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Sample records for randomness stochastic algorithms

  1. Recycling random numbers in the stochastic simulation algorithm.

    PubMed

    Yates, Christian A; Klingbeil, Guido

    2013-03-01

    The stochastic simulation algorithm (SSA) was introduced by Gillespie and in a different form by Kurtz. Since its original formulation there have been several attempts at improving the efficiency and hence the speed of the algorithm. We briefly discuss some of these methods before outlining our own simple improvement, the recycling direct method (RDM), and demonstrating that it is capable of increasing the speed of most stochastic simulations. The RDM involves the statistically acceptable recycling of random numbers in order to reduce the computational cost associated with their generation and is compatible with several of the pre-existing improvements on the original SSA. Our improvement is also sufficiently simple (one additional line of code) that we hope will be adopted by both trained mathematical modelers and experimentalists wishing to simulate their model systems. PMID:23485273

  2. Algorithmic advances in stochastic programming

    SciTech Connect

    Morton, D.P.

    1993-07-01

    Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.

  3. A retrodictive stochastic simulation algorithm

    SciTech Connect

    Vaughan, T.G. Drummond, P.D.; Drummond, A.J.

    2010-05-20

    In this paper we describe a simple method for inferring the initial states of systems evolving stochastically according to master equations, given knowledge of the final states. This is achieved through the use of a retrodictive stochastic simulation algorithm which complements the usual predictive stochastic simulation approach. We demonstrate the utility of this new algorithm by applying it to example problems, including the derivation of likely ancestral states of a gene sequence given a Markovian model of genetic mutation.

  4. RES: Regularized Stochastic BFGS Algorithm

    NASA Astrophysics Data System (ADS)

    Mokhtari, Aryan; Ribeiro, Alejandro

    2014-12-01

    RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.

  5. Enhanced algorithms for stochastic programming

    SciTech Connect

    Krishna, A.S.

    1993-09-01

    In this dissertation, we present some of the recent advances made in solving two-stage stochastic linear programming problems of large size and complexity. Decomposition and sampling are two fundamental components of techniques to solve stochastic optimization problems. We describe improvements to the current techniques in both these areas. We studied different ways of using importance sampling techniques in the context of Stochastic programming, by varying the choice of approximation functions used in this method. We have concluded that approximating the recourse function by a computationally inexpensive piecewise-linear function is highly efficient. This reduced the problem from finding the mean of a computationally expensive functions to finding that of a computationally inexpensive function. Then we implemented various variance reduction techniques to estimate the mean of a piecewise-linear function. This method achieved similar variance reductions in orders of magnitude less time than, when we directly applied variance-reduction techniques directly on the given problem. In solving a stochastic linear program, the expected value problem is usually solved before a stochastic solution and also to speed-up the algorithm by making use of the information obtained from the solution of the expected value problem. We have devised a new decomposition scheme to improve the convergence of this algorithm.

  6. Algorithm refinement for the stochastic Burgers' equation

    SciTech Connect

    Bell, John B.; Foo, Jasmine; Garcia, Alejandro L. . E-mail: algarcia@algarcia.org

    2007-04-10

    In this paper, we develop an algorithm refinement (AR) scheme for an excluded random walk model whose mean field behavior is given by the viscous Burgers' equation. AR hybrids use the adaptive mesh refinement framework to model a system using a molecular algorithm where desired while allowing a computationally faster continuum representation to be used in the remainder of the domain. The focus in this paper is the role of fluctuations on the dynamics. In particular, we demonstrate that it is necessary to include a stochastic forcing term in Burgers' equation to accurately capture the correct behavior of the system. The conclusion we draw from this study is that the fidelity of multiscale methods that couple disparate algorithms depends on the consistent modeling of fluctuations in each algorithm and on a coupling, such as algorithm refinement, that preserves this consistency.

  7. Enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Technical report

    SciTech Connect

    Morton, D.P.

    1994-01-01

    Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems. Stochastic programming, Hydroelectric scheduling, Large-scale Systems.

  8. Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation-Maximization (SAEM) Algorithm.

    PubMed

    Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu

    2016-03-01

    The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed. PMID:25416456

  9. A Stochastic Collocation Algorithm for Uncertainty Analysis

    NASA Technical Reports Server (NTRS)

    Mathelin, Lionel; Hussaini, M. Yousuff; Zang, Thomas A. (Technical Monitor)

    2003-01-01

    This report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters.

  10. A novel stochastic optimization algorithm.

    PubMed

    Li, B; Jiang, W

    2000-01-01

    This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: (1) it is not the simple mix of SAA, GA, and CA; (2) it works from a population; (3) it can be easily used to solve optimization problems either with continuous variables or with discrete variables, and it does not need coding and decoding,; and (4) it can easily escape from local minima and converge quickly. Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA. PMID:18244742

  11. Stochastic structure formation in random media

    NASA Astrophysics Data System (ADS)

    Klyatskin, V. I.

    2016-01-01

    Stochastic structure formation in random media is considered using examples of elementary dynamical systems related to the two-dimensional geophysical fluid dynamics (Gaussian random fields) and to stochastically excited dynamical systems described by partial differential equations (lognormal random fields). In the latter case, spatial structures (clusters) may form with a probability of one in almost every system realization due to rare events happening with vanishing probability. Problems involving stochastic parametric excitation occur in fluid dynamics, magnetohydrodynamics, plasma physics, astrophysics, and radiophysics. A more complicated stochastic problem dealing with anomalous structures on the sea surface (rogue waves) is also considered, where the random Gaussian generation of sea surface roughness is accompanied by parametric excitation.

  12. Bootstrap performance profiles in stochastic algorithms assessment

    SciTech Connect

    Costa, Lino; Espírito Santo, Isabel A.C.P.; Oliveira, Pedro

    2015-03-10

    Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.

  13. Linear-scaling and parallelisable algorithms for stochastic quantum chemistry

    NASA Astrophysics Data System (ADS)

    Booth, George H.; Smart, Simon D.; Alavi, Ali

    2014-07-01

    For many decades, quantum chemical method development has been dominated by algorithms which involve increasingly complex series of tensor contractions over one-electron orbital spaces. Procedures for their derivation and implementation have evolved to require the minimum amount of logic and rely heavily on computationally efficient library-based matrix algebra and optimised paging schemes. In this regard, the recent development of exact stochastic quantum chemical algorithms to reduce computational scaling and memory overhead requires a contrasting algorithmic philosophy, but one which when implemented efficiently can achieve higher accuracy/cost ratios with small random errors. Additionally, they can exploit the continuing trend for massive parallelisation which hinders the progress of deterministic high-level quantum chemical algorithms. In the Quantum Monte Carlo community, stochastic algorithms are ubiquitous but the discrete Fock space of quantum chemical methods is often unfamiliar, and the methods introduce new concepts required for algorithmic efficiency. In this paper, we explore these concepts and detail an algorithm used for Full Configuration Interaction Quantum Monte Carlo (FCIQMC), which is implemented and available in MOLPRO and as a standalone code, and is designed for high-level parallelism and linear-scaling with walker number. Many of the algorithms are also in use in, or can be transferred to, other stochastic quantum chemical methods and implementations. We apply these algorithms to the strongly correlated chromium dimer to demonstrate their efficiency and parallelism.

  14. Stochastic template placement algorithm for gravitational wave data analysis

    SciTech Connect

    Harry, I. W.; Sathyaprakash, B. S.; Allen, B.

    2009-11-15

    This paper presents an algorithm for constructing matched-filter template banks in an arbitrary parameter space. The method places templates at random, then removes those which are 'too close' together. The properties and optimality of stochastic template banks generated in this manner are investigated for some simple models. The effectiveness of these template banks for gravitational wave searches for binary inspiral waveforms is also examined. The properties of a stochastic template bank are then compared to the deterministically placed template banks that are currently used in gravitational wave data analysis.

  15. Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique

    PubMed Central

    Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep

    2015-01-01

    In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. PMID:26552032

  16. Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique.

    PubMed

    Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep

    2015-01-01

    In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. PMID:26552032

  17. An exact accelerated stochastic simulation algorithm

    PubMed Central

    Mjolsness, Eric; Orendorff, David; Chatelain, Philippe; Koumoutsakos, Petros

    2009-01-01

    An exact method for stochastic simulation of chemical reaction networks, which accelerates the stochastic simulation algorithm (SSA), is proposed. The present “ER-leap” algorithm is derived from analytic upper and lower bounds on the multireaction probabilities sampled by SSA, together with rejection sampling and an adaptive multiplicity for reactions. The algorithm is tested on a number of well-quantified reaction networks and is found experimentally to be very accurate on test problems including a chaotic reaction network. At the same time ER-leap offers a substantial speedup over SSA with a simulation time proportional to the 2∕3 power of the number of reaction events in a Galton–Watson process. PMID:19368432

  18. Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

    NASA Technical Reports Server (NTRS)

    Williams Colin P.

    1999-01-01

    Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

  19. Stochastic Evolutionary Algorithms for Planning Robot Paths

    NASA Technical Reports Server (NTRS)

    Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

    2006-01-01

    A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

  20. Random musings on stochastics (Lorenz Lecture)

    NASA Astrophysics Data System (ADS)

    Koutsoyiannis, D.

    2014-12-01

    In 1960 Lorenz identified the chaotic nature of atmospheric dynamics, thus highlighting the importance of the discovery of chaos by Poincare, 70 years earlier, in the motion of three bodies. Chaos in the macroscopic world offered a natural way to explain unpredictability, that is, randomness. Concurrently with Poincare's discovery, Boltzmann introduced statistical physics, while soon after Borel and Lebesgue laid the foundation of measure theory, later (in 1930s) used by Kolmogorov as the formal foundation of probability theory. Subsequently, Kolmogorov and Khinchin introduced the concepts of stochastic processes and stationarity, and advanced the concept of ergodicity. All these areas are now collectively described by the term "stochastics", which includes probability theory, stochastic processes and statistics. As paradoxical as it may seem, stochastics offers the tools to deal with chaos, even if it results from deterministic dynamics. As chaos entails uncertainty, it is more informative and effective to replace the study of exact system trajectories with that of probability densities. Also, as the exact laws of complex systems can hardly be deduced by synthesis of the detailed interactions of system components, these laws should inevitably be inferred by induction, based on observational data and using statistics. The arithmetic of stochastics is quite different from that of regular numbers. Accordingly, it needs the development of intuition and interpretations which differ from those built upon deterministic considerations. Using stochastic tools in a deterministic context may result in mistaken conclusions. In an attempt to contribute to a more correct interpretation and use of stochastic concepts in typical tasks of nonlinear systems, several examples are studied, which aim (a) to clarify the difference in the meaning of linearity in deterministic and stochastic context; (b) to contribute to a more attentive use of stochastic concepts (entropy, statistical

  1. The theory of hybrid stochastic algorithms

    SciTech Connect

    Kennedy, A.D. . Supercomputer Computations Research Inst.)

    1989-11-21

    These lectures introduce the family of Hybrid Stochastic Algorithms for performing Monte Carlo calculations in Quantum Field Theory. After explaining the basic concepts of Monte Carlo integration we discuss the properties of Markov processes and one particularly useful example of them: the Metropolis algorithm. Building upon this framework we consider the Hybrid and Langevin algorithms from the viewpoint that they are approximate versions of the Hybrid Monte Carlo method; and thus we are led to consider Molecular Dynamics using the Leapfrog algorithm. The lectures conclude by reviewing recent progress in these areas, explaining higher-order integration schemes, the asymptotic large-volume behaviour of the various algorithms, and some simple exact results obtained by applying them to free field theory. It is attempted throughout to give simple yet correct proofs of the various results encountered. 38 refs.

  2. A hierarchical exact accelerated stochastic simulation algorithm

    PubMed Central

    Orendorff, David; Mjolsness, Eric

    2012-01-01

    A new algorithm, “HiER-leap” (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled “blocks” and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms. PMID:23231214

  3. Perspective: Stochastic algorithms for chemical kinetics

    PubMed Central

    Gillespie, Daniel T.; Hellander, Andreas; Petzold, Linda R.

    2013-01-01

    We outline our perspective on stochastic chemical kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional chemical kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with chemical reactions inside the subvolumes. PMID:23656106

  4. Stochastic algorithms for Markov models estimation with intermittent missing data.

    PubMed

    Deltour, I; Richardson, S; Le Hesran, J Y

    1999-06-01

    Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for estimating Markov chain models in the case of intermittent missing data in longitudinal studies, a stochastic EM algorithm and the Gibbs sampler, are described. The first can be viewed as a random perturbation of the EM algorithm and is appropriate when the M step is straightforward but the E step is computationally burdensome. It leads to a good approximation of the maximum likelihood estimates. The Gibbs sampler is used for a full Bayesian inference. The performances of the two algorithms are illustrated on two simulated data sets. A motivating example concerned with the modelling of the evolution of parasitemia by Plasmodium falciparum (malaria) in a cohort of 105 young children in Cameroon is described and briefly analyzed. PMID:11318215

  5. Path sampling with stochastic dynamics: Some new algorithms

    SciTech Connect

    Stoltz, Gabriel . E-mail: stoltz@cermics.enpc.fr

    2007-07-01

    We propose here some new sampling algorithms for path sampling in the case when stochastic dynamics are used. In particular, we present a new proposal function for equilibrium sampling of paths with a Monte-Carlo dynamics (the so-called 'brownian tube' proposal). This proposal is based on the continuity of the dynamics with respect to the random forcing, and generalizes all previous approaches when stochastic dynamics are used. The efficiency of this proposal is demonstrated using some measure of decorrelation in path space. We also discuss a switching strategy that allows to transform ensemble of paths at a finite rate while remaining at equilibrium, in contrast with the usual Jarzynski like switching. This switching is very interesting to sample constrained paths starting from unconstrained paths, or to perform simulated annealing in a rigorous way.

  6. A stochastic approximation algorithm for estimating mixture proportions

    NASA Technical Reports Server (NTRS)

    Sparra, J.

    1976-01-01

    A stochastic approximation algorithm for estimating the proportions in a mixture of normal densities is presented. The algorithm is shown to converge to the true proportions in the case of a mixture of two normal densities.

  7. Constant-complexity stochastic simulation algorithm with optimal binning

    SciTech Connect

    Sanft, Kevin R.; Othmer, Hans G.

    2015-08-21

    At the molecular level, biochemical processes are governed by random interactions between reactant molecules, and the dynamics of such systems are inherently stochastic. When the copy numbers of reactants are large, a deterministic description is adequate, but when they are small, such systems are often modeled as continuous-time Markov jump processes that can be described by the chemical master equation. Gillespie’s Stochastic Simulation Algorithm (SSA) generates exact trajectories of these systems, but the amount of computational work required for each step of the original SSA is proportional to the number of reaction channels, leading to computational complexity that scales linearly with the problem size. The original SSA is therefore inefficient for large problems, which has prompted the development of several alternative formulations with improved scaling properties. We describe an exact SSA that uses a table data structure with event time binning to achieve constant computational complexity with respect to the number of reaction channels for weakly coupled reaction networks. We present a novel adaptive binning strategy and discuss optimal algorithm parameters. We compare the computational efficiency of the algorithm to existing methods and demonstrate excellent scaling for large problems. This method is well suited for generating exact trajectories of large weakly coupled models, including those that can be described by the reaction-diffusion master equation that arises from spatially discretized reaction-diffusion processes.

  8. Second-order stochastic leapfrog algorithm for multiplicative noise brownian motion

    PubMed

    Qiang; Habib

    2000-11-01

    A stochastic leapfrog algorithm for the numerical integration of Brownian motion stochastic differential equations with multiplicative noise is proposed and tested. The algorithm has a second-order convergence of moments in a finite time interval and requires the sampling of only one uniformly distributed random variable per time step. The noise may be white or colored. We apply the algorithm to a study of the approach towards equilibrium of an oscillator coupled nonlinearly to a heat bath and investigate the effect of the multiplicative noise (arising from the nonlinear coupling) on the relaxation time. This allows us to test the regime of validity of the energy-envelope approximation method. PMID:11102105

  9. Second-order stochastic leapfrog algorithm for multiplicative noise Brownian motion

    NASA Astrophysics Data System (ADS)

    Qiang, Ji; Habib, Salman

    2000-11-01

    A stochastic leapfrog algorithm for the numerical integration of Brownian motion stochastic differential equations with multiplicative noise is proposed and tested. The algorithm has a second-order convergence of moments in a finite time interval and requires the sampling of only one uniformly distributed random variable per time step. The noise may be white or colored. We apply the algorithm to a study of the approach towards equilibrium of an oscillator coupled nonlinearly to a heat bath and investigate the effect of the multiplicative noise (arising from the nonlinear coupling) on the relaxation time. This allows us to test the regime of validity of the energy-envelope approximation method.

  10. Stochastic Formal Correctness of Numerical Algorithms

    NASA Technical Reports Server (NTRS)

    Daumas, Marc; Lester, David; Martin-Dorel, Erik; Truffert, Annick

    2009-01-01

    We provide a framework to bound the probability that accumulated errors were never above a given threshold on numerical algorithms. Such algorithms are used for example in aircraft and nuclear power plants. This report contains simple formulas based on Levy's and Markov's inequalities and it presents a formal theory of random variables with a special focus on producing concrete results. We selected four very common applications that fit in our framework and cover the common practices of systems that evolve for a long time. We compute the number of bits that remain continuously significant in the first two applications with a probability of failure around one out of a billion, where worst case analysis considers that no significant bit remains. We are using PVS as such formal tools force explicit statement of all hypotheses and prevent incorrect uses of theorems.

  11. Parameter identification using a creeping-random-search algorithm

    NASA Technical Reports Server (NTRS)

    Parrish, R. V.

    1971-01-01

    A creeping-random-search algorithm is applied to different types of problems in the field of parameter identification. The studies are intended to demonstrate that a random-search algorithm can be applied successfully to these various problems, which often cannot be handled by conventional deterministic methods, and, also, to introduce methods that speed convergence to an extremal of the problem under investigation. Six two-parameter identification problems with analytic solutions are solved, and two application problems are discussed in some detail. Results of the study show that a modified version of the basic creeping-random-search algorithm chosen does speed convergence in comparison with the unmodified version. The results also show that the algorithm can successfully solve problems that contain limits on state or control variables, inequality constraints (both independent and dependent, and linear and nonlinear), or stochastic models.

  12. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization

    NASA Astrophysics Data System (ADS)

    López-Ibáñez, Manuel; Paquete, Luís; Stützle, Thomas

    This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.

  13. Stochastic reaction–diffusion algorithms for macromolecular crowding

    NASA Astrophysics Data System (ADS)

    Sturrock, Marc

    2016-06-01

    Compartment-based (lattice-based) reaction–diffusion algorithms are often used for studying complex stochastic spatio-temporal processes inside cells. In this paper the influence of macromolecular crowding on stochastic reaction–diffusion simulations is investigated. Reaction–diffusion processes are considered on two different kinds of compartmental lattice, a cubic lattice and a hexagonal close packed lattice, and solved using two different algorithms, the stochastic simulation algorithm and the spatiocyte algorithm (Arjunan and Tomita 2010 Syst. Synth. Biol. 4, 35–53). Obstacles (modelling macromolecular crowding) are shown to have substantial effects on the mean squared displacement and average number of molecules in the domain but the nature of these effects is dependent on the choice of lattice, with the cubic lattice being more susceptible to the effects of the obstacles. Finally, improvements for both algorithms are presented.

  14. Multiscale stochastic simulation algorithm with stochastic partial equilibrium assumption for chemically reacting systems

    SciTech Connect

    Cao Yang . E-mail: ycao@cs.ucsb.edu; Gillespie, Dan . E-mail: GillespieDT@mailaps.org; Petzold, Linda . E-mail: petzold@engineering.ucsb.edu

    2005-07-01

    In this paper, we introduce a multiscale stochastic simulation algorithm (MSSA) which makes use of Gillespie's stochastic simulation algorithm (SSA) together with a new stochastic formulation of the partial equilibrium assumption (PEA). This method is much more efficient than SSA alone. It works even with a very small population of fast species. Implementation details are discussed, and an application to the modeling of the heat shock response of E. Coli is presented which demonstrates the excellent efficiency and accuracy obtained with the new method.

  15. Random Walk-Based Solution to Triple Level Stochastic Point Location Problem.

    PubMed

    Jiang, Wen; Huang, De-Shuang; Li, Shenghong

    2016-06-01

    This paper considers the stochastic point location (SPL) problem as a learning mechanism trying to locate a point on a real line via interacting with a random environment. Compared to the stochastic environment in the literatures that confines the learning mechanism to moving in two directions, i.e., left or right, this paper introduces a general triple level stochastic environment which not only tells the learning mechanism to go left or right, but also informs it to stay unmoved. It is easy to understand, as we will prove in this paper, that the environment reported in the previous literatures is just a special case of the triple level environment. And a new learning algorithm, named as random walk-based triple level learning algorithm, is proposed to locate an unknown point under this new type of environment. In order to examine the performance of this algorithm, we divided the triple level SPL problems into four distinguished scenarios by the properties of the unknown point and the stochastic environment, and proved that even under the triple level nonstationary environment and the convergence condition having not being satisfied for some time, which are rarely considered in existing SPL problems, the proposed learning algorithm is still working properly whenever the unknown point is static or evolving with time. Extensive experiments validate our theoretical analyses and demonstrate that the proposed learning algorithms are quite effective and efficient. PMID:26168455

  16. Random attractor of non-autonomous stochastic Boussinesq lattice system

    SciTech Connect

    Zhao, Min Zhou, Shengfan

    2015-09-15

    In this paper, we first consider the existence of tempered random attractor for second-order non-autonomous stochastic lattice dynamical system of nonlinear Boussinesq equations effected by time-dependent coupled coefficients and deterministic forces and multiplicative white noise. Then, we establish the upper semicontinuity of random attractors as the intensity of noise approaches zero.

  17. Designing Stochastic Optimization Algorithms for Real-world Applications

    NASA Astrophysics Data System (ADS)

    Someya, Hiroshi; Handa, Hisashi; Koakutsu, Seiichi

    This article presents a review of recent advances in stochastic optimization algorithms. Novel algorithms achieving highly adaptive and efficient searches, theoretical analyses to deepen our understanding of search behavior, successful implementation on parallel computers, attempts to build benchmark suites for industrial use, and techniques applied to real-world problems are included. A list of resources is provided.

  18. A GENERALIZED STOCHASTIC COLLOCATION APPROACH TO CONSTRAINED OPTIMIZATION FOR RANDOM DATA IDENTIFICATION PROBLEMS

    SciTech Connect

    Webster, Clayton G; Gunzburger, Max D

    2013-01-01

    We present a scalable, parallel mechanism for stochastic identification/control for problems constrained by partial differential equations with random input data. Several identification objectives will be discussed that either minimize the expectation of a tracking cost functional or minimize the difference of desired statistical quantities in the appropriate $L^p$ norm, and the distributed parameters/control can both deterministic or stochastic. Given an objective we prove the existence of an optimal solution, establish the validity of the Lagrange multiplier rule and obtain a stochastic optimality system of equations. The modeling process may describe the solution in terms of high dimensional spaces, particularly in the case when the input data (coefficients, forcing terms, boundary conditions, geometry, etc) are affected by a large amount of uncertainty. For higher accuracy, the computer simulation must increase the number of random variables (dimensions), and expend more effort approximating the quantity of interest in each individual dimension. Hence, we introduce a novel stochastic parameter identification algorithm that integrates an adjoint-based deterministic algorithm with the sparse grid stochastic collocation FEM approach. This allows for decoupled, moderately high dimensional, parameterized computations of the stochastic optimality system, where at each collocation point, deterministic analysis and techniques can be utilized. The advantage of our approach is that it allows for the optimal identification of statistical moments (mean value, variance, covariance, etc.) or even the whole probability distribution of the input random fields, given the probability distribution of some responses of the system (quantities of physical interest). Our rigorously derived error estimates, for the fully discrete problems, will be described and used to compare the efficiency of the method with several other techniques. Numerical examples illustrate the theoretical

  19. Evolving Stochastic Learning Algorithm based on Tsallis entropic index

    NASA Astrophysics Data System (ADS)

    Anastasiadis, A. D.; Magoulas, G. D.

    2006-03-01

    In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method.

  20. On stochastic approximation algorithms for classes of PAC learning problems

    SciTech Connect

    Rao, N.S.V.; Uppuluri, V.R.R.; Oblow, E.M.

    1994-03-01

    The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [o,1]{sup d}. Under some assumptions on different ability of the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of non-polynomial units (e.g. artificial neural networks). Conditions on the sizes of these samples required to ensure the error bounds are derived using martingale inequalities.

  1. Fast Quantum Algorithms for Numerical Integrals and Stochastic Processes

    NASA Technical Reports Server (NTRS)

    Abrams, D.; Williams, C.

    1999-01-01

    We discuss quantum algorithms that calculate numerical integrals and descriptive statistics of stochastic processes. With either of two distinct approaches, one obtains an exponential speed increase in comparison to the fastest known classical deterministic algotithms and a quadratic speed increase incomparison to classical Monte Carlo methods.

  2. Point group identification algorithm in dynamic response analysis of nonlinear stochastic systems

    NASA Astrophysics Data System (ADS)

    Li, Tao; Chen, Jian-bing; Li, Jie

    2016-03-01

    The point group identification (PGI) algorithm is proposed to determine the representative point sets in response analysis of nonlinear stochastic dynamic systems. The PGI algorithm is employed to identify point groups and their feature points in an initial point set by combining subspace clustering analysis and the graph theory. Further, the representative point set of the random-variate space is determined according to the minimum generalized F-discrepancy. The dynamic responses obtained by incorporating the algorithm PGI into the probability density evolution method (PDEM) are compared with those by the Monte Carlo simulation method. The investigations indicate that the proposed method can reduce the number of the representative points, lower the generalized F-discrepancy of the representative point set, and also ensure the accuracy of stochastic structural dynamic analysis.

  3. Quantum stochastic walks: A generalization of classical random walks and quantum walks

    NASA Astrophysics Data System (ADS)

    Aspuru-Guzik, Alan

    2010-03-01

    We introduce the quantum stochastic walk (QSW), which determines the evolution of generalized quantum mechanical walk on a graph that obeys a quantum stochastic equation of motion. Using an axiomatic approach, we specify the rules for all possible quantum, classical and quantum-stochastic transitions from a vertex as defined by its connectivity. We show how the family of possible QSWs encompasses both the classical random walk (CRW) and the quantum walk (QW) as special cases, but also includes more general probability distributions. As an example, we study the QSW on a line, the QW to CRW transition and transitions to genearlized QSWs that go beyond the CRW and QW. QSWs provide a new framework to the study of quantum algorithms as well as of quantum walks with environmental effects.

  4. Random search optimization based on genetic algorithm and discriminant function

    NASA Technical Reports Server (NTRS)

    Kiciman, M. O.; Akgul, M.; Erarslanoglu, G.

    1990-01-01

    The general problem of optimization with arbitrary merit and constraint functions, which could be convex, concave, monotonic, or non-monotonic, is treated using stochastic methods. To improve the efficiency of the random search methods, a genetic algorithm for the search phase and a discriminant function for the constraint-control phase were utilized. The validity of the technique is demonstrated by comparing the results to published test problem results. Numerical experimentation indicated that for cases where a quick near optimum solution is desired, a general, user-friendly optimization code can be developed without serious penalties in both total computer time and accuracy.

  5. Randomized Algorithms for Matrices and Data

    NASA Astrophysics Data System (ADS)

    Mahoney, Michael W.

    2012-03-01

    This chapter reviews recent work on randomized matrix algorithms. By “randomized matrix algorithms,” we refer to a class of recently developed random sampling and random projection algorithms for ubiquitous linear algebra problems such as least-squares (LS) regression and low-rank matrix approximation. These developments have been driven by applications in large-scale data analysis—applications which place very different demands on matrices than traditional scientific computing applications. Thus, in this review, we will focus on highlighting the simplicity and generality of several core ideas that underlie the usefulness of these randomized algorithms in scientific applications such as genetics (where these algorithms have already been applied) and astronomy (where, hopefully, in part due to this review they will soon be applied). The work we will review here had its origins within theoretical computer science (TCS). An important feature in the use of randomized algorithms in TCS more generally is that one must identify and then algorithmically deal with relevant “nonuniformity structure” in the data. For the randomized matrix algorithms to be reviewed here and that have proven useful recently in numerical linear algebra (NLA) and large-scale data analysis applications, the relevant nonuniformity structure is defined by the so-called statistical leverage scores. Defined more precisely below, these leverage scores are basically the diagonal elements of the projection matrix onto the dominant part of the spectrum of the input matrix. As such, they have a long history in statistical data analysis, where they have been used for outlier detection in regression diagnostics. More generally, these scores often have a very natural interpretation in terms of the data and processes generating the data. For example, they can be interpreted in terms of the leverage or influence that a given data point has on, say, the best low-rank matrix approximation; and this

  6. A random search algorithm for laboratory computers

    NASA Technical Reports Server (NTRS)

    Curry, R. E.

    1975-01-01

    The small laboratory computer is ideal for experimental control and data acquisition. Postexperimental data processing is often performed on large computers because of the availability of sophisticated programs, but costs and data compatibility are negative factors. Parameter optimization can be accomplished on the small computer, offering ease of programming, data compatibility, and low cost. A previously proposed random-search algorithm ('random creep') was found to be very slow in convergence. A method is proposed (the 'random leap' algorithm) which starts in a global search mode and automatically adjusts step size to speed convergence. A FORTRAN executive program for the random-leap algorithm is presented which calls a user-supplied function subroutine. An example of a function subroutine is given which calculates maximum-likelihood estimates of receiver operating-characteristic parameters from binary response data. Other applications in parameter estimation, generalized least squares, and matrix inversion are discussed.

  7. A fast and convergent stochastic MLP learning algorithm.

    PubMed

    Sakurai, A

    2001-12-01

    We propose a stochastic learning algorithm for multilayer perceptrons of linear-threshold function units, which theoretically converges with probability one and experimentally exhibits 100% convergence rate and remarkable speed on parity and classification problems with typical generalization accuracy. For learning the n bit parity function with n hidden units, the algorithm converged on all the trials we tested (n=2 to 12) after 5.8 x 4.1(n) presentations for 0.23 x 4.0(n-6) seconds on a 533MHz Alpha 21164A chip on average, which is five to ten times faster than Levenberg-Marquardt algorithm with restarts. For a medium size classification problem known as Thyroid in UCI repository, the algorithm is faster in speed and comparative in generalization accuracy than the standard backpropagation and Levenberg-Marquardt algorithms. PMID:11852440

  8. Stochastic search in structural optimization - Genetic algorithms and simulated annealing

    NASA Technical Reports Server (NTRS)

    Hajela, Prabhat

    1993-01-01

    An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

  9. Random variable transformation for generalized stochastic radiative transfer in finite participating slab media

    NASA Astrophysics Data System (ADS)

    El-Wakil, S. A.; Sallah, M.; El-Hanbaly, A. M.

    2015-10-01

    The stochastic radiative transfer problem is studied in a participating planar finite continuously fluctuating medium. The problem is considered for specular- and diffusly-reflecting boundaries with linear anisotropic scattering. Random variable transformation (RVT) technique is used to get the complete average for the solution functions, that are represented by the probability-density function (PDF) of the solution process. In the RVT algorithm, a simple integral transformation to the input stochastic process (the extinction function of the medium) is applied. This linear transformation enables us to rewrite the stochastic transport equations in terms of the optical random variable (x) and the optical random thickness (L). Then the transport equation is solved deterministically to get a closed form for the solution as a function of x and L. So, the solution is used to obtain the PDF of the solution functions applying the RVT technique among the input random variable (L) and the output process (the solution functions). The obtained averages of the solution functions are used to get the complete analytical averages for some interesting physical quantities, namely, reflectivity and transmissivity at the medium boundaries. In terms of the average reflectivity and transmissivity, the average of the partial heat fluxes for the generalized problem with internal source of radiation are obtained and represented graphically.

  10. Validation of a stochastic digital packing algorithm for porosity prediction in fluvial gravel deposits

    NASA Astrophysics Data System (ADS)

    Liang, Rui; Schruff, Tobias; Jia, Xiaodong; Schüttrumpf, Holger; Frings, Roy M.

    2015-11-01

    Porosity as one of the key properties of sediment mixtures is poorly understood. Most of the existing porosity predictors based upon grain size characteristics have been unable to produce satisfying results for fluvial sediment porosity, due to the lack of consideration of other porosity-controlling factors like grain shape and depositional condition. Considering this, a stochastic digital packing algorithm was applied in this work, which provides an innovative way to pack particles of arbitrary shapes and sizes based on digitization of both particles and packing space. The purpose was to test the applicability of this packing algorithm in predicting fluvial sediment porosity by comparing its predictions with outcomes obtained from laboratory measurements. Laboratory samples examined were two natural fluvial sediments from the Rhine River and Kall River (Germany), and commercial glass beads (spheres). All samples were artificially combined into seven grain size distributions: four unimodal distributions and three bimodal distributions. Our study demonstrates that apart from grain size, grain shape also has a clear impact on porosity. The stochastic digital packing algorithm successfully reproduced the measured variations in porosity for the three different particle sources. However, the packing algorithm systematically overpredicted the porosity measured in random dense packing conditions, mainly because the random motion of particles during settling introduced unwanted kinematic sorting and shape effects. The results suggest that the packing algorithm produces loose packing structures, and is useful for trend analysis of packing porosity.

  11. Stochastic deletion-insertion algorithm to construct dense linkage maps

    PubMed Central

    Wu, Jixiang; Lou, Xiang-Yang; Gonda, Michael

    2011-01-01

    In this study, we proposed a stochastic deletion-insertion (SDI) algorithm for constructing large-scale linkage maps. This SDI algorithm was compared with three published approximation approaches, the seriation (SER), neighbor mapping (NM), and unidirectional growth (UG) approaches, on the basis of simulated F2 data with different population sizes, missing genotype rates, and numbers of markers. Simulation results showed that the SDI method had a similar or higher percentage of correct linkage orders than the other three methods. This SDI algorithm was also applied to a real dataset and compared with the other three methods. The total linkage map distance (cM) obtained by the SDI method (148.08 cM) was smaller than the distance obtained by SER (225.52 cM) and two published distances (150.11 cM and 150.38 cM). Since this SDI algorithm is stochastic, a more accurate linkage order can be quickly obtained by repeating this algorithm. Thus, this SDI method, which combines the advantages of accuracy and speed, is an important addition to the current linkage mapping toolkit for constructing improved linkage maps. PMID:21927641

  12. Stochastic simulation for imaging spatial uncertainty: Comparison and evaluation of available algorithms

    SciTech Connect

    Gotway, C.A.; Rutherford, B.M.

    1993-09-01

    Stochastic simulation has been suggested as a viable method for characterizing the uncertainty associated with the prediction of a nonlinear function of a spatially-varying parameter. Geostatistical simulation algorithms generate realizations of a random field with specified statistical and geostatistical properties. A nonlinear function is evaluated over each realization to obtain an uncertainty distribution of a system response that reflects the spatial variability and uncertainty in the parameter. Crucial management decisions, such as potential regulatory compliance of proposed nuclear waste facilities and optimal allocation of resources in environmental remediation, are based on the resulting system response uncertainty distribution. Many geostatistical simulation algorithms have been developed to generate the random fields, and each algorithm will produce fields with different statistical properties. These different properties will result in different distributions for system response, and potentially, different managerial decisions. The statistical properties of the resulting system response distributions are not completely understood, nor is the ability of the various algorithms to generate response distributions that adequately reflect the associated uncertainty. This paper reviews several of the algorithms available for generating random fields. Algorithms are compared in a designed experiment using seven exhaustive data sets with different statistical and geostatistical properties. For each exhaustive data set, a number of realizations are generated using each simulation algorithm. The realizations are used with each of several deterministic transfer functions to produce a cumulative uncertainty distribution function of a system response. The uncertainty distributions are then compared to the single value obtained from the corresponding exhaustive data set.

  13. Random attractors for the stochastic coupled fractional Ginzburg-Landau equation with additive noise

    SciTech Connect

    Shu, Ji E-mail: 530282863@qq.com; Li, Ping E-mail: 530282863@qq.com; Zhang, Jia; Liao, Ou

    2015-10-15

    This paper is concerned with the stochastic coupled fractional Ginzburg-Landau equation with additive noise. We first transform the stochastic coupled fractional Ginzburg-Landau equation into random equations whose solutions generate a random dynamical system. Then we prove the existence of random attractor for random dynamical system.

  14. Stochastic Kinetic Monte Carlo algorithms for long-range Hamiltonians

    SciTech Connect

    Mason, D R; Rudd, R E; Sutton, A P

    2003-10-13

    We present a higher order kinetic Monte Carlo methodology suitable to model the evolution of systems in which the transition rates are non- trivial to calculate or in which Monte Carlo moves are likely to be non- productive flicker events. The second order residence time algorithm first introduced by Athenes et al.[1] is rederived from the n-fold way algorithm of Bortz et al.[2] as a fully stochastic algorithm. The second order algorithm can be dynamically called when necessary to eliminate unproductive flickering between a metastable state and its neighbors. An algorithm combining elements of the first order and second order methods is shown to be more efficient, in terms of the number of rate calculations, than the first order or second order methods alone while remaining statistically identical. This efficiency is of prime importance when dealing with computationally expensive rate functions such as those arising from long- range Hamiltonians. Our algorithm has been developed for use when considering simulations of vacancy diffusion under the influence of elastic stress fields. We demonstrate the improved efficiency of the method over that of the n-fold way in simulations of vacancy diffusion in alloys. Our algorithm is seen to be an order of magnitude more efficient than the n-fold way in these simulations. We show that when magnesium is added to an Al-2at.%Cu alloy, this has the effect of trapping vacancies. When trapping occurs, we see that our algorithm performs thousands of events for each rate calculation performed.

  15. Implementing Quality Control on a Random Number Stream to Improve a Stochastic Weather Generator

    Technology Transfer Automated Retrieval System (TEKTRAN)

    For decades stochastic modelers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated d...

