased to the high degree side. However, the NR strategy performs better for the average clustering coefficient of the subnet. In the real weighted USAir networks, some obvious characters like the larger clustering coefficient and the fluctuation of degree distribution are reproduced well by these random walk strategies.

DNA-based random number generation in security circuitry.

Science.gov (United States)

Gearheart, Christy M; Arazi, Benjamin; Rouchka, Eric C

2010-06-01

DNA-based circuit design is an area of research in which traditional silicon-based technologies are replaced by naturally occurring phenomena taken from biochemistry and molecular biology. This research focuses on further developing DNA-based methodologies to mimic digital data manipulation. While exhibiting fundamental principles, this work was done in conjunction with the vision that DNA-based circuitry, when the technology matures, will form the basis for a tamper-proof security module, revolutionizing the meaning and concept of tamper-proofing and possibly preventing it altogether based on accurate scientific observations. A paramount part of such a solution would be self-generation of random numbers. A novel prototype schema employs solid phase synthesis of oligonucleotides for random construction of DNA sequences; temporary storage and retrieval is achieved through plasmid vectors. A discussion of how to evaluate sequence randomness is included, as well as how these techniques are applied to a simulation of the random number generation circuitry. Simulation results show generated sequences successfully pass three selected NIST random number generation tests specified for security applications.

Establishing a national knowledge translation and generation network in kidney disease: the CAnadian KidNey KNowledge TraNslation and GEneration NeTwork.

Science.gov (United States)

Manns, Braden; Barrett, Brendan; Evans, Michael; Garg, Amit; Hemmelgarn, Brenda; Kappel, Joanne; Klarenbach, Scott; Madore, Francois; Parfrey, Patrick; Samuel, Susan; Soroka, Steven; Suri, Rita; Tonelli, Marcello; Wald, Ron; Walsh, Michael; Zappitelli, Michael

2014-01-01

Patients with chronic kidney disease (CKD) do not always receive care consistent with guidelines, in part due to complexities in CKD management, lack of randomized trial data to inform care, and a failure to disseminate best practice. At a 2007 conference of key Canadian stakeholders in kidney disease, attendees noted that the impact of Canadian Society of Nephrology (CSN) guidelines was attenuated given limited formal linkages between the CSN Clinical Practice Guidelines Group, kidney researchers, decision makers and knowledge users, and that further knowledge was required to guide care in patients with kidney disease. The idea for the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET) developed from this meeting. CANN-NET is a pan-Canadian network established in partnership with CSN, the Kidney Foundation of Canada and other professional societies to improve the care and outcomes of patients with and at risk for kidney disease. The initial priority areas for knowledge translation include improving optimal timing of dialysis initiation, and increasing the appropriate use of home dialysis. Given the urgent need for new knowledge, CANN-NET has also brought together a national group of experienced Canadian researchers to address knowledge gaps by encouraging and supporting multicentre randomized trials in priority areas, including management of cardiovascular disease in patients with kidney failure.

Robust Power Supply Restoration for Self-Healing Active Distribution Networks Considering the Availability of Distributed Generation

Directory of Open Access Journals (Sweden)

Qiang Yang

2018-01-01

Full Text Available The increasing penetration of distributed generations (DGs with intermittent and stochastic characteristics into current power distribution networks can lead to increased fault levels and degradation in network protection. As one of the key requirements of active network management (ANM, efficient power supply restoration solution to guarantee network self-healing capability with full consideration of DG uncertainties is demanded. This paper presents a joint power supply restoration through combining the DG local restoration and switcher operation-based restoration to enhance the self-healing capability in active distribution networks considering the availability of distributed generation. The restoration algorithmic solution is designed to be able to carry out power restoration in parallel upon multiple simultaneous faults to maximize the load restoration while additionally minimizing power loss, topology variation and power flow changes due to switcher operations. The performance of the proposed solution is validated based on a 53-bus distribution network with wind power generators through extensive simulation experiments for a range of fault cases and DG scenarios generated based on Heuristic Moment Matching (HMM method to fully consider the DG randomness. The numerical result in comparison with the existing solutions demonstrates the effectiveness of the proposed power supply restoration solution.

A special purpose computer for the calculation of the electric conductivity of a random resistor network

International Nuclear Information System (INIS)

Hajjar, Mansour

1987-01-01

The special purpose computer PERCOLA is designed for long numerical simulations on a percolation problem in Statistical Mechanics of disordered media. Our aim is to improve the actual values of the critical exponents characterizing the behaviour of random resistance networks at percolation threshold. The architecture of PERCOLA is based on an efficient iterative algorithm used to compute the electric conductivity of such networks. The calculator has the characteristics of a general purpose 64 bits floating point micro-programmable computer that can run programs for various types of problems with a peak performance of 25 Mflops. This high computing speed is a result of the pipeline architecture based on internal parallelism and separately micro-code controlled units such as: data memories, a micro-code memory, ALUs and multipliers (both WEITEK components), various data paths, a sequencer (ANALOG DEVICES component), address generators and a random number generator. Thus, the special purpose computer runs percolation problem program 10 percent faster than the supercomputer CRAY XMP. (author) [fr

A hybrid-type quantum random number generator

Science.gov (United States)

Hai-Qiang, Ma; Wu, Zhu; Ke-Jin, Wei; Rui-Xue, Li; Hong-Wei, Liu

2016-05-01

This paper proposes a well-performing hybrid-type truly quantum random number generator based on the time interval between two independent single-photon detection signals, which is practical and intuitive, and generates the initial random number sources from a combination of multiple existing random number sources. A time-to-amplitude converter and multichannel analyzer are used for qualitative analysis to demonstrate that each and every step is random. Furthermore, a carefully designed data acquisition system is used to obtain a high-quality random sequence. Our scheme is simple and proves that the random number bit rate can be dramatically increased to satisfy practical requirements. Project supported by the National Natural Science Foundation of China (Grant Nos. 61178010 and 11374042), the Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications), China, and the Fundamental Research Funds for the Central Universities of China (Grant No. bupt2014TS01).

Microcomputer-Assisted Discoveries: Generate Your Own Random Numbers.

Science.gov (United States)

Kimberling, Clark

1984-01-01

Having students try to generate their own random numbers can lead to much discovery learning as one tries to create 'patternlessness' from formulas. Developing an equidistribution test and runs test, plus other ideas for generating random numbers, is discussed, with computer programs given. (MNS)

Random Linear Network Coding for 5G Mobile Video Delivery

Directory of Open Access Journals (Sweden)

Dejan Vukobratovic

2018-03-01

Full Text Available An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G 3GPP New Radio (NR standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC. In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.

Pseudo-Random Number Generators

Science.gov (United States)

Howell, L. W.; Rheinfurth, M. H.

1984-01-01

Package features comprehensive selection of probabilistic distributions. Monte Carlo simulations resorted to whenever systems studied not amenable to deterministic analyses or when direct experimentation not feasible. Random numbers having certain specified distribution characteristic integral part of simulations. Package consists of collector of "pseudorandom" number generators for use in Monte Carlo simulations.

Microcomputer Unit: Generating Random Numbers.

Science.gov (United States)

Haigh, William E.

1986-01-01

Presents an activity, suitable for students in grades 6-12, on generating random numbers. Objectives, equipment needed, list of prerequisite experiences, instructional strategies, and ready-to-copy student worksheets are included. (JN)

Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

Energy Technology Data Exchange (ETDEWEB)

Perumalla, Kalyan S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Alam, Maksudul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-10-01

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.

a Pseudo-Random Number Generator Employing Multiple RÉNYI Maps

Science.gov (United States)

Lui, Oi-Yan; Yuen, Ching-Hung; Wong, Kwok-Wo

2013-11-01

The increasing risk along with the drastic development of multimedia data transmission has raised a big concern on data security. A good pseudo-random number generator is an essential tool in cryptography. In this paper, we propose a novel pseudo-random number generator based on the controlled combination of the outputs of several digitized chaotic Rényi maps. The generated pseudo-random sequences have passed both the NIST 800-22 Revision 1a and the DIEHARD tests. Moreover, simulation results show that the proposed pseudo-random number generator requires less operation time than existing generators and is highly sensitive to the seed.

Optical Subsystems for Next Generation Access Networks

DEFF Research Database (Denmark)

Lazaro, J.A; Polo, V.; Schrenk, B.

2011-01-01

Recent optical technologies are providing higher flexibility to next generation access networks: on the one hand, providing progressive FTTx and specifically FTTH deployment, progressively shortening the copper access network; on the other hand, also opening fixed-mobile convergence solutions...... in next generation PON architectures. It is provided an overview of the optical subsystems developed for the implementation of the proposed NG-Access Networks....

Network formation determined by the diffusion process of random walkers

International Nuclear Information System (INIS)

Ikeda, Nobutoshi

2008-01-01

We studied the diffusion process of random walkers in networks formed by their traces. This model considers the rise and fall of links determined by the frequency of transports of random walkers. In order to examine the relation between the formed network and the diffusion process, a situation in which multiple random walkers start from the same vertex is investigated. The difference in diffusion rate of random walkers according to the difference in dimension of the initial lattice is very important for determining the time evolution of the networks. For example, complete subgraphs can be formed on a one-dimensional lattice while a graph with a power-law vertex degree distribution is formed on a two-dimensional lattice. We derived some formulae for predicting network changes for the 1D case, such as the time evolution of the size of nearly complete subgraphs and conditions for their collapse. The networks formed on the 2D lattice are characterized by the existence of clusters of highly connected vertices and their life time. As the life time of such clusters tends to be small, the exponent of the power-law distribution changes from γ ≅ 1-2 to γ ≅ 3

Random networks of Boolean cellular automata

Energy Technology Data Exchange (ETDEWEB)

Miranda, Enrique [Comision Nacional de Energia Atomica, San Carlos de Bariloche (Argentina). Centro Atomico Bariloche

1990-01-01

Some recent results about random networks of Boolean automata -the Kauffman model- are reviewed. The structure of configuration space is explored. Ultrametricity between cycles is analyzed and the effects of noise in the dynamics are studied. (Author).

Random networks of Boolean cellular automata

International Nuclear Information System (INIS)

Miranda, Enrique

1990-01-01

Some recent results about random networks of Boolean automata -the Kauffman model- are reviewed. The structure of configuration space is explored. Ultrametricity between cycles is analyzed and the effects of noise in the dynamics are studied. (Author)

Micro-generation network connection (renewables)

Energy Technology Data Exchange (ETDEWEB)

Thornycroft, J.; Russell, T.; Curran, J.

2003-07-01

The drive to reduce emissions of carbon dioxide will result in an increase in the number of small generation units seeking connection to the electric power distribution network. The objectives of this study were to consider connection issues relating to micro-generation from renewables and their integration into the UK distribution network. The document is divided into two sections. The first section describes the present system which includes input from micro-generation, the technical impacts and the financial considerations. The second part discusses technical, financial and governance options for the future. A summary of preferred options and recommendations is given. The study was carried out by the Halcrow Group Ltd under contract to the DTI.

Experimental nonlocality-based randomness generation with nonprojective measurements

Science.gov (United States)

Gómez, S.; Mattar, A.; Gómez, E. S.; Cavalcanti, D.; Farías, O. Jiménez; Acín, A.; Lima, G.

2018-04-01

We report on an optical setup generating more than one bit of randomness from one entangled bit (i.e., a maximally entangled state of two qubits). The amount of randomness is certified through the observation of Bell nonlocal correlations. To attain this result we implemented a high-purity entanglement source and a nonprojective three-outcome measurement. Our implementation achieves a gain of 27% of randomness as compared with the standard methods using projective measurements. Additionally, we estimate the amount of randomness certified in a one-sided device-independent scenario, through the observation of Einstein-Podolsky-Rosen steering. Our results prove that nonprojective quantum measurements allow extending the limits for nonlocality-based certified randomness generation using current technology.

Analysis of random number generators in abnormal usage conditions

International Nuclear Information System (INIS)

Soucarros, M.

2012-01-01

Random numbers have been used through the ages for games of chance, more recently for secret codes and today they are necessary to the execution of computer programs. Random number generators have now evolved from simple dices to electronic circuits and algorithms. Accordingly, the ability to distinguish between random and non-random numbers has become more difficult. Furthermore, whereas in the past dices were loaded in order to increase winning chances, it is now possible to influence the outcome of random number generators. In consequence, this subject is still very much an issue and has recently made the headlines. Indeed, there was talks about the PS3 game console which generates constant random numbers and redundant distribution of secret keys on the internet. This thesis presents a study of several generators as well as different means to perturb them. It shows the inherent defects of their conceptions and possible consequences of their failure when they are embedded inside security components. Moreover, this work highlights problems yet to be solved concerning the testing of random numbers and the post-processing eliminating bias in these numbers distribution. (author) [fr

Pseudo-random number generation using a 3-state cellular automaton

Science.gov (United States)

Bhattacharjee, Kamalika; Paul, Dipanjyoti; Das, Sukanta

This paper investigates the potentiality of pseudo-random number generation of a 3-neighborhood 3-state cellular automaton (CA) under periodic boundary condition. Theoretical and empirical tests are performed on the numbers, generated by the CA, to observe the quality of it as pseudo-random number generator (PRNG). We analyze the strength and weakness of the proposed PRNG and conclude that the selected CA is a good random number generator.

Epidemic transmission on random mobile network with diverse infection periods

Science.gov (United States)

Li, Kezan; Yu, Hong; Zeng, Zhaorong; Ding, Yong; Ma, Zhongjun

2015-05-01

The heterogeneity of individual susceptibility and infectivity and time-varying topological structure are two realistic factors when we study epidemics on complex networks. Current research results have shown that the heterogeneity of individual susceptibility and infectivity can increase the epidemic threshold in a random mobile dynamical network with the same infection period. In this paper, we will focus on random mobile dynamical networks with diverse infection periods due to people's different constitutions and external circumstances. Theoretical results indicate that the epidemic threshold of the random mobile network with diverse infection periods is larger than the counterpart with the same infection period. Moreover, the heterogeneity of individual susceptibility and infectivity can play a significant impact on disease transmission. In particular, the homogeneity of individuals will avail to the spreading of epidemics. Numerical examples verify further our theoretical results very well.

NASA's Next Generation Space Geodesy Network

Science.gov (United States)

Desai, S. D.; Gross, R. S.; Hilliard, L.; Lemoine, F. G.; Long, J. L.; Ma, C.; McGarry, J. F.; Merkowitz, S. M.; Murphy, D.; Noll, C. E.;

Building next-generation converged networks theory and practice

CERN Document Server

Pathan, Al-Sakib Khan

2013-01-01

Supplying a comprehensive introduction to next-generation networks, Building Next-Generation Converged Networks: Theory and Practice strikes a balance between how and why things work and how to make them work. It compiles recent advancements along with basic issues from the wide range of fields related to next generation networks. Containing the contributions of 56 industry experts and researchers from 16 different countries, the book presents relevant theoretical frameworks and the latest research. It investigates new technologies such as IPv6 over Low Power Wireless Personal Area Network (6L

Physical Principle for Generation of Randomness

Science.gov (United States)

Zak, Michail

2009-01-01

A physical principle (more precisely, a principle that incorporates mathematical models used in physics) has been conceived as the basis of a method of generating randomness in Monte Carlo simulations. The principle eliminates the need for conventional random-number generators. The Monte Carlo simulation method is among the most powerful computational methods for solving high-dimensional problems in physics, chemistry, economics, and information processing. The Monte Carlo simulation method is especially effective for solving problems in which computational complexity increases exponentially with dimensionality. The main advantage of the Monte Carlo simulation method over other methods is that the demand on computational resources becomes independent of dimensionality. As augmented by the present principle, the Monte Carlo simulation method becomes an even more powerful computational method that is especially useful for solving problems associated with dynamics of fluids, planning, scheduling, and combinatorial optimization. The present principle is based on coupling of dynamical equations with the corresponding Liouville equation. The randomness is generated by non-Lipschitz instability of dynamics triggered and controlled by feedback from the Liouville equation. (In non-Lipschitz dynamics, the derivatives of solutions of the dynamical equations are not required to be bounded.)

Network information provision to potential generators

Energy Technology Data Exchange (ETDEWEB)

Nicholson, G.

2001-07-01

At the time of finalising this report, an Ofgem consultation is underway on the form of Distribution Licence Condition 25, which will state the requirements for Distribution Network Operators to provide and publish data. This report is also relevant to the DTI Ofgem Embedded Generation Working Group (EGWG), which has recently completed its report and recommendations. It is hoped that this document will provide an overview of the status, importance, role and benefits of network information, which can be utilised by Generators, Network Operators and other industry players in framing their responses to this and future consultations. (Authors)

Search for a perfect generator of random numbers

International Nuclear Information System (INIS)

Musyck, E.

1977-01-01

Theoretical tests have been carried out by COVEYOU and MAC PHERSON to verify the applications of the LEHMER algorithm. In a similar way, a theoretical method is proposed to evaluate in a rigorous way the random character of numbers generated by a shift register. This theory introduces the concept of ''degree of randomness'' of the elements, taken in a definite order, of a shift register. It permits making the judicious choice of the elements of the shift register which will produce the bits of the random numbers. On the other hand, a calculation method is developed in order to verify the primitive character of any shift register of high complexity. A new test, called ''slice test'', of empirical and theoretical use is also described; it constitutes a significant contribution to the understanding of certain properties of pseudo-random sequences. As a practical example, a random number generator structure formed with 32 bits, built out of a shift register with 61 elements and 60 modulo-2 adder circuits was made. The author is convinced that this generator can be considered to be practically perfect for all empirical applications of random numbers, particularly for the solution of Monte-Carlo problems. (author)

Random-Number Generator Validity in Simulation Studies: An Investigation of Normality.

Science.gov (United States)

Bang, Jung W.; Schumacker, Randall E.; Schlieve, Paul L.

1998-01-01

The normality of number distributions generated by various random-number generators were studied, focusing on when the random-number generator reached a normal distribution and at what sample size. Findings suggest the steps that should be followed when using a random-number generator in a Monte Carlo simulation. (SLD)

Organization of growing random networks

International Nuclear Information System (INIS)

Krapivsky, P. L.; Redner, S.