  16. A random walk approach to quantum algorithms.

    PubMed

    Kendon, Vivien M

    2006-12-15

    The development of quantum algorithms based on quantum versions of random walks is placed in the context of the emerging field of quantum computing. Constructing a suitable quantum version of a random walk is not trivial; pure quantum dynamics is deterministic, so randomness only enters during the measurement phase, i.e. when converting the quantum information into classical information. The outcome of a quantum random walk is very different from the corresponding classical random walk owing to the interference between the different possible paths. The upshot is that quantum walkers find themselves further from their starting point than a classical walker on average, and this forms the basis of a quantum speed up, which can be exploited to solve problems faster. Surprisingly, the effect of making the walk slightly less than perfectly quantum can optimize the properties of the quantum walk for algorithmic applications. Looking to the future, even with a small quantum computer available, the development of quantum walk algorithms might proceed more rapidly than it has, especially for solving real problems. PMID:17090467

  17. Random migration processes between two stochastic epidemic centers.

    PubMed

    Sazonov, Igor; Kelbert, Mark; Gravenor, Michael B

    2016-04-01

    We consider the epidemic dynamics in stochastic interacting population centers coupled by random migration. Both the epidemic and the migration processes are modeled by Markov chains. We derive explicit formulae for the probability distribution of the migration process, and explore the dependence of outbreak patterns on initial parameters, population sizes and coupling parameters, using analytical and numerical methods. We show the importance of considering the movement of resident and visitor individuals separately. The mean field approximation for a general migration process is derived and an approximate method that allows the computation of statistical moments for networks with highly populated centers is proposed and tested numerically. PMID:26877075

  18. Efficient stochastic Galerkin methods for random diffusion equations

    SciTech Connect

    Xiu Dongbin Shen Jie

    2009-02-01

    We discuss in this paper efficient solvers for stochastic diffusion equations in random media. We employ generalized polynomial chaos (gPC) expansion to express the solution in a convergent series and obtain a set of deterministic equations for the expansion coefficients by Galerkin projection. Although the resulting system of diffusion equations are coupled, we show that one can construct fast numerical methods to solve them in a decoupled fashion. The methods are based on separation of the diagonal terms and off-diagonal terms in the matrix of the Galerkin system. We examine properties of this matrix and show that the proposed method is unconditionally stable for unsteady problems and convergent for steady problems with a convergent rate independent of discretization parameters. Numerical examples are provided, for both steady and unsteady random diffusions, to support the analysis.

  19. An adaptive multi-level simulation algorithm for stochastic biological systems

    NASA Astrophysics Data System (ADS)

    Lester, C.; Yates, C. A.; Giles, M. B.; Baker, R. E.

    2015-01-01

    Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, "Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics," SIAM Multiscale Model. Simul. 10(1), 146-179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the

  20. An adaptive multi-level simulation algorithm for stochastic biological systems

    SciTech Connect

    Lester, C. Giles, M. B.; Baker, R. E.; Yates, C. A.

    2015-01-14

    Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the

  1. State-dependent doubly weighted stochastic simulation algorithm for automatic characterization of stochastic biochemical rare events

    NASA Astrophysics Data System (ADS)

    Roh, Min K.; Daigle, Bernie J.; Gillespie, Dan T.; Petzold, Linda R.

    2011-12-01

    In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys. 129, 165101 (2008)], 10.1063/1.2987701. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA—the state-dependent doubly weighted SSA (sdwSSA)—that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency.

  2. Weighted Flow Algorithms (WFA) for stochastic particle coagulation

    SciTech Connect

    DeVille, R.E.L.; Riemer, N.; West, M.

    2011-09-20

    Stochastic particle-resolved methods are a useful way to compute the time evolution of the multi-dimensional size distribution of atmospheric aerosol particles. An effective approach to improve the efficiency of such models is the use of weighted computational particles. Here we introduce particle weighting functions that are power laws in particle size to the recently-developed particle-resolved model PartMC-MOSAIC and present the mathematical formalism of these Weighted Flow Algorithms (WFA) for particle coagulation and growth. We apply this to an urban plume scenario that simulates a particle population undergoing emission of different particle types, dilution, coagulation and aerosol chemistry along a Lagrangian trajectory. We quantify the performance of the Weighted Flow Algorithm for number and mass-based quantities of relevance for atmospheric sciences applications.

  3. Stochastic permanence of an SIQS epidemic model with saturated incidence and independent random perturbations

    NASA Astrophysics Data System (ADS)

    Wei, Fengying; Chen, Fangxiang

    2016-07-01

    This article discusses a stochastic SIQS epidemic model with saturated incidence. We assume that random perturbations always fluctuate at the endemic equilibrium. The existence of a global positive solution is obtained by constructing a suitable Lyapunov function. Under some suitable conditions, we derive the stochastic boundedness and stochastic permanence of the solutions of a stochastic SIQS model. Some numerical simulations are carried out to check our results.

  4. Stochastic optimization algorithm selection in hydrological model calibration based on fitness landscape characterization

    NASA Astrophysics Data System (ADS)

    Arsenault, Richard; Brissette, François P.; Poulin, Annie; Côté, Pascal; Martel, Jean-Luc

    2014-05-01

    The process of hydrological model parameter calibration is routinely performed with the help of stochastic optimization algorithms. Many such algorithms have been created and they sometimes provide varying levels of performance (as measured by an efficiency metric such as Nash-Sutcliffe). This is because each algorithm is better suited for one type of optimization problem rather than another. This research project's aim was twofold. First, it was sought upon to find various features in the calibration problem fitness landscapes to map the encountered problem types to the best possible optimization algorithm. Second, the optimal number of model evaluations in order to minimize resources usage and maximize overall model quality was investigated. A total of five stochastic optimization algorithms (SCE-UA, CMAES, DDS, PSO and ASA) were used to calibrate four lumped hydrological models (GR4J, HSAMI, HMETS and MOHYSE) on 421 basins from the US MOPEX database. Each of these combinations was performed using three objective functions (Log(RMSE), NSE, and a metric combining NSE, RMSE and BIAS) to add sufficient diversity to the fitness landscapes. Each run was performed 30 times for statistical analysis. With every parameter set tested during the calibration process, the validation value was taken on a separate period. It was then possible to outline the calibration skill versus the validation skill for the different algorithms. Fitness landscapes were characterized by various metrics, such as the dispersion metric, the mean distance between random points and their respective local minima (found through simple hill-climbing algorithms) and the mean distance between the local minima and the best local optimum found. These metrics were then compared to the calibration score of the various optimization algorithms. Preliminary results tend to show that fitness landscapes presenting a globally convergent structure are more prevalent than other types of landscapes in this

  5. A parallel algorithm for random searches

    NASA Astrophysics Data System (ADS)

    Wosniack, M. E.; Raposo, E. P.; Viswanathan, G. M.; da Luz, M. G. E.

    2015-11-01

    We discuss a parallelization procedure for a two-dimensional random search of a single individual, a typical sequential process. To assure the same features of the sequential random search in the parallel version, we analyze the former spatial patterns of the encountered targets for different search strategies and densities of homogeneously distributed targets. We identify a lognormal tendency for the distribution of distances between consecutively detected targets. Then, by assigning the distinct mean and standard deviation of this distribution for each corresponding configuration in the parallel simulations (constituted by parallel random walkers), we are able to recover important statistical properties, e.g., the target detection efficiency, of the original problem. The proposed parallel approach presents a speedup of nearly one order of magnitude compared with the sequential implementation. This algorithm can be easily adapted to different instances, as searches in three dimensions. Its possible range of applicability covers problems in areas as diverse as automated computer searchers in high-capacity databases and animal foraging.

  6. Efficient generation and optimization of stochastic template banks by a neighboring cell algorithm

    NASA Astrophysics Data System (ADS)

    Fehrmann, Henning; Pletsch, Holger J.

    2014-12-01

    Placing signal templates (grid points) as efficiently as possible to cover a multidimensional parameter space is crucial in computing-intensive matched-filtering searches for gravitational waves, but also in similar searches in other fields of astronomy. To generate efficient coverings of arbitrary parameter spaces, stochastic template banks have been advocated, where templates are placed at random while rejecting those too close to others. However, in this simple scheme, for each new random point its distance to every template in the existing bank is computed. This rapidly increasing number of distance computations can render the acceptance of new templates computationally prohibitive, particularly for wide parameter spaces or in large dimensions. This paper presents a neighboring cell algorithm that can dramatically improve the efficiency of constructing a stochastic template bank. By dividing the parameter space into subvolumes (cells), for an arbitrary point an efficient hashing technique is exploited to obtain the index of its enclosing cell along with the parameters of its neighboring templates. Hence only distances to these neighboring templates in the bank are computed, massively lowering the overall computing cost, as demonstrated in simple examples. Furthermore, we propose a novel method based on this technique to increase the fraction of covered parameter space solely by directed template shifts, without adding any templates. As is demonstrated in examples, this method can be highly effective.

  7. Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs

    DOE PAGESBeta

    Gade, Dinakar; Hackebeil, Gabriel; Ryan, Sarah M.; Watson, Jean -Paul; Wets, Roger J.-B.; Woodruff, David L.

    2016-04-02

    We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorithm by using dual prices that are calculated during execution of the standard PHA. In conclusion, we report computational results on stochastic unit commitment and stochastic server location problem instances, and explore the relationship between key PHA parameters and the quality of the resulting lower bounds.

  8. Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

    NASA Astrophysics Data System (ADS)

    Mahdi Alavi, S. M.; Saif, Mehrdad

    2013-12-01

    This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

  9. Stochastic simulations of sediment connectivity using random forests

    NASA Astrophysics Data System (ADS)

    Masselink, Rens; Keesstra, Saskia; Temme, Arnaud

    2016-04-01

    Modelling sediment connectivity, i.e. determining sediment sources, sinks and pathways has often been done by applying spatially explicit models to catchments and calibrating those models using data obtained at a catchment outlet. This means that modelled locations of sediment sources, sinks and pathways are directly derived from the input data of the model (especially the digital elevation model) and the calibration parameters. On the other hand, measured sediment transport data, e.g. from erosion plots or sediment tracers (e.g. Be7, Cs137, Rare earth oxides) is often only available from small plots or hillslopes. Extrapolation of these measured values often lead to an overestimation of erosion at catchment scale. There is a need to link both the small scale erosion/deposition measurements with large scale catchment scale sediment yield. In this study we propose using random forests (RF) for multivariable regression for determining to which extent certain variables influence sediment transport. The independent variables for the RF are derivatives of a high-resolution digital elevation model and vegetation parameters and the independent variables are sediment erosion and deposition data. For the erosion and deposition data we use sediment tracers (rare-earth oxides) applied at a single hillslope in the winter of 2014/2015. Subsequently, we will do stochastic simulations (e.g. sequential Gaussian simulation) for the entire catchment using the RF output and its residuals. These simulations will then be compared to the total suspended sediment output at the catchment outlet. This way, we hope to get a better view of both sediment yield at the catchment scale and locations of sediment sources, sinks and pathways.

  10. Note on coefficient matrices from stochastic Galerkin methods for random diffusion equations

    SciTech Connect

    Zhou Tao; Tang Tao

    2010-11-01

    In a recent work by Xiu and Shen [D. Xiu, J. Shen, Efficient stochastic Galerkin methods for random diffusion equations, J. Comput. Phys. 228 (2009) 266-281], the Galerkin methods are used to solve stochastic diffusion equations in random media, where some properties for the coefficient matrix of the resulting system are provided. They also posed an open question on the properties of the coefficient matrix. In this work, we will provide some results related to the open question.

  11. Fluorescence microscopy image noise reduction using a stochastically-connected random field model

    PubMed Central

    Haider, S. A.; Cameron, A.; Siva, P.; Lui, D.; Shafiee, M. J.; Boroomand, A.; Haider, N.; Wong, A.

    2016-01-01

    Fluorescence microscopy is an essential part of a biologist’s toolkit, allowing assaying of many parameters like subcellular localization of proteins, changes in cytoskeletal dynamics, protein-protein interactions, and the concentration of specific cellular ions. A fundamental challenge with using fluorescence microscopy is the presence of noise. This study introduces a novel approach to reducing noise in fluorescence microscopy images. The noise reduction problem is posed as a Maximum A Posteriori estimation problem, and solved using a novel random field model called stochastically-connected random field (SRF), which combines random graph and field theory. Experimental results using synthetic and real fluorescence microscopy data show the proposed approach achieving strong noise reduction performance when compared to several other noise reduction algorithms, using quantitative metrics. The proposed SRF approach was able to achieve strong performance in terms of signal-to-noise ratio in the synthetic results, high signal to noise ratio and contrast to noise ratio in the real fluorescence microscopy data results, and was able to maintain cell structure and subtle details while reducing background and intra-cellular noise. PMID:26884148

  12. A new stochastic algorithm for inversion of dust aerosol size distribution

    NASA Astrophysics Data System (ADS)

    Wang, Li; Li, Feng; Yang, Ma-ying

    2015-08-01

    Dust aerosol size distribution is an important source of information about atmospheric aerosols, and it can be determined from multiwavelength extinction measurements. This paper describes a stochastic inverse technique based on artificial bee colony (ABC) algorithm to invert the dust aerosol size distribution by light extinction method. The direct problems for the size distribution of water drop and dust particle, which are the main elements of atmospheric aerosols, are solved by the Mie theory and the Lambert-Beer Law in multispectral region. And then, the parameters of three widely used functions, i.e. the log normal distribution (L-N), the Junge distribution (J-J), and the normal distribution (N-N), which can provide the most useful representation of aerosol size distributions, are inversed by the ABC algorithm in the dependent model. Numerical results show that the ABC algorithm can be successfully applied to recover the aerosol size distribution with high feasibility and reliability even in the presence of random noise.

  13. Emergence of patterns in random processes. II. Stochastic structure in random events

    NASA Astrophysics Data System (ADS)

    Newman, William I.

    2014-06-01

    Random events can present what appears to be a pattern in the length of peak-to-peak sequences in time series and other point processes. Previously, we showed that this was the case in both individual and independently distributed processes as well as for Brownian walks. In addition, we introduced the use of the discrete form of the Langevin equation of statistical mechanics as a device for connecting the two limiting sets of behaviors, which we then compared with a variety of observations from the physical and social sciences. Here, we establish a probabilistic framework via the Smoluchowski equation for exploring the Langevin equation and its expected peak-to-peak sequence lengths, and we introduce a concept we call "stochastic structure in random events," or SSRE. We extend the Brownian model to include antipersistent processes via autoregressive (AR) models. We relate the latter to describe the behavior of Old Faithful Geyser in Yellowstone National Park, and we devise a further test for the validity of the Langevin and AR models. Given our analytic results, we show how the Langevin equation can be adapted to describe population cycles of three to four years observed among many mammalian species in biology.

  14. Emergence of patterns in random processes. II. Stochastic structure in random events.

    PubMed

    Newman, William I

    2014-06-01

    Random events can present what appears to be a pattern in the length of peak-to-peak sequences in time series and other point processes. Previously, we showed that this was the case in both individual and independently distributed processes as well as for Brownian walks. In addition, we introduced the use of the discrete form of the Langevin equation of statistical mechanics as a device for connecting the two limiting sets of behaviors, which we then compared with a variety of observations from the physical and social sciences. Here, we establish a probabilistic framework via the Smoluchowski equation for exploring the Langevin equation and its expected peak-to-peak sequence lengths, and we introduce a concept we call "stochastic structure in random events," or SSRE. We extend the Brownian model to include antipersistent processes via autoregressive (AR) models. We relate the latter to describe the behavior of Old Faithful Geyser in Yellowstone National Park, and we devise a further test for the validity of the Langevin and AR models. Given our analytic results, we show how the Langevin equation can be adapted to describe population cycles of three to four years observed among many mammalian species in biology. PMID:25019731

  15. One-dimensional random field Ising model and discrete stochastic mappings

    SciTech Connect

    Behn, U.; Zagrebnov, V.A.

    1987-06-01

    Previous results relating the one-dimensional random field Ising model to a discrete stochastic mapping are generalized to a two-valued correlated random (Markovian) field and to the case of zero temperature. The fractal dimension of the support of the invariant measure is calculated in a simple approximation and its dependence on the physical parameters is discussed.

  16. Cover art: Issues in the metric-guided and metric-less placement of random and stochastic template banks

    SciTech Connect

    Manca, Gian Mario; Vallisneri, Michele

    2010-01-15

    The efficient placement of signal templates in source-parameter space is a crucial requisite for exhaustive matched-filtering searches of modeled gravitational-wave sources, as well as other searches based on more general detection statistics. Unfortunately, the current placement algorithms based on regular parameter-space meshes are difficult to generalize beyond simple signal models with few parameters. Various authors have suggested that a general, flexible, yet efficient alternative can be found in randomized placement strategies such as random placement and stochastic placement, which enhances random placement by selectively rejecting templates that are too close to others. In this article we explore several theoretical and practical issues in randomized placement: the size and performance of the resulting template banks; the very general, purely geometric effects of parameter-space boundaries; the use of quasirandom (self-avoiding) number sequences; most important, the implementation of these algorithms in curved signal manifolds with and without the use of a Riemannian signal metric, which may be difficult to obtain. Specifically, we show how the metric can be replaced with a discrete triangulation-based representation of local geometry. We argue that the broad class of randomized placement algorithms offers a promising answer to many search problems, but that the specific choice of a scheme and its implementation details will still need to be fine-tuned separately for each problem.

  17. Quantum stochastic walks: A generalization of classical random walks and quantum walks

    NASA Astrophysics Data System (ADS)

    Whitfield, James D.; Rodríguez-Rosario, César A.; Aspuru-Guzik, Alán

    2010-02-01

    We introduce the quantum stochastic walk (QSW), which determines the evolution of a generalized quantum-mechanical walk on a graph that obeys a quantum stochastic equation of motion. Using an axiomatic approach, we specify the rules for all possible quantum, classical, and quantum-stochastic transitions from a vertex as defined by its connectivity. We show how the family of possible QSWs encompasses both the classical random walk (CRW) and the quantum walk (QW) as special cases but also includes more general probability distributions. As an example, we study the QSW on a line and the glued tree of depth three to observe the behavior of the QW-to-CRW transition.

  18. Evaluation of a Geothermal Prospect Using a Stochastic Joint Inversion Algorithm

    NASA Astrophysics Data System (ADS)

    Tompson, A. F.; Mellors, R. J.; Ramirez, A.; Dyer, K.; Yang, X.; Trainor-Guitton, W.; Wagoner, J. L.

    2013-12-01

    A stochastic joint inverse algorithm to analyze diverse geophysical and hydrologic data for a geothermal prospect is developed. The purpose is to improve prospect evaluation by finding an ensemble of hydrothermal flow models that are most consistent with multiple types of data sets. The staged approach combines Bayesian inference within a Markov Chain Monte Carlo (MCMC) global search algorithm. The method is highly flexible and capable of accommodating multiple and diverse datasets as a means to maximize the utility of all available data to understand system behavior. An initial application is made at a geothermal prospect located near Superstition Mountain in the western Salton Trough in California. Readily available data include three thermal gradient exploration boreholes, borehole resistivity logs, magnetotelluric and gravity geophysical surveys, surface heat flux measurements, and other nearby hydrologic and geologic information. Initial estimates of uncertainty in structural or parametric characteristics of the prospect are used to drive large numbers of simulations of hydrothermal fluid flow and related geophysical processes using random realizations of the conceptual geothermal system. Uncertainty in the results is represented within a ranked subset of model realizations that best match all available data within a specified norm or tolerance. Statistical (posterior) characteristics of these solutions reflect reductions in the perceived (prior) uncertainties. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-641792.

  19. Limited variance control in statistical low thrust guidance analysis. [stochastic algorithm for SEP comet Encke flyby mission

    NASA Technical Reports Server (NTRS)

    Jacobson, R. A.

    1975-01-01

    Difficulties arise in guiding a solar electric propulsion spacecraft due to nongravitational accelerations caused by random fluctuations in the magnitude and direction of the thrust vector. These difficulties may be handled by using a low thrust guidance law based on the linear-quadratic-Gaussian problem of stochastic control theory with a minimum terminal miss performance criterion. Explicit constraints are imposed on the variances of the control parameters, and an algorithm based on the Hilbert space extension of a parameter optimization method is presented for calculation of gains in the guidance law. The terminal navigation of a 1980 flyby mission to the comet Encke is used as an example.

  20. A new model for realistic random perturbations of stochastic oscillators

    NASA Astrophysics Data System (ADS)

    Dieci, Luca; Li, Wuchen; Zhou, Haomin

    2016-08-01

    Classical theories predict that solutions of differential equations will leave any neighborhood of a stable limit cycle, if white noise is added to the system. In reality, many engineering systems modeled by second order differential equations, like the van der Pol oscillator, show incredible robustness against noise perturbations, and the perturbed trajectories remain in the neighborhood of a stable limit cycle for all times of practical interest. In this paper, we propose a new model of noise to bridge this apparent discrepancy between theory and practice. Restricting to perturbations from within this new class of noise, we consider stochastic perturbations of second order differential systems that -in the unperturbed case- admit asymptotically stable limit cycles. We show that the perturbed solutions are globally bounded and remain in a tubular neighborhood of the underlying deterministic periodic orbit. We also define stochastic Poincaré map(s), and further derive partial differential equations for the transition density function.

  1. D-leaping: Accelerating stochastic simulation algorithms for reactions with delays

    SciTech Connect

    Bayati, Basil; Chatelain, Philippe; Koumoutsakos, Petros

    2009-09-01

    We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays. The present work extends existing accelerated algorithms by distributing, in a time adaptive fashion, the delayed reactions so as to minimize the computational effort while preserving their accuracy. The accuracy of the present algorithm is assessed by comparing its results to those of the corresponding delay differential equations for a representative biochemical system. In addition, the fluctuations produced from the present algorithm are comparable to those from an exact stochastic simulation with delays. The algorithm is used to simulate biochemical systems that model oscillatory gene expression. The results indicate that the present algorithm is competitive with existing works for several benchmark problems while it is orders of magnitude faster for certain systems of biochemical reactions.

  2. Genetic algorithms as global random search methods

    NASA Technical Reports Server (NTRS)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  3. Genetic algorithms as global random search methods

    NASA Technical Reports Server (NTRS)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  4. Performance of a benchmark parallel implementation of the Van Slyke and Wets algorithm for two-stage stochastic programs on the Sequent/Balance

    SciTech Connect

    Ariyawansa, K.A.; Hudson, D.D.

    1989-01-01

    We describe a benchmark parallel version of the Van Slyke and Wets algorithm for two-stage stochastic programs and an implementation of that algorithm on the Sequent/Balance. We also report results of a numerical experiment using random test problems and our implementation. These performance results, to the best of our knowledge, are the first available for the Van Slyke and Wets algorithm on a parallel processor. They indicate that the benchmark implementation parallelizes well, and that even with the use of parallel processing, problems with random variables having large numbers of realizations can take prohibitively large amounts of computation for solution. Thus, they demonstrate the need for exploiting both parallelization and approximation for the solution of stochastic programs. 15 refs., 18 tabs.

  5. Investigation of stochastic radiation transport methods in random heterogeneous mixtures

    NASA Astrophysics Data System (ADS)

    Reinert, Dustin Ray

    Among the most formidable challenges facing our world is the need for safe, clean, affordable energy sources. Growing concerns over global warming induced climate change and the rising costs of fossil fuels threaten conventional means of electricity production and are driving the current nuclear renaissance. One concept at the forefront of international development efforts is the High Temperature Gas-Cooled Reactor (HTGR). With numerous passive safety features and a meltdown-proof design capable of attaining high thermodynamic efficiencies for electricity generation as well as high temperatures useful for the burgeoning hydrogen economy, the HTGR is an extremely promising technology. Unfortunately, the fundamental understanding of neutron behavior within HTGR fuels lags far behind that of more conventional water-cooled reactors. HTGRs utilize a unique heterogeneous fuel element design consisting of thousands of tiny fissile fuel kernels randomly mixed with a non-fissile graphite matrix. Monte Carlo neutron transport simulations of the HTGR fuel element geometry in its full complexity are infeasible and this has motivated the development of more approximate computational techniques. A series of MATLAB codes was written to perform Monte Carlo simulations within HTGR fuel pebbles to establish a comprehensive understanding of the parameters under which the accuracy of the approximate techniques diminishes. This research identified the accuracy of the chord length sampling method to be a function of the matrix scattering optical thickness, the kernel optical thickness, and the kernel packing density. Two new Monte Carlo methods designed to focus the computational effort upon the parameter conditions shown to contribute most strongly to the overall computational error were implemented and evaluated. An extended memory chord length sampling routine that recalls a neutron's prior material traversals was demonstrated to be effective in fixed source calculations containing

  6. Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models

    NASA Technical Reports Server (NTRS)

    Mjoisness, Eric; Castano, Rebecca; Gray, Alexander

    1999-01-01

    We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.

  7. Simple randomized algorithms for online learning with kernels.

    PubMed

    He, Wenwu; Kwok, James T

    2014-12-01

    In online learning with kernels, it is vital to control the size (budget) of the support set because of the curse of kernelization. In this paper, we propose two simple and effective stochastic strategies for controlling the budget. Both algorithms have an expected regret that is sublinear in the horizon. Experimental results on a number of benchmark data sets demonstrate encouraging performance in terms of both efficacy and efficiency. PMID:25108150

  8. Stochastic algorithms for the analysis of numerical flame simulations

    SciTech Connect

    Bell, John B.; Day, Marcus S.; Grcar, Joseph F.; Lijewski, Michael J.

    2004-04-26

    Recent progress in simulation methodologies and high-performance parallel computers have made it is possible to perform detailed simulations of multidimensional reacting flow phenomena using comprehensive kinetics mechanisms. As simulations become larger and more complex, it becomes increasingly difficult to extract useful information from the numerical solution, particularly regarding the interactions of the chemical reaction and diffusion processes. In this paper we present a new diagnostic tool for analysis of numerical simulations of reacting flow. Our approach is based on recasting an Eulerian flow solution in a Lagrangian frame. Unlike a conventional Lagrangian view point that follows the evolution of a volume of the fluid, we instead follow specific chemical elements, e.g., carbon, nitrogen, etc., as they move through the system . From this perspective an ''atom'' is part of some molecule of a species that is transported through the domain by advection and diffusion. Reactions cause the atom to shift from one chemical host species to another and the subsequent transport of the atom is given by the movement of the new species. We represent these processes using a stochastic particle formulation that treats advection deterministically and models diffusion and chemistry as stochastic processes. In this paper, we discuss the numerical issues in detail and demonstrate that an ensemble of stochastic trajectories can accurately capture key features of the continuum solution. The capabilities of this diagnostic are then demonstrated by applications to study the modulation of carbon chemistry during a vortex-flame interaction, and the role of cyano chemistry in rm NO{sub x} production for a steady diffusion flame.

  9. A stochastic multiscale framework for modeling flow through random heterogeneous porous media

    SciTech Connect

    Ganapathysubramanian, B.; Zabaras, N.

    2009-02-01

    Flow through porous media is ubiquitous, occurring from large geological scales down to the microscopic scales. Several critical engineering phenomena like contaminant spread, nuclear waste disposal and oil recovery rely on accurate analysis and prediction of these multiscale phenomena. Such analysis is complicated by inherent uncertainties as well as the limited information available to characterize the system. Any realistic modeling of these transport phenomena has to resolve two key issues: (i) the multi-length scale variations in permeability that these systems exhibit, and (ii) the inherently limited information available to quantify these property variations that necessitates posing these phenomena as stochastic processes. A stochastic variational multiscale formulation is developed to incorporate uncertain multiscale features. A stochastic analogue to a mixed multiscale finite element framework is used to formulate the physical stochastic multiscale process. Recent developments in linear and non-linear model reduction techniques are used to convert the limited information available about the permeability variation into a viable stochastic input model. An adaptive sparse grid collocation strategy is used to efficiently solve the resulting stochastic partial differential equations (SPDEs). The framework is applied to analyze flow through random heterogeneous media when only limited statistics about the permeability variation are given.

  10. Hybrid discrete/continuum algorithms for stochastic reaction networks

    SciTech Connect

    Safta, Cosmin Sargsyan, Khachik Debusschere, Bert Najm, Habib N.

    2015-01-15

    Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker–Planck equation. The Fokker–Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components. The numerical construction at the interface between the discrete and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. The performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.

  11. Hybrid discrete/continuum algorithms for stochastic reaction networks

    NASA Astrophysics Data System (ADS)

    Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.

    2015-01-01

    Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components. The numerical construction at the interface between the discrete and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. The performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.

  12. Hybrid discrete/continuum algorithms for stochastic reaction networks

    SciTech Connect

    Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.

    2014-10-22

    Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discrete and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.

  13. Global Mapping Analysis: Stochastic Gradient Algorithm in Multidimensional Scaling

    NASA Astrophysics Data System (ADS)

    Matsuda, Yoshitatsu; Yamaguchi, Kazunori

    In order to implement multidimensional scaling (MDS) efficiently, we propose a new method named “global mapping analysis” (GMA), which applies stochastic approximation to minimizing MDS criteria. GMA can solve MDS more efficiently in both the linear case (classical MDS) and non-linear one (e.g., ALSCAL) if only the MDS criteria are polynomial. GMA separates the polynomial criteria into the local factors and the global ones. Because the global factors need to be calculated only once in each iteration, GMA is of linear order in the number of objects. Numerical experiments on artificial data verify the efficiency of GMA. It is also shown that GMA can find out various interesting structures from massive document collections.

  14. Hybrid discrete/continuum algorithms for stochastic reaction networks

    DOE PAGESBeta

    Safta, Cosmin; Sargsyan, Khachik; Debusschere, Bert; Najm, Habib N.

    2014-10-22

    Direct solutions of the Chemical Master Equation (CME) governing Stochastic Reaction Networks (SRNs) are generally prohibitively expensive due to excessive numbers of possible discrete states in such systems. To enhance computational efficiency we develop a hybrid approach where the evolution of states with low molecule counts is treated with the discrete CME model while that of states with large molecule counts is modeled by the continuum Fokker-Planck equation. The Fokker-Planck equation is discretized using a 2nd order finite volume approach with appropriate treatment of flux components to avoid negative probability values. The numerical construction at the interface between the discretemore » and continuum regions implements the transfer of probability reaction by reaction according to the stoichiometry of the system. As a result, the performance of this novel hybrid approach is explored for a two-species circadian model with computational efficiency gains of about one order of magnitude.« less

  15. A stochastic model of randomly accelerated walkers for human mobility.

    PubMed

    Gallotti, Riccardo; Bazzani, Armando; Rambaldi, Sandro; Barthelemy, Marc

    2016-01-01

    Recent studies of human mobility largely focus on displacements patterns and power law fits of empirical long-tailed distributions of distances are usually associated to scale-free superdiffusive random walks called Lévy flights. However, drawing conclusions about a complex system from a fit, without any further knowledge of the underlying dynamics, might lead to erroneous interpretations. Here we show, on the basis of a data set describing the trajectories of 780,000 private vehicles in Italy, that the Lévy flight model cannot explain the behaviour of travel times and speeds. We therefore introduce a class of accelerated random walks, validated by empirical observations, where the velocity changes due to acceleration kicks at random times. Combining this mechanism with an exponentially decaying distribution of travel times leads to a short-tailed distribution of distances which could indeed be mistaken with a truncated power law. These results illustrate the limits of purely descriptive models and provide a mechanistic view of mobility. PMID:27573984

  16. Stochastic EM algorithm for nonlinear state estimation with model uncertainties

    NASA Astrophysics Data System (ADS)

    Zia, Amin; Kirubarajan, Thiagalingam; Reilly, James P.; Shirani, Shahram

    2004-01-01

    In most solutions to state estimation problems like, for example, target tracking, it is generally assumed that the state evolution and measurement models are known a priori. The model parameters include process and measurement matrices or functions as well as the corresponding noise statistics. However, there are situations where the model parameters are not known a priori or are known only partially (i.e., with some uncertainty). Moreover, there are situations where the measurement is biased. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the problem with uncertain model parameters is considered as a special case of maximum likelihood estimation with incomplete-data, for which a standard solution called the expectation-maximization (EM) algorithm exists. In this paper a new extension to the EM algorithm is proposed to solve the more general problem of joint state estimation and model parameter identification for nonlinear systems with possibly non-Gaussian noise. In the expectation (E) step, it is shown that the best variational distribution over the state variables is the conditional posterior distribution of states given all the available measurements and inputs. Therefore, a particular type of particle filter is used to estimate and update the posterior distribution. In the maximization (M) step the nonlinear measurement process parameters are approximated using a nonlinear regression method for adjusting the parameters of a mixture of Gaussians (MofG). The proposed algorithm is used to solve a nonlinear bearing-only tracking problem similar to the one reported recently with uncertain measurement process. It is shown that the algorithm is capable of accurately tracking the state vector while identifying the unknown measurement dynamics. Simulation results show the advantages of the new technique over standard

  17. Stochastic EM algorithm for nonlinear state estimation with model uncertainties

    NASA Astrophysics Data System (ADS)

    Zia, Amin; Kirubarajan, Thiagalingam; Reilly, James P.; Shirani, Shahram

    2003-12-01

    In most solutions to state estimation problems like, for example, target tracking, it is generally assumed that the state evolution and measurement models are known a priori. The model parameters include process and measurement matrices or functions as well as the corresponding noise statistics. However, there are situations where the model parameters are not known a priori or are known only partially (i.e., with some uncertainty). Moreover, there are situations where the measurement is biased. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the problem with uncertain model parameters is considered as a special case of maximum likelihood estimation with incomplete-data, for which a standard solution called the expectation-maximization (EM) algorithm exists. In this paper a new extension to the EM algorithm is proposed to solve the more general problem of joint state estimation and model parameter identification for nonlinear systems with possibly non-Gaussian noise. In the expectation (E) step, it is shown that the best variational distribution over the state variables is the conditional posterior distribution of states given all the available measurements and inputs. Therefore, a particular type of particle filter is used to estimate and update the posterior distribution. In the maximization (M) step the nonlinear measurement process parameters are approximated using a nonlinear regression method for adjusting the parameters of a mixture of Gaussians (MofG). The proposed algorithm is used to solve a nonlinear bearing-only tracking problem similar to the one reported recently with uncertain measurement process. It is shown that the algorithm is capable of accurately tracking the state vector while identifying the unknown measurement dynamics. Simulation results show the advantages of the new technique over standard

  18. Recursive state estimation for discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks

    NASA Astrophysics Data System (ADS)

    Ding, Derui; Shen, Yuxuan; Song, Yan; Wang, Yongxiong

    2016-07-01

    This paper is concerned with the state estimation problem for a class of discrete time-varying stochastic nonlinear systems with randomly occurring deception attacks. The stochastic nonlinearity described by statistical means which covers several classes of well-studied nonlinearities as special cases is taken into discussion. The randomly occurring deception attacks are modelled by a set of random variables obeying Bernoulli distributions with given probabilities. The purpose of the addressed state estimation problem is to design an estimator with hope to minimize the upper bound for estimation error covariance at each sampling instant. Such an upper bound is minimized by properly designing the estimator gain. The proposed estimation scheme in the form of two Riccati-like difference equations is of a recursive form. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed scheme.

  19. Random attractors for stochastic 2D-Navier-Stokes equations in some unbounded domains

    NASA Astrophysics Data System (ADS)

    Brzeźniak, Z.; Caraballo, T.; Langa, J. A.; Li, Y.; Łukaszewicz, G.; Real, J.

    We show that the stochastic flow generated by the 2-dimensional Stochastic Navier-Stokes equations with rough noise on a Poincaré-like domain has a unique random attractor. One of the technical problems associated with the rough noise is overcomed by the use of the corresponding Cameron-Martin (or reproducing kernel Hilbert) space. Our results complement the result by Brzeźniak and Li (2006) [10] who showed that the corresponding flow is asymptotically compact and also generalize Caraballo et al. (2006) [12] who proved existence of a unique attractor for the time-dependent deterministic Navier-Stokes equations.

  20. A stochastic learning algorithm for layered neural networks

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E.

    1992-12-31

    The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

  1. A stochastic learning algorithm for layered neural networks

    SciTech Connect

    Bartlett, E.B. . Dept. of Mechanical Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering)

    1992-01-01

    The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

  2. Random Volumetric MRI Trajectories via Genetic Algorithms

    PubMed Central

    Curtis, Andrew Thomas; Anand, Christopher Kumar

    2008-01-01

    A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils. PMID:18604305

  3. Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics.

    PubMed

    Marquez-Lago, Tatiana T; Burrage, Kevin

    2007-09-14

    In cell biology, cell signaling pathway problems are often tackled with deterministic temporal models, well mixed stochastic simulators, and/or hybrid methods. But, in fact, three dimensional stochastic spatial modeling of reactions happening inside the cell is needed in order to fully understand these cell signaling pathways. This is because noise effects, low molecular concentrations, and spatial heterogeneity can all affect the cellular dynamics. However, there are ways in which important effects can be accounted without going to the extent of using highly resolved spatial simulators (such as single-particle software), hence reducing the overall computation time significantly. We present a new coarse grained modified version of the next subvolume method that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods. Benchmarking of the simulation algorithm was performed through comparison with the next subvolume method and well mixed models (MATLAB), as well as stochastic particle reaction and transport simulations (CHEMCELL, Sandia National Laboratories). Additionally, we construct a model based on a set of chemical reactions in the epidermal growth factor receptor pathway. For this particular application and a bistable chemical system example, we analyze and outline the advantages of our presented binomial tau-leap spatial stochastic simulation algorithm, in terms of efficiency and accuracy, in scenarios of both molecular homogeneity and heterogeneity. PMID:17867731

  4. THE LOSS OF ACCURACY OF STOCHASTIC COLLOCATION METHOD IN SOLVING NONLINEAR DIFFERENTIAL EQUATIONS WITH RANDOM INPUT DATA

    SciTech Connect

    Webster, Clayton G; Tran, Hoang A; Trenchea, Catalin S

    2013-01-01

    n this paper we show how stochastic collocation method (SCM) could fail to con- verge for nonlinear differential equations with random coefficients. First, we consider Navier-Stokes equation with uncertain viscosity and derive error estimates for stochastic collocation discretization. Our analysis gives some indicators on how the nonlinearity negatively affects the accuracy of the method. The stochastic collocation method is then applied to noisy Lorenz system. Simulation re- sults demonstrate that the solution of a nonlinear equation could be highly irregular on the random data and in such cases, stochastic collocation method cannot capture the correct solution.

  5. Discrete Randomness in Discrete Time Quantum Walk: Study Via Stochastic Averaging

    NASA Astrophysics Data System (ADS)

    Ellinas, D.; Bracken, A. J.; Smyrnakis, I.

    2012-10-01

    The role of classical noise in quantum walks (QW) on integers is investigated in the form of discrete dichotomic random variable affecting its reshuffling matrix parametrized as a SU2)/U (1) coset element. Analysis in terms of quantum statistical moments and generating functions, derived by the completely positive trace preserving (CPTP) map governing evolution, reveals a pronounced eventual transition in walk's diffusion mode, from a quantum ballistic regime with rate O(t) to a classical diffusive regime with rate O(√{t}), when condition (strength of noise parameter)2 × (number of steps) = 1, is satisfied. The role of classical randomness is studied showing that the randomized QW, when treated on the stochastic average level by means of an appropriate CPTP averaging map, turns out to be equivalent to a novel quantized classical walk without randomness. This result emphasizes the dual role of quantization/randomization in the context of classical random walk.

  6. A stochastic model and Monte Carlo algorithm for fluctuation-induced H2 formation on the surface of interstellar dust grains

    NASA Astrophysics Data System (ADS)

    Sabelfeld, K. K.

    2015-09-01

    A stochastic algorithm for simulation of fluctuation-induced kinetics of H2 formation on grain surfaces is suggested as a generalization of the technique developed in our recent studies [1] where this method was developed to describe the annihilation of spatially separate electrons and holes in a disordered semiconductor. The stochastic model is based on the spatially inhomogeneous, nonlinear integro-differential Smoluchowski equations with random source term. In this paper we derive the general system of Smoluchowski type equations for the formation of H2 from two hydrogen atoms on the surface of interstellar dust grains with physisorption and chemisorption sites. We focus in this study on the spatial distribution, and numerically investigate the segregation in the case of a source with a continuous generation in time and randomly distributed in space. The stochastic particle method presented is based on a probabilistic interpretation of the underlying process as a stochastic Markov process of interacting particle system in discrete but randomly progressed time instances. The segregation is analyzed through the correlation analysis of the vector random field of concentrations which appears to be isotropic in space and stationary in time.

  7. Stochastic responses of a viscoelastic-impact system under additive and multiplicative random excitations

    NASA Astrophysics Data System (ADS)

    Zhao, Xiangrong; Xu, Wei; Yang, Yongge; Wang, Xiying

    2016-06-01

    This paper deals with the stochastic responses of a viscoelastic-impact system under additive and multiplicative random excitations. The viscoelastic force is replaced by a combination of stiffness and damping terms. The non-smooth transformation of the state variables is utilized to transform the original system to a new system without the impact term. The stochastic averaging method is applied to yield the stationary probability density functions. The validity of the analytical method is verified by comparing the analytical results with the numerical results. It is invaluable to note that the restitution coefficient, the viscoelastic parameters and the damping coefficients can induce the occurrence of stochastic P-bifurcation. Furthermore, the joint stationary probability density functions with three peaks are explored.

  8. Cycles, randomness, and transport from chaotic dynamics to stochastic processes.

    PubMed

    Gaspard, Pierre

    2015-09-01

    An overview of advances at the frontier between dynamical systems theory and nonequilibrium statistical mechanics is given. Sensitivity to initial conditions is a mechanism at the origin of dynamical randomness-alias temporal disorder-in deterministic dynamical systems. In spatially extended systems, sustaining transport processes, such as diffusion, relationships can be established between the characteristic quantities of dynamical chaos and the transport coefficients, bringing new insight into the second law of thermodynamics. With methods from dynamical systems theory, the microscopic time-reversal symmetry can be shown to be broken at the statistical level of description in nonequilibrium systems. In this way, the thermodynamic entropy production turns out to be related to temporal disorder and its time asymmetry away from equilibrium. PMID:26428559

  9. A stochastic analysis of steady and transient heat conduction in random media using a homogenization approach

    SciTech Connect

    Zhijie Xu

    2014-07-01

    We present a new stochastic analysis for steady and transient one-dimensional heat conduction problem based on the homogenization approach. Thermal conductivity is assumed to be a random field K consisting of random variables of a total number N. Both steady and transient solutions T are expressed in terms of the homogenized solution (symbol) and its spatial derivatives (equation), where homogenized solution (symbol) is obtained by solving the homogenized equation with effective thermal conductivity. Both mean and variance of stochastic solutions can be obtained analytically for K field consisting of independent identically distributed (i.i.d) random variables. The mean and variance of T are shown to be dependent only on the mean and variance of these i.i.d variables, not the particular form of probability distribution function of i.i.d variables. Variance of temperature field T can be separated into two contributions: the ensemble contribution (through the homogenized temperature (symbol)); and the configurational contribution (through the random variable Ln(x)Ln(x)). The configurational contribution is shown to be proportional to the local gradient of (symbol). Large uncertainty of T field was found at locations with large gradient of (symbol) due to the significant configurational contributions at these locations. Numerical simulations were implemented based on a direct Monte Carlo method and good agreement is obtained between numerical Monte Carlo results and the proposed stochastic analysis.

  10. Simple-random-sampling-based multiclass text classification algorithm.

    PubMed

    Liu, Wuying; Wang, Lin; Yi, Mianzhu

    2014-01-01

    Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm. Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems. The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements. PMID:24778587

  11. Simple-Random-Sampling-Based Multiclass Text Classification Algorithm

    PubMed Central

    Liu, Wuying; Wang, Lin; Yi, Mianzhu

    2014-01-01

    Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm. Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems. The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements. PMID:24778587

  12. Existence and continuity of bi-spatial random attractors and application to stochastic semilinear Laplacian equations

    NASA Astrophysics Data System (ADS)

    Li, Yangrong; Gu, Anhui; Li, Jia

    2015-01-01

    A concept of a bi-spatial random attractor for a random dynamical system is introduced. A unified result about existence and upper semi-continuity for a family of bi-spatial random attractors is obtained if a family of random systems is convergent, uniformly absorbing in an initial space and uniformly omega-compact in both initial and terminate spaces. The upper semi-continuity result improves all existing results even for single-spatial attractors. As an application of the abstract result, it is shown that every semilinear Laplacian equation on the entire space perturbed by a multiplicative and stochastic noise possesses an (L2, Lq)-random attractor with q > 2. Moreover, it is proved that the family of obtained attractors is upper semi-continuous at any density of noises and the family of attractors for the corresponding compact systems is both upper and lower semi-continuous at infinity under the topology of both spaces.

  13. Random transitions described by the stochastic Smoluchowski-Poisson system and by the stochastic Keller-Segel model.

    PubMed

    Chavanis, P H; Delfini, L

    2014-03-01

    We study random transitions between two metastable states that appear below a critical temperature in a one-dimensional self-gravitating Brownian gas with a modified Poisson equation experiencing a second order phase transition from a homogeneous phase to an inhomogeneous phase [P. H. Chavanis and L. Delfini, Phys. Rev. E 81, 051103 (2010)]. We numerically solve the N-body Langevin equations and the stochastic Smoluchowski-Poisson system, which takes fluctuations (finite N effects) into account. The system switches back and forth between the two metastable states (bistability) and the particles accumulate successively at the center or at the boundary of the domain. We explicitly show that these random transitions exhibit the phenomenology of the ordinary Kramers problem for a Brownian particle in a double-well potential. The distribution of the residence time is Poissonian and the average lifetime of a metastable state is given by the Arrhenius law; i.e., it is proportional to the exponential of the barrier of free energy ΔF divided by the energy of thermal excitation kBT. Since the free energy is proportional to the number of particles N for a system with long-range interactions, the lifetime of metastable states scales as eN and is considerable for N≫1. As a result, in many applications, metastable states of systems with long-range interactions can be considered as stable states. However, for moderate values of N, or close to a critical point, the lifetime of the metastable states is reduced since the barrier of free energy decreases. In that case, the fluctuations become important and the mean field approximation is no more valid. This is the situation considered in this paper. By an appropriate change of notations, our results also apply to bacterial populations experiencing chemotaxis in biology. Their dynamics can be described by a stochastic Keller-Segel model that takes fluctuations into account and goes beyond the usual mean field approximation. PMID

  14. Random vibration of nonlinear beams by the new stochastic linearization technique

    NASA Technical Reports Server (NTRS)

    Fang, J.

    1994-01-01

    In this paper, the beam under general time dependent stationary random excitation is investigated, when exact solution is unavailable. Numerical simulations are carried out to compare its results with those yielded by the conventional linearization techniques. It is found that the modified version of the stochastic linearization technique yields considerably more accurate results for the mean square displacement of the beam than the conventional equivalent linearization technique, especially in the case of large nonlinearity.

  15. Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm

    NASA Astrophysics Data System (ADS)

    Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel

    2011-09-01

    The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.

  16. Nuclear space-valued stochastic differential equations driven by Poisson random measures

    SciTech Connect

    Xiong, J.

    1992-01-01

    The thesis is devoted primarily to the study of stochastic differential equations on duals of nuclear spaces driven by Poisson random measures. The existence of a weak solution is obtained by the Galerkin method and the uniqueness is established by implementing the Yamada-Watanabe argument in the present setup. When the magnitudes of the driving terms are small enough and the Poisson streams occur frequently enough, it is proved that the stochastic differential equations mentioned above can be approximated by diffusion equations. Finally, the author considers a system of interacting stochastic differential equations driven by Poisson random measures. Let (X[sup n][sub i](t), [center dot][center dot][center dot], X[sup n][sub n](t)) be the solution of this system and consider the empirical measures [zeta]n([omega],B) [identical to] (1/n) (sum of j=1 to n) [delta]x[sup n][sub j]([center dot],[omega])(B) (n[>=]1). It is provided that [zeta][sub n] converges in distribution to a non-random measure which is the unique solution of a McKean-Vlasov equation. The above problems are motivated by applications to neurophysiology, in particular, to the fluctuation of voltage potentials of spatially distributed neurons and to the study of asymptotic behavior of large systems of interacting neurons.

  17. The stochastic evolution of a protocell: the Gillespie algorithm in a dynamically varying volume.

    PubMed

    Carletti, T; Filisetti, A

    2012-01-01

    We propose an improvement of the Gillespie algorithm allowing us to study the time evolution of an ensemble of chemical reactions occurring in a varying volume, whose growth is directly related to the amount of some specific molecules, belonging to the reactions set. This allows us to study the stochastic evolution of a protocell, whose volume increases because of the production of container molecules. Several protocell models are considered and compared with the deterministic models. PMID:22536297

  18. Effect of randomness on multi-frequency aeroelastic responses resolved by Unsteady Adaptive Stochastic Finite Elements

    SciTech Connect

    Witteveen, Jeroen A.S. Bijl, Hester

    2009-10-01

    The Unsteady Adaptive Stochastic Finite Elements (UASFE) method resolves the effect of randomness in numerical simulations of single-mode aeroelastic responses with a constant accuracy in time for a constant number of samples. In this paper, the UASFE framework is extended to multi-frequency responses and continuous structures by employing a wavelet decomposition pre-processing step to decompose the sampled multi-frequency signals into single-frequency components. The effect of the randomness on the multi-frequency response is then obtained by summing the results of the UASFE interpolation at constant phase for the different frequency components. Results for multi-frequency responses and continuous structures show a three orders of magnitude reduction of computational costs compared to crude Monte Carlo simulations in a harmonically forced oscillator, a flutter panel problem, and the three-dimensional transonic AGARD 445.6 wing aeroelastic benchmark subject to random fields and random parameters with various probability distributions.

  19. Genetic Algorithm and Tabu Search for Vehicle Routing Problems with Stochastic Demand

    NASA Astrophysics Data System (ADS)

    Ismail, Zuhaimy; Irhamah

    2010-11-01

    This paper presents a problem of designing solid waste collection routes, involving scheduling of vehicles where each vehicle begins at the depot, visits customers and ends at the depot. It is modeled as a Vehicle Routing Problem with Stochastic Demands (VRPSD). A data set from a real world problem (a case) is used in this research. We developed Genetic Algorithm (GA) and Tabu Search (TS) procedure and these has produced the best possible result. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities.

  20. Exactly averaged stochastic equations for flow and transport in random media

    SciTech Connect

    Shvidler, Mark; Karasaki, Kenzi

    2001-11-30

    It is well known that exact averaging of the equations of flow and transport in random porous media are at present realized only for a small number of special, occasionally exotic, fields. On the other hand, the properties of approximate averaging methods are not yet fully understood. For example, the convergence behavior and the accuracy of truncated perturbation series are not well known. Furthermore, the calculation of the high-order perturbations is very complicated. These problems for a long time have stimulated attempts to find the answer for the question: Are there in existence some exact general and sufficiently universal forms of averaged equations? If the answer is positive, there arises the problem of the construction of these equations and analyzing them. There exist many publications related to these problems and oriented on different applications: hydrodynamics, flow and transport in porous media, theory of elasticity, acoustic and electromagnetic waves in random fields, etc. We present a method of finding some general forms of exactly averaged equations for flow and transport in random fields by using (1) an assumption of the existence of Green's functions for appropriate stochastic problems, (2 ) some general properties of the Green's functions, and (3) the some basic information about the random fields of the conductivity, porosity and flow velocity. We present some general forms of the exactly averaged non-local equations for the following cases. 1. Steady-state flow with sources in porous media with random conductivity. 2. Transient flow with sources in compressible media with random conductivity and porosity. 3. Non-reactive solute transport in random porous media. We discuss the problem of uniqueness and the properties of the non-local averaged equations, for the cases with some types of symmetry (isotropic, transversal isotropic, orthotropic) and we analyze the hypothesis of the structure of non-local equations in a general case of

  1. A solution algorithm for the fluid dynamic equations based on a stochastic model for molecular motion

    SciTech Connect

    Jenny, Patrick Torrilhon, Manuel; Heinz, Stefan

    2010-02-20

    In this paper, a stochastic model is presented to simulate the flow of gases, which are not in thermodynamic equilibrium, like in rarefied or micro situations. For the interaction of a particle with others, statistical moments of the local ensemble have to be evaluated, but unlike in molecular dynamics simulations or DSMC, no collisions between computational particles are considered. In addition, a novel integration technique allows for time steps independent of the stochastic time scale. The stochastic model represents a Fokker-Planck equation in the kinetic description, which can be viewed as an approximation to the Boltzmann equation. This allows for a rigorous investigation of the relation between the new model and classical fluid and kinetic equations. The fluid dynamic equations of Navier-Stokes and Fourier are fully recovered for small relaxation times, while for larger values the new model extents into the kinetic regime. Numerical studies demonstrate that the stochastic model is consistent with Navier-Stokes in that limit, but also that the results become significantly different, if the conditions for equilibrium are invalid. The application to the Knudsen paradox demonstrates the correctness and relevance of this development, and comparisons with existing kinetic equations and standard solution algorithms reveal its advantages. Moreover, results of a test case with geometrically complex boundaries are presented.

  2. Generalized stochastic resonance in a linear fractional system with a random delay

    NASA Astrophysics Data System (ADS)

    Gao, Shi-Long

    2012-12-01

    The generalized stochastic resonance (GSR) phenomena in a linear fractional random-delayed system driven by a weak periodic signal and an additive noise are considered in this paper. A random delay is considered for a linear fractional Langevin equation to describe the intercellular signal transmission and material exchange processes in ion channels. By virtue of the small delay approximation and Laplace transformation, the analytical expression for the amplitude of the first-order steady state moment is obtained. The simulation results show that the amplitude curves as functions of different system parameters behave non-monotonically and exhibit typical characteristics of GSR phenomena. Furthermore, a physical explanation for all the GSR phenomena is given and the cooperative effects of random delay and the fractional memory are also discussed.

  3. Monotonic continuous-time random walks with drift and stochastic reset events

    NASA Astrophysics Data System (ADS)

    Montero, Miquel; Villarroel, Javier

    2013-01-01

    In this paper we consider a stochastic process that may experience random reset events which suddenly bring the system to the starting value and analyze the relevant statistical magnitudes. We focus our attention on monotonic continuous-time random walks with a constant drift: The process increases between the reset events, either by the effect of the random jumps, or by the action of the deterministic drift. As a result of all these combined factors interesting properties emerge, like the existence (for any drift strength) of a stationary transition probability density function, or the faculty of the model to reproduce power-law-like behavior. General formulas for two extreme statistics, the survival probability, and the mean exit time are also derived. To corroborate in an independent way the results of the paper, Monte Carlo methods were used. These numerical estimations are in full agreement with the analytical predictions.

  4. An iterative curvelet thresholding algorithm for seismic random noise attenuation

    NASA Astrophysics Data System (ADS)

    Wang, De-Li; Tong, Zhong-Fei; Tang, Chen; Zhu, Heng

    2010-12-01

    In this paper, we explore the use of iterative curvelet thresholding for seismic random noise attenuation. A new method for combining the curvelet transform with iterative thresholding to suppress random noise is demonstrated and the issue is described as a linear inverse optimal problem using the L1 norm. Random noise suppression in seismic data is transformed into an L1 norm optimization problem based on the curvelet sparsity transform. Compared to the conventional methods such as median filter algorithm, FX deconvolution, and wavelet thresholding, the results of synthetic and field data processing show that the iterative curvelet thresholding proposed in this paper can sufficiently improve signal to noise radio (SNR) and give higher signal fidelity at the same time. Furthermore, to make better use of the curvelet transform such as multiple scales and multiple directions, we control the curvelet direction of the result after iterative curvelet thresholding to further improve the SNR.

  5. An adaptive algorithm for simulation of stochastic reaction-diffusion processes

    SciTech Connect

    Ferm, Lars Hellander, Andreas Loetstedt, Per

    2010-01-20

    We propose an adaptive hybrid method suitable for stochastic simulation of diffusion dominated reaction-diffusion processes. For such systems, simulation of the diffusion requires the predominant part of the computing time. In order to reduce the computational work, the diffusion in parts of the domain is treated macroscopically, in other parts with the tau-leap method and in the remaining parts with Gillespie's stochastic simulation algorithm (SSA) as implemented in the next subvolume method (NSM). The chemical reactions are handled by SSA everywhere in the computational domain. A trajectory of the process is advanced in time by an operator splitting technique and the timesteps are chosen adaptively. The spatial adaptation is based on estimates of the errors in the tau-leap method and the macroscopic diffusion. The accuracy and efficiency of the method are demonstrated in examples from molecular biology where the domain is discretized by unstructured meshes.

  6. Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations

    NASA Astrophysics Data System (ADS)

    Vrettas, Michail D.; Opper, Manfred; Cornford, Dan

    2015-01-01

    This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.

  7. Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm

    NASA Astrophysics Data System (ADS)

    Takaishi, Tetsuya

    2013-04-01

    The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.

  8. Deterministic and stochastic algorithms for resolving the flow fields in ducts and networks using energy minimization

    NASA Astrophysics Data System (ADS)

    Sochi, Taha

    2016-09-01

    Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of computational fluid dynamics for solving the flow fields in tubes and networks for various types of Newtonian and non-Newtonian fluids.

  9. R-leaping: Accelerating the stochastic simulation algorithm by reaction leaps

    NASA Astrophysics Data System (ADS)

    Auger, Anne; Chatelain, Philippe; Koumoutsakos, Petros

    2006-08-01

    A novel algorithm is proposed for the acceleration of the exact stochastic simulation algorithm by a predefined number of reaction firings (R-leaping) that may occur across several reaction channels. In the present approach, the numbers of reaction firings are correlated binomial distributions and the sampling procedure is independent of any permutation of the reaction channels. This enables the algorithm to efficiently handle large systems with disparate rates, providing substantial computational savings in certain cases. Several mechanisms for controlling the accuracy and the appearance of negative species are described. The advantages and drawbacks of R-leaping are assessed by simulations on a number of benchmark problems and the results are discussed in comparison with established methods.

  10. Dynamic phasing of multichannel cw laser radiation by means of a stochastic gradient algorithm

    SciTech Connect

    Volkov, V A; Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A

    2013-09-30

    The phasing of a multichannel laser beam by means of an iterative stochastic parallel gradient (SPG) algorithm has been numerically and experimentally investigated. The operation of the SPG algorithm is simulated, the acceptable range of amplitudes of probe phase shifts is found, and the algorithm parameters at which the desired Strehl number can be obtained with a minimum number of iterations are determined. An experimental bench with phase modulators based on lithium niobate, which are controlled by a multichannel electronic unit with a real-time microcontroller, has been designed. Phasing of 16 cw laser beams at a system response bandwidth of 3.7 kHz and phase thermal distortions in a frequency band of about 10 Hz is experimentally demonstrated. The experimental data are in complete agreement with the calculation results. (control of laser radiation parameters)

  11. Theory of weak scattering of stochastic electromagnetic fields from deterministic and random media

    SciTech Connect

    Tong Zhisong; Korotkova, Olga

    2010-09-15

    The theory of scattering of scalar stochastic fields from deterministic and random media is generalized to the electromagnetic domain under the first-order Born approximation. The analysis allows for determining the changes in spectrum, coherence, and polarization of electromagnetic fields produced on their propagation from the source to the scattering volume, interaction with the scatterer, and propagation from the scatterer to the far field. An example of scattering of a field produced by a {delta}-correlated partially polarized source and scattered from a {delta}-correlated medium is provided.

  12. Stochastic simulation for the propagation of high-frequency acoustic waves through a random velocity field

    NASA Astrophysics Data System (ADS)

    Lu, B.; Darmon, M.; Leymarie, N.; Chatillon, S.; Potel, C.

    2012-05-01

    In-service inspection of Sodium-Cooled Fast Reactors (SFR) requires the development of non-destructive techniques adapted to the harsh environment conditions and the examination complexity. From past experiences, ultrasonic techniques are considered as suitable candidates. The ultrasonic telemetry is a technique used to constantly insure the safe functioning of reactor inner components by determining their exact position: it consists in measuring the time of flight of the ultrasonic response obtained after propagation of a pulse emitted by a transducer and its interaction with the targets. While in-service the sodium flow creates turbulences that lead to temperature inhomogeneities, which translates into ultrasonic velocity inhomogeneities. These velocity variations could directly impact the accuracy of the target locating by introducing time of flight variations. A stochastic simulation model has been developed to calculate the propagation of ultrasonic waves in such an inhomogeneous medium. Using this approach, the travel time is randomly generated by a stochastic process whose inputs are the statistical moments of travel times known analytically. The stochastic model predicts beam deviations due to velocity inhomogeneities, which are similar to those provided by a determinist method, such as the ray method.

  13. Stochastic simulation for the propagation of high-frequency acoustic waves through a random velocity field

    SciTech Connect

    Lu, B.; Darmon, M.; Leymarie, N.; Chatillon, S.; Potel, C.

    2012-05-17

    In-service inspection of Sodium-Cooled Fast Reactors (SFR) requires the development of non-destructive techniques adapted to the harsh environment conditions and the examination complexity. From past experiences, ultrasonic techniques are considered as suitable candidates. The ultrasonic telemetry is a technique used to constantly insure the safe functioning of reactor inner components by determining their exact position: it consists in measuring the time of flight of the ultrasonic response obtained after propagation of a pulse emitted by a transducer and its interaction with the targets. While in-service the sodium flow creates turbulences that lead to temperature inhomogeneities, which translates into ultrasonic velocity inhomogeneities. These velocity variations could directly impact the accuracy of the target locating by introducing time of flight variations. A stochastic simulation model has been developed to calculate the propagation of ultrasonic waves in such an inhomogeneous medium. Using this approach, the travel time is randomly generated by a stochastic process whose inputs are the statistical moments of travel times known analytically. The stochastic model predicts beam deviations due to velocity inhomogeneities, which are similar to those provided by a determinist method, such as the ray method.

  14. Stochastic coalescence in finite systems: an algorithm for the numerical solution of the multivariate master equation.

    NASA Astrophysics Data System (ADS)

    Alfonso, Lester; Zamora, Jose; Cruz, Pedro

    2015-04-01

    The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.

  15. Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

    PubMed

    Chang, Joshua; Paydarfar, David

    2014-12-01

    Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue. PMID:25145955

  16. Application of stochastic weighted algorithms to a multidimensional silica particle model

    SciTech Connect

    Menz, William J.; Patterson, Robert I.A.; Wagner, Wolfgang; Kraft, Markus

    2013-09-01

    Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associated majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.

  17. Random Matrix Approach to Quantum Adiabatic Evolution Algorithms

    NASA Technical Reports Server (NTRS)

    Boulatov, Alexei; Smelyanskiy, Vadier N.

    2004-01-01

    We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.

  18. Algorithmic randomness, physical entropy, measurements, and the second law

    SciTech Connect

    Zurek, W.H.

    1989-01-01

    Algorithmic information content is equal to the size -- in the number of bits -- of the shortest program for a universal Turing machine which can reproduce a state of a physical system. In contrast to the statistical Boltzmann-Gibbs-Shannon entropy, which measures ignorance, the algorithmic information content is a measure of the available information. It is defined without a recourse to probabilities and can be regarded as a measure of randomness of a definite microstate. I suggest that the physical entropy S -- that is, the quantity which determines the amount of the work {Delta}W which can be extracted in the cyclic isothermal expansion process through the equation {Delta}W = k{sub B}T{Delta}S -- is a sum of two contributions: the mission information measured by the usual statistical entropy and the known randomness measured by the algorithmic information content. The sum of these two contributions is a constant of motion'' in the process of a dissipation less measurement on an equilibrium ensemble. This conservation under a measurement, which can be traced back to the noiseless coding theorem of Shannon, is necessary to rule out existence of a successful Maxwell's demon. 17 refs., 3 figs.

  19. Combinatorial approximation algorithms for MAXCUT using random walks.

    SciTech Connect

    Seshadhri, Comandur; Kale, Satyen

    2010-11-01

    We give the first combinatorial approximation algorithm for MaxCut that beats the trivial 0.5 factor by a constant. The main partitioning procedure is very intuitive, natural, and easily described. It essentially performs a number of random walks and aggregates the information to provide the partition. We can control the running time to get an approximation factor-running time tradeoff. We show that for any constant b > 1.5, there is an {tilde O}(n{sup b}) algorithm that outputs a (0.5 + {delta})-approximation for MaxCut, where {delta} = {delta}(b) is some positive constant. One of the components of our algorithm is a weak local graph partitioning procedure that may be of independent interest. Given a starting vertex i and a conductance parameter {phi}, unless a random walk of length {ell} = O(log n) starting from i mixes rapidly (in terms of {phi} and {ell}), we can find a cut of conductance at most {phi} close to the vertex. The work done per vertex found in the cut is sublinear in n.

  20. A multi-sensor RSS spatial sensing-based robust stochastic optimization algorithm for enhanced wireless tethering.

    PubMed

    Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the "server-relay-client" framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

  1. A Multi-Sensor RSS Spatial Sensing-Based Robust Stochastic Optimization Algorithm for Enhanced Wireless Tethering

    PubMed Central

    Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel

    2014-01-01

    The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions. PMID:25615734

  2. Stochastic sampled data robust stabilisation of T-S fuzzy neutral systems with randomly occurring uncertainties and time-varying delays

    NASA Astrophysics Data System (ADS)

    Rakkiyappan, R.; Chandrasekar, A.; Lakshmanan, S.

    2016-07-01

    This paper is concerned with the stochastic sampled data robust stabilisation of T-S fuzzy neutral systems with randomly occurring uncertainties and time-varying delays. The sampling period is assumed to be m in number, whose occurrence probabilities are given constants and satisfy Bernoulli distribution. By introducing an improved Lyapunov-Krasovskii functional with new triple integral terms and by combining both the convex combination technique and reciprocal convex technique, delay-dependent robust stability criteria are obtained in terms of linear matrix inequalities. These linear matrix inequalities can be easily solved by using standard convex optimisation algorithms. The designed stochastic sampled data fuzzy controller gain can be obtained. Finally, three numerical examples are given to illustrate the effectiveness of the proposed methods.

  3. Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process.

    PubMed

    Hermann, Philipp; Mrkvička, Tomáš; Mattfeldt, Torsten; Minárová, Mária; Helisová, Kateřina; Nicolis, Orietta; Wartner, Fabian; Stehlík, Milan

    2015-08-15

    Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper. PMID:25847279

  4. A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

    PubMed Central

    Roberts, William M; Augustine, Steven B; Lawton, Kristy J; Lindsay, Theodore H; Thiele, Tod R; Izquierdo, Eduardo J; Faumont, Serge; Lindsay, Rebecca A; Britton, Matthew Cale; Pokala, Navin; Bargmann, Cornelia I; Lockery, Shawn R

    2016-01-01

    Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms. DOI: http://dx.doi.org/10.7554/eLife.12572.001 PMID:26824391

  5. A Bloch decomposition-based stochastic Galerkin method for quantum dynamics with a random external potential

    NASA Astrophysics Data System (ADS)

    Wu, Zhizhang; Huang, Zhongyi

    2016-07-01

    In this paper, we consider the numerical solution of the one-dimensional Schrödinger equation with a periodic lattice potential and a random external potential. This is an important model in solid state physics where the randomness results from complicated phenomena that are not exactly known. Here we generalize the Bloch decomposition-based time-splitting pseudospectral method to the stochastic setting using the generalized polynomial chaos with a Galerkin procedure so that the main effects of dispersion and periodic potential are still computed together. We prove that our method is unconditionally stable and numerical examples show that it has other nice properties and is more efficient than the traditional method. Finally, we give some numerical evidence for the well-known phenomenon of Anderson localization.

  6. From doubly stochastic representations of K distributions to random walks and back again: an optics tale

    NASA Astrophysics Data System (ADS)

    French, O. E.

    2009-06-01

    A random walk model with a negative binomially fluctuating number of steps is considered in the case where the mean of the number fluctuations, \\bar{N} , is finite. The asymptotic behaviour of the resultant statistics in the large \\bar{N} limit is derived and shown to give the K distribution. The equivalence of this model to the hitherto unrelated doubly stochastic representation of the K distribution is also demonstrated. The convergence to the K distribution of the probability density function generated by a random walk with a finite mean number of steps is examined along with the moments, and the non-Gaussian statistics are shown to be a direct result of discreteness and bunching effects.

  7. Polynomial iterative algorithms for coloring and analyzing random graphs.

    PubMed

    Braunstein, A; Mulet, R; Pagnani, A; Weigt, M; Zecchina, R

    2003-09-01

    We study the graph coloring problem over random graphs of finite average connectivity c. Given a number q of available colors, we find that graphs with low connectivity admit almost always a proper coloring whereas graphs with high connectivity are uncolorable. Depending on q, we find with a one-step replica-symmetry breaking approximation the precise value of the critical average connectivity c(q). Moreover, we show that below c(q) there exists a clustering phase c in [c(d),c(q)] in which ground states spontaneously divide into an exponential number of clusters. Furthermore, we extended our considerations to the case of single instances showing consistent results. This leads us to propose a different algorithm that is able to color in polynomial time random graphs in the hard but colorable region, i.e., when c in [c(d),c(q)]. PMID:14524921

  8. Using genetic algorithm to solve a new multi-period stochastic optimization model

    NASA Astrophysics Data System (ADS)

    Zhang, Xin-Li; Zhang, Ke-Cun

    2009-09-01

    This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

  9. Use of a Stochastic Joint Inversion Modeling Algorithm to Develop a Hydrothermal Flow Model at a Geothermal Prospect

    NASA Astrophysics Data System (ADS)

    Tompson, A. F. B.; Mellors, R. J.; Dyer, K.; Yang, X.; Chen, M.; Trainor Guitton, W.; Wagoner, J. L.; Ramirez, A. L.

    2014-12-01

    A stochastic joint inverse algorithm is used to analyze diverse geophysical and hydrologic data associated with a geothermal prospect. The approach uses a Markov Chain Monte Carlo (MCMC) global search algorithm to develop an ensemble of hydrothermal groundwater flow models that are most consistent with the observations. The algorithm utilizes an initial conceptual model descriptive of structural (geology), parametric (permeability) and hydrothermal (saturation, temperature) characteristics of the geologic system. Initial (a-priori) estimates of uncertainty in these characteristics are used to drive simulations of hydrothermal fluid flow and related geophysical processes in a large number of random realizations of the conceptual geothermal system spanning these uncertainties. The process seeks to improve the conceptual model by developing a ranked subset of model realizations that best match all available data within a specified norm or tolerance. Statistical (posterior) characteristics of these solutions reflect reductions in the a-priori uncertainties. The algorithm has been tested on a geothermal prospect located at Superstition Mountain, California and has been successful in creating a suite of models compatible with available temperature, surface resistivity, and magnetotelluric (MT) data. Although the MCMC method is highly flexible and capable of accommodating multiple and diverse datasets, a typical inversion may require the evaluation of thousands of possible model runs whose sophistication and complexity may evolve with the magnitude of data considered. As a result, we are testing the use of sensitivity analyses to better identify critical uncertain variables, lower order surrogate models to streamline computational costs, and value of information analyses to better assess optimal use of related data. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL

  10. Stochastic models: theory and simulation.

    SciTech Connect

    Field, Richard V., Jr.

    2008-03-01

    Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.

  11. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications

    NASA Astrophysics Data System (ADS)

    Decelle, Aurelien; Krzakala, Florent; Moore, Cristopher; Zdeborová, Lenka

    2011-12-01

    In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network. We use the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability-undetectability phase transition and the easy-hard phase transition for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world networks and discuss its performance.

  12. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications.

    PubMed

    Decelle, Aurelien; Krzakala, Florent; Moore, Cristopher; Zdeborová, Lenka

    2011-12-01

    In this paper we extend our previous work on the stochastic block model, a commonly used generative model for social and biological networks, and the problem of inferring functional groups or communities from the topology of the network. We use the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram. We describe in detail properties of the detectability-undetectability phase transition and the easy-hard phase transition for the community detection problem. Our analysis translates naturally into a belief propagation algorithm for inferring the group memberships of the nodes in an optimal way, i.e., that maximizes the overlap with the underlying group memberships, and learning the underlying parameters of the block model. Finally, we apply the algorithm to two examples of real-world networks and discuss its performance. PMID:22304154

  13. 2D stochastic-integral models for characterizing random grain noise in titanium alloys

    SciTech Connect

    Sabbagh, Harold A.; Murphy, R. Kim; Sabbagh, Elias H.; Cherry, Matthew; Pilchak, Adam; Knopp, Jeremy S.; Blodgett, Mark P.

    2014-02-18

    We extend our previous work, in which we applied high-dimensional model representation (HDMR) and analysis of variance (ANOVA) concepts to the characterization of a metallic surface that has undergone a shot-peening treatment to reduce residual stresses, and has, therefore, become a random conductivity field. That example was treated as a onedimensional problem, because those were the only data available. In this study, we develop a more rigorous two-dimensional model for characterizing random, anisotropic grain noise in titanium alloys. Such a model is necessary if we are to accurately capture the 'clumping' of crystallites into long chains that appear during the processing of the metal into a finished product. The mathematical model starts with an application of the Karhunen-Loève (K-L) expansion for the random Euler angles, θ and φ, that characterize the orientation of each crystallite in the sample. The random orientation of each crystallite then defines the stochastic nature of the electrical conductivity tensor of the metal. We study two possible covariances, Gaussian and double-exponential, which are the kernel of the K-L integral equation, and find that the double-exponential appears to satisfy measurements more closely of the two. Results based on data from a Ti-7Al sample will be given, and further applications of HDMR and ANOVA will be discussed.

  14. Random Process Simulation for stochastic fatigue analysis. Ph.D. Thesis - Rice Univ., Houston, Tex.

    NASA Technical Reports Server (NTRS)

    Larsen, Curtis E.

    1988-01-01

    A simulation technique is described which directly synthesizes the extrema of a random process and is more efficient than the Gaussian simulation method. Such a technique is particularly useful in stochastic fatigue analysis because the required stress range moment E(R sup m), is a function only of the extrema of the random stress process. The family of autoregressive moving average (ARMA) models is reviewed and an autoregressive model is presented for modeling the extrema of any random process which has a unimodal power spectral density (psd). The proposed autoregressive technique is found to produce rainflow stress range moments which compare favorably with those computed by the Gaussian technique and to average 11.7 times faster than the Gaussian technique. The autoregressive technique is also adapted for processes having bimodal psd's. The adaptation involves using two autoregressive processes to simulate the extrema due to each mode and the superposition of these two extrema sequences. The proposed autoregressive superposition technique is 9 to 13 times faster than the Gaussian technique and produces comparable values for E(R sup m) for bimodal psd's having the frequency of one mode at least 2.5 times that of the other mode.

  15. Global stability and stochastic permanence of a non-autonomous logistic equation with random perturbation

    NASA Astrophysics Data System (ADS)

    Jiang, Daqing; Shi, Ningzhong; Li, Xiaoyue

    2008-04-01

    This paper discusses a randomized non-autonomous logistic equation , where B(t) is a 1-dimensional standard Brownian motion. In [D.Q. Jiang, N.Z. Shi, A note on non-autonomous logistic equation with random perturbation, J. Math. Anal. Appl. 303 (2005) 164-172], the authors show that E[1/N(t)] has a unique positive T-periodic solution E[1/Np(t)] provided a(t), b(t) and [alpha](t) are continuous T-periodic functions, a(t)>0, b(t)>0 and . We show that this equation is stochastically permanent and the solution Np(t) is globally attractive provided a(t), b(t) and [alpha](t) are continuous T-periodic functions, a(t)>0, b(t)>0 and mint[set membership, variant][0,T]a(t)>maxt[set membership, variant][0,T][alpha]2(t). By the way, the similar results of a generalized non-autonomous logistic equation with random perturbation are yielded.

  16. Stochastic Seismic Response of an Algiers Site with Random Depth to Bedrock

    SciTech Connect

    Badaoui, M.; Mebarki, A.; Berrah, M. K.

    2010-05-21

    Among the important effects of the Boumerdes earthquake (Algeria, May 21{sup st} 2003) was that, within the same zone, the destructions in certain parts were more important than in others. This phenomenon is due to site effects which alter the characteristics of seismic motions and cause concentration of damage during earthquakes. Local site effects such as thickness and mechanical properties of soil layers have important effects on the surface ground motions.This paper deals with the effect of the randomness aspect of the depth to bedrock (soil layers heights) which is assumed to be a random variable with lognormal distribution. This distribution is suitable for strictly non-negative random variables with large values of the coefficient of variation. In this case, Monte Carlo simulations are combined with the stiffness matrix method, used herein as a deterministic method, for evaluating the effect of the depth to bedrock uncertainty on the seismic response of a multilayered soil. This study considers a P and SV wave propagation pattern using input accelerations collected at Keddara station, located at 20 km from the epicenter, as it is located directly on the bedrock.A parametric study is conducted do derive the stochastic behavior of the peak ground acceleration and its response spectrum, the transfer function and the amplification factors. It is found that the soil height heterogeneity causes a widening of the frequency content and an increase in the fundamental frequency of the soil profile, indicating that the resonance phenomenon concerns a larger number of structures.

  17. Stochastic Seismic Response of an Algiers Site with Random Depth to Bedrock

    NASA Astrophysics Data System (ADS)

    Badaoui, M.; Berrah, M. K.; Mébarki, A.

    2010-05-01

    Among the important effects of the Boumerdes earthquake (Algeria, May 21st 2003) was that, within the same zone, the destructions in certain parts were more important than in others. This phenomenon is due to site effects which alter the characteristics of seismic motions and cause concentration of damage during earthquakes. Local site effects such as thickness and mechanical properties of soil layers have important effects on the surface ground motions. This paper deals with the effect of the randomness aspect of the depth to bedrock (soil layers heights) which is assumed to be a random variable with lognormal distribution. This distribution is suitable for strictly non-negative random variables with large values of the coefficient of variation. In this case, Monte Carlo simulations are combined with the stiffness matrix method, used herein as a deterministic method, for evaluating the effect of the depth to bedrock uncertainty on the seismic response of a multilayered soil. This study considers a P and SV wave propagation pattern using input accelerations collected at Keddara station, located at 20 km from the epicenter, as it is located directly on the bedrock. A parametric study is conducted do derive the stochastic behavior of the peak ground acceleration and its response spectrum, the transfer function and the amplification factors. It is found that the soil height heterogeneity causes a widening of the frequency content and an increase in the fundamental frequency of the soil profile, indicating that the resonance phenomenon concerns a larger number of structures.