2001-01-01

The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A k . When A k grows more slowly than linearly with k, the number of nodes with k links, N k (t), decays faster than a power law in k, while for A k growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A k is asymptotically linear, N k (t)∼tk -ν , with ν dependent on details of the attachment probability, but in the range 2 -2 power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network

Establishing a National Knowledge Translation and Generation Network in Kidney Disease: The CAnadian KidNey KNowledge TraNslation and GEneration NeTwork

Directory of Open Access Journals (Sweden)

Braden Manns

2014-04-01

Full Text Available Patients with chronic kidney disease (CKD do not always receive care consistent with guidelines, in part due to complexities in CKD management, lack of randomized trial data to inform care, and a failure to disseminate best practice. At a 2007 conference of key Canadian stakeholders in kidney disease, attendees noted that the impact of Canadian Society of Nephrology (CSN guidelines was attenuated given limited formal linkages between the CSN Clinical Practice Guidelines Group, kidney researchers, decision makers and knowledge users, and that further knowledge was required to guide care in patients with kidney disease. The idea for the Canadian Kidney Knowledge Translation and Generation Network (CANN-NET developed from this meeting. CANN-NET is a pan-Canadian network established in partnership with CSN, the Kidney Foundation of Canada and other professional societies to improve the care and outcomes of patients with and at risk for kidney disease. The initial priority areas for knowledge translation include improving optimal timing of dialysis initiation, and increasing the appropriate use of home dialysis. Given the urgent need for new knowledge, CANN-NET has also brought together a national group of experienced Canadian researchers to address knowledge gaps by encouraging and supporting multicentre randomized trials in priority areas, including management of cardiovascular disease in patients with kidney failure.

Pseudo-random number generator for the Sigma 5 computer

Science.gov (United States)

Carroll, S. N.

1983-01-01

A technique is presented for developing a pseudo-random number generator based on the linear congruential form. The two numbers used for the generator are a prime number and a corresponding primitive root, where the prime is the largest prime number that can be accurately represented on a particular computer. The primitive root is selected by applying Marsaglia's lattice test. The technique presented was applied to write a random number program for the Sigma 5 computer. The new program, named S:RANDOM1, is judged to be superior to the older program named S:RANDOM. For applications requiring several independent random number generators, a table is included showing several acceptable primitive roots. The technique and programs described can be applied to any computer having word length different from that of the Sigma 5.

Device-independent randomness generation from several Bell estimators

Science.gov (United States)

Nieto-Silleras, Olmo; Bamps, Cédric; Silman, Jonathan; Pironio, Stefano

2018-02-01

Device-independent randomness generation and quantum key distribution protocols rely on a fundamental relation between the non-locality of quantum theory and its random character. This relation is usually expressed in terms of a trade-off between the probability of guessing correctly the outcomes of measurements performed on quantum systems and the amount of violation of a given Bell inequality. However, a more accurate assessment of the randomness produced in Bell experiments can be obtained if the value of several Bell expressions is simultaneously taken into account, or if the full set of probabilities characterizing the behavior of the device is considered. We introduce protocols for device-independent randomness generation secure against classical side information, that rely on the estimation of an arbitrary number of Bell expressions or even directly on the experimental frequencies of measurement outcomes. Asymptotically, this results in an optimal generation of randomness from experimental data (as measured by the min-entropy), without having to assume beforehand that the devices violate a specific Bell inequality.

Distributed clone detection in static wireless sensor networks: random walk with network division.

Science.gov (United States)

Khan, Wazir Zada; Aalsalem, Mohammed Y; Saad, N M

2015-01-01

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

Distributed clone detection in static wireless sensor networks: random walk with network division.

Directory of Open Access Journals (Sweden)

Wazir Zada Khan

Full Text Available Wireless Sensor Networks (WSNs are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

Use of Hot Rolling for Generating Low Deviation Twins and a Disconnected Random Boundary Network in Inconel 600 Alloy

Science.gov (United States)

Sahu, Sandeep; Yadav, Prabhat Chand; Shekhar, Shashank

2018-02-01

In this investigation, Inconel 600 alloy was thermomechanically processed to different strains via hot rolling followed by a short-time annealing treatment to determine an appropriate thermomechanical process to achieve a high fraction of low-Σ CSL boundaries. Experimental results demonstrate that a certain level of deformation is necessary to obtain effective "grain boundary engineering"; i.e., the deformation must be sufficiently high to provide the required driving force for postdeformation static recrystallization, yet it should be low enough to retain a large fraction of original twin boundaries. Samples processed in such a fashion exhibited 77 pct length fraction of low-Σ CSL boundaries, a dominant fraction of which was from Σ3 ( 64 pct), the latter with very low deviation from its theoretical misorientation. The application of hot rolling also resulted in a very low fraction of Σ1 ( 1 pct) boundaries, as desired. The process also leads to so-called "triple junction engineering" with the generation of special triple junctions, which are very effective in disrupting the connectivity of the random grain boundary network.

Carrier ethernet network control plane based on the Next Generation Network

DEFF Research Database (Denmark)

Fu, Rong; Wang, Yanmeng; Berger, Michael Stubert

2008-01-01

This paper contributes on presenting a step towards the realization of Carrier Ethernet control plane based on the next generation network (NGN). Specifically, transport MPLS (T-MPLS) is taken as the transport technology in Carrier Ethernet. It begins with providing an overview of the evolving...... architecture of the next generation network (NGN). As an essential candidate among the NGN transport technologies, the definition of Carrier Ethernet (CE) is also introduced here. The second part of this paper depicts the contribution on the T-MPLS based Carrier Ethernet network with control plane based on NGN...... at illustrating the improvement of the Carrier Ethernet network with the NGN control plane....

Statistical mechanics of the fashion game on random networks

International Nuclear Information System (INIS)

Sun, YiFan

2016-01-01

A model of fashion on networks is studied. This model consists of two groups of agents that are located on a network and have opposite viewpoints towards being fashionable: behaving consistently with either the majority or the minority of adjacent agents. Checking whether the fashion game has a pure Nash equilibrium (pure NE) is a non-deterministic polynomial complete problem. Using replica-symmetric mean field theory, the largest proportion of satisfied agents and the region where at least one pure NE should exist are determined for several types of random networks. Furthermore, a quantitive analysis of the asynchronous best response dynamics yields the phase diagram of existence and detectability of pure NE in the fashion game on some random networks. (paper: classical statistical mechanics, equilibrium and non-equilibrium).

About the problem of generating three-dimensional pseudo-random points.

Science.gov (United States)

Carpintero, D. D.

The author demonstrates that a popular pseudo-random number generator is not adequate in some circumstances to generate n-dimensional random points, n > 2. This problem is particularly noxious when direction cosines are generated. He proposes several soultions, among them a good generator that satisfies all statistical criteria.

Computer simulation of randomly cross-linked polymer networks

International Nuclear Information System (INIS)

Williams, Timothy Philip

2002-01-01

In this work, Monte Carlo and Stochastic Dynamics computer simulations of mesoscale model randomly cross-linked networks were undertaken. Task parallel implementations of the lattice Monte Carlo Bond Fluctuation model and Kremer-Grest Stochastic Dynamics bead-spring continuum model were designed and used for this purpose. Lattice and continuum precursor melt systems were prepared and then cross-linked to varying degrees. The resultant networks were used to study structural changes during deformation and relaxation dynamics. The effects of a random network topology featuring a polydisperse distribution of strand lengths and an abundance of pendant chain ends, were qualitatively compared to recent published work. A preliminary investigation into the effects of temperature on the structural and dynamical properties was also undertaken. Structural changes during isotropic swelling and uniaxial deformation, revealed a pronounced non-affine deformation dependant on the degree of cross-linking. Fractal heterogeneities were observed in the swollen model networks and were analysed by considering constituent substructures of varying size. The network connectivity determined the length scales at which the majority of the substructure unfolding process occurred. Simulated stress-strain curves and diffraction patterns for uniaxially deformed swollen networks, were found to be consistent with experimental findings. Analysis of the relaxation dynamics of various network components revealed a dramatic slowdown due to the network connectivity. The cross-link junction spatial fluctuations for networks close to the sol-gel threshold, were observed to be at least comparable with the phantom network prediction. The dangling chain ends were found to display the largest characteristic relaxation time. (author)

Optimal power flow for distribution networks with distributed generation

Directory of Open Access Journals (Sweden)

Radosavljević Jordan

2015-01-01

Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046

Image encryption using random sequence generated from generalized information domain

International Nuclear Information System (INIS)

Zhang Xia-Yan; Wu Jie-Hua; Zhang Guo-Ji; Li Xuan; Ren Ya-Zhou

2016-01-01

A novel image encryption method based on the random sequence generated from the generalized information domain and permutation–diffusion architecture is proposed. The random sequence is generated by reconstruction from the generalized information file and discrete trajectory extraction from the data stream. The trajectory address sequence is used to generate a P-box to shuffle the plain image while random sequences are treated as keystreams. A new factor called drift factor is employed to accelerate and enhance the performance of the random sequence generator. An initial value is introduced to make the encryption method an approximately one-time pad. Experimental results show that the random sequences pass the NIST statistical test with a high ratio and extensive analysis demonstrates that the new encryption scheme has superior security. (paper)

Hierarchy in directed random networks.

Science.gov (United States)

Mones, Enys

2013-02-01

In recent years, the theory and application of complex networks have been quickly developing in a markable way due to the increasing amount of data from real systems and the fruitful application of powerful methods used in statistical physics. Many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions. Hierarchy is one of these features that can be formulated in the language of networks. In this paper we present some (qualitative) analytic results on the hierarchical properties of random network models with zero correlations and also investigate, mainly numerically, the effects of different types of correlations. The behavior of the hierarchy is different in the absence and the presence of giant components. We show that the hierarchical structure can be drastically different if there are one-point correlations in the network. We also show numerical results suggesting that the hierarchy does not change monotonically with the correlations and there is an optimal level of nonzero correlations maximizing the level of hierarchy.

Application of random matrix theory to biological networks

Energy Technology Data Exchange (ETDEWEB)

Luo Feng [Department of Computer Science, Clemson University, 100 McAdams Hall, Clemson, SC 29634 (United States); Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhong Jianxin [Department of Physics, Xiangtan University, Hunan 411105 (China) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhongjn@ornl.gov; Yang Yunfeng [Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Scheuermann, Richard H. [Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072 (United States); Zhou Jizhong [Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019 (United States) and Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)]. E-mail: zhouj@ornl.gov

2006-09-25

We show that spectral fluctuation of interaction matrices of a yeast protein-protein interaction network and a yeast metabolic network follows the description of the Gaussian orthogonal ensemble (GOE) of random matrix theory (RMT). Furthermore, we demonstrate that while the global biological networks evaluated belong to GOE, removal of interactions between constituents transitions the networks to systems of isolated modules described by the Poisson distribution. Our results indicate that although biological networks are very different from other complex systems at the molecular level, they display the same statistical properties at network scale. The transition point provides a new objective approach for the identification of functional modules.

Autocatalytic polymerization generates persistent random walk of crawling cells.

Science.gov (United States)

Sambeth, R; Baumgaertner, A

2001-05-28

The autocatalytic polymerization kinetics of the cytoskeletal actin network provides the basic mechanism for a persistent random walk of a crawling cell. It is shown that network remodeling by branching processes near the cell membrane is essential for the bimodal spatial stability of the network which induces a spontaneous breaking of isotropic cell motion. Details of the phenomena are analyzed using a simple polymerization model studied by analytical and simulation methods.

A Markov model for the temporal dynamics of balanced random networks of finite size

Science.gov (United States)

Lagzi, Fereshteh; Rotter, Stefan

2014-01-01

The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between

Physical Configuration of the Next Generation Home Network

Science.gov (United States)

Terada, Shohei; Kakishima, Yu; Hanawa, Dai; Oguchi, Kimio

The number of broadband users is rapidly increasing worldwide. Japan already has over 10 million FTTH users. Another trend is the rapid digitalization of home electrical equipment e. g. digital cameras and hard disc recorders. These trends will encourage the emergence of the next generation home network. In this paper, we introduce the next generation home network image and describe the five domains into which home devices can be classified. We then clarify the optimum medium with which to configure the network given the requirements imposed by the home environment. Wiring cable lengths for three network topologies are calculated. The results gained from the next generation home network implemented on the first phase testbed are shown. Finally, our conclusions are given.

Optimized Energy Procurement for Cellular Networks with Uncertain Renewable Energy Generation

KAUST Repository

Rached, Nadhir B.

2017-02-07

Renewable energy (RE) is an emerging solution for reducing carbon dioxide (CO2) emissions from cellular networks. One of the challenges of using RE sources is to handle its inherent uncertainty. In this paper, a RE powered cellular network is investigated. For a one-day operation cycle, the cellular network aims to reduce energy procurement costs from the smart grid by optimizing the amounts of energy procured from their locally deployed RE sources as well as from the smart grid. In addition to that, it aims to determine the extra amount of energy to be sold to the electrical grid at each time period. Chance constrained optimization is first proposed to deal with the randomness in the RE generation. Then, to make the optimization problem tractable, two well- know convex approximation methods, namely; Chernoff and Chebyshev based-approaches, are analyzed in details. Numerical results investigate the optimized energy procurement for various daily scenarios and compare between the performances of the employed convex approximation approaches.

Generating equilateral random polygons in confinement

International Nuclear Information System (INIS)

Diao, Y; Ernst, C; Montemayor, A; Ziegler, U

2011-01-01

One challenging problem in biology is to understand the mechanism of DNA packing in a confined volume such as a cell. It is known that confined circular DNA is often knotted and hence the topology of the extracted (and relaxed) circular DNA can be used as a probe of the DNA packing mechanism. However, in order to properly estimate the topological properties of the confined circular DNA structures using mathematical models, it is necessary to generate large ensembles of simulated closed chains (i.e. polygons) of equal edge lengths that are confined in a volume such as a sphere of certain fixed radius. Finding efficient algorithms that properly sample the space of such confined equilateral random polygons is a difficult problem. In this paper, we propose a method that generates confined equilateral random polygons based on their probability distribution. This method requires the creation of a large database initially. However, once the database has been created, a confined equilateral random polygon of length n can be generated in linear time in terms of n. The errors introduced by the method can be controlled and reduced by the refinement of the database. Furthermore, our numerical simulations indicate that these errors are unbiased and tend to cancel each other in a long polygon. (paper)

Super fast physical-random number generation using laser diode frequency noises

Science.gov (United States)

Ushiki, Tetsuro; Doi, Kohei; Maehara, Shinya; Sato, Takashi; Ohkawa, Masashi; Ohdaira, Yasuo

2011-02-01

Random numbers can be classified as either pseudo- or physical-random in character. Pseudo-random numbers' periodicity renders them inappropriate for use in cryptographic applications, but naturally-generated physical-random numbers have no calculable periodicity, thereby making them ideally-suited to the task. The laser diode naturally produces a wideband "noise" signal that is believed to have tremendous capacity and great promise, for the rapid generation of physical-random numbers for use in cryptographic applications. We measured a laser diode's output, at a fast photo detector and generated physical-random numbers from frequency noises. We then identified and evaluated the binary-number-line's statistical properties. The result shows that physical-random number generation, at speeds as high as 40Gbps, is obtainable, using the laser diode's frequency noise characteristic.

An X-ray CCD signal generator with true random arrival time

International Nuclear Information System (INIS)

Huo Jia; Xu Yuming; Chen Yong; Cui Weiwei; Li Wei; Zhang Ziliang; Han Dawei; Wang Yusan; Wang Juan

2011-01-01

An FPGA-based true random signal generator with adjustable amplitude and exponential distribution of time interval is presented. Since traditional true random number generators (TRNG) are resource costly and difficult to transplant, we employed a method of random number generation based on jitter and phase noise in ring oscillators formed by gates in an FPGA. In order to improve the random characteristics, a combination of two different pseudo-random processing circuits is used for post processing. The effects of the design parameters, such as sample frequency are discussed. Statistical tests indicate that the generator can well simulate the timing behavior of random signals with Poisson distribution. The X-ray CCD signal generator will be used in debugging the CCD readout system of the Low Energy X-ray Instrument onboard the Hard X-ray Modulation Telescope (HXMT). (authors)

Efficient Raman generation in a waveguide: A route to ultrafast quantum random number generation

Energy Technology Data Exchange (ETDEWEB)

England, D. G.; Bustard, P. J.; Moffatt, D. J.; Nunn, J.; Lausten, R.; Sussman, B. J., E-mail: ben.sussman@nrc.ca [National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1A 0R6 (Canada)

2014-02-03

The inherent uncertainty in quantum mechanics offers a source of true randomness which can be used to produce unbreakable cryptographic keys. We discuss the development of a high-speed random number generator based on the quantum phase fluctuations in spontaneously initiated stimulated Raman scattering (SISRS). We utilize the tight confinement and long interaction length available in a Potassium Titanyl Phosphate waveguide to generate highly efficient SISRS using nanojoule pulse energies, reducing the high pump power requirements of the previous approaches. We measure the random phase of the Stokes output using a simple interferometric setup to yield quantum random numbers at 145 Mbps.

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Directory of Open Access Journals (Sweden)

J. C. Ochoa-Rivera

2002-01-01

Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..

Quantum random-number generator based on a photon-number-resolving detector

International Nuclear Information System (INIS)

Ren Min; Wu, E; Liang Yan; Jian Yi; Wu Guang; Zeng Heping

2011-01-01

We demonstrated a high-efficiency quantum random number generator which takes inherent advantage of the photon number distribution randomness of a coherent light source. This scheme was realized by comparing the photon flux of consecutive pulses with a photon number resolving detector. The random bit generation rate could reach 2.4 MHz with a system clock of 6.0 MHz, corresponding to a random bit generation efficiency as high as 40%. The random number files passed all the stringent statistical tests.

Secure self-calibrating quantum random-bit generator

International Nuclear Information System (INIS)

Fiorentino, M.; Santori, C.; Spillane, S. M.; Beausoleil, R. G.; Munro, W. J.

2007-01-01

Random-bit generators (RBGs) are key components of a variety of information processing applications ranging from simulations to cryptography. In particular, cryptographic systems require 'strong' RBGs that produce high-entropy bit sequences, but traditional software pseudo-RBGs have very low entropy content and therefore are relatively weak for cryptography. Hardware RBGs yield entropy from chaotic or quantum physical systems and therefore are expected to exhibit high entropy, but in current implementations their exact entropy content is unknown. Here we report a quantum random-bit generator (QRBG) that harvests entropy by measuring single-photon and entangled two-photon polarization states. We introduce and implement a quantum tomographic method to measure a lower bound on the 'min-entropy' of the system, and we employ this value to distill a truly random-bit sequence. This approach is secure: even if an attacker takes control of the source of optical states, a secure random sequence can be distilled

The additive congruential random number generator--A special case of a multiple recursive generator

Science.gov (United States)

Wikramaratna, Roy S.

2008-07-01

This paper considers an approach to generating uniformly distributed pseudo-random numbers which works well in serial applications but which also appears particularly well-suited for application on parallel processing systems. Additive Congruential Random Number (ACORN) generators are straightforward to implement for arbitrarily large order and modulus; if implemented using integer arithmetic, it becomes possible to generate identical sequences on any machine. Previously published theoretical analysis has demonstrated that a kth order ACORN sequence approximates to being uniformly distributed in up to k dimensions, for any given k. ACORN generators can be constructed to give period lengths exceeding any given number (for example, with period length in excess of 230p, for any given p). Results of empirical tests have demonstrated that, if p is greater than or equal to 2, then the ACORN generator can be used successfully for generating double precision uniform random variates. This paper demonstrates that an ACORN generator is a particular case of a multiple recursive generator (and, therefore, also a special case of a matrix generator). Both these latter approaches have been widely studied, and it is to be hoped that the results given in the present paper will lead to greater confidence in using the ACORN generators.

A priori data-driven multi-clustered reservoir generation algorithm for echo state network.

Directory of Open Access Journals (Sweden)

Xiumin Li

Full Text Available Echo state networks (ESNs with multi-clustered reservoir topology perform better in reservoir computing and robustness than those with random reservoir topology. However, these ESNs have a complex reservoir topology, which leads to difficulties in reservoir generation. This study focuses on the reservoir generation problem when ESN is used in environments with sufficient priori data available. Accordingly, a priori data-driven multi-cluster reservoir generation algorithm is proposed. The priori data in the proposed algorithm are used to evaluate reservoirs by calculating the precision and standard deviation of ESNs. The reservoirs are produced using the clustering method; only the reservoir with a better evaluation performance takes the place of a previous one. The final reservoir is obtained when its evaluation score reaches the preset requirement. The prediction experiment results obtained using the Mackey-Glass chaotic time series show that the proposed reservoir generation algorithm provides ESNs with extra prediction precision and increases the structure complexity of the network. Further experiments also reveal the appropriate values of the number of clusters and time window size to obtain optimal performance. The information entropy of the reservoir reaches the maximum when ESN gains the greatest precision.

Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators

Science.gov (United States)

Parks, Bradley; Bapna, Mukund; Igbokwe, Julianne; Almasi, Hamid; Wang, Weigang; Majetich, Sara A.

2018-05-01

Superparamagnetic perpendicular magnetic tunnel junctions are fabricated and analyzed for use in random number generators. Time-resolved resistance measurements are used as streams of bits in statistical tests for randomness. Voltage control of the thermal stability enables tuning the average speed of random bit generation up to 70 kHz in a 60 nm diameter device. In its most efficient operating mode, the device generates random bits at an energy cost of 600 fJ/bit. A narrow range of magnetic field tunes the probability of a given state from 0 to 1, offering a means of probabilistic computing.

Listening to the Noise: Random Fluctuations Reveal Gene Network Parameters

Science.gov (United States)

Munsky, Brian; Trinh, Brooke; Khammash, Mustafa

2010-03-01

The cellular environment is abuzz with noise originating from the inherent random motion of reacting molecules in the living cell. In this noisy environment, clonal cell populations exhibit cell-to-cell variability that can manifest significant prototypical differences. Noise induced stochastic fluctuations in cellular constituents can be measured and their statistics quantified using flow cytometry, single molecule fluorescence in situ hybridization, time lapse fluorescence microscopy and other single cell and single molecule measurement techniques. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. We use theoretical investigations to establish experimental guidelines for the identification of gene regulatory networks, and we apply these guideline to experimentally identify predictive models for different regulatory mechanisms in bacteria and yeast.

Quasirandom geometric networks from low-discrepancy sequences

Science.gov (United States)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Study on random number generator in Monte Carlo code

International Nuclear Information System (INIS)

Oya, Kentaro; Kitada, Takanori; Tanaka, Shinichi

2011-01-01

The Monte Carlo code uses a sequence of pseudo-random numbers with a random number generator (RNG) to simulate particle histories. A pseudo-random number has its own period depending on its generation method and the period is desired to be long enough not to exceed the period during one Monte Carlo calculation to ensure the correctness especially for a standard deviation of results. The linear congruential generator (LCG) is widely used as Monte Carlo RNG and the period of LCG is not so long by considering the increasing rate of simulation histories in a Monte Carlo calculation according to the remarkable enhancement of computer performance. Recently, many kinds of RNG have been developed and some of their features are better than those of LCG. In this study, we investigate the appropriate RNG in a Monte Carlo code as an alternative to LCG especially for the case of enormous histories. It is found that xorshift has desirable features compared with LCG, and xorshift has a larger period, a comparable speed to generate random numbers, a better randomness, and good applicability to parallel calculation. (author)

Random Item Generation Is Affected by Age

Science.gov (United States)

Multani, Namita; Rudzicz, Frank; Wong, Wing Yiu Stephanie; Namasivayam, Aravind Kumar; van Lieshout, Pascal

2016-01-01

Purpose: Random item generation (RIG) involves central executive functioning. Measuring aspects of random sequences can therefore provide a simple method to complement other tools for cognitive assessment. We examine the extent to which RIG relates to specific measures of cognitive function, and whether those measures can be estimated using RIG…

A fast ergodic algorithm for generating ensembles of equilateral random polygons

Science.gov (United States)

Varela, R.; Hinson, K.; Arsuaga, J.; Diao, Y.

2009-03-01

Knotted structures are commonly found in circular DNA and along the backbone of certain proteins. In order to properly estimate properties of these three-dimensional structures it is often necessary to generate large ensembles of simulated closed chains (i.e. polygons) of equal edge lengths (such polygons are called equilateral random polygons). However finding efficient algorithms that properly sample the space of equilateral random polygons is a difficult problem. Currently there are no proven algorithms that generate equilateral random polygons with its theoretical distribution. In this paper we propose a method that generates equilateral random polygons in a 'step-wise uniform' way. We prove that this method is ergodic in the sense that any given equilateral random polygon can be generated by this method and we show that the time needed to generate an equilateral random polygon of length n is linear in terms of n. These two properties make this algorithm a big improvement over the existing generating methods. Detailed numerical comparisons of our algorithm with other widely used algorithms are provided.

Parameters affecting the resilience of scale-free networks to random failures.

Energy Technology Data Exchange (ETDEWEB)

Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran (University of New Mexico, Albuquerque, NM); Saia, Jared (University of New Mexico, Albuquerque, NM)

2005-09-01

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

Experimentally generated randomness certified by the impossibility of superluminal signals.

Science.gov (United States)

Bierhorst, Peter; Knill, Emanuel; Glancy, Scott; Zhang, Yanbao; Mink, Alan; Jordan, Stephen; Rommal, Andrea; Liu, Yi-Kai; Christensen, Bradley; Nam, Sae Woo; Stevens, Martin J; Shalm, Lynden K

2018-04-01

From dice to modern electronic circuits, there have been many attempts to build better devices to generate random numbers. Randomness is fundamental to security and cryptographic systems and to safeguarding privacy. A key challenge with random-number generators is that it is hard to ensure that their outputs are unpredictable 1-3 . For a random-number generator based on a physical process, such as a noisy classical system or an elementary quantum measurement, a detailed model that describes the underlying physics is necessary to assert unpredictability. Imperfections in the model compromise the integrity of the device. However, it is possible to exploit the phenomenon of quantum non-locality with a loophole-free Bell test to build a random-number generator that can produce output that is unpredictable to any adversary that is limited only by general physical principles, such as special relativity 1-11 . With recent technological developments, it is now possible to carry out such a loophole-free Bell test 12-14,22 . Here we present certified randomness obtained from a photonic Bell experiment and extract 1,024 random bits that are uniformly distributed to within 10 -12 . These random bits could not have been predicted according to any physical theory that prohibits faster-than-light (superluminal) signalling and that allows independent measurement choices. To certify and quantify the randomness, we describe a protocol that is optimized for devices that are characterized by a low per-trial violation of Bell inequalities. Future random-number generators based on loophole-free Bell tests may have a role in increasing the security and trust of our cryptographic systems and infrastructure.

Quantum random flip-flop and its applications in random frequency synthesis and true random number generation

Energy Technology Data Exchange (ETDEWEB)

Stipčević, Mario, E-mail: mario.stipcevic@irb.hr [Photonics and Quantum Optics Research Unit, Center of Excellence for Advanced Materials and Sensing Devices, Ruđer Bošković Institute, Bijenička 54, 10000 Zagreb (Croatia)

2016-03-15

In this work, a new type of elementary logic circuit, named random flip-flop (RFF), is proposed, experimentally realized, and studied. Unlike conventional Boolean logic circuits whose action is deterministic and highly reproducible, the action of a RFF is intentionally made maximally unpredictable and, in the proposed realization, derived from a fundamentally random process of emission and detection of light quanta. We demonstrate novel applications of RFF in randomness preserving frequency division, random frequency synthesis, and random number generation. Possible usages of these applications in the information and communication technology, cryptographic hardware, and testing equipment are discussed.

Distributed Detection with Collisions in a Random, Single-Hop Wireless Sensor Network

Science.gov (United States)

2013-05-26

public release; distribution is unlimited. Distributed detection with collisions in a random, single-hop wireless sensor network The views, opinions...1274 2 ABSTRACT Distributed detection with collisions in a random, single-hop wireless sensor network Report Title We consider the problem of... WIRELESS SENSOR NETWORK Gene T. Whipps?† Emre Ertin† Randolph L. Moses† ?U.S. Army Research Laboratory, Adelphi, MD 20783 †The Ohio State University

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

Science.gov (United States)

Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

2015-07-21

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

Randomizing growing networks with a time-respecting null model

Science.gov (United States)

Ren, Zhuo-Ming; Mariani, Manuel Sebastian; Zhang, Yi-Cheng; Medo, Matúš

2018-05-01

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology—a time-respecting null model—that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Fast random-number generation using a diode laser's frequency noise characteristic

Science.gov (United States)

Takamori, Hiroki; Doi, Kohei; Maehara, Shinya; Kawakami, Kohei; Sato, Takashi; Ohkawa, Masashi; Ohdaira, Yasuo

2012-02-01

Random numbers can be classified as either pseudo- or physical-random, in character. Pseudo-random numbers are generated by definite periodicity, so, their usefulness in cryptographic applications is somewhat limited. On the other hand, naturally-generated physical-random numbers have no calculable periodicity, thereby making them ideal for the task. Diode lasers' considerable wideband noise gives them tremendous capacity for generating physical-random numbers, at a high rate of speed. We measured a diode laser's output with a fast photo detector, and evaluated the binary-numbers from the diode laser's frequency noise characteristics. We then identified and evaluated the binary-number-line's statistical properties. We also investigate the possibility that much faster physical-random number parallel-generation is possible, using separate outputs of different optical-path length and character, which we refer to as "coherence collapse".

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

Safro, I. M. (Mathematics and Computer Science)

2012-02-24

Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

Properties making a chaotic system a good Pseudo Random Number Generator

OpenAIRE

Falcioni, Massimo; Palatella, Luigi; Pigolotti, Simone; Vulpiani, Angelo

2005-01-01

We discuss two properties making a deterministic algorithm suitable to generate a pseudo random sequence of numbers: high value of Kolmogorov-Sinai entropy and high-dimensionality. We propose the multi dimensional Anosov symplectic (cat) map as a Pseudo Random Number Generator. We show what chaotic features of this map are useful for generating Pseudo Random Numbers and investigate numerically which of them survive in the discrete version of the map. Testing and comparisons with other generat...

A Repetition Test for Pseudo-Random Number Generators

OpenAIRE

Gil, Manuel; Gonnet, Gaston H.; Petersen, Wesley P.

2017-01-01

A new statistical test for uniform pseudo-random number generators (PRNGs) is presented. The idea is that a sequence of pseudo-random numbers should have numbers reappear with a certain probability. The expectation time that a repetition occurs provides the metric for the test. For linear congruential generators (LCGs) failure can be shown theoretically. Empirical test results for a number of commonly used PRNGs are reported, showing that some PRNGs considered to have good statistical propert...

Large-size, high-uniformity, random silver nanowire networks as transparent electrodes for crystalline silicon wafer solar cells.

Science.gov (United States)

Xie, Shouyi; Ouyang, Zi; Jia, Baohua; Gu, Min

2013-05-06

Metal nanowire networks are emerging as next generation transparent electrodes for photovoltaic devices. We demonstrate the application of random silver nanowire networks as the top electrode on crystalline silicon wafer solar cells. The dependence of transmittance and sheet resistance on the surface coverage is measured. Superior optical and electrical properties are observed due to the large-size, highly-uniform nature of these networks. When applying the nanowire networks on the solar cells with an optimized two-step annealing process, we achieved as large as 19% enhancement on the energy conversion efficiency. The detailed analysis reveals that the enhancement is mainly caused by the improved electrical properties of the solar cells due to the silver nanowire networks. Our result reveals that this technology is a promising alternative transparent electrode technology for crystalline silicon wafer solar cells.

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

OpenAIRE

Zhang, Han; Xu, Tao; Li, Hongsheng; Zhang, Shaoting; Wang, Xiaogang; Huang, Xiaolei; Metaxas, Dimitris

2017-01-01

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given...

Network clustering analysis using mixture exponential-family random graph models and its application in genetic interaction data.

Science.gov (United States)

Wang, Yishu; Zhao, Hongyu; Deng, Minghua; Fang, Huaying; Yang, Dejie

2017-08-24

Epistatic miniarrary profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. It provides an incredible set of molecular tools and advanced technologies that should be efficiently understanding the relationship between the genotypes and phenotypes of individuals. However, the network information gained from EMAP cannot be fully exploited using the traditional statistical network models. Because the genetic network is always heterogeneous, for example, the network structure features for one subset of nodes are different from those of the left nodes. Exponentialfamily random graph models (ERGMs) are a family of statistical models, which provide a principled and flexible way to describe the structural features (e.g. the density, centrality and assortativity) of an observed network. However, the single ERGM is not enough to capture this heterogeneity of networks. In this paper, we consider a mixture ERGM (MixtureEGRM) networks, which model a network with several communities, where each community is described by a single EGRM.

Scaling of peak flows with constant flow velocity in random self-similar networks

Science.gov (United States)

Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

2011-01-01

A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios

Simulation of nonlinear random vibrations using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Paez, T.L.; Tucker, S.; O`Gorman, C.

1997-02-01

The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

Generating functionals for quantum field theories with random potentials

International Nuclear Information System (INIS)

Jain, Mudit; Vanchurin, Vitaly

2016-01-01

We consider generating functionals for computing correlators in quantum field theories with random potentials. Examples of such theories include cosmological systems in context of the string theory landscape (e.g. cosmic inflation) or condensed matter systems with quenched disorder (e.g. spin glass). We use the so-called replica trick to define two different generating functionals for calculating correlators of the quantum fields averaged over a given distribution of random potentials. The first generating functional is appropriate for calculating averaged (in-out) amplitudes and involves a single replica of fields, but the replica limit is taken to an (unphysical) negative one number of fields outside of the path integral. When the number of replicas is doubled the generating functional can also be used for calculating averaged probabilities (squared amplitudes) using the in-in construction. The second generating functional involves an infinite number of replicas, but can be used for calculating both in-out and in-in correlators and the replica limits are taken to only a zero number of fields. We discuss the formalism in details for a single real scalar field, but the generalization to more fields or to different types of fields is straightforward. We work out three examples: one where the mass of scalar field is treated as a random variable and two where the functional form of interactions is random, one described by a Gaussian random field and the other by a Euclidean action in the field configuration space.

Security Flaws in an Efficient Pseudo-Random Number Generator for Low-Power Environments

Science.gov (United States)

Peris-Lopez, Pedro; Hernandez-Castro, Julio C.; Tapiador, Juan M. E.; Millán, Enrique San; van der Lubbe, Jan C. A.

In 2004, Settharam and Rhee tackled the design of a lightweight Pseudo-Random Number Generator (PRNG) suitable for low-power environments (e.g. sensor networks, low-cost RFID tags). First, they explicitly fixed a set of requirements for this primitive. Then, they proposed a PRNG conforming to these requirements and using a free-running timer [9]. We analyze this primitive discovering important security faults. The proposed algorithm fails to pass even relatively non-stringent batteries of randomness such as ENT (i.e. a pseudorandom number sequence test program). We prove that their recommended PRNG has a very short period due to the flawed design of its core. The internal state can be easily revealed, compromising its backward and forward security. Additionally, the rekeying algorithm is defectively designed mainly related to the unpractical value proposed for this purpose.

Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations.

Science.gov (United States)

Ghersi, Dario; Parakh, Abhishek; Mezei, Mihaly

2017-12-05

Four pseudorandom number generators were compared with a physical, quantum-based random number generator using the NIST suite of statistical tests, which only the quantum-based random number generator could successfully pass. We then measured the effect of the five random number generators on various calculated properties in different Markov-chain Monte Carlo simulations. Two types of systems were tested: conformational sampling of a small molecule in aqueous solution and liquid methanol under constant temperature and pressure. The results show that poor quality pseudorandom number generators produce results that deviate significantly from those obtained with the quantum-based random number generator, particularly in the case of the small molecule in aqueous solution setup. In contrast, the widely used Mersenne Twister pseudorandom generator and a 64-bit Linear Congruential Generator with a scrambler produce results that are statistically indistinguishable from those obtained with the quantum-based random number generator. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Brain potentials index executive functions during random number generation.