  18. A stochastic control approach to Slotted-ALOHA random access protocol

    NASA Astrophysics Data System (ADS)

    Pietrabissa, Antonio

    2013-12-01

    ALOHA random access protocols are distributed protocols based on transmission probabilities, that is, each node decides upon packet transmissions according to a transmission probability value. In the literature, ALOHA protocols are analysed by giving necessary and sufficient conditions for the stability of the queues of the node buffers under a control vector (whose elements are the transmission probabilities assigned to the nodes), given an arrival rate vector (whose elements represent the rates of the packets arriving in the node buffers). The innovation of this work is that, given an arrival rate vector, it computes the optimal control vector by defining and solving a stochastic control problem aimed at maximising the overall transmission efficiency, while keeping a grade of fairness among the nodes. Furthermore, a more general case in which the arrival rate vector changes in time is considered. The increased efficiency of the proposed solution with respect to the standard ALOHA approach is evaluated by means of numerical simulations.

  19. Tools and Algorithms to Link Horizontal Hydrologic and Vertical Hydrodynamic Models and Provide a Stochastic Modeling Framework

    NASA Astrophysics Data System (ADS)

    Salah, Ahmad M.; Nelson, E. James; Williams, Gustavious P.

    2010-04-01

    We present algorithms and tools we developed to automatically link an overland flow model to a hydrodynamic water quality model with different spatial and temporal discretizations. These tools run the linked models which provide a stochastic simulation frame. We also briefly present the tools and algorithms we developed to facilitate and analyze stochastic simulations of the linked models. We demonstrate the algorithms by linking the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model for overland flow with the CE-QUAL-W2 model for water quality and reservoir hydrodynamics. GSSHA uses a two-dimensional horizontal grid while CE-QUAL-W2 uses a two-dimensional vertical grid. We implemented the algorithms and tools in the Watershed Modeling System (WMS) which allows modelers to easily create and use models. The algorithms are general and could be used for other models. Our tools create and analyze stochastic simulations to help understand uncertainty in the model application. While a number of examples of linked models exist, the ability to perform automatic, unassisted linking is a step forward and provides the framework to easily implement stochastic modeling studies.

  20. Randomized tree construction algorithm to explore energy landscapes.

    PubMed

    Jaillet, Léonard; Corcho, Francesc J; Pérez, Juan-Jesús; Cortés, Juan

    2011-12-01

    In this work, a new method for exploring conformational energy landscapes is described. The method, called transition-rapidly exploring random tree (T-RRT), combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased toward yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved due to a self-tuning mechanism. The method is able to efficiently find both energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and the alanine dipeptide. PMID:21919017

  1. Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays.

    PubMed

    Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa

    2016-01-01

    This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387

  2. Networked Fusion Filtering from Outputs with Stochastic Uncertainties and Correlated Random Transmission Delays

    PubMed Central

    Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa

    2016-01-01

    This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387

  3. Random matrix approach to quantum adiabatic evolution algorithms

    SciTech Connect

    Boulatov, A.; Smelyanskiy, V.N.

    2005-05-15

    We analyze the power of the quantum adiabatic evolution algorithm (QAA) for solving random computationally hard optimization problems within a theoretical framework based on random matrix theory (RMT). We present two types of driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that nonadiabatic corrections in the QAA are due to the interaction of the ground state with the 'cloud' formed by most of the excited states, confirming that in driven RMT models, the Landau-Zener scenario of pairwise level repulsions is not relevant for the description of nonadiabatic corrections. We show that the QAA has a finite probability of success in a certain range of parameters, implying a polynomial complexity of the algorithm. The second model corresponds to the standard QAA with the problem Hamiltonian taken from the RMT Gaussian unitary ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. For this reason, the driven GUE model can also lead to polynomial complexity of the QAA. The main contribution to the failure probability of the QAA comes from the nonadiabatic corrections to the eigenstates, which only depend on the absolute values of the transition amplitudes. Due to the mapping between the two models, these absolute values are the same in both cases. Our results indicate that this 'phase irrelevance' is the leading effect that can make both the Markovian- and GUE-type QAAs successful.

  4. A Stochastic Framework For Sediment Concentration Estimation By Accounting Random Arrival Processes Of Incoming Particles Into Receiving Waters

    NASA Astrophysics Data System (ADS)

    Tsai, C.; Hung, R. J.

    2015-12-01

    This study attempts to apply queueing theory to develop a stochastic framework that could account for the random-sized batch arrivals of incoming sediment particles into receiving waters. Sediment particles, control volume, mechanics of sediment transport (such as mechanics of suspension, deposition and resuspension) are treated as the customers, service facility and the server respectively in queueing theory. In the framework, the stochastic diffusion particle tracking model (SD-PTM) and resuspension of particles are included to simulate the random transport trajectories of suspended particles. The most distinguished characteristic of queueing theory is that customers come to the service facility in a random manner. In analogy to sediment transport, this characteristic is adopted to model the random-sized batch arrival process of sediment particles including the random occurrences and random magnitude of incoming sediment particles. The random occurrences of arrivals are simulated by Poisson process while the number of sediment particles in each arrival can be simulated by a binominal distribution. Simulations of random arrivals and random magnitude are proposed individually to compare with the random-sized batch arrival simulations. Simulation results are a probabilistic description for discrete sediment transport through ensemble statistics (i.e. ensemble means and ensemble variances) of sediment concentrations and transport rates. Results reveal the different mechanisms of incoming particles will result in differences in the ensemble variances of concentrations and transport rates under the same mean incoming rate of sediment particles.

  5. Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization

    NASA Astrophysics Data System (ADS)

    Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo

    A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.

  6. A new algorithm for calculating the curvature perturbations in stochastic inflation

    SciTech Connect

    Fujita, Tomohiro; Kawasaki, Masahiro; Tada, Yuichiro; Takesako, Tomohiro E-mail: kawasaki@icrr.u-tokyo.ac.jp E-mail: takesako@icrr.u-tokyo.ac.jp

    2013-12-01

    We propose a new approach for calculating the curvature perturbations produced during inflation in the stochastic formalism. In our formalism, the fluctuations of the e-foldings are directly calculated without perturbatively expanding the inflaton field and they are connected to the curvature perturbations by the δN formalism. The result automatically includes the contributions of the higher order perturbations because we solve the equation of motion non-perturbatively. In this paper, we analytically prove that our result (the power spectrum and the nonlinearity parameter) is consistent with the standard result in single field slow-roll inflation. We also describe the algorithm for numerical calculations of the curvature perturbations in more general inflation models.

  7. Stochastic generation of explicit pore structures by thresholding Gaussian random fields

    SciTech Connect

    Hyman, Jeffrey D.; Winter, C. Larrabee

    2014-11-15

    We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1]) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone. -- Graphical abstract: -- Highlights: •An efficient method to stochastically generate realistic pore structures is provided. •Samples are generated by applying a level threshold to a Gaussian field realization. •Two user prescribed quantities determine the topology and geometry of the pore space. •Multiple pore structures and preferential flow directions can be produced. •A pore space based on Berea sandstone is generated.

  8. The Application of Multiobjective Genetic Algorithm to the Parameter Optimization of Single-Well Potential Stochastic Resonance Algorithm Aimed at Simultaneous Determination of Multiple Weak Chromatographic Peaks

    PubMed Central

    Xiang, Bingren; Wu, Xiaohong; Liu, Dan

    2014-01-01

    Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses. PMID:24526920

  9. A matrix product algorithm for stochastic dynamics on locally tree-like graphs

    NASA Astrophysics Data System (ADS)

    Barthel, Thomas; de Bacco, Caterina; Franz, Silvio

    In this talk, I describe a novel algorithm for the efficient simulation of generic stochastic dynamics of classical degrees of freedom defined on the vertices of locally tree-like graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon the cavity method and ideas from quantum many-body theory, the algorithm is based on a matrix product approximation of the so-called edge messages - conditional probabilities of vertex variable trajectories. The matrix product edge messages (MPEM) are constructed recursively. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the MPEM in truncations. In contrast to Monte Carlo simulations, the approach has a better error scaling and works for both, single instances as well as the thermodynamic limit. Due to the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations with unprecedented accuracy. The method is demonstrated for the prototypical non-equilibrium Glauber dynamics of an Ising spin system. Reference: arXiv:1508.03295.

  10. An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

    PubMed Central

    2009-01-01

    Background Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches. Results In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay. Conclusions By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net. PMID:20003312

  11. Random finite set multi-target trackers: stochastic geometry for space situational awareness

    NASA Astrophysics Data System (ADS)

    Vo, Ba-Ngu; Vo, Ba-Tuong

    2015-05-01

    This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchmark for the RFS approach. Since there is a suite of RFS-based multi-target tracking algorithms, benchmarking tracking performance of the RFS approach by using the PHD filter, the cheapest of these, is misleading. Such benchmarking should be performed with more sophisticated RFS algorithms. In this paper we outline the high-performance RFS-based multi-target trackers such that the Generalized Labled Multi-Bernoulli filter, and a number of efficient approximations and discuss extensions and applications of these filters. Applications to space situational awareness are discussed.

  12. A stochastic simulation method for the assessment of resistive random access memory retention reliability

    SciTech Connect

    Berco, Dan Tseng, Tseung-Yuen

    2015-12-21

    This study presents an evaluation method for resistive random access memory retention reliability based on the Metropolis Monte Carlo algorithm and Gibbs free energy. The method, which does not rely on a time evolution, provides an extremely efficient way to compare the relative retention properties of metal-insulator-metal structures. It requires a small number of iterations and may be used for statistical analysis. The presented approach is used to compare the relative robustness of a single layer ZrO{sub 2} device with a double layer ZnO/ZrO{sub 2} one, and obtain results which are in good agreement with experimental data.

  13. A stochastic simulation method for the assessment of resistive random access memory retention reliability

    NASA Astrophysics Data System (ADS)

    Berco, Dan; Tseng, Tseung-Yuen

    2015-12-01

    This study presents an evaluation method for resistive random access memory retention reliability based on the Metropolis Monte Carlo algorithm and Gibbs free energy. The method, which does not rely on a time evolution, provides an extremely efficient way to compare the relative retention properties of metal-insulator-metal structures. It requires a small number of iterations and may be used for statistical analysis. The presented approach is used to compare the relative robustness of a single layer ZrO2 device with a double layer ZnO/ZrO2 one, and obtain results which are in good agreement with experimental data.

  14. Performance of a benchmark implementation of the Van Slyke and Wets algorithm for stochastic programs on the Alliant FX/8

    SciTech Connect

    Ariyawansa, K.A.

    1991-04-01

    A benchmark parallel implementation of the Van Slyke and Wets algorithm for stochastic linear programs, and the results of a carefully designed numerical experiment on the Sequent/Balance using the implementation are presented. An important use of this implementation is as a benchmark to assess the performance of approximation algorithms for stochastic linear programs. These approximation algorithms are best suited for implementation on parallel vector processes like the Alliant FX/8. Therefore, the performance of the benchmark implementation on the Alliant FX/8 is of interest. In this paper, we present results observed when a portion of the numerical experiment is performed on the Alliant FX/8. These results indicate that the implementation makes satisfactory use of the concurrency capabilities of the Alliant FX/8. They also indicate that the vectorization capabilities of the Alliant FX/8 are not satisfactorily utilized by the implementation. 9 refs., 9 tabs.

  15. Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

    NASA Astrophysics Data System (ADS)

    Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

    2009-05-01

    bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

  16. A Stochastic Algorithm for the Isobaric-Isothermal Ensemble with Ewald Summations for All Long Range Forces.

    PubMed

    Di Pierro, Michele; Elber, Ron; Leimkuhler, Benedict

    2015-12-01

    We present an algorithm termed COMPEL (COnstant Molecular Pressure with Ewald sum for Long range forces) to conduct simulations in the NPT ensemble. The algorithm combines novel features recently proposed in the literature to obtain a highly efficient and accurate numerical integrator. COMPEL exploits the concepts of molecular pressure, rapid stochastic relaxation to equilibrium, exact calculation of the contribution to the pressure of long-range nonbonded forces with Ewald summation, and the use of Trotter expansion to generate a robust, highly stable, symmetric, and accurate algorithm. Explicit implementation in the MOIL program and illustrative numerical examples are discussed. PMID:26616351

  17. Stochastic generation of explicit pore structures by thresholding Gaussian random fields

    NASA Astrophysics Data System (ADS)

    Hyman, Jeffrey D.; Winter, C. Larrabee

    2014-11-01

    We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1]) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone.

  18. Biased Randomized Algorithm for Fast Model-Based Diagnosis

    NASA Technical Reports Server (NTRS)

    Williams, Colin; Vartan, Farrokh

    2005-01-01

    A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the

  19. The analysis of a sparse grid stochastic collocation method for partial differential equations with high-dimensional random input data.

    SciTech Connect

    Webster, Clayton; Tempone, Raul; Nobile, Fabio

    2007-12-01

    This work describes the convergence analysis of a Smolyak-type sparse grid stochastic collocation method for the approximation of statistical quantities related to the solution of partial differential equations with random coefficients and forcing terms (input data of the model). To compute solution statistics, the sparse grid stochastic collocation method uses approximate solutions, produced here by finite elements, corresponding to a deterministic set of points in the random input space. This naturally requires solving uncoupled deterministic problems and, as such, the derived strong error estimates for the fully discrete solution are used to compare the computational efficiency of the proposed method with the Monte Carlo method. Numerical examples illustrate the theoretical results and are used to compare this approach with several others, including the standard Monte Carlo.

  20. A Hybrid of the Chemical Master Equation and the Gillespie Algorithm for Efficient Stochastic Simulations of Sub-Networks

    PubMed Central

    Albert, Jaroslav

    2016-01-01

    Modeling stochastic behavior of chemical reaction networks is an important endeavor in many aspects of chemistry and systems biology. The chemical master equation (CME) and the Gillespie algorithm (GA) are the two most fundamental approaches to such modeling; however, each of them has its own limitations: the GA may require long computing times, while the CME may demand unrealistic memory storage capacity. We propose a method that combines the CME and the GA that allows one to simulate stochastically a part of a reaction network. First, a reaction network is divided into two parts. The first part is simulated via the GA, while the solution of the CME for the second part is fed into the GA in order to update its propensities. The advantage of this method is that it avoids the need to solve the CME or stochastically simulate the entire network, which makes it highly efficient. One of its drawbacks, however, is that most of the information about the second part of the network is lost in the process. Therefore, this method is most useful when only partial information about a reaction network is needed. We tested this method against the GA on two systems of interest in biology - the gene switch and the Griffith model of a genetic oscillator—and have shown it to be highly accurate. Comparing this method to four different stochastic algorithms revealed it to be at least an order of magnitude faster than the fastest among them. PMID:26930199

  1. MULTILEVEL ACCELERATION OF STOCHASTIC COLLOCATION METHODS FOR PDE WITH RANDOM INPUT DATA

    SciTech Connect

    Webster, Clayton G; Jantsch, Peter A; Teckentrup, Aretha L; Gunzburger, Max D

    2013-01-01

    Stochastic Collocation (SC) methods for stochastic partial differential equa- tions (SPDEs) suffer from the curse of dimensionality, whereby increases in the stochastic dimension cause an explosion of computational effort. To combat these challenges, multilevel approximation methods seek to decrease computational complexity by balancing spatial and stochastic discretization errors. As a form of variance reduction, multilevel techniques have been successfully applied to Monte Carlo (MC) methods, but may be extended to accelerate other methods for SPDEs in which the stochastic and spatial degrees of freedom are de- coupled. This article presents general convergence and computational complexity analysis of a multilevel method for SPDEs, demonstrating its advantages with regard to standard, single level approximation. The numerical results will highlight conditions under which multilevel sparse grid SC is preferable to the more traditional MC and SC approaches.

  2. Phase locking of a seven-channel continuous wave fibre laser system by a stochastic parallel gradient algorithm

    SciTech Connect

    Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Sinyavin, D N; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A

    2014-11-30

    A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)

  3. Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

    PubMed Central

    Orio, Patricio; Soudry, Daniel

    2012-01-01

    Background The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled gating particles, while the DA was modeled using uncoupled gating particles. Implementations of DA with coupled particles, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. Main Contributions We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when

  4. A novel quantum random number generation algorithm used by smartphone camera

    NASA Astrophysics Data System (ADS)

    Wu, Nan; Wang, Kun; Hu, Haixing; Song, Fangmin; Li, Xiangdong

    2015-05-01

    We study an efficient algorithm to extract quantum random numbers (QRN) from the raw data obtained by charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) based sensors, like a camera used in a commercial smartphone. Based on NIST statistical test for random number generators, the proposed algorithm has a high QRN generation rate and high statistical randomness. This algorithm provides a kind of simple, low-priced and reliable devices as a QRN generator for quantum key distribution (QKD) or other cryptographic applications.

  5. A stochastic simulation framework for the prediction of strategic noise mapping and occupational noise exposure using the random walk approach.

    PubMed

    Han, Lim Ming; Haron, Zaiton; Yahya, Khairulzan; Bakar, Suhaimi Abu; Dimon, Mohamad Ngasri

    2015-01-01

    Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces. PMID:25875019

  6. A Stochastic Simulation Framework for the Prediction of Strategic Noise Mapping and Occupational Noise Exposure Using the Random Walk Approach

    PubMed Central

    Haron, Zaiton; Bakar, Suhaimi Abu; Dimon, Mohamad Ngasri

    2015-01-01

    Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces. PMID:25875019

  7. Stochastic contraction-based observer and controller design algorithm with application to a flight vehicle

    NASA Astrophysics Data System (ADS)

    Mohamed, Majeed; Narayan Kar, Indra

    2015-11-01

    This paper focuses on a stochastic version of contraction theory to construct observer-controller structure for a flight dynamic system with noisy velocity measurement. A nonlinear stochastic observer is designed to estimate the pitch rate, the pitch angle, and the velocity of an aircraft example model using stochastic contraction theory. Estimated states are used to compute feedback control for solving a tracking problem. The structure and gain selection of the observer is carried out using Itô's stochastic differential equations and the contraction theory. The contraction property of integrated observer-controller structure is derived to ensure the exponential convergence of the trajectories of closed-loop nonlinear system. The upper bound of the state estimation error is explicitly derived and the efficacy of the proposed observer-controller structure has been shown through the numerical simulations.

  8. Random traveling wave and bifurcations of asymptotic behaviors in the stochastic KPP equation driven by dual noises

    NASA Astrophysics Data System (ADS)

    Huang, Zhehao; Liu, Zhengrong

    2016-07-01

    In this paper, we study the influences of dually environmental noises on the traveling wave which develops from the deterministic KPP equation. We prove that if the strengths of noises satisfy some condition, the solution of the stochastic KPP equation with Heaviside initial condition develops a random traveling wave, whose wave speed is deterministic and depends on the strengths of noises. If the strengths of noises satisfy some other conditions, the solution tends to zero as time tends to infinity. Therefore, there exist bifurcations of asymptotic behaviors of solution induced by the strengths of dual noises.

  9. Diffusion and stochastic island generation in the magnetic field line random walk

    SciTech Connect

    Vlad, M.; Spineanu, F.

    2014-08-10

    The cross-field diffusion of field lines in stochastic magnetic fields described by the 2D+slab model is studied using a semi-analytic statistical approach, the decorrelation trajectory method. We show that field line trapping and the associated stochastic magnetic islands strongly influence the diffusion coefficients, leading to dependences on the parameters that are different from the quasilinear and Bohm regimes. A strong amplification of the diffusion is produced by a small slab field in the presence of trapping. The diffusion regimes are determined and the corresponding physical processes are identified.

  10. Stochastic algorithm for size-extensive vibrational self-consistent field methods on fully anharmonic potential energy surfaces.

    PubMed

    Hermes, Matthew R; Hirata, So

    2014-12-28

    A stochastic algorithm based on Metropolis Monte Carlo (MC) is presented for the size-extensive vibrational self-consistent field methods (XVSCF(n) and XVSCF[n]) for anharmonic molecular vibrations. The new MC-XVSCF methods substitute stochastic evaluations of a small number of high-dimensional integrals of functions of the potential energy surface (PES), which is sampled on demand, for diagrammatic equations involving high-order anharmonic force constants. This algorithm obviates the need to evaluate and store any high-dimensional partial derivatives of the potential and can be applied to the fully anharmonic PES without any Taylor-series approximation in an intrinsically parallelizable algorithm. The MC-XVSCF methods reproduce deterministic XVSCF calculations on the same Taylor-series PES in all energies, frequencies, and geometries. Calculations using the fully anharmonic PES evaluated on the fly with electronic structure methods report anharmonic effects on frequencies and geometries of much greater magnitude than deterministic XVSCF calculations, reflecting an underestimation of anharmonic effects in a Taylor-series approximation to the PES. PMID:25554137

  11. A hybrid meta-heuristic algorithm for the vehicle routing problem with stochastic travel times considering the driver's satisfaction

    NASA Astrophysics Data System (ADS)

    Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges

    2012-05-01

    A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.

  12. Stochastic algorithm for size-extensive vibrational self-consistent field methods on fully anharmonic potential energy surfaces

    SciTech Connect

    Hermes, Matthew R.; Hirata, So

    2014-12-28

    A stochastic algorithm based on Metropolis Monte Carlo (MC) is presented for the size-extensive vibrational self-consistent field methods (XVSCF(n) and XVSCF[n]) for anharmonic molecular vibrations. The new MC-XVSCF methods substitute stochastic evaluations of a small number of high-dimensional integrals of functions of the potential energy surface (PES), which is sampled on demand, for diagrammatic equations involving high-order anharmonic force constants. This algorithm obviates the need to evaluate and store any high-dimensional partial derivatives of the potential and can be applied to the fully anharmonic PES without any Taylor-series approximation in an intrinsically parallelizable algorithm. The MC-XVSCF methods reproduce deterministic XVSCF calculations on the same Taylor-series PES in all energies, frequencies, and geometries. Calculations using the fully anharmonic PES evaluated on the fly with electronic structure methods report anharmonic effects on frequencies and geometries of much greater magnitude than deterministic XVSCF calculations, reflecting an underestimation of anharmonic effects in a Taylor-series approximation to the PES.

  13. Stochastic nonlinear wave equation with memory driven by compensated Poisson random measures

    SciTech Connect

    Liang, Fei; Department of Mathematics, Xi An University of Science and Technology, Xi An 710054 ; Gao, Hongjun

    2014-03-15

    In this paper, we study a class of stochastic nonlinear wave equation with memory driven by Lévy noise. We first show the existence and uniqueness of global mild solutions using a suitable energy function. Second, under some additional assumptions we prove the exponential stability of the solutions.

  14. A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters: Theory and application to microfilaments

    SciTech Connect

    Li, Tong; Gu, YuanTong

    2014-04-15

    As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

  15. Space resection model calculation based on Random Sample Consensus algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Xinzhu; Kang, Zhizhong

    2016-03-01

    Resection has been one of the most important content in photogrammetry. It aims at the position and attitude information of camera at the shooting point. However in some cases, the observed values for calculating are with gross errors. This paper presents a robust algorithm that using RANSAC method with DLT model can effectually avoiding the difficulties to determine initial values when using co-linear equation. The results also show that our strategies can exclude crude handicap and lead to an accurate and efficient way to gain elements of exterior orientation.

  16. Probability-dependent H ∞ filtering for nonlinear stochastic systems with missing measurements and randomly occurring communication delays

    NASA Astrophysics Data System (ADS)

    Che, Yan; Shu, Huisheng; Yang, Hua; Ding, Derui

    2013-07-01

    In this article, the H ∞ filtering problem is investigated for a class of nonlinear stochastic systems with incomplete measurements. The considered incomplete measurements include both the missing measurements and the randomly occurring communication delays. By using a set of Kronecker delta functions, a unified measurement model is employed to describe the phenomena of random communication delays and missing measurements. The purpose of the problem addressed is to design an H ∞ filter such that, for all nonlinearities, incomplete measurements and external disturbances, the filtering error dynamics is exponentially mean-square stable and the H ∞-norm requirement is satisfied. A sufficient condition for the existence of the desired filter is established in terms of certain linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed filter scheme.

  17. Comparing three stochastic search algorithms for computational protein design: Monte Carlo, replica exchange Monte Carlo, and a multistart, steepest-descent heuristic.

    PubMed

    Mignon, David; Simonson, Thomas

    2016-07-15

    Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc. PMID:27197555

  18. Evaluation of Monte Carlo Electron-Transport Algorithms in the Integrated Tiger Series Codes for Stochastic-Media Simulations

    NASA Astrophysics Data System (ADS)

    Franke, Brian C.; Kensek, Ronald P.; Prinja, Anil K.

    2014-06-01

    Stochastic-media simulations require numerous boundary crossings. We consider two Monte Carlo electron transport approaches and evaluate accuracy with numerous material boundaries. In the condensed-history method, approximations are made based on infinite-medium solutions for multiple scattering over some track length. Typically, further approximations are employed for material-boundary crossings where infinite-medium solutions become invalid. We have previously explored an alternative "condensed transport" formulation, a Generalized Boltzmann-Fokker-Planck GBFP method, which requires no special boundary treatment but instead uses approximations to the electron-scattering cross sections. Some limited capabilities for analog transport and a GBFP method have been implemented in the Integrated Tiger Series (ITS) codes. Improvements have been made to the condensed history algorithm. The performance of the ITS condensed-history and condensed-transport algorithms are assessed for material-boundary crossings. These assessments are made both by introducing artificial material boundaries and by comparison to analog Monte Carlo simulations.

  19. Exact Mapping of the Stochastic Field Theory for Manna Sandpiles to Interfaces in Random Media

    NASA Astrophysics Data System (ADS)

    Le Doussal, Pierre; Wiese, Kay Jörg

    2015-03-01

    We show that the stochastic field theory for directed percolation in the presence of an additional conservation law [the conserved directed-percolation (C-DP) class] can be mapped exactly to the continuum theory for the depinning of an elastic interface in short-range correlated quenched disorder. Along one line of the parameters commonly studied, this mapping leads to the simplest overdamped dynamics. Away from this line, an additional memory term arises in the interface dynamics; we argue that this does not change the universality class. Since C-DP is believed to describe the Manna class of self-organized criticality, this shows that Manna stochastic sandpiles and disordered elastic interfaces (i.e., the quenched Edwards-Wilkinson model) share the same universal large-scale behavior.

  20. Exact mapping of the stochastic field theory for Manna sandpiles to interfaces in random media.

    PubMed

    Le Doussal, Pierre; Wiese, Kay Jörg

    2015-03-20

    We show that the stochastic field theory for directed percolation in the presence of an additional conservation law [the conserved directed-percolation (C-DP) class] can be mapped exactly to the continuum theory for the depinning of an elastic interface in short-range correlated quenched disorder. Along one line of the parameters commonly studied, this mapping leads to the simplest overdamped dynamics. Away from this line, an additional memory term arises in the interface dynamics; we argue that this does not change the universality class. Since C-DP is believed to describe the Manna class of self-organized criticality, this shows that Manna stochastic sandpiles and disordered elastic interfaces (i.e., the quenched Edwards-Wilkinson model) share the same universal large-scale behavior. PMID:25839253

  1. Can stochastic, dissipative wave fields be treated as random walk generators

    NASA Technical Reports Server (NTRS)

    Weinstock, J.

    1986-01-01

    A suggestion by Meek et al. (1985) that the gravity wave field be viewed as stochastic, with significant nonlinearities, is applied to calculate diffusivities. The purpose here is to calculate the diffusivity for stochastic wave model and compare it with previous diffusivity estimates. The researchers do this for an idealized case in which the wind velocity changes but slowly, and for which saturation is the principal mechanism by which wave energy is lost. A related calculation was given in a very brief way (Weinstock, 1976), but the approximations were not fully justified, nor were the physical pre-suppositions clearly explained. The observations of Meek et al. (1985) have clarified the pre-suppositions for the researchers and provided a rationalization and improvement of the approximations employed.

  2. Introducing Stochastic Simulation of Chemical Reactions Using the Gillespie Algorithm and MATLAB: Revisited and Augmented

    ERIC Educational Resources Information Center

    Argoti, A.; Fan, L. T.; Cruz, J.; Chou, S. T.

    2008-01-01

    The stochastic simulation of chemical reactions, specifically, a simple reversible chemical reaction obeying the first-order, i.e., linear, rate law, has been presented by Martinez-Urreaga and his collaborators in this journal. The current contribution is intended to complement and augment their work in two aspects. First, the simple reversible…

  3. Fault detection of aircraft system with random forest algorithm and similarity measure.

    PubMed

    Lee, Sanghyuk; Park, Wookje; Jung, Sikhang

    2014-01-01

    Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

  4. An efficient algorithm for generating random number pairs drawn from a bivariate normal distribution

    NASA Technical Reports Server (NTRS)

    Campbell, C. W.

    1983-01-01

    An efficient algorithm for generating random number pairs from a bivariate normal distribution was developed. Any desired value of the two means, two standard deviations, and correlation coefficient can be selected. Theoretically the technique is exact and in practice its accuracy is limited only by the quality of the uniform distribution random number generator, inaccuracies in computer function evaluation, and arithmetic. A FORTRAN routine was written to check the algorithm and good accuracy was obtained. Some small errors in the correlation coefficient were observed to vary in a surprisingly regular manner. A simple model was developed which explained the qualities aspects of the errors.

  5. Experimental implementation of a quantum random-walk search algorithm using strongly dipolar coupled spins

    SciTech Connect

    Lu Dawei; Peng Xinhua; Du Jiangfeng; Zhu Jing; Zou Ping; Yu Yihua; Zhang Shanmin; Chen Qun

    2010-02-15

    An important quantum search algorithm based on the quantum random walk performs an oracle search on a database of N items with O({radical}(phN)) calls, yielding a speedup similar to the Grover quantum search algorithm. The algorithm was implemented on a quantum information processor of three-qubit liquid-crystal nuclear magnetic resonance (NMR) in the case of finding 1 out of 4, and the diagonal elements' tomography of all the final density matrices was completed with comprehensible one-dimensional NMR spectra. The experimental results agree well with the theoretical predictions.

  6. Experimental implementation of a quantum random-walk search algorithm using strongly dipolar coupled spins

    NASA Astrophysics Data System (ADS)

    Lu, Dawei; Zhu, Jing; Zou, Ping; Peng, Xinhua; Yu, Yihua; Zhang, Shanmin; Chen, Qun; Du, Jiangfeng

    2010-02-01

    An important quantum search algorithm based on the quantum random walk performs an oracle search on a database of N items with O(phN) calls, yielding a speedup similar to the Grover quantum search algorithm. The algorithm was implemented on a quantum information processor of three-qubit liquid-crystal nuclear magnetic resonance (NMR) in the case of finding 1 out of 4, and the diagonal elements’ tomography of all the final density matrices was completed with comprehensible one-dimensional NMR spectra. The experimental results agree well with the theoretical predictions.

  7. Study on 2D random medium inversion algorithm based on Fuzzy C-means Clustering theory

    NASA Astrophysics Data System (ADS)

    Xu, Z.; Zhu, P.; Gu, Y.; Yang, X.; Jiang, J.

    2015-12-01

    Abstract: In seismic exploration for metal deposits, the traditional seismic inversion method based on layered homogeneous medium theory seems difficult to inverse small scale inhomogeneity and spatial variation of the actual medium. The reason is that physical properties of actual medium are more likely random distribution rather than layered. Thus, it is necessary to investigate a random medium inversion algorithm. The velocity of 2D random medium can be described as a function of five parameters: the background velocity (V0), the standard deviation of velocity (σ), the horizontal and vertical autocorrelation lengths (A and B), and the autocorrelation angle (θ). In this study, we propose an inversion algorithm for random medium based on the Fuzzy C-means Clustering (FCM) theory, whose basic idea is that FCM is used to control the inversion process to move forward to the direction we desired by clustering the estimated parameters into groups. Our method can be divided into three steps: firstly, the three parameters (A, B, θ) are estimated from 2D post-stack seismic data using the non-stationary random medium parameter estimation method, and then the estimated parameters are clustered to different groups according to FCM; secondly, the initial random medium model is constructed with clustered groups and the rest two parameters (V0 and σ) obtained from the well logging data; at last, inversion of the random medium are conducted to obtain velocity, impedance and random medium parameters using the Conjugate Gradient Method. The inversion experiments of synthetic seismic data show that the velocity models inverted by our algorithm are close to the real velocity distribution and the boundary of different media can be distinguished clearly.Key words: random medium, inversion, FCM, parameter estimation

  8. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

    PubMed

    Polan, Daniel F; Brady, Samuel L; Kaufman, Robert A

    2016-09-01

    There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 (n) , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21

  9. Polarization of an electromagnetic wave in a randomly birefringent medium: A stochastic theory of the Stokes parameters

    SciTech Connect

    Botet, Robert; Kuratsuji, Hiroshi

    2010-03-15

    We present a framework for the stochastic features of the polarization state of an electromagnetic wave propagating through the optical medium with both deterministic (controlled) and disordered birefringence. In this case, the Stokes parameters obey a Langevin-type equation on the Poincare sphere. The functional integral method provides for a natural tool to derive the Fokker-Planck equation for the probability distribution of the Stokes parameters. We solve the Fokker-Planck equation in the case of a random anisotropic active medium submitted to a homogeneous electromagnetic field. The possible dissipation and relaxation phenomena are studied in general and in various cases, and we give hints about how to validate experimentally the corresponding phenomenological equations.

  10. An iterative algorithm for analysis of coupled structural-acoustic systems subject to random excitations

    NASA Astrophysics Data System (ADS)

    Zhao, Guo-Zhong; Chen, Gang; Kang, Zhan

    2012-04-01

    This paper analyzes the random response of structural-acoustic coupled systems. Most existing works on coupled structural-acoustic analysis are limited to systems under deterministic excitations due to high computational cost required by a random response analysis. To reduce the computational burden involved in the coupled random analysis, an iterative procedure based on the Pseudo excitation method has been developed. It is found that this algorithm has an overwhelming advantage in computing efficiency over traditional methods, as demonstrated by some numerical examples given in this paper.

  11. Algorithms for Performance, Dependability, and Performability Evaluation using Stochastic Activity Networks

    NASA Technical Reports Server (NTRS)

    Deavours, Daniel D.; Qureshi, M. Akber; Sanders, William H.

    1997-01-01

    Modeling tools and technologies are important for aerospace development. At the University of Illinois, we have worked on advancing the state of the art in modeling by Markov reward models in two important areas: reducing the memory necessary to numerically solve systems represented as stochastic activity networks and other stochastic Petri net extensions while still obtaining solutions in a reasonable amount of time, and finding numerically stable and memory-efficient methods to solve for the reward accumulated during a finite mission time. A long standing problem when modeling with high level formalisms such as stochastic activity networks is the so-called state space explosion, where the number of states increases exponentially with size of the high level model. Thus, the corresponding Markov model becomes prohibitively large and solution is constrained by the the size of primary memory. To reduce the memory necessary to numerically solve complex systems, we propose new methods that can tolerate such large state spaces that do not require any special structure in the model (as many other techniques do). First, we develop methods that generate row and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix, permitting us to cache portions of the matrix and hence reduce the solution time. Finally, we develop a new memory and computationally efficient technique for Gauss-Seidel based solvers that avoids the need for generating rows of A in order to solve Ax = b. This is a significant performance improvement for on-the-fly methods as well as other recent solution techniques based on Kronecker operators. Taken together, these new results show that one can solve very large models without any special structure.

  12. Neural network based adaptive control of nonlinear plants using random search optimization algorithms

    NASA Technical Reports Server (NTRS)

    Boussalis, Dhemetrios; Wang, Shyh J.

    1992-01-01

    This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

  13. Beyond the random coil: stochastic conformational switching in intrinsically disordered proteins.

    PubMed

    Choi, Ucheor B; McCann, James J; Weninger, Keith R; Bowen, Mark E

    2011-04-13

    Intrinsically disordered proteins (IDPs) participate in critical cellular functions that exploit the flexibility and rapid conformational fluctuations of their native state. Limited information about the native state of IDPs can be gained by the averaging over many heterogeneous molecules that is unavoidable in ensemble approaches. We used single molecule fluorescence to characterize native state conformational dynamics in five synaptic proteins confirmed to be disordered by other techniques. For three of the proteins, SNAP-25, synaptobrevin and complexin, their conformational dynamics could be described with a simple semiflexible polymer model. Surprisingly, two proteins, neuroligin and the NMDAR-2B glutamate receptor, were observed to stochastically switch among distinct conformational states despite the fact that they appeared intrinsically disordered by other measures. The hop-like intramolecular diffusion found in these proteins is suggested to define a class of functionality previously unrecognized for IDPs. PMID:21481779

  14. Solving the chemical master equation by a fast adaptive finite state projection based on the stochastic simulation algorithm.

    PubMed

    Sidje, R B; Vo, H D

    2015-11-01

    The mathematical framework of the chemical master equation (CME) uses a Markov chain to model the biochemical reactions that are taking place within a biological cell. Computing the transient probability distribution of this Markov chain allows us to track the composition of molecules inside the cell over time, with important practical applications in a number of areas such as molecular biology or medicine. However the CME is typically difficult to solve, since the state space involved can be very large or even countably infinite. We present a novel way of using the stochastic simulation algorithm (SSA) to reduce the size of the finite state projection (FSP) method. Numerical experiments that demonstrate the effectiveness of the reduction are included. PMID:26319118

  15. An accelerated algorithm for discrete stochastic simulation of reaction–diffusion systems using gradient-based diffusion and tau-leaping

    PubMed Central

    Koh, Wonryull; Blackwell, Kim T.