Science.gov (United States)

Joppich, Gregor; Däuper, Jan; Dengler, Reinhard; Johannes, Sönke; Rodriguez-Fornells, Antoni; Münte, Thomas F

2004-06-01

The generation of random sequences is considered to tax different executive functions. To explore the involvement of these functions further, brain potentials were recorded in 16 healthy young adults while either engaging in random number generation (RNG) by pressing the number keys on a computer keyboard in a random sequence or in ordered number generation (ONG) necessitating key presses in the canonical order. Key presses were paced by an external auditory stimulus to yield either fast (1 press/800 ms) or slow (1 press/1300 ms) sequences in separate runs. Attentional demands of random and ordered tasks were assessed by the introduction of a secondary task (key-press to a target tone). The P3 amplitude to the target tone of this secondary task was reduced during RNG, reflecting the greater consumption of attentional resources during RNG. Moreover, RNG led to a left frontal negativity peaking 140 ms after the onset of the pacing stimulus, whenever the subjects produced a true random response. This negativity could be attributed to the left dorsolateral prefrontal cortex and was absent when numbers were repeated. This negativity was interpreted as an index for the inhibition of habitual responses. Finally, in response locked ERPs a negative component was apparent peaking about 50 ms after the key-press that was more prominent during RNG. Source localization suggested a medial frontal source. This effect was tentatively interpreted as a reflection of the greater monitoring demands during random sequence generation.

Post-processing Free Quantum Random Number Generator Based on Avalanche Photodiode Array

International Nuclear Information System (INIS)

Li Yang; Liao Sheng-Kai; Liang Fu-Tian; Shen Qi; Liang Hao; Peng Cheng-Zhi

2016-01-01

Quantum random number generators adopting single photon detection have been restricted due to the non-negligible dead time of avalanche photodiodes (APDs). We propose a new approach based on an APD array to improve the generation rate of random numbers significantly. This method compares the detectors' responses to consecutive optical pulses and generates the random sequence. We implement a demonstration experiment to show its simplicity, compactness and scalability. The generated numbers are proved to be unbiased, post-processing free, ready to use, and their randomness is verified by using the national institute of standard technology statistical test suite. The random bit generation efficiency is as high as 32.8% and the potential generation rate adopting the 32 × 32 APD array is up to tens of Gbits/s. (paper)

A true random number generator based on mouse movement and chaotic cryptography

International Nuclear Information System (INIS)

Hu Yue; Liao Xiaofeng; Wong, Kwok-wo; Zhou Qing

2009-01-01

True random number generators are in general more secure than pseudo random number generators. In this paper, we propose a novel true random number generator which generates a 256-bit random number by computer mouse movement. It is cheap, convenient and universal for personal computers. To eliminate the effect of similar movement patterns generated by the same user, three chaos-based approaches, namely, discretized 2D chaotic map permutation, spatiotemporal chaos and 'MASK' algorithm, are adopted to post-process the captured mouse movements. Random bits generated by three users are tested using NIST statistical tests. Both the spatiotemporal chaos approach and the 'MASK' algorithm pass the tests successfully. However, the latter has a better performance in terms of efficiency and effectiveness and so is more practical for common personal computer applications.

Truly random dynamics generated by autonomous dynamical systems

Science.gov (United States)

González, J. A.; Reyes, L. I.

2001-09-01

We investigate explicit functions that can produce truly random numbers. We use the analytical properties of the explicit functions to show that a certain class of autonomous dynamical systems can generate random dynamics. This dynamics presents fundamental differences with the known chaotic systems. We present real physical systems that can produce this kind of random time-series. Some applications are discussed.

Mean First Passage Time of Preferential Random Walks on Complex Networks with Applications

Directory of Open Access Journals (Sweden)

Zhongtuan Zheng

2017-01-01

Full Text Available This paper investigates, both theoretically and numerically, preferential random walks (PRW on weighted complex networks. By using two different analytical methods, two exact expressions are derived for the mean first passage time (MFPT between two nodes. On one hand, the MFPT is got explicitly in terms of the eigenvalues and eigenvectors of a matrix associated with the transition matrix of PRW. On the other hand, the center-product-degree (CPD is introduced as one measure of node strength and it plays a main role in determining the scaling of the MFPT for the PRW. Comparative studies are also performed on PRW and simple random walks (SRW. Numerical simulations of random walks on paradigmatic network models confirm analytical predictions and deepen discussions in different aspects. The work may provide a comprehensive approach for exploring random walks on complex networks, especially biased random walks, which may also help to better understand and tackle some practical problems such as search and routing on networks.

Ryu-Takayanagi formula for symmetric random tensor networks

Science.gov (United States)

Chirco, Goffredo; Oriti, Daniele; Zhang, Mingyi

2018-06-01

We consider the special case of random tensor networks (RTNs) endowed with gauge symmetry constraints on each tensor. We compute the Rényi entropy for such states and recover the Ryu-Takayanagi (RT) formula in the large-bond regime. The result provides first of all an interesting new extension of the existing derivations of the RT formula for RTNs. Moreover, this extension of the RTN formalism brings it in direct relation with (tensorial) group field theories (and spin networks), and thus provides new tools for realizing the tensor network/geometry duality in the context of background-independent quantum gravity, and for importing quantum gravity tools into tensor network research.

Recommendations and illustrations for the evaluation of photonic random number generators

Science.gov (United States)

Hart, Joseph D.; Terashima, Yuta; Uchida, Atsushi; Baumgartner, Gerald B.; Murphy, Thomas E.; Roy, Rajarshi

2017-09-01

The never-ending quest to improve the security of digital information combined with recent improvements in hardware technology has caused the field of random number generation to undergo a fundamental shift from relying solely on pseudo-random algorithms to employing optical entropy sources. Despite these significant advances on the hardware side, commonly used statistical measures and evaluation practices remain ill-suited to understand or quantify the optical entropy that underlies physical random number generation. We review the state of the art in the evaluation of optical random number generation and recommend a new paradigm: quantifying entropy generation and understanding the physical limits of the optical sources of randomness. In order to do this, we advocate for the separation of the physical entropy source from deterministic post-processing in the evaluation of random number generators and for the explicit consideration of the impact of the measurement and digitization process on the rate of entropy production. We present the Cohen-Procaccia estimate of the entropy rate h (𝜖 ,τ ) as one way to do this. In order to provide an illustration of our recommendations, we apply the Cohen-Procaccia estimate as well as the entropy estimates from the new NIST draft standards for physical random number generators to evaluate and compare three common optical entropy sources: single photon time-of-arrival detection, chaotic lasers, and amplified spontaneous emission.

Recommendations and illustrations for the evaluation of photonic random number generators

Directory of Open Access Journals (Sweden)

Joseph D. Hart

2017-09-01

Full Text Available The never-ending quest to improve the security of digital information combined with recent improvements in hardware technology has caused the field of random number generation to undergo a fundamental shift from relying solely on pseudo-random algorithms to employing optical entropy sources. Despite these significant advances on the hardware side, commonly used statistical measures and evaluation practices remain ill-suited to understand or quantify the optical entropy that underlies physical random number generation. We review the state of the art in the evaluation of optical random number generation and recommend a new paradigm: quantifying entropy generation and understanding the physical limits of the optical sources of randomness. In order to do this, we advocate for the separation of the physical entropy source from deterministic post-processing in the evaluation of random number generators and for the explicit consideration of the impact of the measurement and digitization process on the rate of entropy production. We present the Cohen-Procaccia estimate of the entropy rate h(,τ as one way to do this. In order to provide an illustration of our recommendations, we apply the Cohen-Procaccia estimate as well as the entropy estimates from the new NIST draft standards for physical random number generators to evaluate and compare three common optical entropy sources: single photon time-of-arrival detection, chaotic lasers, and amplified spontaneous emission.

Parallel random number generator for inexpensive configurable hardware cells

Science.gov (United States)

Ackermann, J.; Tangen, U.; Bödekker, B.; Breyer, J.; Stoll, E.; McCaskill, J. S.

2001-11-01

A new random number generator ( RNG) adapted to parallel processors has been created. This RNG can be implemented with inexpensive hardware cells. The correlation between neighboring cells is suppressed with smart connections. With such connection structures, sequences of pseudo-random numbers are produced. Numerical tests including a self-avoiding random walk test and the simulation of the order parameter and energy of the 2D Ising model give no evidence for correlation in the pseudo-random sequences. Because the new random number generator has suppressed the correlation between neighboring cells which is usually observed in cellular automaton implementations, it is applicable for extended time simulations. It gives an immense speed-up factor if implemented directly in configurable hardware, and has recently been used for long time simulations of spatially resolved molecular evolution.

A pseudo-random number generator and its spectral test

International Nuclear Information System (INIS)

Wang Lai

1998-01-01

The author introduces a pseudo-random number generator and describes its algorithm and C language implementation. The performance of the generator is tested and compared with some well known LCG generators

Generation of Random Numbers and Parallel Random Number Streams for Monte Carlo Simulations

Directory of Open Access Journals (Sweden)

L. Yu. Barash

2012-01-01

Full Text Available Modern methods and libraries for high quality pseudorandom number generation and for generation of parallel random number streams for Monte Carlo simulations are considered. The probability equidistribution property and the parameters when the property holds at dimensions up to logarithm of mesh size are considered for Multiple Recursive Generators.

Random generation of RNA secondary structures according to native distributions

Directory of Open Access Journals (Sweden)

Nebel Markus E

2011-10-01

Full Text Available Abstract Background Random biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate. Accordingly, the generation of random sequences has been investigated for a long time. Similarly, random object of a more complicated structure like RNA molecules or proteins are of interest. Results In this article, we present a new general framework for deriving algorithms for the non-uniform random generation of combinatorial objects according to the encoding and probability distribution implied by a stochastic context-free grammar. Briefly, the framework extends on the well-known recursive method for (uniform random generation and uses the popular framework of admissible specifications of combinatorial classes, introducing weighted combinatorial classes to allow for the non-uniform generation by means of unranking. This framework is used to derive an algorithm for the generation of RNA secondary structures of a given fixed size. We address the random generation of these structures according to a realistic distribution obtained from real-life data by using a very detailed context-free grammar (that models the class of RNA secondary structures by distinguishing between all known motifs in RNA structure. Compared to well-known sampling approaches used in several structure prediction tools (such as SFold ours has two major advantages: Firstly, after a preprocessing step in time O(n2 for the computation of all weighted class sizes needed, with our approach a set of m random secondary structures of a given structure size n can be computed in worst-case time complexity Om⋅n⋅ log(n while other algorithms typically have a runtime in O(m⋅n2. Secondly, our approach works with integer arithmetic only which is faster and saves us from all the discomforting details of using floating point arithmetic with

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Decentralized formation of random regular graphs for robust multi-agent networks

KAUST Repository

Yazicioglu, A. Yasin

2014-12-15

Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs have significant impact on the robustness of networked systems. One family of robust graphs is the random regular graphs. In this paper, we present a locally applicable reconfiguration scheme to build random regular graphs through self-organization. For any connected initial graph, the proposed scheme maintains connectivity and the average degree while minimizing the degree differences and randomizing the links. As such, if the average degree of the initial graph is an integer, then connected regular graphs are realized uniformly at random as time goes to infinity.

Quantum Random Networks for Type 2 Quantum Computers

National Research Council Canada - National Science Library

Allara, David L; Hasslacher, Brosl

2006-01-01

Random boolean networks (RBNs) have been studied theoretically and computationally in order to be able to use their remarkable self-healing and large basins of altercation properties as quantum computing architectures, especially...

Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services

DEFF Research Database (Denmark)

Tassi, Andrea; Chatzigeorgiou, Ioannis; Roetter, Daniel Enrique Lucani

2016-01-01

Point-to-multipoint communications are expected to play a pivotal role in next-generation networks. This paper refers to a cellular system transmitting layered multicast services to a multicast group of users. Reliability of communications is ensured via different random linear network coding (RLNC......) techniques. We deal with a fundamental problem: the computational complexity of the RLNC decoder. The higher the number of decoding operations is, the more the user's computational overhead grows and, consequently, the faster the battery of mobile devices drains. By referring to several sparse RLNC...... techniques, and without any assumption on the implementation of the RLNC decoder in use, we provide an efficient way to characterize the performance of users targeted by ultra-reliable layered multicast services. The proposed modeling allows to efficiently derive the average number of coded packet...

Deep Convolutional Generative Adversarial Network for Procedural 3D Landscape Generation Based on DEM

OpenAIRE

Wulff-Jensen, Andreas; Rant, Niclas Nerup; Møller, Tobias Nordvig; Billeskov, Jonas Aksel

2018-01-01

This paper proposes a novel framework for improving procedural generation of 3D landscapes using machine learning. We utilized a Deep Convolutional Generative Adversarial Network (DC-GAN) to generate heightmaps. The network was trained on a dataset consisting of Digital Elevation Maps (DEM) of the alps. During map generation, the batch size and learning rate were optimized for the most efficient and satisfying map production. The diversity of the final output was tested against Perlin noise u...

Dynamic Output Feedback Control for Nonlinear Networked Control Systems with Random Packet Dropout and Random Delay

Directory of Open Access Journals (Sweden)

Shuiqing Yu

2013-01-01

Full Text Available This paper investigates the dynamic output feedback control for nonlinear networked control systems with both random packet dropout and random delay. Random packet dropout and random delay are modeled as two independent random variables. An observer-based dynamic output feedback controller is designed based upon the Lyapunov theory. The quantitative relationship of the dropout rate, transition probability matrix, and nonlinear level is derived by solving a set of linear matrix inequalities. Finally, an example is presented to illustrate the effectiveness of the proposed method.

Recent results for random networks of automata

International Nuclear Information System (INIS)

Flyvbjerg, H.

1987-05-01

After a very brief historical and contextual introduction to random networks of automata we review recent numerical and analytical results. Open questions and unsolved problems are pointed out and discussed. One such question is also answered: it is shown that the size of the stable core can be used as order parameter for a transition between phases of frozen and chaotic network behavior. A mean-field-like but exact selfconsistency equation for the size of the stable core is given. A new derivation of critical parameter values follows from it. (orig.)

Pseudo-random bit generator based on lag time series

Science.gov (United States)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Order-based representation in random networks of cortical neurons.

Directory of Open Access Journals (Sweden)

Goded Shahaf

2008-11-01

Full Text Available The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

Modeling documents with Generative Adversarial Networks

OpenAIRE

Glover, John

2016-01-01

This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.

A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

Science.gov (United States)

Grauer, Jared A.

2017-01-01

Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

The European Nuclear Society Young Generation Network: Five years of networking experience

International Nuclear Information System (INIS)

Meskens, Gaston

2000-01-01

In 1995, Mr Jan Runermark (Sweden), aware of a need for an exchange of knowledge from the older to the younger generation, came up with the idea of starting a European Nuclear Society Young Generation Network. A first network was formed with Sweden, the Netherlands, Spain, Finland, Germany and Belgium. The ENSYGN is now affiliated to the European Nuclear Society and brings together young students and professionals from 21 member countries Belgium, Bulgaria, Croatia, Czech Republic, Denmark Finland, France, Germany, Hungary, Italy, Netherlands, Poland, Romania, Russia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Ukraine, and United Kingdom, The ENSYGN Core group meets (at least) twice a year and elects its own chair and co chair for a term of two years. The ENSYGN chair has a seat in the ENS Steering Committee and in the ENS Board. The ENSYGN works closely together with other young generation networks from the US, Australia, Japan and South America. ENSYGN organises workshops and courses on European level, takes part in international meetings (fl. UNFCCC, OECD) and stimulates networking on national level

Achieving universal access to next generation networks

DEFF Research Database (Denmark)

Falch, Morten; Henten, Anders

The paper examines investment dimensions of next generation networks in a universal service perspective in a European context. The question is how new network infrastructures for getting access to communication, information and entertainment services in the present and future information society...

A method for generating skewed random numbers using two overlapping uniform distributions

International Nuclear Information System (INIS)

Ermak, D.L.; Nasstrom, J.S.

1995-02-01

The objective of this work was to implement and evaluate a method for generating skewed random numbers using a combination of uniform random numbers. The method provides a simple and accurate way of generating skewed random numbers from the specified first three moments without an a priori specification of the probability density function. We describe the procedure for generating skewed random numbers from unifon-n random numbers, and show that it accurately produces random numbers with the desired first three moments over a range of skewness values. We also show that in the limit of zero skewness, the distribution of random numbers is an accurate approximation to the Gaussian probability density function. Future work win use this method to provide skewed random numbers for a Langevin equation model for diffusion in skewed turbulence

Generating variable and random schedules of reinforcement using Microsoft Excel macros.

Science.gov (United States)

Bancroft, Stacie L; Bourret, Jason C

2008-01-01

Variable reinforcement schedules are used to arrange the availability of reinforcement following varying response ratios or intervals of time. Random reinforcement schedules are subtypes of variable reinforcement schedules that can be used to arrange the availability of reinforcement at a constant probability across number of responses or time. Generating schedule values for variable and random reinforcement schedules can be difficult. The present article describes the steps necessary to write macros in Microsoft Excel that will generate variable-ratio, variable-interval, variable-time, random-ratio, random-interval, and random-time reinforcement schedule values.

Realization of a Quantum Random Generator Certified with the Kochen-Specker Theorem

Science.gov (United States)

Kulikov, Anatoly; Jerger, Markus; Potočnik, Anton; Wallraff, Andreas; Fedorov, Arkady

2017-12-01

Random numbers are required for a variety of applications from secure communications to Monte Carlo simulation. Yet randomness is an asymptotic property, and no output string generated by a physical device can be strictly proven to be random. We report an experimental realization of a quantum random number generator (QRNG) with randomness certified by quantum contextuality and the Kochen-Specker theorem. The certification is not performed in a device-independent way but through a rigorous theoretical proof of each outcome being value indefinite even in the presence of experimental imperfections. The analysis of the generated data confirms the incomputable nature of our QRNG.