    2011-01-01

    Stochastic simulation of reaction–diffusion systems enables the investigation of stochastic events arising from the small numbers and heterogeneous distribution of molecular species in biological cells. Stochastic variations in intracellular microdomains and in diffusional gradients play a significant part in the spatiotemporal activity and behavior of cells. Although an exact stochastic simulation that simulates every individual reaction and diffusion event gives a most accurate trajectory of the system's state over time, it can be too slow for many practical applications. We present an accelerated algorithm for discrete stochastic simulation of reaction–diffusion systems designed to improve the speed of simulation by reducing the number of time-steps required to complete a simulation run. This method is unique in that it employs two strategies that have not been incorporated in existing spatial stochastic simulation algorithms. First, diffusive transfers between neighboring subvolumes are based on concentration gradients. This treatment necessitates sampling of only the net or observed diffusion events from higher to lower concentration gradients rather than sampling all diffusion events regardless of local concentration gradients. Second, we extend the non-negative Poisson tau-leaping method that was originally developed for speeding up nonspatial or homogeneous stochastic simulation algorithms. This method calculates each leap time in a unified step for both reaction and diffusion processes while satisfying the leap condition that the propensities do not change appreciably during the leap and ensuring that leaping does not cause molecular populations to become negative. Numerical results are presented that illustrate the improvement in simulation speed achieved by incorporating these two new strategies. PMID:21513371

  16. High-resolution climate data over conterminous US using random forest algorithm

    NASA Astrophysics Data System (ADS)

    Hashimoto, H.; Nemani, R. R.; Wang, W.

    2014-12-01

    We developed a new methodology to create high-resolution precipitation data using the random forest algorithm. We have used two approaches: physical downscaling from GCM data using a regional climate model, and interpolation from ground observation data. Physical downscaling method can be applied only for a small region because it is computationally expensive and complex to deploy. On the other hand, interpolation schemes from ground observations do not consider physical processes. In this study, we utilized the random forest algorithm to integrate atmospheric reanalysis data, satellite data, topography data, and ground observation data. First we considered situations where precipitation is same across the domain, largely dominated by storm like systems. We then picked several points to train random forest algorithm. The random forest algorithm estimates out-of-bag errors spatially, and produces the relative importance of each of the input variable.This methodology has the following advantages. (1) The methodology can ingest any spatial dataset to improve downscaling. Even non-precipitation datasets can be ingested such as satellite cloud cover data, radar reflectivity image, or modeled convective available potential energy. (2) The methodology is purely statistical so that physical assumptions are not required. Meanwhile, most of interpolation schemes assume empirical relationship between precipitation and elevation for orographic precipitation. (3) Low quality value in ingested data does not cause critical bias in the results because of the ensemble feature of random forest. Therefore, users do not need to pay a special attention to quality control of input data compared to other interpolation methodologies. (4) Same methodology can be applied to produce other high-resolution climate datasets, such as wind and cloud cover. Those variables are usually hard to be interpolated by conventional algorithms. In conclusion, the proposed methodology can produce reasonable

  17. Magnetic localization and orientation of the capsule endoscope based on a random complex algorithm

    PubMed Central

    He, Xiaoqi; Zheng, Zizhao; Hu, Chao

    2015-01-01

    The development of the capsule endoscope has made possible the examination of the whole gastrointestinal tract without much pain. However, there are still some important problems to be solved, among which, one important problem is the localization of the capsule. Currently, magnetic positioning technology is a suitable method for capsule localization, and this depends on a reliable system and algorithm. In this paper, based on the magnetic dipole model as well as magnetic sensor array, we propose nonlinear optimization algorithms using a random complex algorithm, applied to the optimization calculation for the nonlinear function of the dipole, to determine the three-dimensional position parameters and two-dimensional direction parameters. The stability and the antinoise ability of the algorithm is compared with the Levenberg–Marquart algorithm. The simulation and experiment results show that in terms of the error level of the initial guess of magnet location, the random complex algorithm is more accurate, more stable, and has a higher “denoise” capacity, with a larger range for initial guess values. PMID:25914561

  18. A random fatigue of mechanize titanium abutment studied with Markoff chain and stochastic finite element formulation.

    PubMed

    Prados-Privado, María; Prados-Frutos, Juan Carlos; Calvo-Guirado, José Luis; Bea, José Antonio

    2016-11-01

    To measure fatigue in dental implants and in its components, it is necessary to use a probabilistic analysis since the randomness in the output depends on a number of parameters (such as fatigue properties of titanium and applied loads, unknown beforehand as they depend on mastication habits). The purpose is to apply a probabilistic approximation in order to predict fatigue life, taking into account the randomness of variables. More accuracy on the results has been obtained by taking into account different load blocks with different amplitudes, as happens with bite forces during the day and allowing us to know how effects have different type of bruxism on the piece analysed. PMID:27073012

  19. A preliminary numerical evaluation of a parallel algorithm for approximating the values and subgradients of the recourse function in a stochastic program with complete recourse

    SciTech Connect

    Lessor, K.S.

    1988-08-26

    The parallel algorithm of Ariyawansa, Sorensen, and Wets for approximating the values and subgradients of the recourse function in a stochastic program with complete recourse is implemented and timing results are reported for limited experimental trials. 14 refs., 6 figs., 8 tabs.

  20. Development of Semi-Stochastic Algorithm for Optimizing Alloy Composition of High-Temperature Austenitic Stainless Steels (H-Series) for Desired Mechanical and Corrosion Properties.

    SciTech Connect

    Dulikravich, George S.; Sikka, Vinod K.; Muralidharan, G.

    2006-06-01

    The goal of this project was to adapt and use an advanced semi-stochastic algorithm for constrained multiobjective optimization and combine it with experimental testing and verification to determine optimum concentrations of alloying elements in heat-resistant and corrosion-resistant H-series stainless steel alloys that will simultaneously maximize a number of alloy's mechanical and corrosion properties.

  1. A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

    PubMed

    Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

    2015-01-01

    Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations. PMID:26405895

  2. Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions.

    PubMed

    de Andrade, Carlos Eduardo; Toso, Rodrigo Franco; Resende, Mauricio G C; Miyazawa, Flávio Keidi

    2015-01-01

    In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions. PMID:25299242

  3. Autoclassification of the Variable 3XMM Sources Using the Random Forest Machine Learning Algorithm

    NASA Astrophysics Data System (ADS)

    Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

    2015-11-01

    In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

  4. Evaluation of stochastic algorithms for financial mathematics problems from point of view of energy-efficiency

    NASA Astrophysics Data System (ADS)

    Atanassov, E.; Dimitrov, D.; Gurov, T.

    2015-10-01

    The recent developments in the area of high-performance computing are driven not only by the desire for ever higher performance but also by the rising costs of electricity. The use of various types of accelerators like GPUs, Intel Xeon Phi has become mainstream and many algorithms and applications have been ported to make use of them where available. In Financial Mathematics the question of optimal use of computational resources should also take into account the limitations on space, because in many use cases the servers are deployed close to the exchanges. In this work we evaluate various algorithms for option pricing that we have implemented for different target architectures in terms of their energy and space efficiency. Since it has been established that low-discrepancy sequences may be better than pseudorandom numbers for these types of algorithms, we also test the Sobol and Halton sequences. We present the raw results, the computed metrics and conclusions from our tests.

  5. Evaluation of stochastic algorithms for financial mathematics problems from point of view of energy-efficiency

    SciTech Connect

    Atanassov, E.; Dimitrov, D. E-mail: emanouil@parallel.bas.bg Gurov, T.

    2015-10-28

    The recent developments in the area of high-performance computing are driven not only by the desire for ever higher performance but also by the rising costs of electricity. The use of various types of accelerators like GPUs, Intel Xeon Phi has become mainstream and many algorithms and applications have been ported to make use of them where available. In Financial Mathematics the question of optimal use of computational resources should also take into account the limitations on space, because in many use cases the servers are deployed close to the exchanges. In this work we evaluate various algorithms for option pricing that we have implemented for different target architectures in terms of their energy and space efficiency. Since it has been established that low-discrepancy sequences may be better than pseudorandom numbers for these types of algorithms, we also test the Sobol and Halton sequences. We present the raw results, the computed metrics and conclusions from our tests.

  6. Effect of the driving algorithm on the turbulence generated by a random jet array

    NASA Astrophysics Data System (ADS)

    Pérez-Alvarado, Alejandro; Mydlarski, Laurent; Gaskin, Susan

    2016-02-01

    Different driving algorithms for a large random jet array (RJA) were tested and their performance characterized by comparing the statistics of the turbulence generated downstream of the RJA. Of particular interest was the spatial configuration of the jets operating at any given instant (an aspect that has not been documented in previous RJAs studies), as well as the statistics of their respective on/off times. All algorithms generated flows with nonzero skewnesses of the velocity fluctuation normal to the plane of the RJA (identified as an inherent limitation of the system resulting from the unidirectional forcing imposed from only one side of the RJA), and slightly super-Gaussian kurtoses of the velocity fluctuations in all directions. It was observed that algorithms imposing spatial configurations generated the most isotropic flows; however, they suffered from high mean flows and low turbulent kinetic energies. The algorithm identified as RANDOM (also referred to as the "sunbathing algorithm") generated the flow that, on an overall basis, most closely approximated zero-mean-flow homogeneous isotropic turbulence, with variations in horizontal and vertical homogeneities of RMS velocities of no more than ±6 %, deviations from isotropy ( w RMS/ u RMS) in the range of 0.62-0.77, and mean flows on the order of 7 % of the RMS velocities (determined by averaging their absolute values over the three velocity components and three downstream distances). A relatively high turbulent Reynolds number ( Re T = u T ℓ/ ν = 2360, where ℓ is the integral length scale of the flow and u T is a characteristic RMS velocity) was achieved using the RANDOM algorithm and the integral length scale ( ℓ = 11.5 cm) is the largest reported to date. The quality of the turbulence in our large facility demonstrates the ability of RJAs to be scaled-up and to be the laboratory system most capable of generating the largest quasi-homogeneous isotropic turbulent regions with zero mean flow.

  7. Fast randomized Hough transformation track initiation algorithm based on multi-scale clustering

    NASA Astrophysics Data System (ADS)

    Wan, Minjie; Gu, Guohua; Chen, Qian; Qian, Weixian; Wang, Pengcheng

    2015-10-01

    A fast randomized Hough transformation track initiation algorithm based on multi-scale clustering is proposed to overcome existing problems in traditional infrared search and track system(IRST) which cannot provide movement information of the initial target and select the threshold value of correlation automatically by a two-dimensional track association algorithm based on bearing-only information . Movements of all the targets are presumed to be uniform rectilinear motion throughout this new algorithm. Concepts of space random sampling, parameter space dynamic linking table and convergent mapping of image to parameter space are developed on the basis of fast randomized Hough transformation. Considering the phenomenon of peak value clustering due to shortcomings of peak detection itself which is built on threshold value method, accuracy can only be ensured on condition that parameter space has an obvious peak value. A multi-scale idea is added to the above-mentioned algorithm. Firstly, a primary association is conducted to select several alternative tracks by a low-threshold .Then, alternative tracks are processed by multi-scale clustering methods , through which accurate numbers and parameters of tracks are figured out automatically by means of transforming scale parameters. The first three frames are processed by this algorithm in order to get the first three targets of the track , and then two slightly different gate radius are worked out , mean value of which is used to be the global threshold value of correlation. Moreover, a new model for curvilinear equation correction is applied to the above-mentioned track initiation algorithm for purpose of solving the problem of shape distortion when a space three-dimensional curve is mapped to a two-dimensional bearing-only space. Using sideways-flying, launch and landing as examples to build models and simulate, the application of the proposed approach in simulation proves its effectiveness , accuracy , and adaptivity

  8. Nonconvergence of the Wang-Landau algorithms with multiple random walkers

    NASA Astrophysics Data System (ADS)

    Belardinelli, R. E.; Pereyra, V. D.

    2016-05-01

    This paper discusses some convergence properties in the entropic sampling Monte Carlo methods with multiple random walkers, particularly in the Wang-Landau (WL) and 1 /t algorithms. The classical algorithms are modified by the use of m -independent random walkers in the energy landscape to calculate the density of states (DOS). The Ising model is used to show the convergence properties in the calculation of the DOS, as well as the critical temperature, while the calculation of the number π by multiple dimensional integration is used in the continuum approximation. In each case, the error is obtained separately for each walker at a fixed time, t ; then, the average over m walkers is performed. It is observed that the error goes as 1 /√{m } . However, if the number of walkers increases above a certain critical value m >mx , the error reaches a constant value (i.e., it saturates). This occurs for both algorithms; however, it is shown that for a given system, the 1 /t algorithm is more efficient and accurate than the similar version of the WL algorithm. It follows that it makes no sense to increase the number of walkers above a critical value mx, since it does not reduce the error in the calculation. Therefore, the number of walkers does not guarantee convergence.

  9. Mathematical algorithm development and parametric studies with the GEOFRAC three-dimensional stochastic model of natural rock fracture systems

    NASA Astrophysics Data System (ADS)

    Ivanova, Violeta M.; Sousa, Rita; Murrihy, Brian; Einstein, Herbert H.

    2014-06-01

    This paper presents results from research conducted at MIT during 2010-2012 on modeling of natural rock fracture systems with the GEOFRAC three-dimensional stochastic model. Following a background summary of discrete fracture network models and a brief introduction of GEOFRAC, the paper provides a thorough description of the newly developed mathematical and computer algorithms for fracture intensity, aperture, and intersection representation, which have been implemented in MATLAB. The new methods optimize, in particular, the representation of fracture intensity in terms of cumulative fracture area per unit volume, P32, via the Poisson-Voronoi Tessellation of planes into polygonal fracture shapes. In addition, fracture apertures now can be represented probabilistically or deterministically whereas the newly implemented intersection algorithms allow for computing discrete pathways of interconnected fractures. In conclusion, results from a statistical parametric study, which was conducted with the enhanced GEOFRAC model and the new MATLAB-based Monte Carlo simulation program FRACSIM, demonstrate how fracture intensity, size, and orientations influence fracture connectivity.

  10. A Stochastic, Resonance-Free Multiple Time-Step Algorithm for Polarizable Models That Permits Very Large Time Steps.

    PubMed

    Margul, Daniel T; Tuckerman, Mark E

    2016-05-10

    Molecular dynamics remains one of the most widely used computational tools in the theoretical molecular sciences to sample an equilibrium ensemble distribution and/or to study the dynamical properties of a system. The efficiency of a molecular dynamics calculation is limited by the size of the time step that can be employed, which is dictated by the highest frequencies in the system. However, many properties of interest are connected to low-frequency, long time-scale phenomena, requiring many small time steps to capture. This ubiquitous problem can be ameliorated by employing multiple time-step algorithms, which assign different time steps to forces acting on different time scales. In such a scheme, fast forces are evaluated more frequently than slow forces, and as the former are often computationally much cheaper to evaluate, the savings can be significant. Standard multiple time-step approaches are limited, however, by resonance phenomena, wherein motion on the fastest time scales limits the step sizes that can be chosen for the slower time scales. In atomistic models of biomolecular systems, for example, the largest time step is typically limited to around 5 fs. Previously, we introduced an isokinetic extended phase-space algorithm (Minary et al. Phys. Rev. Lett. 2004, 93, 150201) and its stochastic analog (Leimkuhler et al. Mol. Phys. 2013, 111, 3579) that eliminate resonance phenomena through a set of kinetic energy constraints. In simulations of a fixed-charge flexible model of liquid water, for example, the time step that could be assigned to the slow forces approached 100 fs. In this paper, we develop a stochastic isokinetic algorithm for multiple time-step molecular dynamics calculations using a polarizable model based on fluctuating dipoles. The scheme developed here employs two sets of induced dipole moments, specifically, those associated with short-range interactions and those associated with a full set of interactions. The scheme is demonstrated on

  11. A Stochastic Algorithm for Generating Realistic Virtual Interstitial Cell of Cajal Networks.

    PubMed

    Gao, Jerry; Sathar, Shameer; O'Grady, Gregory; Archer, Rosalind; Cheng, Leo K

    2015-08-01

    Interstitial cells of Cajal (ICC) play a central role in coordinating normal gastrointestinal (GI) motility. Depletion of ICC numbers and network integrity contributes to major functional GI motility disorders. However, the mechanisms relating ICC structure to GI function and dysfunction remains unclear, partly because there is a lack of large-scale ICC network imaging data across a spectrum of depletion levels to guide models. Experimental imaging of these large-scale networks remains challenging because of technical constraints, and hence, we propose the generation of realistic virtual ICC networks in silico using the single normal equation simulation (SNESIM) algorithm. ICC network imaging data obtained from wild-type (normal) and 5-HT2B serotonin receptor knockout (depleted ICC) mice were used to inform the algorithm, and the virtual networks generated were assessed using ICC network structural metrics and biophysically-based computational modeling. When the virtual networks were compared to the original networks, there was less than 10% error for four out of five structural metrics and all four functional measures. The SNESIM algorithm was then modified to enable the generation of ICC networks across a spectrum of depletion levels, and as a proof-of-concept, virtual networks were successfully generated with a range of structural and functional properties. The SNESIM and modified SNESIM algorithms, therefore, offer an alternative strategy for obtaining the large-scale ICC network imaging data across a spectrum of depletion levels. These models can be applied to accurately inform the physiological consequences of ICC depletion. PMID:25781477

  12. Parrallel Implementation of Fast Randomized Algorithms for Low Rank Matrix Decomposition

    SciTech Connect

    Lucas, Andrew J.; Stalizer, Mark; Feo, John T.

    2014-03-01

    We analyze the parallel performance of randomized interpolative decomposition by de- composing low rank complex-valued Gaussian random matrices larger than 100 GB. We chose a Cray XMT supercomputer as it provides an almost ideal PRAM model permitting quick investigation of parallel algorithms without obfuscation from hardware idiosyncrasies. We obtain that on non-square matrices performance scales almost linearly with runtime about 100 times faster on 128 processors. We also verify that numerically discovered error bounds still hold on matrices two orders of magnitude larger than those previously tested.

  13. Rotorcraft Blade Mode Damping Identification from Random Responses Using a Recursive Maximum Likelihood Algorithm

    NASA Technical Reports Server (NTRS)

    Molusis, J. A.

    1982-01-01

    An on line technique is presented for the identification of rotor blade modal damping and frequency from rotorcraft random response test data. The identification technique is based upon a recursive maximum likelihood (RML) algorithm, which is demonstrated to have excellent convergence characteristics in the presence of random measurement noise and random excitation. The RML technique requires virtually no user interaction, provides accurate confidence bands on the parameter estimates, and can be used for continuous monitoring of modal damping during wind tunnel or flight testing. Results are presented from simulation random response data which quantify the identified parameter convergence behavior for various levels of random excitation. The data length required for acceptable parameter accuracy is shown to depend upon the amplitude of random response and the modal damping level. Random response amplitudes of 1.25 degrees to .05 degrees are investigated. The RML technique is applied to hingeless rotor test data. The inplane lag regressing mode is identified at different rotor speeds. The identification from the test data is compared with the simulation results and with other available estimates of frequency and damping.

  14. Bad News Comes in Threes: Stochastic Structure in Random Events (Invited)

    NASA Astrophysics Data System (ADS)

    Newman, W. I.; Turcotte, D. L.; Malamud, B. D.

    2013-12-01

    Plots of random numbers have been known for nearly a century to show repetitive peak-to-peak sequences with an average length of 3. Geophysical examples include events such as earthquakes, geyser eruptions, and magnetic substorms. We consider a classic model in statistical physics, the Langevin equation x[n+1] = α*x[n] + η[n], where x[n] is the nth value of a measured quantity and η[n] is a random number, commonly a Gaussian white noise. Here, α is a parameter that ranges from 0, corresponding to independent random data, to 1, corresponding to Brownian motion which preserves memory of past steps. We show that, for α = 0, the mean peak-to-peak sequence length is 3 while, for α = 1, the mean sequence length is 4. We obtain the physical and mathematical properties of this model, including the distribution of peak-to-peak sequence lengths that can be expected. We compare the theory with observations of earthquake magnitudes emerging from large events, observations of the auroral electrojet index as a measure of global electrojet activity, and time intervals observed between successive eruptions of Old Faithful Geyser in Yellowstone National Park. We demonstrate that the largest earthquake events as described by their magnitudes are consistent with our theory for α = 0, thereby confronting the aphorism (and our analytic theory) that "bad news comes in threes." Electrojet activity, on the other hand, demonstrates some memory effects, consistent with the intuitive picture of the magnetosphere presenting a capacitor-plate like system that preserves memory. Old Faithful Geyser, finally, shows strong antipersistence effects between successive events, i.e. long-time intervals are followed by short ones, and vice versa. As an additional application, we apply our theory to the observed 3-4 year mammalian population cycles.

  15. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

    PubMed Central

    Nguyen, Lam-Tung; Schmidt, Heiko A.; von Haeseler, Arndt; Minh, Bui Quang

    2015-01-01

    Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3–97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree. PMID:25371430

  16. Enhancing network robustness against targeted and random attacks using a memetic algorithm

    NASA Astrophysics Data System (ADS)

    Tang, Xianglong; Liu, Jing; Zhou, Mingxing

    2015-08-01

    In the past decades, there has been much interest in the elasticity of infrastructures to targeted and random attacks. In the recent work by Schneider C. M. et al., Proc. Natl. Acad. Sci. U.S.A., 108 (2011) 3838, the authors proposed an effective measure (namely R, here we label it as R t to represent the measure for targeted attacks) to evaluate network robustness against targeted node attacks. Using a greedy algorithm, they found that the optimal structure is an onion-like one. However, real systems are often under threats of both targeted attacks and random failures. So, enhancing networks robustness against both targeted and random attacks is of great importance. In this paper, we first design a random-robustness index (Rr) . We find that the onion-like networks destroyed the original strong ability of BA networks in resisting random attacks. Moreover, the structure of an R r -optimized network is found to be different from that of an onion-like network. To design robust scale-free networks (RSF) which are resistant to both targeted and random attacks (TRA) without changing the degree distribution, a memetic algorithm (MA) is proposed, labeled as \\textit{MA-RSF}\\textit{TRA} . In the experiments, both synthetic scale-free networks and real-world networks are used to validate the performance of \\textit{MA-RSF}\\textit{TRA} . The results show that \\textit{MA-RSF} \\textit{TRA} has a great ability in searching for the most robust network structure that is resistant to both targeted and random attacks.

  17. The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets.

    PubMed

    González-Recio, O; Jiménez-Montero, J A; Alenda, R

    2013-01-01

    In the next few years, with the advent of high-density single nucleotide polymorphism (SNP) arrays and genome sequencing, genomic evaluation methods will need to deal with a large number of genetic variants and an increasing sample size. The boosting algorithm is a machine-learning technique that may alleviate the drawbacks of dealing with such large data sets. This algorithm combines different predictors in a sequential manner with some shrinkage on them; each predictor is applied consecutively to the residuals from the committee formed by the previous ones to form a final prediction based on a subset of covariates. Here, a detailed description is provided and examples using a toy data set are included. A modification of the algorithm called "random boosting" was proposed to increase predictive ability and decrease computation time of genome-assisted evaluation in large data sets. Random boosting uses a random selection of markers to add a subsequent weak learner to the predictive model. These modifications were applied to a real data set composed of 1,797 bulls genotyped for 39,714 SNP. Deregressed proofs of 4 yield traits and 1 type trait from January 2009 routine evaluations were used as dependent variables. A 2-fold cross-validation scenario was implemented. Sires born before 2005 were used as a training sample (1,576 and 1,562 for production and type traits, respectively), whereas younger sires were used as a testing sample to evaluate predictive ability of the algorithm on yet-to-be-observed phenotypes. Comparison with the original algorithm was provided. The predictive ability of the algorithm was measured as Pearson correlations between observed and predicted responses. Further, estimated bias was computed as the average difference between observed and predicted phenotypes. The results showed that the modification of the original boosting algorithm could be run in 1% of the time used with the original algorithm and with negligible differences in accuracy

  18. Representation of high frequency Space Shuttle data by ARMA algorithms and random response spectra

    NASA Technical Reports Server (NTRS)

    Spanos, P. D.; Mushung, L. J.

    1990-01-01

    High frequency Space Shuttle lift-off data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models which lend themselves to incorporation to the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the non-stationarity of the lift-off event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguishable phase of the lift-off event, where stationarity can be expected. The presented results are rather preliminary in nature; it is aimed to call attention to the availability of the discussed digital algorithms and to the need to augment the Space Shuttle data bank as more flights are completed.

  19. Random Walk of Single Gold Nanoparticles in Zebrafish Embryos Leading to Stochastic Toxic Effects on Embryonic Developments

    PubMed Central

    Browning, Lauren M.; Lee, Kerry J.; Huang, Tao; Nallathamby, Prakash D.; Lowman, Jill E.; Xu, Xiao-Hong Nancy

    2010-01-01

    We have synthesized and characterized stable (non-aggregation, non-photobleaching and non-blinking), nearly monodisperse and highly-purified Au nanoparticles, and used them to probe transport of cleavage-stage zebrafish embryos and to study their effects on embryonic development in real time. We found that single Au nanoparticles (11.6 ± 0.9 nm in diameter) passively diffused into chorionic space of the embryos via their chorionic-pore-canals and continued their random-walk through chorionic space and into inner mass of embryos. Diffusion coefficients of single nanoparticles vary dramatically (2.8×10-11 to 1.3×10-8 cm2/s) as nanoparticles diffuse through various parts of embryos, suggesting highly diverse transport barriers and viscosity gradients of embryos. The amount of Au nanoparticles accumulated in embryos increase with its concentration. Interestingly, their effects on embryonic development are not proportionally related to the concentration. Majority of embryos (74% on average) incubated chronically with 0.025-1.2 nM Au nanoparticles for 120 h developed to normal zebrafish, with some (24%) being dead and few (2%) deformed. We developed a new approach to image and characterize individual Au nanoparticles embedded in tissues using histology sample preparation methods and LSRP spectra of single nanoparticles. We found that Au nanoparticles in various parts of normally developed and deformed zebrafish, suggesting that random-walk of nanoparticles in embryos during their development might have led to stochastic effects on embryonic development. These results show that Au nanoparticles are much more biocompatible (less toxic) to the embryos than Ag nanoparticles that we reported previously, suggesting that they are better suited as biocompatible probes for imaging embryos in vivo. The results provide powerful evidences that biocompatibility and toxicity of nanoparticles highly depend on their chemical properties, and the embryos can serve as effective in

  20. Extraction of Cole parameters from the electrical bioimpedance spectrum using stochastic optimization algorithms.

    PubMed

    Gholami-Boroujeny, Shiva; Bolic, Miodrag

    2016-04-01

    Fitting the measured bioimpedance spectroscopy (BIS) data to the Cole model and then extracting the Cole parameters is a common practice in BIS applications. The extracted Cole parameters then can be analysed as descriptors of tissue electrical properties. To have a better evaluation of physiological or pathological properties of biological tissue, accurate extraction of Cole parameters is of great importance. This paper proposes an improved Cole parameter extraction based on bacterial foraging optimization (BFO) algorithm. We employed simulated datasets to test the performance of the BFO fitting method regarding parameter extraction accuracy and noise sensitivity, and we compared the results with those of a least squares (LS) fitting method. The BFO method showed better robustness to the noise and higher accuracy in terms of extracted parameters. In addition, we applied our method to experimental data where bioimpedance measurements were obtained from forearm in three different positions of the arm. The goal of the experiment was to explore how robust Cole parameters are in classifying position of the arm for different people, and measured at different times. The extracted Cole parameters obtained by LS and BFO methods were applied to different classifiers. Two other evolutionary algorithms, GA and PSO were also used for comparison purpose. We showed that when the classifiers are fed with the extracted feature sets by BFO fitting method, higher accuracy is obtained both when applying on training data and test data. PMID:26215520

  1. Epigenetic aspects of lymphocyte antigen receptor gene rearrangement or 'when stochasticity completes randomness'.

    PubMed

    Jaeger, Sébastien; Fernandez, Bastien; Ferrier, Pierre

    2013-06-01

    To perform their specific functional role, B and T lymphocytes, cells of the adaptive immune system of jawed vertebrates, need to express one (and, preferably, only one) form of antigen receptor, i.e. the immunoglobulin or T-cell receptor (TCR), respectively. This end goal depends initially on a series of DNA cis-rearrangement events between randomly chosen units from separate clusters of V, D (at some immunoglobulin and TCR loci) and J gene segments, a biomolecular process collectively referred to as V(D)J recombination. V(D)J recombination takes place in immature T and B cells and relies on the so-called RAG nuclease, a site-specific DNA cleavage apparatus that corresponds to the lymphoid-specific moiety of the VDJ recombinase. At the genome level, this recombinase's mission presents substantial biochemical challenges. These relate to the huge distance between (some of) the gene segments that it eventually rearranges and the need to achieve cell-lineage-restricted and developmentally ordered routines with at times, mono-allelic versus bi-allelic discrimination. The entire process must be completed without any recombination errors, instigators of chromosome instability, translocation and, potentially, tumorigenesis. As expected, such a precisely choreographed and yet potentially risky process demands sophisticated controls; epigenetics demonstrates what is possible when calling upon its many facets. In this vignette, we will recall the evidence that almost from the start appeared to link the two topics, V(D)J recombination and epigenetics, before reviewing the latest advances in our knowledge of this joint venture. PMID:23278765

  2. Enhanced cancer recognition system based on random forests feature elimination algorithm.

    PubMed

    Ozcift, Akin

    2012-08-01

    Accurate classifiers are vital to design precise computer aided diagnosis (CADx) systems. Classification performances of machine learning algorithms are sensitive to the characteristics of data. In this aspect, determining the relevant and discriminative features is a key step to improve performance of CADx. There are various feature extraction methods in the literature. However, there is no universal variable selection algorithm that performs well in every data analysis scheme. Random Forests (RF), an ensemble of trees, is used in classification studies successfully. The success of RF algorithm makes it eligible to be used as kernel of a wrapper feature subset evaluator. We used best first search RF wrapper algorithm to select optimal features of four medical datasets: colon cancer, leukemia cancer, breast cancer and lung cancer. We compared accuracies of 15 widely used classifiers trained with all features versus to extracted features of each dataset. The experimental results demonstrated the efficiency of proposed feature extraction strategy with the increase in most of the classification accuracies of the algorithms. PMID:21567124

  3. Downscaling stream flow time series from monthly to daily scales using an auto-regressive stochastic algorithm: StreamFARM

    NASA Astrophysics Data System (ADS)

    Rebora, N.; Silvestro, F.; Rudari, R.; Herold, C.; Ferraris, L.

    2016-06-01

    Downscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily). In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases.

  4. Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system

    NASA Astrophysics Data System (ADS)

    Huang, Wen-Cheng; Yuan, Lun-Chin; Lee, Chi-Ming

    2002-12-01

    The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse" in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m3/day and 920,000 m3/day to Shihmen during the wet season and dry season, respectively.

  5. Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields

    NASA Astrophysics Data System (ADS)

    Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun

    2015-01-01

    Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.

  6. Personalized PageRank Clustering: A graph clustering algorithm based on random walks

    NASA Astrophysics Data System (ADS)

    A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

    2013-11-01

    Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

  7. Fuzzy stochastic elements method. Spectral approach

    NASA Astrophysics Data System (ADS)

    Sniady, Pawel; Mazur-Sniady, Krystyna; Sieniawska, Roza; Zukowski, Stanislaw

    2013-05-01

    We study a complex dynamic problem, which concerns a structure with uncertain parameters subjected to a stochastic excitation. Formulation of such a problem introduces fuzzy random variables for parameters of the structure and fuzzy stochastic processes for the load process. The uncertainty has two sources, namely the randomness of structural parameters such as geometry characteristics, material and damping properties, load process and imprecision of the theoretical model and incomplete information or uncertain data. All of these have a great influence on the response of the structure. By analyzing such problems we describe the random variability using the probability theory and the imprecision by use of fuzzy sets. Due to the fact that it is difficult to find an analytic expression for the inversion of the stochastic operator in the stochastic differential equation, a number of approximate methods have been proposed in the literature which can be connected to the finite element method. To evaluate the effects of excitation in the frequency domain we use the spectral density function. The spectral analysis is widely used in stochastic dynamics field of linear systems for stationary random excitation. The concept of the evolutionary spectral density is used in the case of non-stationary random excitation. We solve the considered problem using fuzzy stochastic finite element method. The solution is based on the idea of a fuzzy random frequency response vector for stationary input excitation and a transient fuzzy random frequency response vector for the fuzzy non-stationary one. We use the fuzzy random frequency response vector and the transient fuzzy random frequency response vector in the context of spectral analysis in order to determine the influence of structural uncertainty on the fuzzy random response of the structure. We study a linear system with random parameters subjected to two particular cases of stochastic excitation in a frequency domain. The first one

  8. Analysis of (1+1) evolutionary algorithm and randomized local search with memory.

    PubMed

    Sung, Chi Wan; Yuen, Shiu Yin

    2011-01-01

    This paper considers the scenario of the (1+1) evolutionary algorithm (EA) and randomized local search (RLS) with memory. Previously explored solutions are stored in memory until an improvement in fitness is obtained; then the stored information is discarded. This results in two new algorithms: (1+1) EA-m (with a raw list and hash table option) and RLS-m+ (and RLS-m if the function is a priori known to be unimodal). These two algorithms can be regarded as very simple forms of tabu search. Rigorous theoretical analysis of the expected time to find the globally optimal solutions for these algorithms is conducted for both unimodal and multimodal functions. A unified mathematical framework, involving the new concept of spatially invariant neighborhood, is proposed. Under this framework, both (1+1) EA with standard uniform mutation and RLS can be considered as particular instances and in the most general cases, all functions can be considered to be unimodal. Under this framework, it is found that for unimodal functions, the improvement by memory assistance is always positive but at most by one half. For multimodal functions, the improvement is significant; for functions with gaps and another hard function, the order of growth is reduced; for at least one example function, the order can change from exponential to polynomial. Empirical results, with a reasonable fitness evaluation time assumption, verify that (1+1) EA-m and RLS-m+ are superior to their conventional counterparts. Both new algorithms are promising for use in a memetic algorithm. In particular, RLS-m+ makes the previously impractical RLS practical, and surprisingly, does not require any extra memory in actual implementation. PMID:20868262

  9. Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification.

    PubMed

    Donos, Cristian; Dümpelmann, Matthias; Schulze-Bonhage, Andreas

    2015-08-01

    The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour. PMID:26022388

  10. Random Search Algorithm for Solving the Nonlinear Fredholm Integral Equations of the Second Kind

    PubMed Central

    Hong, Zhimin; Yan, Zaizai; Yan, Jiao

    2014-01-01

    In this paper, a randomized numerical approach is used to obtain approximate solutions for a class of nonlinear Fredholm integral equations of the second kind. The proposed approach contains two steps: at first, we define a discretized form of the integral equation by quadrature formula methods and solution of this discretized form converges to the exact solution of the integral equation by considering some conditions on the kernel of the integral equation. And then we convert the problem to an optimal control problem by introducing an artificial control function. Following that, in the next step, solution of the discretized form is approximated by a kind of Monte Carlo (MC) random search algorithm. Finally, some examples are given to show the efficiency of the proposed approach. PMID:25072373

  11. Investigation and appreciation of optimal output feedback. Volume 1: A convergent algorithm for the stochastic infinite-time discrete optimal output feedback problem

    NASA Technical Reports Server (NTRS)

    Halyo, N.; Broussard, J. R.

    1984-01-01

    The stochastic, infinite time, discrete output feedback problem for time invariant linear systems is examined. Two sets of sufficient conditions for the existence of a stable, globally optimal solution are presented. An expression for the total change in the cost function due to a change in the feedback gain is obtained. This expression is used to show that a sequence of gains can be obtained by an algorithm, so that the corresponding cost sequence is monotonically decreasing and the corresponding sequence of the cost gradient converges to zero. The algorithm is guaranteed to obtain a critical point of the cost function. The computational steps necessary to implement the algorithm on a computer are presented. The results are applied to a digital outer loop flight control problem. The numerical results for this 13th order problem indicate a rate of convergence considerably faster than two other algorithms used for comparison.

  12. Evolving random fractal Cantor superlattices for the infrared using a genetic algorithm

    PubMed Central

    Bossard, Jeremy A.; Lin, Lan; Werner, Douglas H.

    2016-01-01

    Ordered and chaotic superlattices have been identified in Nature that give rise to a variety of colours reflected by the skin of various organisms. In particular, organisms such as silvery fish possess superlattices that reflect a broad range of light from the visible to the UV. Such superlattices have previously been identified as ‘chaotic’, but we propose that apparent ‘chaotic’ natural structures, which have been previously modelled as completely random structures, should have an underlying fractal geometry. Fractal geometry, often described as the geometry of Nature, can be used to mimic structures found in Nature, but deterministic fractals produce structures that are too ‘perfect’ to appear natural. Introducing variability into fractals produces structures that appear more natural. We suggest that the ‘chaotic’ (purely random) superlattices identified in Nature are more accurately modelled by multi-generator fractals. Furthermore, we introduce fractal random Cantor bars as a candidate for generating both ordered and ‘chaotic’ superlattices, such as the ones found in silvery fish. A genetic algorithm is used to evolve optimal fractal random Cantor bars with multiple generators targeting several desired optical functions in the mid-infrared and the near-infrared. We present optimized superlattices demonstrating broadband reflection as well as single and multiple pass bands in the near-infrared regime. PMID:26763335

  13. Evolving random fractal Cantor superlattices for the infrared using a genetic algorithm.

    PubMed

    Bossard, Jeremy A; Lin, Lan; Werner, Douglas H

    2016-01-01

    Ordered and chaotic superlattices have been identified in Nature that give rise to a variety of colours reflected by the skin of various organisms. In particular, organisms such as silvery fish possess superlattices that reflect a broad range of light from the visible to the UV. Such superlattices have previously been identified as 'chaotic', but we propose that apparent 'chaotic' natural structures, which have been previously modelled as completely random structures, should have an underlying fractal geometry. Fractal geometry, often described as the geometry of Nature, can be used to mimic structures found in Nature, but deterministic fractals produce structures that are too 'perfect' to appear natural. Introducing variability into fractals produces structures that appear more natural. We suggest that the 'chaotic' (purely random) superlattices identified in Nature are more accurately modelled by multi-generator fractals. Furthermore, we introduce fractal random Cantor bars as a candidate for generating both ordered and 'chaotic' superlattices, such as the ones found in silvery fish. A genetic algorithm is used to evolve optimal fractal random Cantor bars with multiple generators targeting several desired optical functions in the mid-infrared and the near-infrared. We present optimized superlattices demonstrating broadband reflection as well as single and multiple pass bands in the near-infrared regime. PMID:26763335

  14. Fault diagnosis in spur gears based on genetic algorithm and random forest

    NASA Astrophysics Data System (ADS)

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  15. Urban Road Detection in Airbone Laser Scanning Point Cloud Using Random Forest Algorithm

    NASA Astrophysics Data System (ADS)

    Kaczałek, B.; Borkowski, A.