Note on Marsaglia\\'s Xorshift Random Number Generators

Directory of Open Access Journals (Sweden)

Richard P. Brent

2004-08-01

Full Text Available Marsaglia (2003 has described a class of Xorshift random number generators (RNGs with periods 2n - 1 for n = 32, 64, etc. We show that the sequences generated by these RNGs are identical to the sequences generated by certain linear feedback shift register (LFSR generators using "exclusive or" (xor operations on n-bit words, with a recurrence defined by a primitive polynomial of degree n.

Young generation network: facing the future

International Nuclear Information System (INIS)

Berk, R.

1997-01-01

The future of the nuclear industry lies with the young generation. That's why in 1995, ENS supported the creation of the Young Generation Network (YGN). The YGN aims to fulfill the needs and interests of young people working in the nuclear business by organizing special programs with interesting opportunities and activities. (author)

A novel Random Walk algorithm with Compulsive Evolution for heat exchanger network synthesis

International Nuclear Information System (INIS)

Xiao, Yuan; Cui, Guomin

2017-01-01

Highlights: • A novel Random Walk Algorithm with Compulsive Evolution is proposed for HENS. • A simple and feasible evolution strategy is presented in RWCE algorithm. • The integer and continuous variables of HEN are optimized simultaneously in RWCE. • RWCE is demonstrated a relatively strong global search ability in HEN optimization. - Abstract: The heat exchanger network (HEN) synthesis can be characterized as highly combinatorial, nonlinear and nonconvex, contributing to unmanageable computational time and a challenge in identifying the global optimal network design. Stochastic methods are robust and show a powerful global optimizing ability. Based on the common characteristic of different stochastic methods, namely randomness, a novel Random Walk algorithm with Compulsive Evolution (RWCE) is proposed to achieve the best possible total annual cost of heat exchanger network with the relatively simple and feasible evolution strategy. A population of heat exchanger networks is first randomly initialized. Next, the heat load of heat exchanger for each individual is randomly expanded or contracted in order to optimize both the integer and continuous variables simultaneously and to obtain the lowest total annual cost. Besides, when individuals approach to local optima, there is a certain probability for them to compulsively accept the imperfect networks in order to keep the population diversity and ability of global optimization. The presented method is then applied to heat exchanger network synthesis cases from the literature to compare the best results published. RWCE consistently has a lower computed total annual cost compared to previously published results.

Shortest loops are pacemakers in random networks of electrically coupled axons

Directory of Open Access Journals (Sweden)

Nikita eVladimirov

2012-04-01

Full Text Available High-frequency oscillations (HFOs are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bidirectional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100-200 Hz are predicted to be caused by spontaneously spiking axons in a network with strong (high-conductance gap junctions. Type II oscillations (200-300 Hz require no spontaneous spiking and relatively weak (low-conductance gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network's loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate.The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.

GASPRNG: GPU accelerated scalable parallel random number generator library

Science.gov (United States)

Gao, Shuang; Peterson, Gregory D.

2013-04-01

Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or

Quantum games on evolving random networks

OpenAIRE

Pawela, Łukasz

2015-01-01

We study the advantages of quantum strategies in evolutionary social dilemmas on evolving random networks. We focus our study on the two-player games: prisoner's dilemma, snowdrift and stag-hunt games. The obtained result show the benefits of quantum strategies for the prisoner's dilemma game. For the other two games, we obtain regions of parameters where the quantum strategies dominate, as well as regions where the classical strategies coexist.

Pseudo-random-number generators and the square site percolation threshold.

Science.gov (United States)

Lee, Michael J

2008-09-01

Selected pseudo-random-number generators are applied to a Monte Carlo study of the two-dimensional square-lattice site percolation model. A generator suitable for high precision calculations is identified from an application specific test of randomness. After extended computation and analysis, an ostensibly reliable value of p_{c}=0.59274598(4) is obtained for the percolation threshold.

Scaling of peak flows with constant flow velocity in random self-similar networks

Directory of Open Access Journals (Sweden)

R. Mantilla

2011-07-01

Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters p_i and p_e, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β^(E and φ^(E that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β^(E and φ^(E and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ^(E and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit

Analytical connection between thresholds and immunization strategies of SIS model in random networks

Science.gov (United States)

Zhou, Ming-Yang; Xiong, Wen-Man; Liao, Hao; Wang, Tong; Wei, Zong-Wen; Fu, Zhong-Qian

2018-05-01

Devising effective strategies for hindering the propagation of viruses and protecting the population against epidemics is critical for public security and health. Despite a number of studies based on the susceptible-infected-susceptible (SIS) model devoted to this topic, we still lack a general framework to compare different immunization strategies in completely random networks. Here, we address this problem by suggesting a novel method based on heterogeneous mean-field theory for the SIS model. Our method builds the relationship between the thresholds and different immunization strategies in completely random networks. Besides, we provide an analytical argument that the targeted large-degree strategy achieves the best performance in random networks with arbitrary degree distribution. Moreover, the experimental results demonstrate the effectiveness of the proposed method in both artificial and real-world networks.

Complex networks: when random walk dynamics equals synchronization

International Nuclear Information System (INIS)

Kriener, Birgit; Anand, Lishma; Timme, Marc

2012-01-01

Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)

Optimizing the next generation optical access networks

DEFF Research Database (Denmark)

Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso

2009-01-01

Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect...

Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks

OpenAIRE

Jalalifar, Seyed Ali; Hasani, Hosein; Aghajan, Hamid

2018-01-01

We present a novel approach to generating photo-realistic images of a face with accurate lip sync, given an audio input. By using a recurrent neural network, we achieved mouth landmarks based on audio features. We exploited the power of conditional generative adversarial networks to produce highly-realistic face conditioned on a set of landmarks. These two networks together are capable of producing a sequence of natural faces in sync with an input audio track.

Non-Stoichiometric SixN Metal-Oxide-Semiconductor Field-Effect Transistor for Compact Random Number Generator with 0.3 Mbit/s Generation Rate

Science.gov (United States)

Matsumoto, Mari; Ohba, Ryuji; Yasuda, Shin-ichi; Uchida, Ken; Tanamoto, Tetsufumi; Fujita, Shinobu

2008-08-01

The demand for random numbers for security applications is increasing. A conventional random number generator using thermal noise can generate unpredictable high-quality random numbers, but the circuit is extremely large because of large amplifier circuit for a small thermal signal. On the other hand, a pseudo-random number generator is small but the quality of randomness is bad. For a small circuit and a high quality of randomness, we purpose a non-stoichiometric SixN metal-oxide-semiconductor field-effect transistor (MOSFET) noise source device. This device generates a very large noise signal without an amplifier circuit. As a result, it is shown that, utilizing a SiN MOSFET, we can attain a compact random number generator with a high generation rate near 1 Mbit/s, which is suitable for almost all security applications.

Heterogeneous next-generation wireless network interference model-and its applications

KAUST Repository

Mahmood, Nurul Huda

2014-04-01

Next-generation wireless systems facilitating better utilisation of the scarce radio spectrum have emerged as a response to inefficient and rigid spectrum assignment policies. These are comprised of intelligent radio nodes that opportunistically operate in the radio spectrum of existing primary systems, yet unwanted interference at the primary receivers is unavoidable. In order to design efficient next-generation systems and to minimise the adverse effect of their interference, it is necessary to realise how the resulting interference impacts the performance of the primary systems. In this work, a generalised framework for the interference analysis of such a next-generation system is presented where the nextgeneration transmitters may transmit randomly with different transmit powers. The analysis is built around a model developed for the statistical representation of the interference at the primary receivers, which is then used to evaluate various performance measures of the primary system. Applications of the derived interference model in designing the next-generation network system parameters are also demonstrated. Such approach provides a unified and generalised framework, the use of which allows a wide range of performance metrics can be evaluated. Findings of the analytical performance analyses are confirmed through extensive computer-based Monte-Carlo simulations. © 2012 John Wiley & Sons, Ltd.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Random resistor network model of minimal conductivity in graphene.

Science.gov (United States)

Cheianov, Vadim V; Fal'ko, Vladimir I; Altshuler, Boris L; Aleiner, Igor L

2007-10-26

Transport in undoped graphene is related to percolating current patterns in the networks of n- and p-type regions reflecting the strong bipolar charge density fluctuations. Finite transparency of the p-n junctions is vital in establishing the macroscopic conductivity. We propose a random resistor network model to analyze scaling dependencies of the conductance on the doping and disorder, the quantum magnetoresistance and the corresponding dephasing rate.

Research on Some Bus Transport Networks with Random Overlapping Clique Structure

International Nuclear Information System (INIS)

Yang Xuhua; Sun Youxian; Wang Bo; Wang Wanliang

2008-01-01

On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs

Ultrafast quantum random number generation based on quantum phase fluctuations.

Science.gov (United States)

Xu, Feihu; Qi, Bing; Ma, Xiongfeng; Xu, He; Zheng, Haoxuan; Lo, Hoi-Kwong

2012-05-21

A quantum random number generator (QRNG) can generate true randomness by exploiting the fundamental indeterminism of quantum mechanics. Most approaches to QRNG employ single-photon detection technologies and are limited in speed. Here, we experimentally demonstrate an ultrafast QRNG at a rate over 6 Gbits/s based on the quantum phase fluctuations of a laser operating near threshold. Moreover, we consider a potential adversary who has partial knowledge on the raw data and discuss how one can rigorously remove such partial knowledge with postprocessing. We quantify the quantum randomness through min-entropy by modeling our system and employ two randomness extractors--Trevisan's extractor and Toeplitz-hashing--to distill the randomness, which is information-theoretically provable. The simplicity and high-speed of our experimental setup show the feasibility of a robust, low-cost, high-speed QRNG.

Modular Transformations, Order-Chaos Transitions and Pseudo-Random Number Generation

Science.gov (United States)

Bonelli, Antonio; Ruffo, Stefano

Successive pairs of pseudo-random numbers generated by standard linear congruential transformations display ordered patterns of parallel lines. We study the "ordered" and "chaotic" distribution of such pairs by solving the eigenvalue problem for two-dimensional modular transformations over integers. We conjecture that the optimal uniformity for pair distribution is obtained when the slope of linear modular eigenspaces takes the value n opt =maxint (p/√ {p-1}), where p is a prime number. We then propose a new generator of pairs of independent pseudo-random numbers, which realizes an optimal uniform distribution (in the "statistical" sense) of points on the unit square (0, 1] × (0, 1]. The method can be easily generalized to the generation of k-tuples of random numbers (with k>2).

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks.

Science.gov (United States)

Gu, Weiwei; Gong, Li; Lou, Xiaodan; Zhang, Jiang

2017-10-13

Network embedding which encodes all vertices in a network as a set of numerical vectors in accordance with it's local and global structures, has drawn widespread attention. Network embedding not only learns significant features of a network, such as the clustering and linking prediction but also learns the latent vector representation of the nodes which provides theoretical support for a variety of applications, such as visualization, link prediction, node classification, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although those algorithms have drawn much attention for their high scores in learning efficiency and accuracy, there is still a lack of theoretical explanation, and the transparency of those algorithms has been doubted. Here, we propose an approach based on the open-flow network model to reveal the underlying flow structure and its hidden metric space of different random walk strategies on networks. We show that the essence of embedding based on random walks is the latent metric structure defined on the open-flow network. This not only deepens our understanding of random- walk-based embedding algorithms but also helps in finding new potential applications in network embedding.

A model of coauthorship networks

Science.gov (United States)

Zhou, Guochang; Li, Jianping; Xie, Zonglin

2017-10-01

A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

Organization of growing random networks

Energy Technology Data Exchange (ETDEWEB)

Krapivsky, P. L.; Redner, S.

2001-06-01

The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.

Brownian motion properties of optoelectronic random bit generators based on laser chaos.

Science.gov (United States)

Li, Pu; Yi, Xiaogang; Liu, Xianglian; Wang, Yuncai; Wang, Yongge

2016-07-11

The nondeterministic property of the optoelectronic random bit generator (RBG) based on laser chaos are experimentally analyzed from two aspects of the central limit theorem and law of iterated logarithm. The random bits are extracted from an optical feedback chaotic laser diode using a multi-bit extraction technique in the electrical domain. Our experimental results demonstrate that the generated random bits have no statistical distance from the Brownian motion, besides that they can pass the state-of-the-art industry-benchmark statistical test suite (NIST SP800-22). All of them give a mathematically provable evidence that the ultrafast random bit generator based on laser chaos can be used as a nondeterministic random bit source.

Using Computer-Generated Random Numbers to Calculate the Lifetime of a Comet.

Science.gov (United States)

Danesh, Iraj

1991-01-01

An educational technique to calculate the lifetime of a comet using software-generated random numbers is introduced to undergraduate physiques and astronomy students. Discussed are the generation and eligibility of the required random numbers, background literature related to the problem, and the solution to the problem using random numbers.…

Analysis of entropy extraction efficiencies in random number generation systems

Science.gov (United States)

Wang, Chao; Wang, Shuang; Chen, Wei; Yin, Zhen-Qiang; Han, Zheng-Fu

2016-05-01

Random numbers (RNs) have applications in many areas: lottery games, gambling, computer simulation, and, most importantly, cryptography [N. Gisin et al., Rev. Mod. Phys. 74 (2002) 145]. In cryptography theory, the theoretical security of the system calls for high quality RNs. Therefore, developing methods for producing unpredictable RNs with adequate speed is an attractive topic. Early on, despite the lack of theoretical support, pseudo RNs generated by algorithmic methods performed well and satisfied reasonable statistical requirements. However, as implemented, those pseudorandom sequences were completely determined by mathematical formulas and initial seeds, which cannot introduce extra entropy or information. In these cases, “random” bits are generated that are not at all random. Physical random number generators (RNGs), which, in contrast to algorithmic methods, are based on unpredictable physical random phenomena, have attracted considerable research interest. However, the way that we extract random bits from those physical entropy sources has a large influence on the efficiency and performance of the system. In this manuscript, we will review and discuss several randomness extraction schemes that are based on radiation or photon arrival times. We analyze the robustness, post-processing requirements and, in particular, the extraction efficiency of those methods to aid in the construction of efficient, compact and robust physical RNG systems.

A new neural network model for solving random interval linear programming problems.

Science.gov (United States)

Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza

2017-05-01

This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.

A two-stage flow-based intrusion detection model for next-generation networks.

Science.gov (United States)

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

International Nuclear Information System (INIS)

Zhou Gang; Yang Li

2009-01-01

A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

Generating hierarchial scale-free graphs from fractals

Energy Technology Data Exchange (ETDEWEB)

Komjathy, Julia, E-mail: komyju@math.bme.hu [Department of Stochastics, Institute of Mathematics, Technical University of Budapest, H-1529 P.O. Box 91 (Hungary); Simon, Karoly, E-mail: simonk@math.bme.hu [Department of Stochastics, Institute of Mathematics, Technical University of Budapest, H-1529 P.O. Box 91 (Hungary)

2011-08-15

Highlights: > We generate deterministic scale-free networks using graph-directed self similar IFS. > Our model exhibits similar clustering, power law decay properties to real networks. > The average length of shortest path and the diameter of the graph are determined. > Using this model, we generate random graphs with prescribed power law exponent. - Abstract: Motivated by the hierarchial network model of E. Ravasz, A.-L. Barabasi, and T. Vicsek, we introduce deterministic scale-free networks derived from a graph directed self-similar fractal {Lambda}. With rigorous mathematical results we verify that our model captures some of the most important features of many real networks: the scale-free and the high clustering properties. We also prove that the diameter is the logarithm of the size of the system. We point out a connection between the power law exponent of the degree distribution and some intrinsic geometric measure theoretical properties of the underlying fractal. Using our (deterministic) fractal {Lambda} we generate random graph sequence sharing similar properties.

Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-07-01

Full Text Available such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative...

Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding

DEFF Research Database (Denmark)

Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani

2014-01-01

This work proposes a new protocol applying on– the–fly random linear network coding in wireless mesh net- works. The protocol provides increased reliability, low delay, and high throughput to the upper layers, while being oblivious to their specific requirements. This seemingly conflicting goals ...

GRD: An SPSS extension command for generating random data

Directory of Open Access Journals (Sweden)

Bradley Harding

2014-09-01

Full Text Available To master statistics and data analysis tools, it is necessary to understand a number of concepts, manyof which are quite abstract. For example, sampling from a theoretical distribution can help individuals explore andunderstand randomness. Sampling can also be used to build exercises aimed to help students master statistics. Here, we present GRD (Generator of Random Data, an extension command for SPSS (version 17 and above. With GRD, it is possible to get random data from a given distribution. In its simplest use, GRD will return a set of simulated data from a normal distribution.With subcommands to GRD, it is possible to get data from multiple groups, over multiple repeated measures, and with desired effectsizes. Group sizes can be equal or unequal. With further subcommands, it is possible to sample from any theoretical population, (not simply the normal distribution, introduce non-homogeneous variances,fix or randomize subject effects, etc. Finally, GRD’s generated data are in a format ready to be analyzed.

Design Guidelines for New Generation Network Architecture

Science.gov (United States)

Harai, Hiroaki; Fujikawa, Kenji; Kafle, Ved P.; Miyazawa, Takaya; Murata, Masayuki; Ohnishi, Masaaki; Ohta, Masataka; Umezawa, Takeshi

Limitations are found in the recent Internet because a lot of functions and protocols are patched to the original suite of layered protocols without considering global optimization. This reveals that end-to-end argument in the original Internet was neither sufficient for the current societal network and nor for a sustainable network of the future. In this position paper, we present design guidelines for a future network, which we call the New Generation Network, which provides the inclusion of diverse human requirements, reliable connection between the real-world and virtual network space, and promotion of social potentiality for human emergence. The guidelines consist of the crystal synthesis, the reality connection, and the sustainable & evolutional guidelines.

TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

CERN Document Server

Laurito, Andres; The ATLAS collaboration

2017-01-01

Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

CERN Document Server

Laurito, Andres; The ATLAS collaboration

2018-01-01

Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

Delineating social network data anonymization via random edge perturbation

KAUST Repository

Xue, Mingqiang

2012-01-01

Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations. © 2012 ACM.

Transient stability analysis of a distribution network with distributed generators

NARCIS (Netherlands)

Xyngi, I.; Ishchenko, A.; Popov, M.; Sluis, van der L.

2009-01-01

This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are

Ab initio Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism

KAUST Repository

Zenil, Hector

2018-02-18

To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.

Random Generators and Normal Numbers

OpenAIRE

Bailey, David H.; Crandall, Richard E.

2002-01-01

Pursuant to the authors' previous chaotic-dynamical model for random digits of fundamental constants, we investigate a complementary, statistical picture in which pseudorandom number generators (PRNGs) are central. Some rigorous results are achieved: We establish b-normality for constants of the form $\\sum_i 1/(b^{m_i} c^{n_i})$ for certain sequences $(m_i), (n_i)$ of integers. This work unifies and extends previously known classes of explicit normals. We prove that for coprime $b,c>1$ the...

Traffic Management for Next Generation Transport Networks

DEFF Research Database (Denmark)

Yu, Hao

required by the next generation transport network to provide Quality-of-Service (QoS) guaranteed video services. Augmenting network capacity and upgrading network nodes indicate long deployment period, replacement of equipment and thus significant cost to the network service providers. This challenge may...... slacken the steps of some network operators towards providing IPTV services. In this dissertation, the topology-based hierarchical scheduling scheme is proposed to tackle the problem addressed. The scheme simplifies the deployment process by placing an intelligent switch with centralized traffic...... management functions at the edge of the network, scheduling traffic on behalf of the other nodes. The topology-based hierarchical scheduling scheme is able to provide outstanding flow isolation due to its centralized scheduling ability, which is essential for providing IPTV services. In order to reduce...

On the hop count statistics for randomly deployed wireless sensor networks

NARCIS (Netherlands)

Dulman, S.O.; Rossi, M.; Havinga, Paul J.M.; Zorzi, M.

2006-01-01

In this paper we focus on exploiting the information provided by a generally accepted and largely ignored hypothesis (the random deployment of the nodes of an ad hoc or wireless sensor network) to design improved networking protocols. Specifically, we derive the relationship between the number of

High-Performance Pseudo-Random Number Generation on Graphics Processing Units

OpenAIRE

Nandapalan, Nimalan; Brent, Richard P.; Murray, Lawrence M.; Rendell, Alistair

2011-01-01

This work considers the deployment of pseudo-random number generators (PRNGs) on graphics processing units (GPUs), developing an approach based on the xorgens generator to rapidly produce pseudo-random numbers of high statistical quality. The chosen algorithm has configurable state size and period, making it ideal for tuning to the GPU architecture. We present a comparison of both speed and statistical quality with other common parallel, GPU-based PRNGs, demonstrating favourable performance o...

A revision of the subtract-with-borrow random number generators

Science.gov (United States)

Sibidanov, Alexei

2017-12-01

The most popular and widely used subtract-with-borrow generator, also known as RANLUX, is reimplemented as a linear congruential generator using large integer arithmetic with the modulus size of 576 bits. Modern computers, as well as the specific structure of the modulus inferred from RANLUX, allow for the development of a fast modular multiplication - the core of the procedure. This was previously believed to be slow and have too high cost in terms of computing resources. Our tests show a significant gain in generation speed which is comparable with other fast, high quality random number generators. An additional feature is the fast skipping of generator states leading to a seeding scheme which guarantees the uniqueness of random number sequences. Licensing provisions: GPLv3 Programming language: C++, C, Assembler

Autonomous Byte Stream Randomizer

Science.gov (United States)

Paloulian, George K.; Woo, Simon S.; Chow, Edward T.

2013-01-01

Net-centric networking environments are often faced with limited resources and must utilize bandwidth as efficiently as possible. In networking environments that span wide areas, the data transmission has to be efficient without any redundant or exuberant metadata. The Autonomous Byte Stream Randomizer software provides an extra level of security on top of existing data encryption methods. Randomizing the data s byte stream adds an extra layer to existing data protection methods, thus making it harder for an attacker to decrypt protected data. Based on a generated crypto-graphically secure random seed, a random sequence of numbers is used to intelligently and efficiently swap the organization of bytes in data using the unbiased and memory-efficient in-place Fisher-Yates shuffle method. Swapping bytes and reorganizing the crucial structure of the byte data renders the data file unreadable and leaves the data in a deconstructed state. This deconstruction adds an extra level of security requiring the byte stream to be reconstructed with the random seed in order to be readable. Once the data byte stream has been randomized, the software enables the data to be distributed to N nodes in an environment. Each piece of the data in randomized and distributed form is a separate entity unreadable on its own right, but when combined with all N pieces, is able to be reconstructed back to one. Reconstruction requires possession of the key used for randomizing the bytes, leading to the generation of the same cryptographically secure random sequence of numbers used to randomize the data. This software is a cornerstone capability possessing the ability to generate the same cryptographically secure sequence on different machines and time intervals, thus allowing this software to be used more heavily in net-centric environments where data transfer bandwidth is limited.

Generative Adversarial Networks: An Overview

Science.gov (United States)

Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A.

2018-01-01

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

DEFF Research Database (Denmark)

Heide, J; Zhang, Qi; Fitzek, F H P

2013-01-01

This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...

Pseudo-random bit generator based on Chebyshev map

Science.gov (United States)

Stoyanov, B. P.

2013-10-01

In this paper, we study a pseudo-random bit generator based on two Chebyshev polynomial maps. The novel derivative algorithm shows perfect statistical properties established by number of statistical tests.

Neural network based control of Doubly Fed Induction Generator in wind power generation

Science.gov (United States)

Barbade, Swati A.; Kasliwal, Prabha

2012-07-01

To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

A generator for unique quantum random numbers based on vacuum states

DEFF Research Database (Denmark)

Gabriel, C.; Wittmann, C.; Sych, D.

2010-01-01

the purity of a continuous-variable quantum vacuum state to generate unique random numbers. We use the intrinsic randomness in measuring the quadratures of a mode in the lowest energy vacuum state, which cannot be correlated to any other state. The simplicity of our source, combined with its verifiably......Random numbers are a valuable component in diverse applications that range from simulations(1) over gambling to cryptography(2,3). The quest for true randomness in these applications has engendered a large variety of different proposals for producing random numbers based on the foundational...... unpredictability of quantum mechanics(4-11). However, most approaches do not consider that a potential adversary could have knowledge about the generated numbers, so the numbers are not verifiably random and unique(12-15). Here we present a simple experimental setup based on homodyne measurements that uses...

Random Deep Belief Networks for Recognizing Emotions from Speech Signals.

Science.gov (United States)

Wen, Guihua; Li, Huihui; Huang, Jubing; Li, Danyang; Xun, Eryang

2017-01-01

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

Directory of Open Access Journals (Sweden)

Guihua Wen

2017-01-01

Full Text Available Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

Generating prior probabilities for classifiers of brain tumours using belief networks

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2007-09-01

Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

Ring correlations in random networks.

Science.gov (United States)

Sadjadi, Mahdi; Thorpe, M F

2016-12-01

We examine the correlations between rings in random network glasses in two dimensions as a function of their separation. Initially, we use the topological separation (measured by the number of intervening rings), but this leads to pseudo-long-range correlations due to a lack of topological charge neutrality in the shells surrounding a central ring. This effect is associated with the noncircular nature of the shells. It is, therefore, necessary to use the geometrical distance between ring centers. Hence we find a generalization of the Aboav-Weaire law out to larger distances, with the correlations between rings decaying away when two rings are more than about three rings apart.

Testing random number generators for Monte Carlo applications

International Nuclear Information System (INIS)

Sim, L.H.

1992-01-01

Central to any system for modelling radiation transport phenomena using Monte Carlo techniques is the method by which pseudo random numbers are generated. This method is commonly referred to as the Random Number Generator (RNG). It is usually a computer implemented mathematical algorithm which produces a series of numbers uniformly distributed on the interval [0,1]. If this series satisfies certain statistical tests for randomness, then for practical purposes the pseudo random numbers in the series can be considered to be random. Tests of this nature are important not only for new RNGs but also to test the implementation of known RNG algorithms in different computer environments. Six RNGs have been tested using six statistical tests and one visual test. The statistical tests are the moments, frequency (digit and number), serial, gap, and poker tests. The visual test is a simple two dimensional ordered pair display. In addition the RNGs have been tested in a specific Monte Carlo application. This type of test is often overlooked, however it is important that in addition to satisfactory performance in statistical tests, the RNG be able to perform effectively in the applications of interest. The RNGs tested here are based on a variety of algorithms, including multiplicative and linear congruential, lagged Fibonacci, and combination arithmetic and lagged Fibonacci. The effect of the Bays-Durham shuffling algorithm on the output of a known bad RNG has also been investigated. 18 refs., 11 tabs., 4 figs. of

Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

KAUST Repository

Yasin Yazicioǧlu, A.; Egerstedt, Magnus; Shamma, Jeff S.

2015-01-01

Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.

Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

KAUST Repository

Yasin Yazicioǧlu, A.

2015-11-25

Multi-Agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-Agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small number of links. One family of such graphs is the random regular graphs. In this paper, we present a decentralized scheme for transforming any connected interaction graph with a possibly non-integer average degree of k into a connected random m-regular graph for some m ϵ [k+k ] 2. Accordingly, the agents improve the robustness of the network while maintaining a similar number of links as the initial configuration by locally adding or removing some edges. © 2015 IEEE.

Programmable pseudo-random detector-pulse-pattern generator

International Nuclear Information System (INIS)

Putten, R. van der; Nationaal Inst. voor Kernfysica en Hoge-Energiefysica

1990-01-01

This report discusses the design and realization of the digital part of the programmable pseudo-random detector pulse-pattern generator. For the design and realization use has been made of F-TTL and high speed special purpose ic's, in particular FAL's (15 ns). The design possibilities offered by the software for pro-gramming of the FAL's have been utilized as much as possible. In this way counters, registers and a state machine with extended control possibilities have been designed and an advanced 8 channel pulse generator has been developed which is controlled via the VME system bus. the generator possesses an internal clock oscillator of 16 MHZ. The moment when a pulse is generated can be adjusted with a step size of 250 ps. 2000 different periods (time windows) can be stored for generating a pattern. (author). 37 refs.; 6 figs

Critical node lifetimes in random networks via the Chen-Stein method

NARCIS (Netherlands)

Franceschetti, M.; Meester, R.W.J.

2006-01-01

This correspondence considers networks where nodes are connected randomly and can fail at random times. It provides scaling laws that allow to find the critical time at which isolated nodes begin to appear in the system as its size tends to infinity. Applications are in the areas of sensor and

Robustness of networks against propagating attacks under vaccination strategies

International Nuclear Information System (INIS)

Hasegawa, Takehisa; Masuda, Naoki

2011-01-01

We study the effect of vaccination on the robustness of networks against propagating attacks that obey the susceptible–infected–removed model. By extending the generating function formalism developed by Newman (2005 Phys. Rev. Lett. 95 108701), we analytically determine the robustness of networks that depends on the vaccination parameters. We consider the random defense where nodes are vaccinated randomly and the degree-based defense where hubs are preferentially vaccinated. We show that, when vaccines are inefficient, the random graph is more robust against propagating attacks than the scale-free network. When vaccines are relatively efficient, the scale-free network with the degree-based defense is more robust than the random graph with the random defense and the scale-free network with the random defense

The Reliability of Randomly Generated Math Curriculum-Based Measurements

Science.gov (United States)

Strait, Gerald G.; Smith, Bradley H.; Pender, Carolyn; Malone, Patrick S.; Roberts, Jarod; Hall, John D.

2015-01-01

"Curriculum-Based Measurement" (CBM) is a direct method of academic assessment used to screen and evaluate students' skills and monitor their responses to academic instruction and intervention. Interventioncentral.org offers a math worksheet generator at no cost that creates randomly generated "math curriculum-based measures"…

An investigation of the uniform random number generator

Science.gov (United States)

Temple, E. C.

1982-01-01

Most random number generators that are in use today are of the congruential form X(i+1) + AX(i) + C mod M where A, C, and M are nonnegative integers. If C=O, the generator is called the multiplicative type and those for which C/O are called mixed congruential generators. It is easy to see that congruential generators will repeat a sequence of numbers after a maximum of M values have been generated. The number of numbers that a procedure generates before restarting the sequence is called the length or the period of the generator. Generally, it is desirable to make the period as long as possible. A detailed discussion of congruential generators is given. Also, several promising procedures that differ from the multiplicative and mixed procedure are discussed.

Random walk generated by random permutations of {1, 2, 3, ..., n + 1}

International Nuclear Information System (INIS)

Oshanin, G; Voituriez, R

2004-01-01

We study properties of a non-Markovian random walk X (n) l , l = 0, 1, 2, ..., n, evolving in discrete time l on a one-dimensional lattice of integers, whose moves to the right or to the left are prescribed by the rise-and-descent sequences characterizing random permutations π of [n + 1] = {1, 2, 3, ..., n + 1}. We determine exactly the probability of finding the end-point X n = X (n) n of the trajectory of such a permutation-generated random walk (PGRW) at site X, and show that in the limit n → ∞ it converges to a normal distribution with a smaller, compared to the conventional Polya random walk, diffusion coefficient. We formulate, as well, an auxiliary stochastic process whose distribution is identical to the distribution of the intermediate points X (n) l , l < n, which enables us to obtain the probability measure of different excursions and to define the asymptotic distribution of the number of 'turns' of the PGRW trajectories

Features of Random Metal Nanowire Networks with Application in Transparent Conducting Electrodes

KAUST Repository

Maloth, Thirupathi

2017-01-01

in terms of sheet resistance and optical transmittance. However, as the electrical properties of such random networks are achieved thanks to a percolation network, a minimum size of the electrodes is needed so it actually exceeds the representative volume

Novel pseudo-random number generator based on quantum random walks

Science.gov (United States)

Yang, Yu-Guang; Zhao, Qian-Qian

2016-02-01

In this paper, we investigate the potential application of quantum computation for constructing pseudo-random number generators (PRNGs) and further construct a novel PRNG based on quantum random walks (QRWs), a famous quantum computation model. The PRNG merely relies on the equations used in the QRWs, and thus the generation algorithm is simple and the computation speed is fast. The proposed PRNG is subjected to statistical tests such as NIST and successfully passed the test. Compared with the representative PRNG based on quantum chaotic maps (QCM), the present QRWs-based PRNG has some advantages such as better statistical complexity and recurrence. For example, the normalized Shannon entropy and the statistical complexity of the QRWs-based PRNG are 0.999699456771172 and 1.799961178212329e-04 respectively given the number of 8 bits-words, say, 16Mbits. By contrast, the corresponding values of the QCM-based PRNG are 0.999448131481064 and 3.701210794388818e-04 respectively. Thus the statistical complexity and the normalized entropy of the QRWs-based PRNG are closer to 0 and 1 respectively than those of the QCM-based PRNG when the number of words of the analyzed sequence increases. It provides a new clue to construct PRNGs and also extends the applications of quantum computation.

Novel pseudo-random number generator based on quantum random walks.

Science.gov (United States)

Yang, Yu-Guang; Zhao, Qian-Qian

2016-02-04

In this paper, we investigate the potential application of quantum computation for constructing pseudo-random number generators (PRNGs) and further construct a novel PRNG based on quantum random walks (QRWs), a famous quantum computation model. The PRNG merely relies on the equations used in the QRWs, and thus the generation algorithm is simple and the computation speed is fast. The proposed PRNG is subjected to statistical tests such as NIST and successfully passed the test. Compared with the representative PRNG based on quantum chaotic maps (QCM), the present QRWs-based PRNG has some advantages such as better statistical complexity and recurrence. For example, the normalized Shannon entropy and the statistical complexity of the QRWs-based PRNG are 0.999699456771172 and 1.799961178212329e-04 respectively given the number of 8 bits-words, say, 16Mbits. By contrast, the corresponding values of the QCM-based PRNG are 0.999448131481064 and 3.701210794388818e-04 respectively. Thus the statistical complexity and the normalized entropy of the QRWs-based PRNG are closer to 0 and 1 respectively than those of the QCM-based PRNG when the number of words of the analyzed sequence increases. It provides a new clue to construct PRNGs and also extends the applications of quantum computation.

Diffusion in random networks: Asymptotic properties, and numerical and engineering approximations

Science.gov (United States)

Padrino, Juan C.; Zhang, Duan Z.

2016-11-01

The ensemble phase averaging technique is applied to model mass transport by diffusion in random networks. The system consists of an ensemble of random networks, where each network is made of a set of pockets connected by tortuous channels. Inside a channel, we assume that fluid transport is governed by the one-dimensional diffusion equation. Mass balance leads to an integro-differential equation for the pores mass density. The so-called dual porosity model is found to be equivalent to the leading order approximation of the integration kernel when the diffusion time scale inside the channels is small compared to the macroscopic time scale. As a test problem, we consider the one-dimensional mass diffusion in a semi-infinite domain, whose solution is sought numerically. Because of the required time to establish the linear concentration profile inside a channel, for early times the similarity variable is xt- 1 / 4 rather than xt- 1 / 2 as in the traditional theory. This early time sub-diffusive similarity can be explained by random walk theory through the network. In addition, by applying concepts of fractional calculus, we show that, for small time, the governing equation reduces to a fractional diffusion equation with known solution. We recast this solution in terms of special functions easier to compute. Comparison of the numerical and exact solutions shows excellent agreement.

Embedded generation connection incentives for distribution network operators

Energy Technology Data Exchange (ETDEWEB)

Williams, P.; Andrews, S.