    2016-06-01

    The objective of this research is to detect points that describe a road surface in an unclassified point cloud of the airborne laser scanning (ALS). For this purpose we use the Random Forest learning algorithm. The proposed methodology consists of two stages: preparation of features and supervised point cloud classification. In this approach we consider ALS points, representing only the last echo. For these points RGB, intensity, the normal vectors, their mean values and the standard deviations are provided. Moreover, local and global height variations are taken into account as components of a feature vector. The feature vectors are calculated on a basis of the 3D Delaunay triangulation. The proposed methodology was tested on point clouds with the average point density of 12 pts/m2 that represent large urban scene. The significance level of 15% was set up for a decision tree of the learning algorithm. As a result of the Random Forest classification we received two subsets of ALS points. One of those groups represents points belonging to the road network. After the classification evaluation we achieved from 90% of the overall classification accuracy. Finally, the ALS points representing roads were merged and simplified into road network polylines using morphological operations.

  16. Cooperative mobile agents search using beehive partitioned structure and Tabu Random search algorithm

    NASA Astrophysics Data System (ADS)

    Ramazani, Saba; Jackson, Delvin L.; Selmic, Rastko R.

    2013-05-01

    In search and surveillance operations, deploying a team of mobile agents provides a robust solution that has multiple advantages over using a single agent in efficiency and minimizing exploration time. This paper addresses the challenge of identifying a target in a given environment when using a team of mobile agents by proposing a novel method of mapping and movement of agent teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, mobile agents have an efficient travel path while performing searches due to this partitioning approach. Second, we use a team of mobile agents that move in a cooperative manner and utilize the Tabu Random algorithm to search for the target. Due to the ever-increasing use of robotics and Unmanned Aerial Vehicle (UAV) platforms, the field of cooperative multi-agent search has developed many applications recently that would benefit from the use of the approach presented in this work, including: search and rescue operations, surveillance, data collection, and border patrol. In this paper, the increased efficiency of the Tabu Random Search algorithm method in combination with hexagonal partitioning is simulated, analyzed, and advantages of this approach are presented and discussed.

  17. Track-Before-Detect Algorithm for Faint Moving Objects based on Random Sampling and Consensus

    NASA Astrophysics Data System (ADS)

    Dao, P.; Rast, R.; Schlaegel, W.; Schmidt, V.; Dentamaro, A.

    2014-09-01

    There are many algorithms developed for tracking and detecting faint moving objects in congested backgrounds. One obvious application is detection of targets in images where each pixel corresponds to the received power in a particular location. In our application, a visible imager operated in stare mode observes geostationary objects as fixed, stars as moving and non-geostationary objects as drifting in the field of view. We would like to achieve high sensitivity detection of the drifters. The ability to improve SNR with track-before-detect (TBD) processing, where target information is collected and collated before the detection decision is made, allows respectable performance against dim moving objects. Generally, a TBD algorithm consists of a pre-processing stage that highlights potential targets and a temporal filtering stage. However, the algorithms that have been successfully demonstrated, e.g. Viterbi-based and Bayesian-based, demand formidable processing power and memory. We propose an algorithm that exploits the quasi constant velocity of objects, the predictability of the stellar clutter and the intrinsically low false alarm rate of detecting signature candidates in 3-D, based on an iterative method called "RANdom SAmple Consensus” and one that can run real-time on a typical PC. The technique is tailored for searching objects with small telescopes in stare mode. Our RANSAC-MT (Moving Target) algorithm estimates parameters of a mathematical model (e.g., linear motion) from a set of observed data which contains a significant number of outliers while identifying inliers. In the pre-processing phase, candidate blobs were selected based on morphology and an intensity threshold that would normally generate unacceptable level of false alarms. The RANSAC sampling rejects candidates that conform to the predictable motion of the stars. Data collected with a 17 inch telescope by AFRL/RH and a COTS lens/EM-CCD sensor by the AFRL/RD Satellite Assessment Center is

  18. An efficient voting algorithm for finding additive biclusters with random background.

    PubMed

    Xiao, Jing; Wang, Lusheng; Liu, Xiaowen; Jiang, Tao

    2008-12-01

    The biclustering problem has been extensively studied in many areas, including e-commerce, data mining, machine learning, pattern recognition, statistics, and, more recently, computational biology. Given an n x m matrix A (n >or= m), the main goal of biclustering is to identify a subset of rows (called objects) and a subset of columns (called properties) such that some objective function that specifies the quality of the found bicluster (formed by the subsets of rows and of columns of A) is optimized. The problem has been proved or conjectured to be NP-hard for various objective functions. In this article, we study a probabilistic model for the implanted additive bicluster problem, where each element in the n x m background matrix is a random integer from [0, L - 1] for some integer L, and a k x k implanted additive bicluster is obtained from an error-free additive bicluster by randomly changing each element to a number in [0, L - 1] with probability theta. We propose an O(n(2)m) time algorithm based on voting to solve the problem. We show that when k >or= Omega(square root of (n log n)), the voting algorithm can correctly find the implanted bicluster with probability at least 1 - (9/n(2)). We also implement our algorithm as a C++ program named VOTE. The implementation incorporates several ideas for estimating the size of an implanted bicluster, adjusting the threshold in voting, dealing with small biclusters, and dealing with overlapping implanted biclusters. Our experimental results on both simulated and real datasets show that VOTE can find biclusters with a high accuracy and speed. PMID:19040364

  19. Precise algorithm to generate random sequential addition of hard hyperspheres at saturation.

    PubMed

    Zhang, G; Torquato, S

    2013-11-01

    The study of the packing of hard hyperspheres in d-dimensional Euclidean space R^{d} has been a topic of great interest in statistical mechanics and condensed matter theory. While the densest known packings are ordered in sufficiently low dimensions, it has been suggested that in sufficiently large dimensions, the densest packings might be disordered. The random sequential addition (RSA) time-dependent packing process, in which congruent hard hyperspheres are randomly and sequentially placed into a system without interparticle overlap, is a useful packing model to study disorder in high dimensions. Of particular interest is the infinite-time saturation limit in which the available space for another sphere tends to zero. However, the associated saturation density has been determined in all previous investigations by extrapolating the density results for nearly saturated configurations to the saturation limit, which necessarily introduces numerical uncertainties. We have refined an algorithm devised by us [S. Torquato, O. U. Uche, and F. H. Stillinger, Phys. Rev. E 74, 061308 (2006)] to generate RSA packings of identical hyperspheres. The improved algorithm produce such packings that are guaranteed to contain no available space in a large simulation box using finite computational time with heretofore unattained precision and across the widest range of dimensions (2≤d≤8). We have also calculated the packing and covering densities, pair correlation function g(2)(r), and structure factor S(k) of the saturated RSA configurations. As the space dimension increases, we find that pair correlations markedly diminish, consistent with a recently proposed "decorrelation" principle, and the degree of "hyperuniformity" (suppression of infinite-wavelength density fluctuations) increases. We have also calculated the void exclusion probability in order to compute the so-called quantizer error of the RSA packings, which is related to the second moment of inertia of the average

  20. Precise algorithm to generate random sequential addition of hard hyperspheres at saturation

    NASA Astrophysics Data System (ADS)

    Zhang, G.; Torquato, S.

    2013-11-01

    The study of the packing of hard hyperspheres in d-dimensional Euclidean space Rd has been a topic of great interest in statistical mechanics and condensed matter theory. While the densest known packings are ordered in sufficiently low dimensions, it has been suggested that in sufficiently large dimensions, the densest packings might be disordered. The random sequential addition (RSA) time-dependent packing process, in which congruent hard hyperspheres are randomly and sequentially placed into a system without interparticle overlap, is a useful packing model to study disorder in high dimensions. Of particular interest is the infinite-time saturation limit in which the available space for another sphere tends to zero. However, the associated saturation density has been determined in all previous investigations by extrapolating the density results for nearly saturated configurations to the saturation limit, which necessarily introduces numerical uncertainties. We have refined an algorithm devised by us [S. Torquato, O. U. Uche, and F. H. Stillinger, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.74.061308 74, 061308 (2006)] to generate RSA packings of identical hyperspheres. The improved algorithm produce such packings that are guaranteed to contain no available space in a large simulation box using finite computational time with heretofore unattained precision and across the widest range of dimensions (2≤d≤8). We have also calculated the packing and covering densities, pair correlation function g2(r), and structure factor S(k) of the saturated RSA configurations. As the space dimension increases, we find that pair correlations markedly diminish, consistent with a recently proposed “decorrelation” principle, and the degree of “hyperuniformity” (suppression of infinite-wavelength density fluctuations) increases. We have also calculated the void exclusion probability in order to compute the so-called quantizer error of the RSA packings, which is related to the

  1. A Random Algorithm for Low-Rank Decomposition of Large-Scale Matrices With Missing Entries.

    PubMed

    Liu, Yiguang; Lei, Yinjie; Li, Chunguang; Xu, Wenzheng; Pu, Yifei

    2015-11-01

    A random submatrix method (RSM) is proposed to calculate the low-rank decomposition U(m×r)V(n×r)(T) (r < m, n) of the matrix Y∈R(m×n) (assuming m > n generally) with known entry percentage 0 < ρ ≤ 1. RSM is very fast as only O(mr(2)ρ(r)) or O(n(3)ρ(3r)) floating-point operations (flops) are required, compared favorably with O(mnr+r(2)(m+n)) flops required by the state-of-the-art algorithms. Meanwhile, RSM has the advantage of a small memory requirement as only max(n(2),mr+nr) real values need to be saved. With the assumption that known entries are uniformly distributed in Y, submatrices formed by known entries are randomly selected from Y with statistical size k×nρ(k) or mρ(l)×l , where k or l takes r+1 usually. We propose and prove a theorem, under random noises the probability that the subspace associated with a smaller singular value will turn into the space associated to anyone of the r largest singular values is smaller. Based on the theorem, the nρ(k)-k null vectors or the l-r right singular vectors associated with the minor singular values are calculated for each submatrix. The vectors ought to be the null vectors of the submatrix formed by the chosen nρ(k) or l columns of the ground truth of V(T). If enough submatrices are randomly chosen, V and U can be estimated accordingly. The experimental results on random synthetic matrices with sizes such as 13 1072 ×10(24) and on real data sets such as dinosaur indicate that RSM is 4.30 ∼ 197.95 times faster than the state-of-the-art algorithms. It, meanwhile, has considerable high precision achieving or approximating to the best. PMID:26208344

  2. Comparing Algorithms for Graph Isomorphism Using Discrete- and Continuous-Time Quantum Random Walks

    SciTech Connect

    Rudinger, Kenneth; Gamble, John King; Bach, Eric; Friesen, Mark; Joynt, Robert; Coppersmith, S. N.

    2013-07-01

    Berry and Wang [Phys. Rev. A 83, 042317 (2011)] show numerically that a discrete-time quan- tum random walk of two noninteracting particles is able to distinguish some non-isomorphic strongly regular graphs from the same family. Here we analytically demonstrate how it is possible for these walks to distinguish such graphs, while continuous-time quantum walks of two noninteracting parti- cles cannot. We show analytically and numerically that even single-particle discrete-time quantum random walks can distinguish some strongly regular graphs, though not as many as two-particle noninteracting discrete-time walks. Additionally, we demonstrate how, given the same quantum random walk, subtle di erences in the graph certi cate construction algorithm can nontrivially im- pact the walk's distinguishing power. We also show that no continuous-time walk of a xed number of particles can distinguish all strongly regular graphs when used in conjunction with any of the graph certi cates we consider. We extend this constraint to discrete-time walks of xed numbers of noninteracting particles for one kind of graph certi cate; it remains an open question as to whether or not this constraint applies to the other graph certi cates we consider.

  3. Comparing Algorithms for Graph Isomorphism Using Discrete- and Continuous-Time Quantum Random Walks

    DOE PAGESBeta

    Rudinger, Kenneth; Gamble, John King; Bach, Eric; Friesen, Mark; Joynt, Robert; Coppersmith, S. N.

    2013-07-01

    Berry and Wang [Phys. Rev. A 83, 042317 (2011)] show numerically that a discrete-time quan- tum random walk of two noninteracting particles is able to distinguish some non-isomorphic strongly regular graphs from the same family. Here we analytically demonstrate how it is possible for these walks to distinguish such graphs, while continuous-time quantum walks of two noninteracting parti- cles cannot. We show analytically and numerically that even single-particle discrete-time quantum random walks can distinguish some strongly regular graphs, though not as many as two-particle noninteracting discrete-time walks. Additionally, we demonstrate how, given the same quantum random walk, subtle di erencesmore » in the graph certi cate construction algorithm can nontrivially im- pact the walk's distinguishing power. We also show that no continuous-time walk of a xed number of particles can distinguish all strongly regular graphs when used in conjunction with any of the graph certi cates we consider. We extend this constraint to discrete-time walks of xed numbers of noninteracting particles for one kind of graph certi cate; it remains an open question as to whether or not this constraint applies to the other graph certi cates we consider.« less

  4. Combining Spectral and Texture Features Using Random Forest Algorithm: Extracting Impervious Surface Area in Wuhan

    NASA Astrophysics Data System (ADS)

    Shao, Zhenfeng; Zhang, Yuan; Zhang, Lei; Song, Yang; Peng, Minjun

    2016-06-01

    Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer's accuracy of 0.98, a user's accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

  5. MetaPIGA v2.0: maximum likelihood large phylogeny estimation using the metapopulation genetic algorithm and other stochastic heuristics

    PubMed Central

    2010-01-01

    Background The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s) but rather by practical issues such as ergonomics and/or the availability of specific functionalities. Results Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood), including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA) together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers. Conclusions The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these algorithms. MetaPIGA v2

  6. Distribution of transition times in a stochastic model of excitable cell: Insights into the cell-intrinsic mechanisms of randomness in neuronal interspike intervals

    NASA Astrophysics Data System (ADS)

    Requena-Carrión, Jesús; Requena-Carrión, Víctor J.

    2016-04-01

    In this paper, we develop an analytical approach to studying random patterns of activity in excitable cells. Our analytical approach uses a two-state stochastic model of excitable system based on the electrophysiological properties of refractoriness and restitution, which characterize cell recovery after excitation. By applying the notion of probability density flux, we derive the distributions of transition times between states and the distribution of interspike interval (ISI) durations for a constant applied stimulus. The derived ISI distribution is unimodal and, provided that the time spent in the excited state is constant, can be approximated by a Rayleigh peak followed by an exponential tail. We then explore the role of the model parameters in determining the shape of the derived distributions and the ISI coefficient of variation. Finally, we use our analytical results to study simulation results from the stochastic Morris-Lecar neuron and from a three-state extension of the proposed stochastic model, which is capable of reproducing multimodal ISI histograms.

  7. Data Security in Ad Hoc Networks Using Randomization of Cryptographic Algorithms

    NASA Astrophysics Data System (ADS)

    Krishna, B. Ananda; Radha, S.; Keshava Reddy, K. Chenna

    Ad hoc networks are a new wireless networking paradigm for mobile hosts. Unlike traditional mobile wireless networks, ad hoc networks do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. The military tactical and other security-sensitive operations are still the main applications of ad hoc networks, although there is a trend to adopt ad hoc networks for commercial uses due to their unique properties. One main challenge in design of these networks is how to feasibly detect and defend the major attacks against data, impersonation and unauthorized data modification. Also, in the same network some nodes may be malicious whose objective is to degrade the network performance. In this study, we propose a security model in which the packets are encrypted and decrypted using multiple algorithms where the selection scheme is random. The performance of the proposed model is analyzed and it is observed that there is no increase in control overhead but a slight delay is introduced due to the encryption process. We conclude that the proposed security model works well for heavily loaded networks with high mobility and can be extended for more cryptographic algorithms.

  8. Randomized selection on the GPU

    SciTech Connect

    Monroe, Laura Marie; Wendelberger, Joanne R; Michalak, Sarah E

    2011-01-13

    We implement here a fast and memory-sparing probabilistic top N selection algorithm on the GPU. To our knowledge, this is the first direct selection in the literature for the GPU. The algorithm proceeds via a probabilistic-guess-and-chcck process searching for the Nth element. It always gives a correct result and always terminates. The use of randomization reduces the amount of data that needs heavy processing, and so reduces the average time required for the algorithm. Probabilistic Las Vegas algorithms of this kind are a form of stochastic optimization and can be well suited to more general parallel processors with limited amounts of fast memory.

  9. From analytical solutions of solute transport equations to multidimensional time-domain random walk (TDRW) algorithms

    NASA Astrophysics Data System (ADS)

    Bodin, Jacques

    2015-03-01

    In this study, new multi-dimensional time-domain random walk (TDRW) algorithms are derived from approximate one-dimensional (1-D), two-dimensional (2-D), and three-dimensional (3-D) analytical solutions of the advection-dispersion equation and from exact 1-D, 2-D, and 3-D analytical solutions of the pure-diffusion equation. These algorithms enable the calculation of both the time required for a particle to travel a specified distance in a homogeneous medium and the mass recovery at the observation point, which may be incomplete due to 2-D or 3-D transverse dispersion or diffusion. The method is extended to heterogeneous media, represented as a piecewise collection of homogeneous media. The particle motion is then decomposed along a series of intermediate checkpoints located on the medium interface boundaries. The accuracy of the multi-dimensional TDRW method is verified against (i) exact analytical solutions of solute transport in homogeneous media and (ii) finite-difference simulations in a synthetic 2-D heterogeneous medium of simple geometry. The results demonstrate that the method is ideally suited to purely diffusive transport and to advection-dispersion transport problems dominated by advection. Conversely, the method is not recommended for highly dispersive transport problems because the accuracy of the advection-dispersion TDRW algorithms degrades rapidly for a low Péclet number, consistent with the accuracy limit of the approximate analytical solutions. The proposed approach provides a unified methodology for deriving multi-dimensional time-domain particle equations and may be applicable to other mathematical transport models, provided that appropriate analytical solutions are available.

  10. Harmonics elimination algorithm for operational modal analysis using random decrement technique

    NASA Astrophysics Data System (ADS)

    Modak, S. V.; Rawal, Chetan; Kundra, T. K.

    2010-05-01

    Operational modal analysis (OMA) extracts modal parameters of a structure using their output response, during operation in general. OMA, when applied to mechanical engineering structures is often faced with the problem of harmonics present in the output response, and can cause erroneous modal extraction. This paper demonstrates for the first time that the random decrement (RD) method can be efficiently employed to eliminate the harmonics from the randomdec signatures. Further, the research work shows effective elimination of large amplitude harmonics also by proposing inclusion of additional random excitation. This obviously need not be recorded for analysis, as is the case with any other OMA method. The free decays obtained from RD have been used for system modal identification using eigen-system realization algorithm (ERA). The proposed harmonic elimination method has an advantage over previous methods in that it does not require the harmonic frequencies to be known and can be used for multiple harmonics, including periodic signals. The theory behind harmonic elimination is first developed and validated. The effectiveness of the method is demonstrated through a simulated study and then by experimental studies on a beam and a more complex F-shape structure, which resembles in shape to the skeleton of a drilling or milling machine tool. Cases with presence of single and multiple harmonics in the response are considered.

  11. Stochastic reconstruction of sandstones

    PubMed

    Manwart; Torquato; Hilfer

    2000-07-01

    A simulated annealing algorithm is employed to generate a stochastic model for a Berea sandstone and a Fontainebleau sandstone, with each a prescribed two-point probability function, lineal-path function, and "pore size" distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples. PMID:11088546

  12. Development and Evaluation of a New Air Exchange Rate Algorithm for the Stochastic Human Exposure and Dose Simulation Model

    EPA Science Inventory

    between-home and between-city variability in residential pollutant infiltration. This is likely a result of differences in home ventilation, or air exchange rates (AER). The Stochastic Human Exposure and Dose Simulation (SHEDS) model is a population exposure model that uses a pro...

  13. Comparison of linear–stochastic and nonlinear–deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death

    PubMed Central

    Skinner, James E; Meyer, Michael; Nester, Brian A; Geary, Una; Taggart, Pamela; Mangione, Antoinette; Ramalanjaona, George; Terregino, Carol; Dalsey, William C

    2009-01-01

    Objective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD). Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data. Methods: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle–Thaler criteria. Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤0.001). The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤0.001); sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤0.001). Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test. PMID:19707283

  14. SU-D-201-06: Random Walk Algorithm Seed Localization Parameters in Lung Positron Emission Tomography (PET) Images

    SciTech Connect

    Soufi, M; Asl, A Kamali; Geramifar, P

    2015-06-15

    Purpose: The objective of this study was to find the best seed localization parameters in random walk algorithm application to lung tumor delineation in Positron Emission Tomography (PET) images. Methods: PET images suffer from statistical noise and therefore tumor delineation in these images is a challenging task. Random walk algorithm, a graph based image segmentation technique, has reliable image noise robustness. Also its fast computation and fast editing characteristics make it powerful for clinical purposes. We implemented the random walk algorithm using MATLAB codes. The validation and verification of the algorithm have been done by 4D-NCAT phantom with spherical lung lesions in different diameters from 20 to 90 mm (with incremental steps of 10 mm) and different tumor to background ratios of 4:1 and 8:1. STIR (Software for Tomographic Image Reconstruction) has been applied to reconstruct the phantom PET images with different pixel sizes of 2×2×2 and 4×4×4 mm{sup 3}. For seed localization, we selected pixels with different maximum Standardized Uptake Value (SUVmax) percentages, at least (70%, 80%, 90% and 100%) SUVmax for foreground seeds and up to (20% to 55%, 5% increment) SUVmax for background seeds. Also, for investigation of algorithm performance on clinical data, 19 patients with lung tumor were studied. The resulted contours from algorithm have been compared with nuclear medicine expert manual contouring as ground truth. Results: Phantom and clinical lesion segmentation have shown that the best segmentation results obtained by selecting the pixels with at least 70% SUVmax as foreground seeds and pixels up to 30% SUVmax as background seeds respectively. The mean Dice Similarity Coefficient of 94% ± 5% (83% ± 6%) and mean Hausdorff Distance of 1 (2) pixels have been obtained for phantom (clinical) study. Conclusion: The accurate results of random walk algorithm in PET image segmentation assure its application for radiation treatment planning and

  15. A fast random walk algorithm for computing the pulsed-gradient spin-echo signal in multiscale porous media.

    PubMed

    Grebenkov, Denis S

    2011-02-01

    A new method for computing the signal attenuation due to restricted diffusion in a linear magnetic field gradient is proposed. A fast random walk (FRW) algorithm for simulating random trajectories of diffusing spin-bearing particles is combined with gradient encoding. As random moves of a FRW are continuously adapted to local geometrical length scales, the method is efficient for simulating pulsed-gradient spin-echo experiments in hierarchical or multiscale porous media such as concrete, sandstones, sedimentary rocks and, potentially, brain or lungs. PMID:21159532

  16. Excitation energies from particle-particle random phase approximation: Davidson algorithm and benchmark studies.

    PubMed

    Yang, Yang; Peng, Degao; Lu, Jianfeng; Yang, Weitao

    2014-09-28

    The particle-particle random phase approximation (pp-RPA) has been used to investigate excitation problems in our recent paper [Y. Yang, H. van Aggelen, and W. Yang, J. Chem. Phys. 139, 224105 (2013)]. It has been shown to be capable of describing double, Rydberg, and charge transfer excitations, which are challenging for conventional time-dependent density functional theory (TDDFT). However, its performance on larger molecules is unknown as a result of its expensive O(N(6)) scaling. In this article, we derive and implement a Davidson iterative algorithm for the pp-RPA to calculate the lowest few excitations for large systems. The formal scaling is reduced to O(N(4)), which is comparable with the commonly used configuration interaction singles (CIS) and TDDFT methods. With this iterative algorithm, we carried out benchmark tests on molecules that are significantly larger than the molecules in our previous paper with a reasonably large basis set. Despite some self-consistent field convergence problems with ground state calculations of (N - 2)-electron systems, we are able to accurately capture lowest few excitations for systems with converged calculations. Compared to CIS and TDDFT, there is no systematic bias for the pp-RPA with the mean signed error close to zero. The mean absolute error of pp-RPA with B3LYP or PBE references is similar to that of TDDFT, which suggests that the pp-RPA is a comparable method to TDDFT for large molecules. Moreover, excitations with relatively large non-HOMO excitation contributions are also well described in terms of excitation energies, as long as there is also a relatively large HOMO excitation contribution. These findings, in conjunction with the capability of pp-RPA for describing challenging excitations shown earlier, further demonstrate the potential of pp-RPA as a reliable and general method to describe excitations, and to be a good alternative to TDDFT methods. PMID:25273409

  17. A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites.

    PubMed

    Wei, Zhi-Sen; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

    2015-10-01

    Protein-protein interactions exist ubiquitously and play important roles in the life cycles of living cells. The interaction sites (residues) are essential to understanding the underlying mechanisms of protein-protein interactions. Previous research has demonstrated that the accurate identification of protein-protein interaction sites (PPIs) is helpful for developing new therapeutic drugs because many drugs will interact directly with those residues. Because of its significant potential in biological research and drug development, the prediction of PPIs has become an important topic in computational biology. However, a severe data imbalance exists in the PPIs prediction problem, where the number of the majority class samples (non-interacting residues) is far larger than that of the minority class samples (interacting residues). Thus, we developed a novel cascade random forests algorithm (CRF) to address the serious data imbalance that exists in the PPIs prediction problem. The proposed CRF resolves the negative effect of data imbalance by connecting multiple random forests in a cascade-like manner, each of which is trained with a balanced training subset that includes all minority samples and a subset of majority samples using an effective ensemble protocol. Based on the proposed CRF, we implemented a new sequence-based PPIs predictor, called CRF-PPI, which takes the combined features of position-specific scoring matrices, averaged cumulative hydropathy, and predicted relative solvent accessibility as model inputs. Benchmark experiments on both the cross validation and independent validation datasets demonstrated that the proposed CRF-PPI outperformed the state-of-the-art sequence-based PPIs predictors. The source code for CRF-PPI and the benchmark datasets are available online at http://csbio.njust.edu.cn/bioinf/CRF-PPI for free academic use. PMID:26441427

  18. Adaptive stochastic cellular automata: Applications

    NASA Astrophysics Data System (ADS)

    Qian, S.; Lee, Y. C.; Jones, R. D.; Barnes, C. W.; Flake, G. W.; O'Rourke, M. K.; Lee, K.; Chen, H. H.; Sun, G. Z.; Zhang, Y. Q.; Chen, D.; Giles, C. L.

    1990-09-01

    The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.

  19. Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm

    NASA Astrophysics Data System (ADS)

    Provost, Floriane; Hibert, Clément; Malet, Jean-Philippe; Stumpf, André; Doubre, Cécile

    2016-04-01

    Different studies have shown the presence of microseismic activity in soft-rock landslides. The seismic signals exhibit significantly different features in the time and frequency domains which allow their classification and interpretation. Most of the classes could be associated with different mechanisms of deformation occurring within and at the surface (e.g. rockfall, slide-quake, fissure opening, fluid circulation). However, some signals remain not fully understood and some classes contain few examples that prevent any interpretation. To move toward a more complete interpretation of the links between the dynamics of soft-rock landslides and the physical processes controlling their behaviour, a complete catalog of the endogeneous seismicity is needed. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forest classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied to the seismicity recorded at the Super-Sauze landslide between 2013 and 2015. We selected a dozen of seismic signal features that characterize precisely its spectral content (e.g. central frequency, spectrum width, energy in several frequency bands, spectrogram shape, spectrum local and global maxima

  20. Highly efficient numerical algorithm based on random trees for accelerating parallel Vlasov-Poisson simulations

    NASA Astrophysics Data System (ADS)

    Acebrón, Juan A.; Rodríguez-Rozas, Ángel

    2013-10-01

    An efficient numerical method based on a probabilistic representation for the Vlasov-Poisson system of equations in the Fourier space has been derived. This has been done theoretically for arbitrary dimensional problems, and particularized to unidimensional problems for numerical purposes. Such a representation has been validated theoretically in the linear regime comparing the solution obtained with the classical results of the linear Landau damping. The numerical strategy followed requires generating suitable random trees combined with a Padé approximant for approximating accurately a given divergent series. Such series are obtained by summing the partial contributions to the solution coming from trees with arbitrary number of branches. These contributions, coming in general from multi-dimensional definite integrals, are efficiently computed by a quasi-Monte Carlo method. It is shown how the accuracy of the method can be effectively increased by considering more terms of the series. The new representation was used successfully to develop a Probabilistic Domain Decomposition method suited for massively parallel computers, which improves the scalability found in classical methods. Finally, a few numerical examples based on classical phenomena such as the non-linear Landau damping, and the two streaming instability are given, illustrating the remarkable performance of the algorithm, when compared the results with those obtained using a classical method.

  1. PIPS-SBB: A Parallel Distributed-Memory Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

    DOE PAGESBeta

    Munguia, Lluis-Miquel; Oxberry, Geoffrey; Rajan, Deepak

    2016-05-01

    Stochastic mixed-integer programs (SMIPs) deal with optimization under uncertainty at many levels of the decision-making process. When solved as extensive formulation mixed- integer programs, problem instances can exceed available memory on a single workstation. In order to overcome this limitation, we present PIPS-SBB: a distributed-memory parallel stochastic MIP solver that takes advantage of parallelism at multiple levels of the optimization process. We also show promising results on the SIPLIB benchmark by combining methods known for accelerating Branch and Bound (B&B) methods with new ideas that leverage the structure of SMIPs. Finally, we expect the performance of PIPS-SBB to improve furthermore » as more functionality is added in the future.« less

  2. Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

    NASA Technical Reports Server (NTRS)

    Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

    2010-01-01

    For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

  3. An automatic water body area monitoring algorithm for satellite images based on Markov Random Fields

    NASA Astrophysics Data System (ADS)

    Elmi, Omid; Tourian, Mohammad J.; Sneeuw, Nico

    2016-04-01

    Our knowledge about spatial and temporal variation of hydrological parameters are surprisingly poor, because most of it is based on in situ stations and the number of stations have reduced dramatically during the past decades. On the other hand, remote sensing techniques have proven their ability to measure different parameters of Earth phenomena. Optical and SAR satellite imagery provide the opportunity to monitor the spatial change in coastline, which can serve as a way to determine the water extent repeatedly in an appropriate time interval. An appropriate classification technique to separate water and land is the backbone of each automatic water body monitoring. Due to changes in the water level, river and lake extent, atmosphere, sunlight radiation and onboard calibration of the satellite over time, most of the pixel-based classification techniques fail to determine accurate water masks. Beyond pixel intensity, spatial correlation between neighboring pixels is another source of information that should be used to decide the label of pixels. Water bodies have strong spatial correlation in satellite images. Therefore including contextual information as additional constraint into the procedure of water body monitoring improves the accuracy of the derived water masks significantly. In this study, we present an automatic algorithm for water body area monitoring based on maximum a posteriori (MAP) estimation of Markov Random Fields (MRF). First we collect all available images from selected case studies during the monitoring period. Then for each image separately we apply a k-means clustering to derive a primary water mask. After that we develop a MRF using pixel values and the primary water mask for each image. Then among the different realizations of the field we select the one that maximizes the posterior estimation. We solve this optimization problem using graph cut techniques. A graph with two terminals is constructed, after which the best labelling structure for

  4. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift.

    PubMed

    Zhang, Yudong; Yang, Jiquan; Yang, Jianfei; Liu, Aijun; Sun, Ping

    2016-01-01

    Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. PMID:27066068

  5. A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

    PubMed Central

    Zhang, Yudong; Yang, Jiquan; Yang, Jianfei; Liu, Aijun; Sun, Ping

    2016-01-01

    Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches. PMID:27066068

  6. Model parameter adaption-based multi-model algorithm for extended object tracking using a random matrix.

    PubMed

    Li, Borui; Mu, Chundi; Han, Shuli; Bai, Tianming

    2014-01-01

    Traditional object tracking technology usually regards the target as a point source object. However, this approximation is no longer appropriate for tracking extended objects such as large targets and closely spaced group objects. Bayesian extended object tracking (EOT) using a random symmetrical positive definite (SPD) matrix is a very effective method to jointly estimate the kinematic state and physical extension of the target. The key issue in the application of this random matrix-based EOT approach is to model the physical extension and measurement noise accurately. Model parameter adaptive approaches for both extension dynamic and measurement noise are proposed in this study based on the properties of the SPD matrix to improve the performance of extension estimation. An interacting multi-model algorithm based on model parameter adaptive filter using random matrix is also presented. Simulation results demonstrate the effectiveness of the proposed adaptive approaches and multi-model algorithm. The estimation performance of physical extension is better than the other algorithms, especially when the target maneuvers. The kinematic state estimation error is lower than the others as well. PMID:24763252

  7. Algorithms for propagating uncertainty across heterogeneous domains

    SciTech Connect

    Cho, Heyrim; Yang, Xiu; Venturi, D.; Karniadakis, George E.

    2015-12-30

    We address an important research area in stochastic multi-scale modeling, namely the propagation of uncertainty across heterogeneous domains characterized by partially correlated processes with vastly different correlation lengths. This class of problems arise very often when computing stochastic PDEs and particle models with stochastic/stochastic domain interaction but also with stochastic/deterministic coupling. The domains may be fully embedded, adjacent or partially overlapping. The fundamental open question we address is the construction of proper transmission boundary conditions that preserve global statistical properties of the solution across different subdomains. Often, the codes that model different parts of the domains are black-box and hence a domain decomposition technique is required. No rigorous theory or even effective empirical algorithms have yet been developed for this purpose, although interfaces defined in terms of functionals of random fields (e.g., multi-point cumulants) can overcome the computationally prohibitive problem of preserving sample-path continuity across domains. The key idea of the different methods we propose relies on combining local reduced-order representations of random fields with multi-level domain decomposition. Specifically, we propose two new algorithms: The first one enforces the continuity of the conditional mean and variance of the solution across adjacent subdomains by using Schwarz iterations. The second algorithm is based on PDE-constrained multi-objective optimization, and it allows us to set more general interface conditions. The effectiveness of these new algorithms is demonstrated in numerical examples involving elliptic problems with random diffusion coefficients, stochastically advected scalar fields, and nonlinear advection-reaction problems with random reaction rates.

  8. Addition of random run FM noise to the KPW time scale algorithm

    NASA Technical Reports Server (NTRS)

    Greenhall, C. A.

    2002-01-01

    The KPW (Kalman plus weights) time scale algorithm uses a Kalman filter to provide frequency and drift information to a basic time scale equation. This paper extends the algorithm to three-state clocks nd gives results for a simulated eight-clock ensemble.

  9. Stochastic models of solute transport in highly heterogeneous geologic media

    SciTech Connect

    Semenov, V.N.; Korotkin, I.A.; Pruess, K.; Goloviznin, V.M.; Sorokovikova, O.S.

    2009-09-15

    A stochastic model of anomalous diffusion was developed in which transport occurs by random motion of Brownian particles, described by distribution functions of random displacements with heavy (power-law) tails. One variant of an effective algorithm for random function generation with a power-law asymptotic and arbitrary factor of asymmetry is proposed that is based on the Gnedenko-Levy limit theorem and makes it possible to reproduce all known Levy {alpha}-stable fractal processes. A two-dimensional stochastic random walk algorithm has been developed that approximates anomalous diffusion with streamline-dependent and space-dependent parameters. The motivation for introducing such a type of dispersion model is the observed fact that tracers in natural aquifers spread at different super-Fickian rates in different directions. For this and other important cases, stochastic random walk models are the only known way to solve the so-called multiscaling fractional order diffusion equation with space-dependent parameters. Some comparisons of model results and field experiments are presented.

  10. An algorithm to detect chimeric clones and random noise in genomic mapping

    SciTech Connect

    Grigoriev, A.; Mott, R.; Lehrach, H.

    1994-07-15

    Experimental noise and contiguous clone inserts can pose serious problems in reconstructing genomic maps from hybridization data. The authors describe an algorithm that easily identifies false positive signals and clones containing chimeric inserts/internal deletions. The algorithm {open_quotes}dechimerizes{close_quotes} clones, splitting them into independent contiguous components and cleaning the initial library into a more consistent data set for further ordering. The effectiveness of the algorithm is demonstrated on both simulated data and the real YAC map of the whole genome genome of the fission yeast Schizosaccharomyces pombe. 8 refs., 3 figs., 1 tab.

  11. A randomized controlled trial of a diagnostic algorithm for symptoms of uncomplicated cystitis at an out-of-hours service

    PubMed Central

    Grude, Nils; Lindbaek, Morten

    2015-01-01

    Objective. To compare the clinical outcome of patients presenting with symptoms of uncomplicated cystitis who were seen by a doctor, with patients who were given treatment following a diagnostic algorithm. Design. Randomized controlled trial. Setting. Out-of-hours service, Oslo, Norway. Intervention. Women with typical symptoms of uncomplicated cystitis were included in the trial in the time period September 2010–November 2011. They were randomized into two groups. One group received standard treatment according to the diagnostic algorithm, the other group received treatment after a regular consultation by a doctor. Subjects. Women (n = 441) aged 16–55 years. Mean age in both groups 27 years. Main outcome measures. Number of days until symptomatic resolution. Results. No significant differences were found between the groups in the basic patient demographics, severity of symptoms, or percentage of urine samples with single culture growth. A median of three days until symptomatic resolution was found in both groups. By day four 79% in the algorithm group and 72% in the regular consultation group were free of symptoms (p = 0.09). The number of patients who contacted a doctor again in the follow-up period and received alternative antibiotic treatment was insignificantly higher (p = 0.08) after regular consultation than after treatment according to the diagnostic algorithm. There were no cases of severe pyelonephritis or hospital admissions during the follow-up period. Conclusion. Using a diagnostic algorithm is a safe and efficient method for treating women with symptoms of uncomplicated cystitis at an out-of-hours service. This simplification of treatment strategy can lead to a more rational use of consultation time and a stricter adherence to National Antibiotic Guidelines for a common disorder. PMID:25961367

  12. Stochastic Processes in Electrochemistry.

    PubMed

    Singh, Pradyumna S; Lemay, Serge G

    2016-05-17

    Stochastic behavior becomes an increasingly dominant characteristic of electrochemical systems as we probe them on the smallest scales. Advances in the tools and techniques of nanoelectrochemistry dictate that stochastic phenomena will become more widely manifest in the future. In this Perspective, we outline the conceptual tools that are required to analyze and understand this behavior. We draw on examples from several specific electrochemical systems where important information is encoded in, and can be derived from, apparently random signals. This Perspective attempts to serve as an accessible introduction to understanding stochastic phenomena in electrochemical systems and outlines why they cannot be understood with conventional macroscopic descriptions. PMID:27120701

  13. On implementation of EM-type algorithms in the stochastic models for a matrix computing on GPU

    SciTech Connect

    Gorshenin, Andrey K.

    2015-03-10

    The paper discusses the main ideas of an implementation of EM-type algorithms for computing on the graphics processors and the application for the probabilistic models based on the Cox processes. An example of the GPU’s adapted MATLAB source code for the finite normal mixtures with the expectation-maximization matrix formulas is given. The testing of computational efficiency for GPU vs CPU is illustrated for the different sample sizes.

  14. Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization

    NASA Astrophysics Data System (ADS)

    Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.

    2011-09-01

    The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.

  15. A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Kar, Mohuya B.; Bera, Shankar; Das, Debasis; Kar, Samarjit

    2015-10-01

    This paper presents a production-inventory model for deteriorating items with stock-dependent demand under inflation in a random planning horizon. The supplier offers the retailer fully permissible delay in payment. It is assumed that the time horizon of the business period is random in nature and follows exponential distribution with a known mean. Here learning effect is also introduced for the production cost and setup cost. The model is formulated as profit maximization problem with respect to the retailer and solved with the help of genetic algorithm (GA) and PSO. Moreover, the convergence of two methods—GA and PSO—is studied against generation numbers and it is seen that GA converges rapidly than PSO. The optimum results from methods are compared both numerically and graphically. It is observed that the performance of GA is marginally better than PSO. We have provided some numerical examples and some sensitivity analyses to illustrate the model.

  16. A multiresolution wavelet analysis and Gaussian Markov random field algorithm for breast cancer screening of digital mammography

    SciTech Connect

    Lee, C.G.; Chen, C.H.

    1996-12-31

    In this paper a novel multiresolution wavelet analysis (MWA) and non-stationary Gaussian Markov random field (GMRF) technique is introduced for the identification of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides a highly efficient technique for microcalcification detection. A Bayesian teaming paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of larger lesions recorded on the mammograms. The effectiveness of the approach has been extensively tested with a number of mammographic images provided by a local hospital.

  17. Mass weighted urn design - a new randomization algorithm for unequal allocations

    PubMed Central

    Zhao, Wenle

    2015-01-01

    Unequal allocations have been used in clinical trials motivated by ethical, efficiency, or feasibility concerns. Commonly used permuted block randomization faces a tradeoff between effective imbalance control with a small block size and accurate allocation target with a large block size. Few other unequal allocation randomization designs have been proposed in literature with applications in real trials hardly ever been reported, partly due to their complexity in implementation compared to the permuted block randomization. Proposed in this paper is the mass weighted urn design, in which the number of balls in the urn equals to the number of treatments, and remains unchanged during the study. The chance a ball being randomly selected is proportional to the mass of the ball. After each treatment assignment, a part of the mass of the selected ball is re-distributed to all balls based on the target allocation ratio. This design allows any desired optimal unequal allocations be accurately targeted without approximation, and provides a consistent imbalance control throughout the allocation sequence. The statistical properties of this new design is evaluated with the Euclidean distance between the observed treatment distribution and the desired treatment distribution as the treatment imbalance measure; and the Euclidean distance between the conditional allocation probability and the target allocation probability as the allocation predictability measure. Computer simulation results are presented comparing the mass weighted urn design with other randomization designs currently available for unequal allocations. PMID:26091947

  18. A Prüfer-Sequence Based Algorithm for Calculating the Size of Ideal Randomly Branched Polymers.

    PubMed

    Singaram, Surendra W; Gopal, Ajaykumar; Ben-Shaul, Avinoam

    2016-07-01

    Branched polymers can be represented as tree graphs. A one-to-one correspondence exists between a tree graph comprised of N labeled vertices and a sequence of N - 2 integers, known as the Prüfer sequence. Permutations of this sequence yield sequences corresponding to tree graphs with the same vertex-degree distribution but (generally) different branching patterns. Repeatedly shuffling the Prüfer sequence we have generated large ensembles of random tree graphs, all with the same degree distributions. We also present and apply an efficient algorithm to determine graph distances directly from their Prüfer sequences. From the (Prüfer sequence derived) graph distances, 3D size metrics, e.g., the polymer's radius of gyration, Rg, and average end-to-end distance, were then calculated using several different theoretical approaches. Applying our method to ideal randomly branched polymers of different vertex-degree distributions, all their 3D size measures are found to obey the usual N(1/4) scaling law. Among the branched polymers analyzed are RNA molecules comprised of equal proportions of the four-randomly distributed-nucleotides. Prior to Prüfer shuffling, the vertices of their representative tree graphs, these "random-sequence" RNAs exhibit an Rg ∼ N(1/3) scaling. PMID:27104292

  19. Using a stochastic gradient boosting algorithm to analyse the effectiveness of Landsat 8 data for montado land cover mapping: Application in southern Portugal

    NASA Astrophysics Data System (ADS)

    Godinho, Sérgio; Guiomar, Nuno; Gil, Artur

    2016-07-01

    This study aims to develop and propose a methodological approach for montado ecosystem mapping using Landsat 8 multi-spectral data, vegetation indices, and the Stochastic Gradient Boosting (SGB) algorithm. Two Landsat 8 scenes (images from spring and summer 2014) of the same area in southern Portugal were acquired. Six vegetation indices were calculated for each scene: the Enhanced Vegetation Index (EVI), the Short-Wave Infrared Ratio (SWIR32), the Carotenoid Reflectance Index 1 (CRI1), the Green Chlorophyll Index (CIgreen), the Normalised Multi-band Drought Index (NMDI), and the Soil-Adjusted Total Vegetation Index (SATVI). Based on this information, two datasets were prepared: (i) Dataset I only included multi-temporal Landsat 8 spectral bands (LS8), and (ii) Dataset II included the same information as Dataset I plus vegetation indices (LS8 + VIs). The integration of the vegetation indices into the classification scheme resulted in a significant improvement in the accuracy of Dataset II's classifications when compared to Dataset I (McNemar test: Z-value = 4.50), leading to a difference of 4.90% in overall accuracy and 0.06 in the Kappa value. For the montado ecosystem, adding vegetation indices in the classification process showed a relevant increment in producer and user accuracies of 3.64% and 6.26%, respectively. By using the variable importance function from the SGB algorithm, it was found that the six most prominent variables (from a total of 24 tested variables) were the following: EVI_summer; CRI1_spring; SWIR32_spring; B6_summer; B5_summer; and CIgreen_summer.

  20. [Segmentation of Winter Wheat Canopy Image Based on Visual Spectral and Random Forest Algorithm].

    PubMed

    Liu, Ya-dong; Cui, Ri-xian

    2015-12-01

    Digital image analysis has been widely used in non-destructive monitoring of crop growth and nitrogen nutrition status due to its simplicity and efficiency. It is necessary to segment winter wheat plant from soil background for accessing canopy cover, intensity level of visible spectrum (R, G, and B) and other color indices derived from RGB. In present study, according to the variation in R, G, and B components of sRGB color space and L*, a*, and b* components of CIEL* a* b* color space between wheat plant and soil background, the segmentation of wheat plant from soil background were conducted by the Otsu's method based on a* component of CIEL* a* b* color space, and RGB based random forest method, and CIEL* a* b* based random forest method, respectively. Also the ability to segment wheat plant from soil background was evaluated with the value of segmentation accuracy. The results showed that all three methods had revealed good ability to segment wheat plant from soil background. The Otsu's method had lowest segmentation accuracy in comparison with the other two methods. There were only little difference in segmentation error between the two random forest methods. In conclusion, the random forest method had revealed its capacity to segment wheat plant from soil background with only the visual spectral information of canopy image without any color components combinations or any color space transformation. PMID:26964234

  1. Heuristic-biased stochastic sampling

    SciTech Connect

    Bresina, J.L.

    1996-12-31

    This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search.

  2. GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm

    PubMed Central

    Hastie, David I.; Zeller, Tanja; Liquet, Benoit; Newcombe, Paul; Yengo, Loic; Wild, Philipp S.; Schillert, Arne; Ziegler, Andreas; Nielsen, Sune F.; Butterworth, Adam S.; Ho, Weang Kee; Castagné, Raphaële; Munzel, Thomas; Tregouet, David; Falchi, Mario; Cambien, François; Nordestgaard, Børge G.; Fumeron, Fredéric; Tybjærg-Hansen, Anne; Froguel, Philippe; Danesh, John; Petretto, Enrico; Blankenberg, Stefan; Tiret, Laurence; Richardson, Sylvia

    2013-01-01

    Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This

  3. Fast Numerical Algorithms for 3-D Scattering from PEC and Dielectric Random Rough Surfaces in Microwave Remote Sensing

    NASA Astrophysics Data System (ADS)

    Zhang, Lisha

    We present fast and robust numerical algorithms for 3-D scattering from perfectly electrical conducting (PEC) and dielectric random rough surfaces in microwave remote sensing. The Coifman wavelets or Coiflets are employed to implement Galerkin's procedure in the method of moments (MoM). Due to the high-precision one-point quadrature, the Coiflets yield fast evaluations of the most off-diagonal entries, reducing the matrix fill effort from O(N2) to O( N). The orthogonality and Riesz basis of the Coiflets generate well conditioned impedance matrix, with rapid convergence for the conjugate gradient solver. The resulting impedance matrix is further sparsified by the matrix-formed standard fast wavelet transform (SFWT). By properly selecting multiresolution levels of the total transformation matrix, the solution precision can be enhanced while matrix sparsity and memory consumption have not been noticeably sacrificed. The unified fast scattering algorithm for dielectric random rough surfaces can asymptotically reduce to the PEC case when the loss tangent grows extremely large. Numerical results demonstrate that the reduced PEC model does not suffer from ill-posed problems. Compared with previous publications and laboratory measurements, good agreement is observed.

  4. Estimation of spatial variability in humidity, wind, and solar radiation using the random forest algorithm for the conterminous USA

    NASA Astrophysics Data System (ADS)

    Hashimoto, H.; Nemani, R. R.

    2015-12-01

    Regional scale ecosystem modeling requires high-resolution data of surface climate variables. Spatial variability in temperature and precipitation has been well studied over the past two decades resulting in several sophisticated algorithms. However, compared to temperature and precipitation, other surface climate variables, such as humidity, solar radiation and wind speed, are not available to use, even though those data are equally important for ecosystem modeling. The main reason for this is the lack of governing physical equations for interpolating observations and the lack of comparable satellite observations. Therefore, scientists have been using reanalysis data or simply interpolated data for ecosystem modeling, though they are too coarse for regional scale ecosystem analysis. In this study, we developed a method to spatially map daily climate variables, including humidity, solar radiation, wind, precipitation, and temperature. We applied the method to the conterminous USA from 1980 to 2015. Previously, we successfully developed a precipitation interpolation method using random forest algorithm, and now we extended it to the other variables. Because this method does not require any assumptions about physical equations, this method can potentially be applicable to any climate variable if measured data are available. The method requires point data along with a host of spatial data sets . Satellite data, reanalysis data, and radar data were used and the importance of each dataset was analyzed using random forest algorithm. The only parameter we need to adjust is the radius from the target point, in which statistically meaningful relationships between observed and spatial co-variate data is calculated. The radius was optimized using mean absolute error and bias. We also analyzed temporal consistency and spatial patterns of the results. Because it is relatively easy to customize the setup depending on user's request, the resulting datasets may be useful for

  5. Exact stochastic simulation of coupled chemical reactions with delays

    NASA Astrophysics Data System (ADS)

    Cai, Xiaodong

    2007-03-01

    Gillespie's exact stochastic simulation algorithm (SSA) [J. Phys. Chem. 81, 2350 (1977)] has been widely used to simulate the stochastic dynamics of chemically reacting systems. In this algorithm, it is assumed that all reactions occur instantly. While this is true in many cases, it is also possible that some chemical reactions, such as gene transcription and translation in living cells, take certain time to finish after they are initiated. Thus, the product of such reactions will emerge after certain delays. Apparently, Gillespie's SSA is not an exact algorithm for chemical reaction systems with delays. In this paper, the author develops an exact SSA for chemical reaction systems with delays, based upon the same fundamental premise of stochastic kinetics used by Gillespie in the development of his SSA. He then shows that an algorithm modified from Gillespie's SSA by Barrio et al. [PLOS Comput. Biol. 2, 1017 (2006)] is also an exact SSA for chemical reaction systems with delays, but it needs to generate more random variables than the author's algorithm.

  6. The adaptive dynamic community detection algorithm based on the non-homogeneous random walking

    NASA Astrophysics Data System (ADS)

    Xin, Yu; Xie, Zhi-Qiang; Yang, Jing

    2016-05-01

    With the changing of the habit and custom, people's social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.

  7. Improved scaling of time-evolving block-decimation algorithm through reduced-rank randomized singular value decomposition

    NASA Astrophysics Data System (ADS)

    Tamascelli, D.; Rosenbach, R.; Plenio, M. B.

    2015-06-01

    When the amount of entanglement in a quantum system is limited, the relevant dynamics of the system is restricted to a very small part of the state space. When restricted to this subspace the description of the system becomes efficient in the system size. A class of algorithms, exemplified by the time-evolving block-decimation (TEBD) algorithm, make use of this observation by selecting the relevant subspace through a decimation technique relying on the singular value decomposition (SVD). In these algorithms, the complexity of each time-evolution step is dominated by the SVD. Here we show that, by applying a randomized version of the SVD routine (RRSVD), the power law governing the computational complexity of TEBD is lowered by one degree, resulting in a considerable speed-up. We exemplify the potential gains in efficiency at the hand of some real world examples to which TEBD can be successfully applied and demonstrate that for those systems RRSVD delivers results as accurate as state-of-the-art deterministic SVD routines.

  8. Pharmacologic treatment for urgency-predominant urinary incontinence in women diagnosed using a simplified algorithm: a randomized trial

    PubMed Central

    Huang, Alison J.; Hess, Rachel; Arya, Lily A.; Richter, Holly E.; Subak, Leslee L.; Bradley, Catherine S.; Rogers, Rebecca G.; Myers, Deborah L.; Johnson, Karen C.; Gregory, W. Thomas; Kraus, Stephen R.; Schembri, Michael; Brown, Jeanette S.

    2013-01-01

    Objective The purpose of this study was to evaluate clinical outcomes associated with the initiation of treatment for urgency-predominant incontinence in women diagnosed by a simple 3-item questionnaire. Study Design We conducted a multicenter, double-blinded, 12-week randomized trial of pharmacologic therapy for urgency-predominant incontinence in ambulatory women diagnosed by the simple 3-item questionnaire. Participants (N = 645) were assigned randomly to fesoterodine therapy (4-8 mg daily) or placebo. Urinary incontinence was assessed with the use of voiding diaries; postvoid residual volume was measured after treatment. Results After 12 weeks, women who had been assigned randomly to fesoterodine therapy reported 0.9 fewer urgency and 1.0 fewer total incontinence episodes/day, compared with placebo (P ≤ .001). Four serious adverse events occurred in each group, none of which was related to treatment. No participant had postvoid residual volume of ≥250 mL after treatment. Conclusion Among ambulatory women with urgency-predominant incontinence diagnosed with a simple 3-item questionnaire, pharmacologic therapy resulted in a moderate decrease in incontinence frequency without increasing significant urinary retention or serious adverse events, which provides support for a streamlined algorithm for diagnosis and treatment of female urgency-predominant incontinence. PMID:22542122

  9. Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm

    NASA Astrophysics Data System (ADS)

    Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne

    2010-02-01

    Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.

  10. Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

    NASA Astrophysics Data System (ADS)

    Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

    2016-01-01

    In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

  11. Random Forest Classification for Surficial Material Mapping in Northern Canada

    NASA Astrophysics Data System (ADS)

    Parkinson, William

    There is a need at the Geological Survey of Canada to apply improved accuracy assessments of satellite image classification and to support remote predictive mapping techniques for geological map production and field operations. Most existing image classification algorithms, however, lack any robust capabilities for assessing classification accuracy and its variability throughout the landscape. In this study, a random forest classification workflow is introduced to improve understanding of overall image classification accuracy and to better describe its spatial variability across a heterogeneous landscape in Northern Canada. Random Forest model is a stochastic implementation of classification and regression trees, which is computationally efficient, effectively handles outlier bias can be used on non-parametric data sources. A variable selection methodology and stochastic accuracy assessment for Random Forest is introduced. Random forest provides an enhanced classification compared to the standard maximum likelihood algorithms improving predictive capacity of satellite imagery for surficial material mapping.

  12. Finite-Size Scaling in Random K-SAT Problems

    NASA Astrophysics Data System (ADS)

    Ha, Meesoon; Lee, Sang Hoon; Jeon, Chanil; Jeong, Hawoong

    2010-03-01

    We propose a comprehensive view of threshold behaviors in random K-satisfiability (K-SAT) problems, in the context of the finite-size scaling (FSS) concept of nonequilibrium absorbing phase transitions using the average SAT (ASAT) algorithm. In particular, we focus on the value of the FSS exponent to characterize the SAT/UNSAT phase transition, which is still debatable. We also discuss the role of the noise (temperature-like) parameter in stochastic local heuristic search algorithms.

  13. Analysis of a stack algorithm for random multiple-access communication

    NASA Astrophysics Data System (ADS)

    Fayolle, G.; Flajolet, P.; Jacquet, P.; Hofri, M.

    1985-03-01

    The present investigation is concerned with the performance of a protocol for managing the use of a single-channel packet switching communications network such as the one employed in the Aloha network. The conducted analysis is mainly concerned with the Capetanakis-Tsybakov-Mikhailov (CTM) access protocols and collision resolution algorithms (CRA), coupled with free, or continuous, access of newly arriving packets into the contention. The considered scheme proves ergodic as long as the rate of generation of new packets is below a certain bound. Attention is given to the basic equation and the collision resolution interval (CRI) duration, the direct evaluation of the mean delay, the distribution of the states of the top of stack, the moments of packet delay time, and the numerical results.

  14. Efficient asymmetric image authentication schemes based on photon counting-double random phase encoding and RSA algorithms.

    PubMed

    Moon, Inkyu; Yi, Faliu; Han, Mingu; Lee, Jieun

    2016-06-01

    Recently, double random phase encoding (DRPE) has been integrated with the photon counting (PC) imaging technique for the purpose of secure image authentication. In this scheme, the same key should be securely distributed and shared between the sender and receiver, but this is one of the most vexing problems of symmetric cryptosystems. In this study, we propose an efficient asymmetric image authentication scheme by combining the PC-DRPE and RSA algorithms, which solves key management and distribution problems. The retrieved image from the proposed authentication method contains photon-limited encrypted data obtained by means of PC-DRPE. Therefore, the original image can be protected while the retrieved image can be efficiently verified using a statistical nonlinear correlation approach. Experimental results demonstrate the feasibility of our proposed asymmetric image authentication method. PMID:27411183

  15. Gray level co-occurrence and random forest algorithm-based gender determination with maxillary tooth plaster images.

    PubMed

    Akkoç, Betül; Arslan, Ahmet; Kök, Hatice

    2016-06-01

    Gender is one of the intrinsic properties of identity, with performance enhancement reducing the cluster when a search is performed. Teeth have durable and resistant structure, and as such are important sources of identification in disasters (accident, fire, etc.). In this study, gender determination is accomplished by maxillary tooth plaster models of 40 people (20 males and 20 females). The images of tooth plaster models are taken with a lighting mechanism set-up. A gray level co-occurrence matrix of the image with segmentation is formed and classified via a Random Forest (RF) algorithm by extracting pertinent features of the matrix. Automatic gender determination has a 90% success rate, with an applicable system to determine gender from maxillary tooth plaster images. PMID:27104495

  16. Ant colony optimization and stochastic gradient descent.

    PubMed

    Meuleau, Nicolas; Dorigo, Marco

    2002-01-01

    In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques. PMID:12171633

  17. Sublinear scaling for time-dependent stochastic density functional theory

    SciTech Connect

    Gao, Yi; Neuhauser, Daniel; Baer, Roi; Rabani, Eran

    2015-01-21

    A stochastic approach to time-dependent density functional theory is developed for computing the absorption cross section and the random phase approximation (RPA) correlation energy. The core idea of the approach involves time-propagation of a small set of stochastic orbitals which are first projected on the occupied space and then propagated in time according to the time-dependent Kohn-Sham equations. The evolving electron density is exactly represented when the number of random orbitals is infinite, but even a small number (≈16) of such orbitals is enough to obtain meaningful results for absorption spectrum and the RPA correlation energy per electron. We implement the approach for silicon nanocrystals using real-space grids and find that the overall scaling of the algorithm is sublinear with computational time and memory.

  18. Q-ary collision resolution algorithms in random-access systems with free or blocked channel access

    NASA Astrophysics Data System (ADS)

    Mathys, P.; Flajolet, P.

    1985-03-01

    The throughput characteristics of contention-based random-access systems (RAS's) which use Q-ary tree algorithms (where Q equal to or greater than 2 is the number of groups into which contending users are split) of the Capetanakis-Tsybakov-Mikhailov-Vvedenskaya type are analyzed for an infinite population of identical users generating packets according to a Poisson process. Both free and blocked channel-access protocols are considered in combination with Q-ary collision resoluton algorithms that exploit either binary ('collision/no collision') or ternary ('collision/success/idle') feedback. For the resulting RAS's, functional equations for transformed generating functions of the first two moments of the collision resolution interval length are obtained and solved. The maximum stable throughput as a function of Q is given. The results of a packet-delay analysis are also given, and the analyzed RAS's are compared among themselves and with the slotted ALOHA system in terms of both system throughput and packet delay. It is concluded that the 'practical optimum' RAS (in terms of ease of implementation combined with good performance) uses free (i.e., immediate) channel access and ternary splitting (i.e., Q = 3) with binary feedback.

  19. Efficient stochastic superparameterization for geophysical turbulence.

    PubMed

    Grooms, Ian; Majda, Andrew J

    2013-03-19

    Efficient computation of geophysical turbulence, such as occurs in the atmosphere and ocean, is a formidable challenge for the following reasons: the complex combination of waves, jets, and vortices; significant energetic backscatter from unresolved small scales to resolved large scales; a lack of dynamical scale separation between large and small scales; and small-scale instabilities, conditional on the large scales, which do not saturate. Nevertheless, efficient methods are needed to allow large ensemble simulations of sufficient size to provide meaningful quantifications of uncertainty in future predictions and past reanalyses through data assimilation and filtering. Here, a class of efficient stochastic superparameterization algorithms is introduced. In contrast to conventional superparameterization, the method here (i) does not require the simulation of nonlinear eddy dynamics on periodic embedded domains, (ii) includes a better representation of unresolved small-scale instabilities, and (iii) allows efficient representation of a much wider range of unresolved scales. The simplest algorithm implemented here radically improves efficiency by representing small-scale eddies at and below the limit of computational resolution by a suitable one-dimensional stochastic model of random-direction plane waves. In contrast to heterogeneous multiscale methods, the methods developed here do not require strong scale separation or conditional equilibration of local statistics. The simplest algorithm introduced here shows excellent performance on a difficult test suite of prototype problems for geophysical turbulence with waves, jets, and vortices, with a speedup of several orders of magnitude compared with direct simulation. PMID:23487800

  20. Efficient stochastic superparameterization for geophysical turbulence

    PubMed Central

    Grooms, Ian; Majda, Andrew J.

    2013-01-01

    Efficient computation of geophysical turbulence, such as occurs in the atmosphere and ocean, is a formidable challenge for the following reasons: the complex combination of waves, jets, and vortices; significant energetic backscatter from unresolved small scales to resolved large scales; a lack of dynamical scale separation between large and small scales; and small-scale instabilities, conditional on the large scales, which do not saturate. Nevertheless, efficient methods are needed to allow large ensemble simulations of sufficient size to provide meaningful quantifications of uncertainty in future predictions and past reanalyses through data assimilation and filtering. Here, a class of efficient stochastic superparameterization algorithms is introduced. In contrast to conventional superparameterization, the method here (i) does not require the simulation of nonlinear eddy dynamics on periodic embedded domains, (ii) includes a better representation of unresolved small-scale instabilities, and (iii) allows efficient representation of a much wider range of unresolved scales. The simplest algorithm implemented here radically improves efficiency by representing small-scale eddies at and below the limit of computational resolution by a suitable one-dimensional stochastic model of random-direction plane waves. In contrast to heterogeneous multiscale methods, the methods developed here do not require strong scale separation or conditional equilibration of local statistics. The simplest algorithm introduced here shows excellent performance on a difficult test suite of prototype problems for geophysical turbulence with waves, jets, and vortices, with a speedup of several orders of magnitude compared with direct simulation. PMID:23487800

  1. Applicability of random sequential adsorption algorithm for simulation of surface plasma polishing kinetics

    NASA Astrophysics Data System (ADS)

    Minárik, Stanislav; Vaňa, Dušan

    2015-11-01

    Applicability of random sequential adsorption (RSA) model for the material removal during a surface plasma polishing is discussed. The mechanical nature of plasma polishing process is taken into consideration in modified version of RSA model. During the plasma polishing the surface layer is aligned such that molecules of material are removed from the surface mechanically as a consequence of the surface deformation induced by plasma particles impact. We propose modification of RSA technique to describe the reduction of material on the surface provided that sequential character of molecules release from the surface is maintained throughout the polishing process. This empirical model is able to estimate depth profile of material density on the surface during the plasma polishing. We have shown that preliminary results obtained from this model are in good agreement with experimental results. We believe that molecular dynamics simulation of the polishing process, possibly also other types of surface treatment, can be based on this model. However influence of material parameters and processing conditions (including plasma characteristics) must be taken into account using appropriate model variables.

  2. Stochastic hybrid modeling of intracellular calcium dynamics

    NASA Astrophysics Data System (ADS)

    Choi, TaiJung; Maurya, Mano Ram; Tartakovsky, Daniel M.; Subramaniam, Shankar

    2010-10-01

    Deterministic models of biochemical processes at the subcellular level might become inadequate when a cascade of chemical reactions is induced by a few molecules. Inherent randomness of such phenomena calls for the use of stochastic simulations. However, being computationally intensive, such simulations become infeasible for large and complex reaction networks. To improve their computational efficiency in handling these networks, we present a hybrid approach, in which slow reactions and fluxes are handled through exact stochastic simulation and their fast counterparts are treated partially deterministically through chemical Langevin equation. The classification of reactions as fast or slow is accompanied by the assumption that in the time-scale of fast reactions, slow reactions do not occur and hence do not affect the probability of the state. Our new approach also handles reactions with complex rate expressions such as Michaelis-Menten kinetics. Fluxes which cannot be modeled explicitly through reactions, such as flux of Ca2+ from endoplasmic reticulum to the cytosol through inositol 1,4,5-trisphosphate receptor channels, are handled deterministically. The proposed hybrid algorithm is used to model the regulation of the dynamics of cytosolic calcium ions in mouse macrophage RAW 264.7 cells. At relatively large number of molecules, the response characteristics obtained with the stochastic and deterministic simulations coincide, which validates our approach in the limit of large numbers. At low doses, the response characteristics of some key chemical species, such as levels of cytosolic calcium, predicted with stochastic simulations, differ quantitatively from their deterministic counterparts. These observations are ubiquitous throughout dose response, sensitivity, and gene-knockdown response analyses. While the relative differences between the peak-heights of the cytosolic [Ca2+] time-courses obtained from stochastic (mean of 16 realizations) and deterministic

  3. Stochastic operator-splitting method for reaction-diffusion systems

    NASA Astrophysics Data System (ADS)

    Choi, TaiJung; Maurya, Mano Ram; Tartakovsky, Daniel M.; Subramaniam, Shankar

    2012-11-01

    Many biochemical processes at the sub-cellular level involve a small number of molecules. The local numbers of these molecules vary in space and time, and exhibit random fluctuations that can only be captured with stochastic simulations. We present a novel stochastic operator-splitting algorithm to model such reaction-diffusion phenomena. The reaction and diffusion steps employ stochastic simulation algorithms and Brownian dynamics, respectively. Through theoretical analysis, we have developed an algorithm to identify if the system is reaction-controlled, diffusion-controlled or is in an intermediate regime. The time-step size is chosen accordingly at each step of the simulation. We have used three examples to demonstrate the accuracy and robustness of the proposed algorithm. The first example deals with diffusion of two chemical species undergoing an irreversible bimolecular reaction. It is used to validate our algorithm by comparing its results with the solution obtained from a corresponding deterministic partial differential equation at low and high number of molecules. In this example, we also compare the results from our method to those obtained using a Gillespie multi-particle (GMP) method. The second example, which models simplified RNA synthesis, is used to study the performance of our algorithm in reaction- and diffusion-controlled regimes and to investigate the effects of local inhomogeneity. The third example models reaction-diffusion of CheY molecules through the cytoplasm of Escherichia coli during chemotaxis. It is used to compare the algorithm's performance against the GMP method. Our analysis demonstrates that the proposed algorithm enables accurate simulation of the kinetics of complex and spatially heterogeneous systems. It is also computationally more efficient than commonly used alternatives, such as the GMP method.

  4. Development and Evaluation of a New Air Exchange Rate Algorithm for the Stochastic Human Exposure and Dose Simulation Model (ISES Presentation)

    EPA Science Inventory

    Previous exposure assessment panel studies have observed considerable seasonal, between-home and between-city variability in residential pollutant infiltration. This is likely a result of differences in home ventilation, or air exchange rates (AER). The Stochastic Human Exposure ...

  5. Stochastic superparameterization in quasigeostrophic turbulence

    SciTech Connect

    Grooms, Ian; Majda, Andrew J.

    2014-08-15

    In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and vortices. Conventional superparameterization simulates large scale dynamics on a coarse grid in a physical domain, and couples these dynamics to high-resolution simulations on periodic domains embedded in the coarse grid. Stochastic superparameterization replaces the nonlinear, deterministic eddy equations on periodic embedded domains by quasilinear stochastic approximations on formally infinite embedded domains. The result is a seamless algorithm which never uses a small scale grid and is far cheaper than conventional SP, but with significant success in difficult test problems. Various design choices in the algorithm are investigated in detail here, including decoupling the timescale of evolution on the embedded domains from the length of the time step used on the coarse grid, and sensitivity to certain assumed properties of the eddies (e.g. the shape of the assumed eddy energy spectrum). We present four closures based on stochastic superparameterization which elucidate the properties of the underlying framework: a ‘null hypothesis’ stochastic closure that uncouples the eddies from the mean, a stochastic closure with nonlinearly coupled eddies and mean, a nonlinear deterministic closure, and a stochastic closure based on energy conservation. The different algorithms are compared and contrasted on a stringent test suite for quasigeostrophic turbulence involving two-layer dynamics on a β-plane forced by an imposed background shear. The success of the algorithms developed here suggests that they may be fruitfully applied to more realistic situations. They are expected to be particularly useful in providing accurate and

  6. Nonlinear optimization for stochastic simulations.

    SciTech Connect

    Johnson, Michael M.; Yoshimura, Ann S.; Hough, Patricia Diane; Ammerlahn, Heidi R.

    2003-12-01

    This report describes research targeting development of stochastic optimization algorithms and their application to mission-critical optimization problems in which uncertainty arises. The first section of this report covers the enhancement of the Trust Region Parallel Direct Search (TRPDS) algorithm to address stochastic responses and the incorporation of the algorithm into the OPT++ optimization library. The second section describes the Weapons of Mass Destruction Decision Analysis Center (WMD-DAC) suite of systems analysis tools and motivates the use of stochastic optimization techniques in such non-deterministic simulations. The third section details a batch programming interface designed to facilitate criteria-based or algorithm-driven execution of system-of-system simulations. The fourth section outlines the use of the enhanced OPT++ library and batch execution mechanism to perform systems analysis and technology trade-off studies in the WMD detection and response problem domain.

  7. Generalized spectral decomposition for stochastic nonlinear problems

    SciTech Connect

    Nouy, Anthony Le Maitre, Olivier P.

    2009-01-10

    We present an extension of the generalized spectral decomposition method for the resolution of nonlinear stochastic problems. The method consists in the construction of a reduced basis approximation of the Galerkin solution and is independent of the stochastic discretization selected (polynomial chaos, stochastic multi-element or multi-wavelets). Two algorithms are proposed for the sequential construction of the successive generalized spectral modes. They involve decoupled resolutions of a series of deterministic and low-dimensional stochastic problems. Compared to the classical Galerkin method, the algorithms allow for significant computational savings and require minor adaptations of the deterministic codes. The methodology is detailed and tested on two model problems, the one-dimensional steady viscous Burgers equation and a two-dimensional nonlinear diffusion problem. These examples demonstrate the effectiveness of the proposed algorithms which exhibit convergence rates with the number of modes essentially dependent on the spectrum of the stochastic solution but independent of the dimension of the stochastic approximation space.

  8. Fock space, symbolic algebra, and analytical solutions for small stochastic systems

    NASA Astrophysics Data System (ADS)

    Santos, Fernando A. N.; Gadêlha, Hermes; Gaffney, Eamonn A.

    2015-12-01

    Randomness is ubiquitous in nature. From single-molecule biochemical reactions to macroscale biological systems, stochasticity permeates individual interactions and often regulates emergent properties of the system. While such systems are regularly studied from a modeling viewpoint using stochastic simulation algorithms, numerous potential analytical tools can be inherited from statistical and quantum physics, replacing randomness due to quantum fluctuations with low-copy-number stochasticity. Nevertheless, classical studies remained limited to the abstract level, demonstrating a more general applicability and equivalence between systems in physics and biology rather than exploiting the physics tools to study biological systems. Here the Fock space representation, used in quantum mechanics, is combined with the symbolic algebra of creation and annihilation operators to consider explicit solutions for the chemical master equations describing small, well-mixed, biochemical, or biological systems. This is illustrated with an exact solution for a Michaelis-Menten single enzyme interacting with limited substrate, including a consideration of very short time scales, which emphasizes when stiffness is present even for small copy numbers. Furthermore, we present a general matrix representation for Michaelis-Menten kinetics with an arbitrary number of enzymes and substrates that, following diagonalization, leads to the solution of this ubiquitous, nonlinear enzyme kinetics problem. For this, a flexible symbolic maple code is provided, demonstrating the prospective advantages of this framework compared to stochastic simulation algorithms. This further highlights the possibilities for analytically based studies of stochastic systems in biology and chemistry using tools from theoretical quantum physics.

  9. Fock space, symbolic algebra, and analytical solutions for small stochastic systems.

    PubMed

    Santos, Fernando A N; Gadêlha, Hermes; Gaffney, Eamonn A

    2015-12-01

    Randomness is ubiquitous in nature. From single-molecule biochemical reactions to macroscale biological systems, stochasticity permeates individual interactions and often regulates emergent properties of the system. While such systems are regularly studied from a modeling viewpoint using stochastic simulation algorithms, numerous potential analytical tools can be inherited from statistical and quantum physics, replacing randomness due to quantum fluctuations with low-copy-number stochasticity. Nevertheless, classical studies remained limited to the abstract level, demonstrating a more general applicability and equivalence between systems in physics and biology rather than exploiting the physics tools to study biological systems. Here the Fock space representation, used in quantum mechanics, is combined with the symbolic algebra of creation and annihilation operators to consider explicit solutions for the chemical master equations describing small, well-mixed, biochemical, or biological systems. This is illustrated with an exact solution for a Michaelis-Menten single enzyme interacting with limited substrate, including a consideration of very short time scales, which emphasizes when stiffness is present even for small copy numbers. Furthermore, we present a general matrix representation for Michaelis-Menten kinetics with an arbitrary number of enzymes and substrates that, following diagonalization, leads to the solution of this ubiquitous, nonlinear enzyme kinetics problem. For this, a flexible symbolic maple code is provided, demonstrating the prospective advantages of this framework compared to stochastic simulation algorithms. This further highlights the possibilities for analytically based studies of stochastic systems in biology and chemistry using tools from theoretical quantum physics. PMID:26764734

  10. Stochastic games

    PubMed Central

    Solan, Eilon; Vieille, Nicolas

    2015-01-01

    In 1953, Lloyd Shapley contributed his paper “Stochastic games” to PNAS. In this paper, he defined the model of stochastic games, which were the first general dynamic model of a game to be defined, and proved that it admits a stationary equilibrium. In this Perspective, we summarize the historical context and the impact of Shapley’s contribution. PMID:26556883

  11. Numerical Methods for Stochastic Partial Differential Equations

    SciTech Connect

    Sharp, D.H.; Habib, S.; Mineev, M.B.

    1999-07-08

    This is the final report of a Laboratory Directed Research and Development (LDRD) project at the Los Alamos National laboratory (LANL). The objectives of this proposal were (1) the development of methods for understanding and control of spacetime discretization errors in nonlinear stochastic partial differential equations, and (2) the development of new and improved practical numerical methods for the solutions of these equations. The authors have succeeded in establishing two methods for error control: the functional Fokker-Planck equation for calculating the time discretization error and the transfer integral method for calculating the spatial discretization error. In addition they have developed a new second-order stochastic algorithm for multiplicative noise applicable to the case of colored noises, and which requires only a single random sequence generation per time step. All of these results have been verified via high-resolution numerical simulations and have been successfully applied to physical test cases. They have also made substantial progress on a longstanding problem in the dynamics of unstable fluid interfaces in porous media. This work has lead to highly accurate quasi-analytic solutions of idealized versions of this problem. These may be of use in benchmarking numerical solutions of the full stochastic PDEs that govern real-world problems.

  12. Variance decomposition in stochastic simulators

    SciTech Connect

    Le Maître, O. P.; Knio, O. M.; Moraes, A.

    2015-06-28

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

  13. Variance decomposition in stochastic simulators

    NASA Astrophysics Data System (ADS)

    Le Maître, O. P.; Knio, O. M.; Moraes, A.

    2015-06-01

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.

  14. Biochemical simulations: stochastic, approximate stochastic and hybrid approaches

    PubMed Central

    2009-01-01

    Computer simulations have become an invaluable tool to study the sometimes counterintuitive temporal dynamics of (bio-)chemical systems. In particular, stochastic simulation methods have attracted increasing interest recently. In contrast to the well-known deterministic approach based on ordinary differential equations, they can capture effects that occur due to the underlying discreteness of the systems and random fluctuations in molecular numbers. Numerous stochastic, approximate stochastic and hybrid simulation methods have been proposed in the literature. In this article, they are systematically reviewed in order to guide the researcher and help her find the appropriate method for a specific problem. PMID:19151097

  15. Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

    NASA Astrophysics Data System (ADS)

    Odindi, John; Adam, Elhadi; Ngubane, Zinhle; Mutanga, Onisimo; Slotow, Rob

    2014-01-01

    Plant species invasion is known to be a major threat to socioeconomic and ecological systems. Due to high cost and limited extents of urban green spaces, high mapping accuracy is necessary to optimize the management of such spaces. We compare the performance of the new-generation WorldView-2 (WV-2) and SPOT-5 images in mapping the bracken fern [Pteridium aquilinum (L) kuhn] in a conserved urban landscape. Using the random forest algorithm, grid-search approaches based on out-of-bag estimate error were used to determine the optimal ntree and mtry combinations. The variable importance and backward feature elimination techniques were further used to determine the influence of the image bands on mapping accuracy. Additionally, the value of the commonly used vegetation indices in enhancing the classification accuracy was tested on the better performing image data. Results show that the performance of the new WV-2 bands was better than that of the traditional bands. Overall classification accuracies of 84.72 and 72.22% were achieved for the WV-2 and SPOT images, respectively. Use of selected indices from the WV-2 bands increased the overall classification accuracy to 91.67%. The findings in this study show the suitability of the new generation in mapping the bracken fern within the often vulnerable urban natural vegetation cover types.