2002-07-01

This is the final report with respect to work commissioned by the Department of Trade and Industry (DTI) as part of the New and Renewable Energy Programme into incentives for distribution network operators (DNOs) for the connection of embedded generation. This report, which incorporates the contents of the interim report submitted in February 2002, considers the implications of changes in the structure and regulation in the UK electricity industry on the successful technical and commercial integrated of embedded generation into distribution networks. The report examines: the obligations of public electricity suppliers (PESs); current DNO practices regarding the connection of embedded generation; the changes introduced by the Utilities Act 2000, including the impact of new obligations placed on DNOs on the connection of embedded generation and the requirements of the new Electricity Distribution Standard Licence conditions; and problems and prospects for DNO incentives.

Mobility management techniques for the next-generation wireless networks

Science.gov (United States)

Sun, Junzhao; Howie, Douglas P.; Sauvola, Jaakko J.

2001-10-01

The tremendous demands from social market are pushing the booming development of mobile communications faster than ever before, leading to plenty of new advanced techniques emerging. With the converging of mobile and wireless communications with Internet services, the boundary between mobile personal telecommunications and wireless computer networks is disappearing. Wireless networks of the next generation need the support of all the advances on new architectures, standards, and protocols. Mobility management is an important issue in the area of mobile communications, which can be best solved at the network layer. One of the key features of the next generation wireless networks is all-IP infrastructure. This paper discusses the mobility management schemes for the next generation mobile networks through extending IP's functions with mobility support. A global hierarchical framework model for the mobility management of wireless networks is presented, in which the mobility management is divided into two complementary tasks: macro mobility and micro mobility. As the macro mobility solution, a basic principle of Mobile IP is introduced, together with the optimal schemes and the advances in IPv6. The disadvantages of the Mobile IP on solving the micro mobility problem are analyzed, on the basis of which three main proposals are discussed as the micro mobility solutions for mobile communications, including Hierarchical Mobile IP (HMIP), Cellular IP, and Handoff-Aware Wireless Access Internet Infrastructure (HAWAII). A unified model is also described in which the different micro mobility solutions can coexist simultaneously in mobile networks.

Efficient pseudo-random number generation for monte-carlo simulations using graphic processors

Science.gov (United States)

Mohanty, Siddhant; Mohanty, A. K.; Carminati, F.

2012-06-01

A hybrid approach based on the combination of three Tausworthe generators and one linear congruential generator for pseudo random number generation for GPU programing as suggested in NVIDIA-CUDA library has been used for MONTE-CARLO sampling. On each GPU thread, a random seed is generated on fly in a simple way using the quick and dirty algorithm where mod operation is not performed explicitly due to unsigned integer overflow. Using this hybrid generator, multivariate correlated sampling based on alias technique has been carried out using both CUDA and OpenCL languages.

Efficient pseudo-random number generation for Monte-Carlo simulations using graphic processors

International Nuclear Information System (INIS)

Mohanty, Siddhant; Mohanty, A K; Carminati, F

2012-01-01

A hybrid approach based on the combination of three Tausworthe generators and one linear congruential generator for pseudo random number generation for GPU programing as suggested in NVIDIA-CUDA library has been used for MONTE-CARLO sampling. On each GPU thread, a random seed is generated on fly in a simple way using the quick and dirty algorithm where mod operation is not performed explicitly due to unsigned integer overflow. Using this hybrid generator, multivariate correlated sampling based on alias technique has been carried out using both CUDA and OpenCL languages.

The Random Walk Model Based on Bipartite Network

Directory of Open Access Journals (Sweden)

Zhang Man-Dun

2016-01-01

Full Text Available With the continuing development of the electronic commerce and growth of network information, there is a growing possibility for citizens to be confused by the information. Though the traditional technology of information retrieval have the ability to relieve the overload of information in some extent, it can not offer a targeted personality service based on user’s interests and activities. In this context, the recommendation algorithm arose. In this paper, on the basis of conventional recommendation, we studied the scheme of random walk based on bipartite network and the application of it. We put forward a similarity measurement based on implicit feedback. In this method, a uneven character vector is imported(the weight of item in the system. We put forward a improved random walk pattern which make use of partial or incomplete neighbor information to create recommendation information. In the end, there is an experiment in the real data set, the recommendation accuracy and practicality are improved. We promise the reality of the result of the experiment

Exponential random graph models for networks with community structure.

Science.gov (United States)

Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

2013-09-01

Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

Relay-aided multi-cell broadcasting with random network coding

DEFF Research Database (Denmark)

Lu, Lu; Sun, Fan; Xiao, Ming

2010-01-01

We investigate a relay-aided multi-cell broadcasting system using random network codes, where the focus is on devising efficient scheduling algorithms between relay and base stations. Two scheduling algorithms are proposed based on different feedback strategies; namely, a one-step scheduling...

Modeling urbanization patterns with generative adversarial networks

OpenAIRE

Albert, Adrian; Strano, Emanuele; Kaur, Jasleen; Gonzalez, Marta

2018-01-01

In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

Voltage regulation in distribution networks with distributed generation

Science.gov (United States)

Blažič, B.; Uljanić, B.; Papič, I.

2012-11-01

The paper deals with the topic of voltage regulation in distribution networks with relatively high distributed energy resources (DER) penetration. The problem of voltage rise is described and different options for voltage regulation are given. The influence of DER on voltage profile and the effectiveness of the investigated solutions are evaluated by means of simulation in DIgSILENT. The simulated network is an actual distribution network in Slovenia with a relatively high penetration of distributed generation. Recommendations for voltage control in networks with DER penetration are given at the end.

Storage capacity and retrieval time of small-world neural networks

International Nuclear Information System (INIS)

Oshima, Hiraku; Odagaki, Takashi

2007-01-01

To understand the influence of structure on the function of neural networks, we study the storage capacity and the retrieval time of Hopfield-type neural networks for four network structures: regular, small world, random networks generated by the Watts-Strogatz (WS) model, and the same network as the neural network of the nematode Caenorhabditis elegans. Using computer simulations, we find that (1) as the randomness of network is increased, its storage capacity is enhanced; (2) the retrieval time of WS networks does not depend on the network structure, but the retrieval time of C. elegans's neural network is longer than that of WS networks; (3) the storage capacity of the C. elegans network is smaller than that of networks generated by the WS model, though the neural network of C. elegans is considered to be a small-world network

Fiber to the home: next generation network

Science.gov (United States)

Yang, Chengxin; Guo, Baoping

2006-07-01

Next generation networks capable of carrying converged telephone, television (TV), very high-speed internet, and very high-speed bi-directional data services (like video-on-demand (VOD), Game etc.) strategy for Fiber To The Home (FTTH) is presented. The potential market is analyzed. The barriers and some proper strategy are also discussed. Several technical problems like various powering methods, optical fiber cables, and different network architecture are discussed too.

A novel approach to generate random surface thermal loads in piping

Energy Technology Data Exchange (ETDEWEB)

Costa Garrido, Oriol, E-mail: oriol.costa@ijs.si; El Shawish, Samir; Cizelj, Leon

2014-07-01

Highlights: • Approach for generating continuous and time-dependent random thermal fields. • Temperature fields simulate fluid mixing thermal loads at fluid–wall interface. • Through plane-wave decomposition, experimental temperature statistics are reproduced. • Validation of the approach with a case study from literature. • Random surface thermal loads generation for future thermal fatigue analyses of piping. - Abstract: There is a need to perform three-dimensional mechanical analyses of pipes, subjected to complex thermo-mechanical loadings such as the ones evolving from turbulent fluid mixing in a T-junction. A novel approach is proposed in this paper for fast and reliable generation of random thermal loads at the pipe surface. The resultant continuous and time-dependent temperature fields simulate the fluid mixing thermal loads at the fluid–wall interface. The approach is based on reproducing discrete fluid temperature statistics, from experimental readings or computational fluid dynamic simulation's results, at interface locations through plane-wave decomposition of temperature fluctuations. The obtained random thermal fields contain large scale instabilities such as cold and hot spots traveling at flow velocities. These low frequency instabilities are believed to be among the major causes of the thermal fatigue in T-junction configurations. The case study found in the literature has been used to demonstrate the generation of random surface thermal loads. The thermal fields generated with the proposed approach are statistically equivalent (within the first two moments) to those from CFD simulations results of similar characteristics. The fields maintain the input data at field locations for a large set of parameters used to generate the thermal loads. This feature will be of great advantage in future sensitivity fatigue analyses of three-dimensional pipe structures.

A novel approach to generate random surface thermal loads in piping

International Nuclear Information System (INIS)

Costa Garrido, Oriol; El Shawish, Samir; Cizelj, Leon

2014-01-01

Highlights: • Approach for generating continuous and time-dependent random thermal fields. • Temperature fields simulate fluid mixing thermal loads at fluid–wall interface. • Through plane-wave decomposition, experimental temperature statistics are reproduced. • Validation of the approach with a case study from literature. • Random surface thermal loads generation for future thermal fatigue analyses of piping. - Abstract: There is a need to perform three-dimensional mechanical analyses of pipes, subjected to complex thermo-mechanical loadings such as the ones evolving from turbulent fluid mixing in a T-junction. A novel approach is proposed in this paper for fast and reliable generation of random thermal loads at the pipe surface. The resultant continuous and time-dependent temperature fields simulate the fluid mixing thermal loads at the fluid–wall interface. The approach is based on reproducing discrete fluid temperature statistics, from experimental readings or computational fluid dynamic simulation's results, at interface locations through plane-wave decomposition of temperature fluctuations. The obtained random thermal fields contain large scale instabilities such as cold and hot spots traveling at flow velocities. These low frequency instabilities are believed to be among the major causes of the thermal fatigue in T-junction configurations. The case study found in the literature has been used to demonstrate the generation of random surface thermal loads. The thermal fields generated with the proposed approach are statistically equivalent (within the first two moments) to those from CFD simulations results of similar characteristics. The fields maintain the input data at field locations for a large set of parameters used to generate the thermal loads. This feature will be of great advantage in future sensitivity fatigue analyses of three-dimensional pipe structures

Analysis of Greedy Decision Making for Geographic Routing for Networks of Randomly Moving Objects

Directory of Open Access Journals (Sweden)

Amber Israr

2016-04-01

Full Text Available Autonomous and self-organizing wireless ad-hoc communication networks for moving objects consist of nodes, which use no centralized network infrastructure. Examples of moving object networks are networks of flying objects, networks of vehicles, networks of moving people or robots. Moving object networks have to face many critical challenges in terms of routing because of dynamic topological changes and asymmetric networks links. A suitable and effective routing mechanism helps to extend the deployment of moving nodes. In this paper an attempt has been made to analyze the performance of the Greedy Decision method (position aware distance based algorithm for geographic routing for network nodes moving according to the random waypoint mobility model. The widely used GPSR (Greedy Packet Stateless Routing protocol utilizes geographic distance and position based data of nodes to transmit packets towards destination nodes. In this paper different scenarios have been tested to develop a concrete set of recommendations for optimum deployment of distance based Greedy Decision of Geographic Routing in randomly moving objects network

Systemic risk on different interbank network topologies

Science.gov (United States)

Lenzu, Simone; Tedeschi, Gabriele

2012-09-01

In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.

Big Data Perspective and Challenges in Next Generation Networks

Directory of Open Access Journals (Sweden)

Kashif Sultan

2018-06-01

Full Text Available With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

Address Translation Problems in IMS Based Next Generation Networks

Directory of Open Access Journals (Sweden)

Balazs Godor

2006-01-01

Full Text Available The development of packed based multimedia networks reached a turning point when the ITU-T and the ETSIhave incorporated the IMS to the NGN. With the fast development of mobile communication more and more services andcontent are available. In contrast with fix network telephony both the services and the devices are personalized in the “mobileworld”. Services, known from the Internet - like e-mail, chat, browsing, presence, etc. – are already available via mobiledevices as well. The IMS originally wanted to exploit both the benefits of mobile networks and the fancy services of theInternet. But today it is already more than that. IMS is the core of the next generation telecommunication networks and abasis for fix-mobile convergent services. The fact however that IMS was originally a “mobile” standard, where IPv6 was notoddity generated some problems for the fix networks, where IPv4 is used. In this article I give an overview of these problemsand mention some solutions as well.

Harmonics: Generation and Suppression in AC System Networks ...

African Journals Online (AJOL)

However, reactive power flow in electrical networks has adverse effects depending on their magnitude and the nature of the supply network. How these harmonics are generated by nonlinear loads and the means by which they can be kept low are the focus of this paper. Keywords: non-linear loads, harmonics, reactive ...

Distributed Synchronization in Networks of Agent Systems With Nonlinearities and Random Switchings.

Science.gov (United States)

Tang, Yang; Gao, Huijun; Zou, Wei; Kurths, Jürgen

2013-02-01

In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

The random signal generator of imitated nuclear radiation pulse

International Nuclear Information System (INIS)

Li Dongcang; Yang Lei; Yuan Shulin; Yang Yinghui; Zang Fujia

2007-01-01

Based in pseudo-random uniformity number, it produces random numbers of Gaussian distribution and exponential distribution by arithmetic. The hardware is the single-chip microcomputer of 89C51. Program language makes use of Keil C. The output pulse amplitude is Gaussian distribution, exponential distribution or uniformity distribution. Likewise, it has two mode or upwards two. The time alternation of output pulse is both periodic and exponential distribution. The generator has achieved output control of multi-mode distribution, imitated random characteristic of nuclear pulse in amplitude and in time. (authors)

DNA based random key generation and management for OTP encryption.

Science.gov (United States)

Zhang, Yunpeng; Liu, Xin; Sun, Manhui

2017-09-01

One-time pad (OTP) is a principle of key generation applied to the stream ciphering method which offers total privacy. The OTP encryption scheme has proved to be unbreakable in theory, but difficult to realize in practical applications. Because OTP encryption specially requires the absolute randomness of the key, its development has suffered from dense constraints. DNA cryptography is a new and promising technology in the field of information security. DNA chromosomes storing capabilities can be used as one-time pad structures with pseudo-random number generation and indexing in order to encrypt the plaintext messages. In this paper, we present a feasible solution to the OTP symmetric key generation and transmission problem with DNA at the molecular level. Through recombinant DNA technology, by using only sender-receiver known restriction enzymes to combine the secure key represented by DNA sequence and the T vector, we generate the DNA bio-hiding secure key and then place the recombinant plasmid in implanted bacteria for secure key transmission. The designed bio experiments and simulation results show that the security of the transmission of the key is further improved and the environmental requirements of key transmission are reduced. Analysis has demonstrated that the proposed DNA-based random key generation and management solutions are marked by high security and usability. Published by Elsevier B.V.

Modeling of steam generator in nuclear power plant using neural network ensemble

International Nuclear Information System (INIS)

Lee, S. K.; Lee, E. C.; Jang, J. W.

2003-01-01

Neural network is now being used in modeling the steam generator is known to be difficult due to the reverse dynamics. However, Neural network is prone to the problem of overfitting. This paper investigates the use of neural network combining methods to model steam generator water level and compares with single neural network. The results show that neural network ensemble is effective tool which can offer improved generalization, lower dependence of the training set and reduced training time

Towards Third-Generation Living Lab Networks in Cities

Directory of Open Access Journals (Sweden)

Seppo Leminen

2017-11-01

Full Text Available Many cities engage in diverse experimentation, innovation, and development activities with a broad variety of environments and stakeholders to the benefit of citizens, companies, municipalities, and other organizations. Hence, this article discusses such engagement in terms of next-generation living lab networks in the city context. In so doing, the study contributes to the discussion on living labs by introducing a framework of collaborative innovation networks in cities and suggesting a typology of third-generation living labs. Our framework is characterized by diverse platforms and participation approaches, resulting in four distinctive modes of collaborative innovation networks where the city is: i a provider, ii a neighbourhood participator, iii a catalyst, or iv a rapid experimenter. The typology is based on an analysis of 118 interviews with participants in six Finnish cities and reveals various ways to organize innovation activities in the city context. In particular, cities can benefit from innovation networks by simultaneously exploiting multiple platforms such as living labs for innovation. We conclude by discussing implications to theory and practice, and suggesting directions for future research.

Random number generation based on digital differential chaos

KAUST Repository

Zidan, Mohammed A.; Radwan, Ahmed G.; Salama, Khaled N.

2012-01-01

In this paper, we present a fully digital differential chaos based random number generator. The output of the digital circuit is proved to be chaotic by calculating the output time series maximum Lyapunov exponent. We introduce a new post processing

Random number generators in support of Monte Carlo problems in physics

International Nuclear Information System (INIS)

Dyadkin, I.G.

1993-01-01

The ability to support a modern users' expectations of random number generators to solve problems in physics is analyzed. The capabilities of the newest concepts and the old pseudo-random algorithms are compared. The author is in favor of multiplicative generators. Due to the 64-bit arithmetic of a modern PC, multiplicative generators have a sufficient number of periods (up to 2 62 ) and are quicker to generate and to govern independent sequences for parallel processing. In addition they are able to replicate sub-sequences (without storing their seeds) for each standard trial in any code and to simulate spatial and planar directions and EXP(-x) distributions often needed as ''bricks'' for simulating events in physics. Hundreds of multipliers for multiplicative generators have been tabulated and tested, and the required speeds have been obtained. (author)

Immunization of Epidemics in Multiplex Networks

Science.gov (United States)

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks. PMID:25401755

Immunization of epidemics in multiplex networks.

Science.gov (United States)

Zhao, Dawei; Wang, Lianhai; Li, Shudong; Wang, Zhen; Wang, Lin; Gao, Bo

2014-01-01

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks.

Gossips and prejudices: ergodic randomized dynamics in social networks

NARCIS (Netherlands)

Frasca, Paolo; Ravazzi, Chiara; Tempo, Roberto; Ishii, Hideaki

In this paper we study a new model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process: following recent literature, we refer to this communication model as “gossiping‿. Second,

Quantum random number generator based on quantum nature of vacuum fluctuations

Science.gov (United States)

Ivanova, A. E.; Chivilikhin, S. A.; Gleim, A. V.

2017-11-01

Quantum random number generator (QRNG) allows obtaining true random bit sequences. In QRNG based on quantum nature of vacuum, optical beam splitter with two inputs and two outputs is normally used. We compare mathematical descriptions of spatial beam splitter and fiber Y-splitter in the quantum model for QRNG, based on homodyne detection. These descriptions were identical, that allows to use fiber Y-splitters in practical QRNG schemes, simplifying the setup. Also we receive relations between the input radiation and the resulting differential current in homodyne detector. We experimentally demonstrate possibility of true random bits generation by using QRNG based on homodyne detection with Y-splitter.

Next-generation science information network for leading-edge applications

International Nuclear Information System (INIS)

Urushidani, S.; Matsukata, J.

2008-01-01

High-speed networks are definitely essential tools for leading-edge applications in many research areas, including nuclear fusion research. This paper describes a number of advanced features in the Japanese next-generation science information network, called SINET3, and gives researchers clues on the uses of advanced high-speed network for their applications. The network services have four categories, multiple layer transfer, enriched virtual private network, enhanced quality-of-service, and bandwidth on demand services, and comprise a versatile service platform. The paper also describes the network architecture and advanced networking capabilities that enable economical service accommodation and flexible network resource assignment as well as effective use of Japan's first 40-Gbps lines

Next-generation science information network for leading-edge applications

Energy Technology Data Exchange (ETDEWEB)

Urushidani, S. [National Institute of Informatics, 2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8430 (Japan)], E-mail: urushi@nii.ac.jp; Matsukata, J. [National Institute of Informatics, 2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8430 (Japan)

2008-04-15

High-speed networks are definitely essential tools for leading-edge applications in many research areas, including nuclear fusion research. This paper describes a number of advanced features in the Japanese next-generation science information network, called SINET3, and gives researchers clues on the uses of advanced high-speed network for their applications. The network services have four categories, multiple layer transfer, enriched virtual private network, enhanced quality-of-service, and bandwidth on demand services, and comprise a versatile service platform. The paper also describes the network architecture and advanced networking capabilities that enable economical service accommodation and flexible network resource assignment as well as effective use of Japan's first 40-Gbps lines.

Random number generation based on digital differential chaos

KAUST Repository

Zidan, Mohammed A.

2012-07-29

In this paper, we present a fully digital differential chaos based random number generator. The output of the digital circuit is proved to be chaotic by calculating the output time series maximum Lyapunov exponent. We introduce a new post processing technique to improve the distribution and statistical properties of the generated data. The post-processed output passes the NIST Sp. 800-22 statistical tests. The system is written in Verilog VHDL and realized on Xilinx Virtex® FPGA. The generator can fit into a very small area and have a maximum throughput of 2.1 Gb/s.

Innovation and networking among entrepreneurs across generations of Asian tigers

DEFF Research Database (Denmark)

Brambini-Pedersen, Jan Vang; Jensen, Kent W; Schøtt, Thomas

2017-01-01

entrepreneurship monitor (GEM) data, this paper aims at reducing this research gap by conducting an analysis of the generational differences between the tiger economies entrepreneurs in respect to their innovative performance, their inclination to network and the importance of the quality of the network......Much attention has been paid to analysing the determinants of the economic development in the different generations of Asian tiger economies. This stream of research has provided valuable insights on the particular generational challenges, the tigers face in implementing successful catching up...

An empirical test of pseudo random number generators by means of an exponential decaying process

International Nuclear Information System (INIS)

Coronel B, H.F.; Hernandez M, A.R.; Jimenez M, M.A.; Mora F, L.E.

2007-01-01

Empirical tests for pseudo random number generators based on the use of processes or physical models have been successfully used and are considered as complementary to theoretical tests of randomness. In this work a statistical methodology for evaluating the quality of pseudo random number generators is presented. The method is illustrated in the context of the so-called exponential decay process, using some pseudo random number generators commonly used in physics. (Author)

The 'emergent scaling' phenomenon and the dielectric properties of random resistor-capacitor networks

CERN Document Server

Bouamrane, R

2003-01-01

An efficient algorithm, based on the Frank-Lobb reduction scheme, for calculating the equivalent dielectric properties of very large random resistor-capacitor (R-C) networks has been developed. It has been used to investigate the network size and composition dependence of dielectric properties and their statistical variability. The dielectric properties of 256 samples of random networks containing: 512, 2048, 8192 and 32 768 components distributed randomly in the ratios 60% R-40% C, 50% R-50% C and 40% R-60% C have been computed. It has been found that these properties exhibit the anomalous power law dependences on frequency known as the 'universal dielectric response' (UDR). Attention is drawn to the contrast between frequency ranges across which percolation determines dielectric response, where considerable variability is found amongst the samples, and those across which power laws define response where very little variability is found between samples. It is concluded that the power law UDRs are emergent pr...

Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks

International Nuclear Information System (INIS)

Szolnoki, Attila; Perc, Matjaz

2009-01-01

We study the evolution of cooperation in the prisoner's dilemma game, whereby a coevolutionary rule is introduced that molds the random topology of the interaction network in two ways. First, existing links are deleted whenever a player adopts a new strategy or its degree exceeds a threshold value; second, new links are added randomly after a given number of game iterations. These coevolutionary processes correspond to the generic formation of new links and deletion of existing links that, especially in human societies, appear frequently as a consequence of ongoing socialization, change of lifestyle or death. Due to the counteraction of deletions and additions of links the initial heterogeneity of the interaction network is qualitatively preserved, and thus cannot be held responsible for the observed promotion of cooperation. Indeed, the coevolutionary rule evokes the spontaneous emergence of a powerful multilevel selection mechanism, which despite the sustained random topology of the evolving network, maintains cooperation across the whole span of defection temptation values.

Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure

International Nuclear Information System (INIS)

Yang Xuhua; Sun Bao; Wang Bo; Sun Youxian

2010-01-01

Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper. (general)

Next Generation Campus Network Deployment Project Based on Softswitch

OpenAIRE

HU Feng; LIU Ziyan

2011-01-01

After analyzing the current networks of Guizhou University，we brought forward a scheme of next generation campus networks based on softswitch technology by choosing SoftX3000 switching system of HuaWei and provided the specific solution of accessing campus networks in this paper. It is proved that this scheme is feasible by using OPNET, which not only accomplished the integration of the PSTN and IP networks but also achieved the combining of voice services and data services.

Convergence of wireless, wireline, and photonics next generation networks

CERN Document Server

Iniewski, Krzysztof

2010-01-01

Filled with illustrations and practical examples from industry, this book provides a brief but comprehensive introduction to the next-generation wireless networks that will soon replace more traditional wired technologies. Written by a mixture of top industrial experts and key academic professors, it is the only book available that covers both wireless networks (such as wireless local area and personal area networks) and optical networks (such as long-haul and metropolitan networks) in one volume. It gives engineers and engineering students the necessary knowledge to meet challenges of next-ge

Pseudo-Random Number Generators for Vector Processors and Multicore Processors

DEFF Research Database (Denmark)

Fog, Agner

2015-01-01

Large scale Monte Carlo applications need a good pseudo-random number generator capable of utilizing both the vector processing capabilities and multiprocessing capabilities of modern computers in order to get the maximum performance. The requirements for such a generator are discussed. New ways...

The generation of 68 Gbps quantum random number by measuring laser phase fluctuations

International Nuclear Information System (INIS)

Nie, You-Qi; Liu, Yang; Zhang, Jun; Pan, Jian-Wei; Huang, Leilei; Payne, Frank

2015-01-01

The speed of a quantum random number generator is essential for practical applications, such as high-speed quantum key distribution systems. Here, we push the speed of a quantum random number generator to 68 Gbps by operating a laser around its threshold level. To achieve the rate, not only high-speed photodetector and high sampling rate are needed but also a very stable interferometer is required. A practical interferometer with active feedback instead of common temperature control is developed to meet the requirement of stability. Phase fluctuations of the laser are measured by the interferometer with a photodetector and then digitalized to raw random numbers with a rate of 80 Gbps. The min-entropy of the raw data is evaluated by modeling the system and is used to quantify the quantum randomness of the raw data. The bias of the raw data caused by other signals, such as classical and detection noises, can be removed by Toeplitz-matrix hashing randomness extraction. The final random numbers can pass through the standard randomness tests. Our demonstration shows that high-speed quantum random number generators are ready for practical usage

True random number generation from mobile telephone photo based on chaotic cryptography

International Nuclear Information System (INIS)

Zhao Liang; Liao Xiaofeng; Xiao Di; Xiang Tao; Zhou Qing; Duan Shukai

2009-01-01

A cheap, convenient and universal TRNG based on mobile telephone photo for producing random bit sequence is proposed. To settle the problem of sequential pixels and comparability, three chaos-based approaches are applied to post-process the generated binary image. The random numbers produced by three users are tested using US NIST RNG statistical test software. The experimental results indicate that the Arnold cat map is the fastest way to generate a random bit sequence and can be accepted on general PC. The 'MASK' algorithm also performs well. Finally, comparing with the TRNG of Hu et al. [Hu Y, Liao X, Wong KW, Zhou Q. A true random number generator based on mouse movement and chaotic cryptography. Chaos, Solitons and Fractals 2007. doi: 10.1016/j.chaos.2007.10.022] which is presented by Hu et al., many merits of the proposed TRNG in this paper has been found.

On the design of henon and logistic map-based random number generator

Science.gov (United States)

Magfirawaty; Suryadi, M. T.; Ramli, Kalamullah

2017-10-01

The key sequence is one of the main elements in the cryptosystem. True Random Number Generators (TRNG) method is one of the approaches to generating the key sequence. The randomness source of the TRNG divided into three main groups, i.e. electrical noise based, jitter based and chaos based. The chaos based utilizes a non-linear dynamic system (continuous time or discrete time) as an entropy source. In this study, a new design of TRNG based on discrete time chaotic system is proposed, which is then simulated in LabVIEW. The principle of the design consists of combining 2D and 1D chaotic systems. A mathematical model is implemented for numerical simulations. We used comparator process as a harvester method to obtain the series of random bits. Without any post processing, the proposed design generated random bit sequence with high entropy value and passed all NIST 800.22 statistical tests.

Finite-time stability of neutral-type neural networks with random time-varying delays

Science.gov (United States)

Ali, M. Syed; Saravanan, S.; Zhu, Quanxin

2017-11-01

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

Problems with the random number generator RANF implemented on the CDC cyber 205

Science.gov (United States)

Kalle, Claus; Wansleben, Stephan

1984-10-01

We show that using RANF may lead to wrong results when lattice models are simulated by Monte Carlo methods. We present a shift-register sequence random number generator which generates two random numbers per cycle on a two pipe CDC Cyber 205.

Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

OpenAIRE

Brown, Suzie; Johnson, Oliver; Tassi, Andrea

2018-01-01

Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kin...

Network meta-analysis incorporating randomized controlled trials and non-randomized comparative cohort studies for assessing the safety and effectiveness of medical treatments: challenges and opportunities

OpenAIRE

Cameron, Chris; Fireman, Bruce; Hutton, Brian; Clifford, Tammy; Coyle, Doug; Wells, George; Dormuth, Colin R.; Platt, Robert; Toh, Sengwee

2015-01-01

Network meta-analysis is increasingly used to allow comparison of multiple treatment alternatives simultaneously, some of which may not have been compared directly in primary research studies. The majority of network meta-analyses published to date have incorporated data from randomized controlled trials (RCTs) only; however, inclusion of non-randomized studies may sometimes be considered. Non-randomized studies can complement RCTs or address some of their limitations, such as short follow-up...

A fast random number generator for the Intel Paragon supercomputer

Science.gov (United States)

Gutbrod, F.

1995-06-01

A pseudo-random number generator is presented which makes optimal use of the architecture of the i860-microprocessor and which is expected to have a very long period. It is therefore a good candidate for use on the parallel supercomputer Paragon XP. In the assembler version, it needs 6.4 cycles for a real∗4 random number. There is a FORTRAN routine which yields identical numbers up to rare and minor rounding discrepancies, and it needs 28 cycles. The FORTRAN performance on other microprocessors is somewhat better. Arguments for the quality of the generator and some numerical tests are given.

A Bidirectional Generalized Synchronization Theorem-Based Chaotic Pseudo-random Number Generator

Directory of Open Access Journals (Sweden)

Han Shuangshuang

2013-07-01

Full Text Available Based on a bidirectional generalized synchronization theorem for discrete chaos system, this paper introduces a new 5-dimensional bidirectional generalized chaos synchronization system (BGCSDS, whose prototype is a novel chaotic system introduced in [12]. Numerical simulation showed that two pair variables of the BGCSDS achieve generalized chaos synchronization via a transform H.A chaos-based pseudo-random number generator (CPNG was designed by the new BGCSDS. Using the FIPS-140-2 tests issued by the National Institute of Standard and Technology (NIST verified the randomness of the 1000 binary number sequences generated via the CPNG and the RC4 algorithm respectively. The results showed that all the tested sequences passed the FIPS-140-2 tests. The confidence interval analysis showed the statistical properties of the randomness of the sequences generated via the CPNG and the RC4 algorithm do not have significant differences.

ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

Directory of Open Access Journals (Sweden)

Zhihuai Xiao

2015-01-01

Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond-Random-Field Ising Model

International Nuclear Information System (INIS)

Ovchinnikov, O. S.; Jesse, S.; Kalinin, S. V.; Bintacchit, P.; Trolier-McKinstry, S.

2009-01-01

An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond-random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.

High-Speed Device-Independent Quantum Random Number Generation without a Detection Loophole

Science.gov (United States)

Liu, Yang; Yuan, Xiao; Li, Ming-Han; Zhang, Weijun; Zhao, Qi; Zhong, Jiaqiang; Cao, Yuan; Li, Yu-Huai; Chen, Luo-Kan; Li, Hao; Peng, Tianyi; Chen, Yu-Ao; Peng, Cheng-Zhi; Shi, Sheng-Cai; Wang, Zhen; You, Lixing; Ma, Xiongfeng; Fan, Jingyun; Zhang, Qiang; Pan, Jian-Wei

2018-01-01

Quantum mechanics provides the means of generating genuine randomness that is impossible with deterministic classical processes. Remarkably, the unpredictability of randomness can be certified in a manner that is independent of implementation devices. Here, we present an experimental study of device-independent quantum random number generation based on a detection-loophole-free Bell test with entangled photons. In the randomness analysis, without the independent identical distribution assumption, we consider the worst case scenario that the adversary launches the most powerful attacks against the quantum adversary. After considering statistical fluctuations and applying an 80 Gb ×45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits /s , with a failure probability less than 10-5. This marks a critical step towards realistic applications in cryptography and fundamental physics tests.

A method of generating moving objects on the constrained network

Science.gov (United States)

Zhang, Jie; Ma, Linbing

2008-10-01

Moving objects databases have become an important research issue in recent years. In case large real data sets acquired by GPS, PDA or other mobile devices are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations over time. In the field of spatiotemporal databases, a number of publications about the generation of test data are restricted to few papers. However, most of the existing moving-object generators assume a fixed and often unrealistic mobility model and do not consider several important characteristics of the network. In this paper, a new generator is presented to solve these problems. First of all, the network is realistic transportation network of Guangzhou. Second, the observation records of vehicle flow are available. Third, in order to simplify the whole simulation process and to help us visualize the process, this framework is built under .Net development platform of Microsoft and ArcEngine9 environment.

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Distributed network generation based on preferential attachment in ABS

NARCIS (Netherlands)

K. Azadbakht (Keyvan); N. Bezirgiannis (Nikolaos); F.S. de Boer (Frank)

2017-01-01

textabstractGeneration of social networks using Preferential Attachment (PA) mechanism is proposed in the Barabasi-Albert model. In this mechanism, new nodes are introduced to the network sequentially and they attach to the existing nodes preferentially where the preference can be based on the

Random number generators tested on quantum Monte Carlo simulations.

Science.gov (United States)

Hongo, Kenta; Maezono, Ryo; Miura, Kenichi

2010-08-01

We have tested and compared several (pseudo) random number generators (RNGs) applied to a practical application, ground state energy calculations of molecules using variational and diffusion Monte Carlo metheds. A new multiple recursive generator with 8th-order recursion (MRG8) and the Mersenne twister generator (MT19937) are tested and compared with the RANLUX generator with five luxury levels (RANLUX-[0-4]). Both MRG8 and MT19937 are proven to give the same total energy as that evaluated with RANLUX-4 (highest luxury level) within the statistical error bars with less computational cost to generate the sequence. We also tested the notorious implementation of linear congruential generator (LCG), RANDU, for comparison. (c) 2010 Wiley Periodicals, Inc.

Quantum random bit generation using energy fluctuations in stimulated Raman scattering.

Science.gov (United States)

Bustard, Philip J; England, Duncan G; Nunn, Josh; Moffatt, Doug; Spanner, Michael; Lausten, Rune; Sussman, Benjamin J

2013-12-02

Random number sequences are a critical resource in modern information processing systems, with applications in cryptography, numerical simulation, and data sampling. We introduce a quantum random number generator based on the measurement of pulse energy quantum fluctuations in Stokes light generated by spontaneously-initiated stimulated Raman scattering. Bright Stokes pulse energy fluctuations up to five times the mean energy are measured with fast photodiodes and converted to unbiased random binary strings. Since the pulse energy is a continuous variable, multiple bits can be extracted from a single measurement. Our approach can be generalized to a wide range of Raman active materials; here we demonstrate a prototype using the optical phonon line in bulk diamond.

Programmable disorder in random DNA tilings

Science.gov (United States)

Tikhomirov, Grigory; Petersen, Philip; Qian, Lulu

2017-03-01

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network—random loops, mazes and trees—on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

Influence of choice of null network on small-world parameters of structural correlation networks.

Directory of Open Access Journals (Sweden)

S M Hadi Hosseini

Full Text Available In recent years, coordinated variations in brain morphology (e.g., volume, thickness have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1 networks constructed by topology randomization (TOP, 2 networks matched to the distributional properties of the observed covariance matrix (HQS, and 3 networks generated from correlation of randomized input data (COR. The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

Science.gov (United States)

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.

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Martin Rosvall

Full Text Available To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.