  16. Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

    NASA Astrophysics Data System (ADS)

    Yin, George; Wang, Le Yi; Zhang, Hongwei

    2014-12-01

    Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomly switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.

  17. Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

    SciTech Connect

    Yin, George; Wang, Le Yi; Zhang, Hongwei

    2014-12-10

    Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomly switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.

  18. Extended Mixed-Efects Item Response Models with the MH-RM Algorithm

    ERIC Educational Resources Information Center

    Chalmers, R. Philip

    2015-01-01

    A mixed-effects item response theory (IRT) model is presented as a logical extension of the generalized linear mixed-effects modeling approach to formulating explanatory IRT models. Fixed and random coefficients in the extended model are estimated using a Metropolis-Hastings Robbins-Monro (MH-RM) stochastic imputation algorithm to accommodate for…

  19. A fast and memory-sparing probabilistic selection algorithm for the GPU

    SciTech Connect

    Monroe, Laura M; Wendelberger, Joanne; Michalak, Sarah

    2010-09-29

    A fast and memory-sparing probabilistic top-N selection algorithm is implemented on the GPU. This probabilistic algorithm gives a deterministic result and always terminates. The use of randomization reduces the amount of data that needs heavy processing, and so reduces both the memory requirements and the average time required for the algorithm. This algorithm is well-suited to more general parallel processors with multiple layers of memory hierarchy. Probabilistic Las Vegas algorithms of this kind are a form of stochastic optimization and can be especially useful for processors having a limited amount of fast memory available.

  20. Efficient prediction designs for random fields

    PubMed Central

    Müller, Werner G; Pronzato, Luc; Rendas, Joao; Waldl, Helmut

    2015-01-01

    For estimation and predictions of random fields, it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging (EK) are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the EK variance when space-filling designs become unsuitable. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, whereas the second uses the surrogate criteria as local heuristic to choose the points at which the (costly) true EK variance is effectively computed. We illustrate the performance of the algorithms presented on both a simple simulated example and a real oceanographic dataset. © 2014 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd. PMID:26300698

  1. Random phase textures: theory and synthesis.

    PubMed

    Galerne, Bruno; Gousseau, Yann; Morel, Jean-Michel

    2011-01-01

    This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample. PMID:20550995

  2. Optimization of Monte Carlo transport simulations in stochastic media

    SciTech Connect

    Liang, C.; Ji, W.

    2012-07-01

    This paper presents an accurate and efficient approach to optimize radiation transport simulations in a stochastic medium of high heterogeneity, like the Very High Temperature Gas-cooled Reactor (VHTR) configurations packed with TRISO fuel particles. Based on a fast nearest neighbor search algorithm, a modified fast Random Sequential Addition (RSA) method is first developed to speed up the generation of the stochastic media systems packed with both mono-sized and poly-sized spheres. A fast neutron tracking method is then developed to optimize the next sphere boundary search in the radiation transport procedure. In order to investigate their accuracy and efficiency, the developed sphere packing and neutron tracking methods are implemented into an in-house continuous energy Monte Carlo code to solve an eigenvalue problem in VHTR unit cells. Comparison with the MCNP benchmark calculations for the same problem indicates that the new methods show considerably higher computational efficiency. (authors)

  3. Epidemiologic programs for computers and calculators. Simple algorithms for the representation of deterministic and stochastic versions of the Reed-Frost epidemic model using a programmable calculator.

    PubMed

    Franco, E L; Simons, A R

    1986-05-01

    Two programs are described for the emulation of the dynamics of Reed-Frost progressive epidemics in a handheld programmable calculator (HP-41C series). The programs provide a complete record of cases, susceptibles, and immunes at each epidemic period using either the deterministic formulation or the trough analogue of the mechanical model for the stochastic version. Both programs can compute epidemics that include a constant rate of influx or outflux of susceptibles and single or double infectivity time periods. PMID:3962973

  4. Handling packet dropouts and random delays for unstable delayed processes in NCS by optimal tuning of PIλDμ controllers with evolutionary algorithms.

    PubMed

    Pan, Indranil; Das, Saptarshi; Gupta, Amitava

    2011-10-01

    The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI(λ)D(μ) controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications. PMID:21621208

  5. Stochastic Vorticity and Associated Filtering Theory

    SciTech Connect

    Amirdjanova, A.; Kallianpur, G.

    2002-12-19

    The focus of this work is on a two-dimensional stochastic vorticity equation for an incompressible homogeneous viscous fluid. We consider a signed measure-valued stochastic partial differential equation for a vorticity process based on the Skorohod-Ito evolution of a system of N randomly moving point vortices. A nonlinear filtering problem associated with the evolution of the vorticity is considered and a corresponding Fujisaki-Kallianpur-Kunita stochastic differential equation for the optimal filter is derived.

  6. Discrete Stochastic Simulation Methods for Chemically Reacting Systems

    PubMed Central

    Cao, Yang; Samuels, David C.

    2012-01-01

    Discrete stochastic chemical kinetics describe the time evolution of a chemically reacting system by taking into account the fact that in reality chemical species are present with integer populations and exhibit some degree of randomness in their dynamical behavior. In recent years, with the development of new techniques to study biochemistry dynamics in a single cell, there are increasing studies using this approach to chemical kinetics in cellular systems, where the small copy number of some reactant species in the cell may lead to deviations from the predictions of the deterministic differential equations of classical chemical kinetics. This chapter reviews the fundamental theory related to stochastic chemical kinetics and several simulation methods that are based on that theory. We focus on non-stiff biochemical systems and the two most important discrete stochastic simulation methods: Gillespie's Stochastic Simulation Algorithm (SSA) and the tau-leaping method. Different implementation strategies of these two methods are discussed. Then we recommend a relatively simple and efficient strategy that combines the strengths of the two methods: the hybrid SSA/tau-leaping method. The implementation details of the hybrid strategy are given here and a related software package is introduced. Finally, the hybrid method is applied to simple biochemical systems as a demonstration of its application. PMID:19216925

  7. Stochastic damage evolution in textile laminates

    NASA Technical Reports Server (NTRS)

    Dzenis, Yuris A.; Bogdanovich, Alexander E.; Pastore, Christopher M.

    1993-01-01

    A probabilistic model utilizing random material characteristics to predict damage evolution in textile laminates is presented. Model is based on a division of each ply into two sublaminas consisting of cells. The probability of cell failure is calculated using stochastic function theory and maximal strain failure criterion. Three modes of failure, i.e. fiber breakage, matrix failure in transverse direction, as well as matrix or interface shear cracking, are taken into account. Computed failure probabilities are utilized in reducing cell stiffness based on the mesovolume concept. A numerical algorithm is developed predicting the damage evolution and deformation history of textile laminates. Effect of scatter of fiber orientation on cell properties is discussed. Weave influence on damage accumulation is illustrated with the help of an example of a Kevlar/epoxy laminate.

  8. Development and comparison of algorithms for generating a scan sequence for a random access scanner. [ZAP (and flow charts for ZIP and SCAN), in FORTRAN for DEC-10

    SciTech Connect

    Eason, R. O.

    1980-09-01

    Many data acquisition systems incorporate high-speed scanners to convert analog signals into digital format for further processing. Some systems multiplex many channels into a single scanner. A random access scanner whose scan sequence is specified by a table in random access memory will permit different scan rates on different channels. Generation of this scan table can be a tedious manual task when there are many channels (e.g. 50), when there are more than a few scan rates (e.g. 5), and/or when the ratio of the highest scan rate to the lowest scan rate becomes large (e.g. 100:1). An algorithm is developed which will generate these scan sequences for the random access scanner and implements the algorithm on a digital computer. Application of number theory to the mathematical statement of the problem led to development of several algorithms which were implemented in FORTRAN. The most efficient of these algorithms operates by partitioning the problem into a set of subproblems. Through recursion they solve each subproblem by partitioning it repeatedly into even smaller parts, continuing until a set of simple problems is created. From this process, a pictorial representation or wheel diagram of the problem can be constructed. From the wheel diagram and a description of the original problem, a scan table can be constructed. In addition, the wheel diagram can be used as a method of storing the scan sequence in a smaller amount of memory. The most efficient partitioning algorithm solved most scan table problems in less than a second of CPU time. Some types of problems, however, required as much as a few minutes of CPU time. 26 figures, 2 tables.

  9. From Complex to Simple: Interdisciplinary Stochastic Models

    ERIC Educational Resources Information Center

    Mazilu, D. A.; Zamora, G.; Mazilu, I.

    2012-01-01

    We present two simple, one-dimensional, stochastic models that lead to a qualitative understanding of very complex systems from biology, nanoscience and social sciences. The first model explains the complicated dynamics of microtubules, stochastic cellular highways. Using the theory of random walks in one dimension, we find analytical expressions…

  10. Attainability analysis in stochastic controlled systems

    SciTech Connect

    Ryashko, Lev

    2015-03-10

    A control problem for stochastically forced nonlinear continuous-time systems is considered. We propose a method for construction of the regulator that provides a preassigned probabilistic distribution of random states in stochastic equilibrium. Geometric criteria of the controllability are obtained. Constructive technique for the specification of attainability sets is suggested.

  11. Discontinuity detection in multivariate space for stochastic simulations

    SciTech Connect

    Archibald, Rick Gelb, Anne Saxena, Rishu Xiu Dongbin

    2009-04-20

    Edge detection has traditionally been associated with detecting physical space jump discontinuities in one dimension, e.g. seismic signals, and two dimensions, e.g. digital images. Hence most of the research on edge detection algorithms is restricted to these contexts. High dimension edge detection can be of significant importance, however. For instance, stochastic variants of classical differential equations not only have variables in space/time dimensions, but additional dimensions are often introduced to the problem by the nature of the random inputs. The stochastic solutions to such problems sometimes contain discontinuities in the corresponding random space and a prior knowledge of jump locations can be very helpful in increasing the accuracy of the final solution. Traditional edge detection methods typically require uniform grid point distribution. They also often involve the computation of gradients and/or Laplacians, which can become very complicated to compute as the number of dimensions increases. The polynomial annihilation edge detection method, on the other hand, is more flexible in terms of its geometric specifications and is furthermore relatively easy to apply. This paper discusses the numerical implementation of the polynomial annihilation edge detection method to high dimensional functions that arise when solving stochastic partial differential equations.

  12. Discontinuity Detection in Multivariate Space for Stochastic Simulations

    SciTech Connect

    Archibald, Richard K; Gelb, Anne; Saxena, Rishu; Xiu, Dongbin

    2009-01-01

    Edge detection has traditionally been associated with detecting physical space jump discontinuities in one dimension, e.g. seismic signals, and two dimensions, e.g. digital images. Hence most of the research on edge detection algorithms is restricted to these contexts. High dimension edge detection can be of significant importance, however. For instance, stochastic variants of classical differential equations not only have variables in space/time dimensions, but additional dimensions are often introduced to the problem by the nature of the random inputs. The stochastic solutions to such problems sometimes contain discontinuities in the corresponding random space and a prior knowledge of jump locations can be very helpful in increasing the accuracy of the final solution. Traditional edge detection methods typically require uniform grid point distribution. They also often involve the computation of gradients and/or Laplacians, which can become very complicated to compute as the number of dimensions increases. The polynomial annihilation edge detection method, on the other hand, is more flexible in terms of its geometric specifications and is furthermore relatively easy to apply. This paper discusses the numerical implementation of the polynomial annihilation edge detection method to high dimensional functions that arise when solving stochastic partial differential equations.

  13. Circumspect descent prevails in solving random constraint satisfaction problems.

    PubMed

    Alava, Mikko; Ardelius, John; Aurell, Erik; Kaski, Petteri; Krishnamurthy, Supriya; Orponen, Pekka; Seitz, Sakari

    2008-10-01

    We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios alpha; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms. PMID:18832149

  14. Stochastic thermodynamics

    NASA Astrophysics Data System (ADS)

    Eichhorn, Ralf; Aurell, Erik

    2014-04-01

    'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response

  15. Stochastic cooling

    SciTech Connect

    Bisognano, J.; Leemann, C.

    1982-03-01

    Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron.

  16. Stochastic partial differential equations in turbulence related problems

    NASA Technical Reports Server (NTRS)

    Chow, P.-L.

    1978-01-01

    The theory of stochastic partial differential equations (PDEs) and problems relating to turbulence are discussed by employing the theories of Brownian motion and diffusion in infinite dimensions, functional differential equations, and functional integration. Relevant results in probablistic analysis, especially Gaussian measures in function spaces and the theory of stochastic PDEs of Ito type, are taken into account. Linear stochastic PDEs are analyzed through linearized Navier-Stokes equations with a random forcing. Stochastic equations for waves in random media as well as model equations in turbulent transport theory are considered. Markovian models in fully developed turbulence are discussed from a stochastic equation viewpoint.

  17. Stochastic model of the residual acceleration environment in microgravity

    NASA Technical Reports Server (NTRS)

    Vinals, Jorge

    1994-01-01

    We describe a theoretical investigation of the effects that stochastic residual accelerations (g-jitter) onboard spacecraft can have on experiments conducted in a microgravity environment. We first introduce a stochastic model of the residual acceleration field, and develop a numerical algorithm to solve the equations governing fluid flow that allow for a stochastic body force. We next summarize our studies of two generic situations: stochastic parametric resonance and the onset of convective flow induced by a fluctuating acceleration field.

  18. Stochastic calculus in physics

    SciTech Connect

    Fox, R.F.

    1987-03-01

    The relationship of Ito-Stratonovich stochastic calculus to studies of weakly colored noise is explained. A functional calculus approach is used to obtain an effective Fokker-Planck equation for the weakly colored noise regime. In a smooth limit, this representation produces the Stratonovich version of the Ito-Stratonovich calculus for white noise. It also provides an approach to steady state behavior for strongly colored noise. Numerical simulation algorithms are explored, and a novel suggestion is made for efficient and accurate simulation of white noise equations.

  19. Stochastic thermodynamics of resetting

    NASA Astrophysics Data System (ADS)

    Fuchs, Jaco; Goldt, Sebastian; Seifert, Udo

    2016-03-01

    Stochastic dynamics with random resetting leads to a non-equilibrium steady state. Here, we consider the thermodynamics of resetting by deriving the first and second law for resetting processes far from equilibrium. We identify the contributions to the entropy production of the system which arise due to resetting and show that they correspond to the rate with which information is either erased or created. Using Landauer's principle, we derive a bound on the amount of work that is required to maintain a resetting process. We discuss different regimes of resetting, including a Maxwell demon scenario where heat is extracted from a bath at constant temperature.

  20. Fluctuating currents in stochastic thermodynamics. II. Energy conversion and nonequilibrium response in kinesin models

    NASA Astrophysics Data System (ADS)

    Altaner, Bernhard; Wachtel, Artur; Vollmer, Jürgen

    2015-10-01

    Unlike macroscopic engines, the molecular machinery of living cells is strongly affected by fluctuations. Stochastic thermodynamics uses Markovian jump processes to model the random transitions between the chemical and configurational states of these biological macromolecules. A recently developed theoretical framework [A. Wachtel, J. Vollmer, and B. Altaner, Phys. Rev. E 92, 042132 (2015), 10.1103/PhysRevE.92.042132] provides a simple algorithm for the determination of macroscopic currents and correlation integrals of arbitrary fluctuating currents. Here we use it to discuss energy conversion and nonequilibrium response in different models for the molecular motor kinesin. Methodologically, our results demonstrate the effectiveness of the algorithm in dealing with parameter-dependent stochastic models. For the concrete biophysical problem our results reveal two interesting features in experimentally accessible parameter regions: the validity of a nonequilibrium Green-Kubo relation at mechanical stalling as well as a negative differential mobility for superstalling forces.

  1. Fluctuating currents in stochastic thermodynamics. II. Energy conversion and nonequilibrium response in kinesin models.

    PubMed

    Altaner, Bernhard; Wachtel, Artur; Vollmer, Jürgen

    2015-10-01

    Unlike macroscopic engines, the molecular machinery of living cells is strongly affected by fluctuations. Stochastic thermodynamics uses Markovian jump processes to model the random transitions between the chemical and configurational states of these biological macromolecules. A recently developed theoretical framework [A. Wachtel, J. Vollmer, and B. Altaner, Phys. Rev. E 92, 042132 (2015)] provides a simple algorithm for the determination of macroscopic currents and correlation integrals of arbitrary fluctuating currents. Here we use it to discuss energy conversion and nonequilibrium response in different models for the molecular motor kinesin. Methodologically, our results demonstrate the effectiveness of the algorithm in dealing with parameter-dependent stochastic models. For the concrete biophysical problem our results reveal two interesting features in experimentally accessible parameter regions: the validity of a nonequilibrium Green-Kubo relation at mechanical stalling as well as a negative differential mobility for superstalling forces. PMID:26565194

  2. Stochastic simulations of minimal cells: the Ribocell model

    PubMed Central

    2012-01-01

    Background Over the last two decades, lipid compartments (liposomes, lipid-coated droplets) have been extensively used as in vitro "minimal" cell models. In particular, simple and complex biomolecular reactions have been carried out inside these self-assembled micro- and nano-sized compartments, leading to the synthesis of RNA and functional proteins inside liposomes. Despite this experimental progress, a detailed physical understanding of the underlying dynamics is missing. In particular, the combination of solute compartmentalization, reactivity and stochastic effects has not yet been clarified. A combination of experimental and computational approaches can reveal interesting mechanisms governing the behavior of micro compartmentalized systems, in particular by highlighting the intrinsic stochastic diversity within a population of "synthetic cells". Methods In this context, we have developed a computational platform called ENVIRONMENT suitable for studying the stochastic time evolution of reacting lipid compartments. This software - which implements a Gillespie Algorithm - is an improvement over a previous program that simulated the stochastic time evolution of homogeneous, fixed-volume, chemically reacting systems, extending it to more general conditions in which a collection of similar such systems interact and change over the course of time. In particular, our approach is focused on elucidating the role of randomness in the time behavior of chemically reacting lipid compartments, such as micelles, vesicles or micro emulsions, in regimes where random fluctuations due to the stochastic nature of reacting events can lead an open system towards unexpected time evolutions. Results This paper analyses the so-called Ribocell (RNA-based cell) model. It consists in a hypothetical minimal cell based on a self-replicating minimum RNA genome coupled with a self-reproducing lipid vesicle compartment. This model assumes the existence of two ribozymes, one able to catalyze

  3. Bayesian Estimation and Inference Using Stochastic Electronics

    PubMed Central

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  4. Bayesian Estimation and Inference Using Stochastic Electronics.

    PubMed

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

  5. Quantum Spontaneous Stochasticity

    NASA Astrophysics Data System (ADS)

    Drivas, Theodore; Eyink, Gregory

    Classical Newtonian dynamics is expected to be deterministic, but recent fluid turbulence theory predicts that a particle advected at high Reynolds-numbers by ''nearly rough'' flows moves nondeterministically. Small stochastic perturbations to the flow velocity or to the initial data lead to persistent randomness, even in the limit where the perturbations vanish! Such ``spontaneous stochasticity'' has profound consequences for astrophysics, geophysics, and our daily lives. We show that a similar effect occurs with a quantum particle in a ''nearly rough'' force, for the semi-classical (large-mass) limit, where spreading of the wave-packet is usually expected to be negligible and dynamics to be deterministic Newtonian. Instead, there are non-zero probabilities to observe multiple, non-unique solutions of the classical equations. Although the quantum wave-function remains split, rapid phase oscillations prevent any coherent superposition of the branches. Classical spontaneous stochasticity has not yet been seen in controlled laboratory experiments of fluid turbulence, but the corresponding quantum effects may be observable by current techniques. We suggest possible experiments with neutral atomic-molecular systems in repulsive electric dipole potentials.

  6. Stochastic kinetic mean field model

    NASA Astrophysics Data System (ADS)

    Erdélyi, Zoltán; Pasichnyy, Mykola; Bezpalchuk, Volodymyr; Tomán, János J.; Gajdics, Bence; Gusak, Andriy M.

    2016-07-01

    This paper introduces a new model for calculating the change in time of three-dimensional atomic configurations. The model is based on the kinetic mean field (KMF) approach, however we have transformed that model into a stochastic approach by introducing dynamic Langevin noise. The result is a stochastic kinetic mean field model (SKMF) which produces results similar to the lattice kinetic Monte Carlo (KMC). SKMF is, however, far more cost-effective and easier to implement the algorithm (open source program code is provided on

  7. A Simple Stochastic Model for Generating Broken Cloud Optical Depth and Top Height Fields

    NASA Technical Reports Server (NTRS)

    Prigarin, Sergei M.; Marshak, Alexander

    2007-01-01

    A simple and fast algorithm for generating two correlated stochastic twodimensional (2D) cloud fields is described. The algorithm is illustrated with two broken cumulus cloud fields: cloud optical depth and cloud top height retrieved from Moderate Resolution Imaging Spectrometer (MODIS). Only two 2D fields are required as an input. The algorithm output is statistical realizations of these two fields with approximately the same correlation and joint distribution functions as the original ones. The major assumption of the algorithm is statistical isotropy of the fields. In contrast to fractals and the Fourier filtering methods frequently used for stochastic cloud modeling, the proposed method is based on spectral models of homogeneous random fields. For keeping the same probability density function as the (first) original field, the method of inverse distribution function is used. When the spatial distribution of the first field has been generated, a realization of the correlated second field is simulated using a conditional distribution matrix. This paper is served as a theoretical justification to the publicly available software that has been recently released by the authors and can be freely downloaded from http://i3rc.gsfc.nasa.gov/Public codes clouds.htm. Though 2D rather than full 3D, stochastic realizations of two correlated cloud fields that mimic statistics of given fields have proved to be very useful to study 3D radiative transfer features of broken cumulus clouds for better understanding of shortwave radiation and interpretation of the remote sensing retrievals.

  8. Improved hybrid optimization algorithm for 3D protein structure prediction.

    PubMed

    Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

    2014-07-01

    A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136

  9. Analysis of stochastic effects in Kaldor-type business cycle discrete model

    NASA Astrophysics Data System (ADS)

    Bashkirtseva, Irina; Ryashko, Lev; Sysolyatina, Anna

    2016-07-01

    We study nonlinear stochastic phenomena in the discrete Kaldor model of business cycles. A numerical parametric analysis of stochastically forced attractors (equilibria, closed invariant curves, discrete cycles) of this model is performed using the stochastic sensitivity functions technique. A spatial arrangement of random states in stochastic attractors is modeled by confidence domains. The phenomenon of noise-induced transitions "chaos-order" is discussed.

  10. Competitive Facility Location with Random Demands

    NASA Astrophysics Data System (ADS)

    Uno, Takeshi; Katagiri, Hideki; Kato, Kosuke

    2009-10-01

    This paper proposes a new location problem of competitive facilities, e.g. shops and stores, with uncertain demands in the plane. By representing the demands for facilities as random variables, the location problem is formulated to a stochastic programming problem, and for finding its solution, three deterministic programming problems: expectation maximizing problem, probability maximizing problem, and satisfying level maximizing problem are considered. After showing that one of their optimal solutions can be found by solving 0-1 programming problems, their solution method is proposed by improving the tabu search algorithm with strategic vibration. Efficiency of the solution method is shown by applying to numerical examples of the facility location problems.

  11. QB1 - Stochastic Gene Regulation

    SciTech Connect

    Munsky, Brian

    2012-07-23

    Summaries of this presentation are: (1) Stochastic fluctuations or 'noise' is present in the cell - Random motion and competition between reactants, Low copy, quantization of reactants, Upstream processes; (2) Fluctuations may be very important - Cell-to-cell variability, Cell fate decisions (switches), Signal amplification or damping, stochastic resonances; and (3) Some tools are available to mode these - Kinetic Monte Carlo simulations (SSA and variants), Moment approximation methods, Finite State Projection. We will see how modeling these reactions can tell us more about the underlying processes of gene regulation.

  12. Stochastic power flow modeling

    SciTech Connect

    Not Available

    1980-06-01

    The stochastic nature of customer demand and equipment failure on large interconnected electric power networks has produced a keen interest in the accurate modeling and analysis of the effects of probabilistic behavior on steady state power system operation. The principle avenue of approach has been to obtain a solution to the steady state network flow equations which adhere both to Kirchhoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques. Clearly the need of the present is to develop sound techniques for producing meaningful data to serve as input. This research has addressed this end and serves to bridge the gap between electric demand modeling, equipment failure analysis, etc., and the area of algorithm development. Therefore, the scope of this work lies squarely on developing an efficient means of producing sensible input information in the form of probability distributions for the many types of solution algorithms that have been developed. Two major areas of development are described in detail: a decomposition of stochastic processes which gives hope of stationarity, ergodicity, and perhaps even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.

  13. Homogeneous Superpixels from Markov Random Walks

    NASA Astrophysics Data System (ADS)

    Perbet, Frank; Stenger, Björn; Maki, Atsuto

    This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

  14. Image segmentation using random-walks on the histogram

    NASA Astrophysics Data System (ADS)

    Morin, Jean-Philippe; Desrosiers, Christian; Duong, Luc

    2012-02-01

    This document presents a novel method for the problem of image segmentation, based on random-walks. This method shares similarities with the Mean-shift algorithm, as it finds the modes of the intensity histogram of images. However, unlike Mean-shift, our proposed method is stochastic and also provides class membership probabilities. Also, unlike other random-walk based methods, our approach does not require any form of user interaction, and can scale to very large images. To illustrate the usefulness, efficiency and scalability of our method, we test it on the task of segmenting anatomical structures present in cardiac CT and brain MRI images.

  15. Accelerating Pseudo-Random Number Generator for MCNP on GPU

    NASA Astrophysics Data System (ADS)

    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.

  16. Competitive Facility Location with Fuzzy Random Demands

    NASA Astrophysics Data System (ADS)

    Uno, Takeshi; Katagiri, Hideki; Kato, Kosuke

    2010-10-01

    This paper proposes a new location problem of competitive facilities, e.g. shops, with uncertainty and vagueness including demands for the facilities in a plane. By representing the demands for facilities as fuzzy random variables, the location problem can be formulated as a fuzzy random programming problem. For solving the fuzzy random programming problem, first the α-level sets for fuzzy numbers are used for transforming it to a stochastic programming problem, and secondly, by using their expectations and variances, it can be reformulated to a deterministic programming problem. After showing that one of their optimal solutions can be found by solving 0-1 programming problems, their solution method is proposed by improving the tabu search algorithm with strategic oscillation. The efficiency of the proposed method is shown by applying it to numerical examples of the facility location problems.

  17. Implementation of Chord Length Sampling for Transport Through a Binary Stochastic Mixture

    SciTech Connect

    T.J. Donovan; T.M. Sutton; Y. Danon

    2002-11-18

    Neutron transport through a special case stochastic mixture is examined, in which spheres of constant radius are uniformly mixed in a matrix material. A Monte Carlo algorithm previously proposed and examined in 2-D has been implemented in a test version of MCNP. The Limited Chord Length Sampling (LCLS) technique provides a means for modeling a binary stochastic mixture as a cell in MCNP. When inside a matrix cell, LCLS uses chord-length sampling to sample the distance to the next stochastic sphere. After a surface crossing into a stochastic sphere, transport is treated explicitly until the particle exits or is killed. Results were computed for a simple model with two different fixed neutron source distributions and three sets of material number densities. Stochastic spheres were modeled as black absorbers and varying degrees of scattering were introduced in the matrix material. Tallies were computed using the LCLS capability and by averaging results obtained from multiple realizations of the random geometry. Results were compared for accuracy and figures of merit were compared to indicate the efficiency gain of the LCLS method over the benchmark method. Results show that LCLS provides very good accuracy if the scattering optical thickness of the matrix is small ({le} 1). Comparisons of figures of merit show an advantage to LCLS varying between factors of 141 and 5. LCLS efficiency and accuracy relative to the benchmark both decrease as scattering is increased in the matrix.

  18. Optimisation of simulations of stochastic processes by removal of opposing reactions

    NASA Astrophysics Data System (ADS)

    Spill, Fabian; Maini, Philip K.; Byrne, Helen M.

    2016-02-01

    Models invoking the chemical master equation are used in many areas of science, and, hence, their simulation is of interest to many researchers. The complexity of the problems at hand often requires considerable computational power, so a large number of algorithms have been developed to speed up simulations. However, a drawback of many of these algorithms is that their implementation is more complicated than, for instance, the Gillespie algorithm, which is widely used to simulate the chemical master equation, and can be implemented with a few lines of code. Here, we present an algorithm which does not modify the way in which the master equation is solved, but instead modifies the transition rates. It works for all models in which reversible reactions occur by replacing such reversible reactions with effective net reactions. Examples of such systems include reaction-diffusion systems, in which diffusion is modelled by a random walk. The random movement of particles between neighbouring sites is then replaced with a net random flux. Furthermore, as we modify the transition rates of the model, rather than its implementation on a computer, our method can be combined with existing algorithms that were designed to speed up simulations of the stochastic master equation. By focusing on some specific models, we show how our algorithm can significantly speed up model simulations while maintaining essential features of the original model.

  19. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor.

    PubMed

    Ma, Xin; Guo, Jing; Xiao, Ke; Sun, Xiao

    2015-01-01

    The prediction of RNA-binding proteins is an incredibly challenging problem in computational biology. Although great progress has been made using various machine learning approaches with numerous features, the problem is still far from being solved. In this study, we attempt to predict RNA-binding proteins directly from amino acid sequences. A novel approach, PRBP predicts RNA-binding proteins using the information of predicted RNA-binding residues in conjunction with a random forest based method. For a given protein, we first predict its RNA-binding residues and then judge whether the protein binds RNA or not based on information from that prediction. If the protein cannot be identified by the information associated with its predicted RNA-binding residues, then a novel random forest predictor is used to determine if the query protein is a RNA-binding protein. We incorporated features of evolutionary information combined with physicochemical features (EIPP) and amino acid composition feature to establish the random forest predictor. Feature analysis showed that EIPP contributed the most to the prediction of RNA-binding proteins. The results also showed that the information from the RNA-binding residue prediction improved the overall performance of our RNA-binding protein prediction. It is anticipated that the PRBP method will become a useful tool for identifying RNA-binding proteins. A PRBP Web server implementation is freely available at http://www.cbi.seu.edu.cn/PRBP/. PMID:26671809

  20. Stochastic uncertainty analysis for solute transport in randomly heterogeneous media using a Karhunen-Loève-based moment equation approach

    USGS Publications Warehouse

    Liu, Gaisheng; Lu, Zhiming; Zhang, Dongxiao

    2007-01-01

    A new approach has been developed for solving solute transport problems in randomly heterogeneous media using the Karhunen-Loève-based moment equation (KLME) technique proposed by Zhang and Lu (2004). The KLME approach combines the Karhunen-Loève decomposition of the underlying random conductivity field and the perturbative and polynomial expansions of dependent variables including the hydraulic head, flow velocity, dispersion coefficient, and solute concentration. The equations obtained in this approach are sequential, and their structure is formulated in the same form as the original governing equations such that any existing simulator, such as Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Groundwater Systems (MT3DMS), can be directly applied as the solver. Through a series of two-dimensional examples, the validity of the KLME approach is evaluated against the classical Monte Carlo simulations. Results indicate that under the flow and transport conditions examined in this work, the KLME approach provides an accurate representation of the mean concentration. For the concentration variance, the accuracy of the KLME approach is good when the conductivity variance is 0.5. As the conductivity variance increases up to 1.0, the mismatch on the concentration variance becomes large, although the mean concentration can still be accurately reproduced by the KLME approach. Our results also indicate that when the conductivity variance is relatively large, neglecting the effects of the cross terms between velocity fluctuations and local dispersivities, as done in some previous studies, can produce noticeable errors, and a rigorous treatment of the dispersion terms becomes more appropriate.

  1. Detecting Character Dependencies in Stochastic Models of Evolution.

    PubMed

    Chakrabarty, Deeparnab; Kannan, Sampath; Tian, Kevin

    2016-03-01

    Stochastic models of biological evolution generally assume that different characters (runs of the stochastic process) are independent and identically distributed. In this article we determine the asymptotic complexity of detecting dependence for some fairly general models of evolution, but simple models of dependence. A key difference from much of the previous work is that our algorithms work without knowledge of the tree topology. Specifically, we consider various stochastic models of evolution ranging from the common ones used by biologists (such as Cavender-Farris-Neyman and Jukes-Cantor models) to very general ones where evolution of different characters can be governed by different transition matrices on each edge of the evolutionary tree (phylogeny). We also consider several models of dependence between two characters. In the most specific model, on each edge of the phylogeny the joint distribution of the dependent characters undergoes a perturbation of a fixed magnitude, in a fixed direction from what it would be if the characters were evolving independently. More general dependence models don't require such a strong "signal." Instead they only require that on each edge, the perturbation of the joint distribution has a significant component in a specific direction. Our main results are nearly tight bounds on the induced or operator norm of the transition matrices that would allow us to detect dependence efficiently for most models of evolution and dependence that we consider. We make essential use of a new concentration result for multistate random variables of a Markov random field on arbitrary trivalent trees: We show that the random variable counting the number of leaves in any particular state has variance that is subquadratic in the number of leaves. PMID:26744770

  2. Variance decomposition in stochastic simulators.

    PubMed

    Le Maître, O P; Knio, O M; Moraes, A

    2015-06-28

    This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models. PMID:26133418

  3. Stochastic Cooling

    SciTech Connect

    Blaskiewicz, M.

    2011-01-01

    Stochastic Cooling was invented by Simon van der Meer and was demonstrated at the CERN ISR and ICE (Initial Cooling Experiment). Operational systems were developed at Fermilab and CERN. A complete theory of cooling of unbunched beams was developed, and was applied at CERN and Fermilab. Several new and existing rings employ coasting beam cooling. Bunched beam cooling was demonstrated in ICE and has been observed in several rings designed for coasting beam cooling. High energy bunched beams have proven more difficult. Signal suppression was achieved in the Tevatron, though operational cooling was not pursued at Fermilab. Longitudinal cooling was achieved in the RHIC collider. More recently a vertical cooling system in RHIC cooled both transverse dimensions via betatron coupling.

  4. Stochastic Wireless Channel Modeling, Estimation and Identification from Measurements

    SciTech Connect

    Olama, Mohammed M; Djouadi, Seddik M; Li, Yanyan

    2008-07-01

    This paper is concerned with stochastic modeling of wireless fading channels, parameter estimation, and system identification from measurement data. Wireless channels are represented by stochastic state-space form, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal measurements. These algorithms estimate the channel inphase and quadrature components and identify the channel parameters recursively. The proposed algorithm is tested using measurement data, and the results are presented.

  5. Connecting the dots: Semi-analytical and random walk numerical solutions of the diffusion–reaction equation with stochastic initial conditions

    SciTech Connect

    Paster, Amir; Bolster, Diogo; Benson, David A.

    2014-04-15

    We study a system with bimolecular irreversible kinetic reaction A+B→∅ where the underlying transport of reactants is governed by diffusion, and the local reaction term is given by the law of mass action. We consider the case where the initial concentrations are given in terms of an average and a white noise perturbation. Our goal is to solve the diffusion–reaction equation which governs the system, and we tackle it with both analytical and numerical approaches. To obtain an analytical solution, we develop the equations of moments and solve them approximately. To obtain a numerical solution, we develop a grid-less Monte Carlo particle tracking approach, where diffusion is modeled by a random walk of the particles, and reaction is modeled by annihilation of particles. The probability of annihilation is derived analytically from the particles' co-location probability. We rigorously derive the relationship between the initial number of particles in the system and the amplitude of white noise represented by that number. This enables us to compare the particle simulations and the approximate analytical solution and offer an explanation of the late time discrepancies. - Graphical abstract:.

  6. Statistical analysis and simulation of random shock waves in scalar conservation laws

    NASA Astrophysics Data System (ADS)

    Venturi, Daniele; Cho, Heyrim; Karniadakis, George

    2014-11-01

    Hyperbolic conservation laws subject to additive random noise and random initial conditions can develop random shock waves at random space-time locations. The statistical analysis of such waves is quite complex, due to non-linearities, high-dimensionality and lack of regularity. By using the Mori-Zwanzig formulation of irreversible statistical mechanics, we derive formally exact reduced-order equations for the one- and two-point probability density function of the solution field. This allows us to perform numerical simulations and determine the statistical properties of the system. We consider the inviscid limit of the stochastic Burgers equation as a model problem and determine its solution in physical and probability spaces by using adaptive discontinuous Galerkin methods. In particular, we study stochastic flows generated by random initial states and random additive noise, yielding multiple interacting shock waves collapsing into clusters and settling down to a similarity state. We also address the question of how random shock waves in space and time manifest themselves in probability space. The mathematical framework is general and it can be applied to other systems, leading to new insights in high-dimensional stochastic dynamics and more efficient computational algorithms. This work was supported by OSD-MURI Grant FA9550-09-1-0613, DOE Grant DE-SC0009247 and NSF/DMS-1216437 grant.

  7. Automated Flight Routing Using Stochastic Dynamic Programming

    NASA Technical Reports Server (NTRS)

    Ng, Hok K.; Morando, Alex; Grabbe, Shon

    2010-01-01

    Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.

  8. Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields: Stochastic inversion of AVA and CSEM data

    SciTech Connect

    Chen, J.; Hoversten, G.M.

    2011-09-15

    Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.

  9. Stochastic Polynomial Dynamic Models of the Yeast Cell Cycle

    NASA Astrophysics Data System (ADS)

    Mitra, Indranil; Dimitrova, Elena; Jarrah, Abdul S.

    2010-03-01

    In the last decade a new holistic approach for tackling biological problems, systems biology, which takes into account the study of the interactions between the components of a biological system to predict function and behavior has emerged. The reverse-engineering of biochemical networks from experimental data have increasingly become important in systems biology. Based on Boolean networks, we propose a time-discrete stochastic framework for the reverse engineering of the yeast cell cycle regulatory network from experimental data. With a suitable choice of state set, we have used powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. Stochasticity is introduced by choosing at each update step a random coordinate function for each variable, chosen from a probability space of update functions. The algorithm is based on a combinatorial structure known as the Gr"obner fans of a polynomial ideal which identifies the underlying network structure and dynamics. The model depicts a correct dynamics of the yeast cell cycle network and reproduces the time sequence of expression patterns along the biological cell cycle. Our findings indicate that the methodolgy has high chance of success when applied to large and complex systems to determine the dynamical properties of corresponding networks.

  10. Multiple Stochastic Point Processes in Gene Expression

    NASA Astrophysics Data System (ADS)

    Murugan, Rajamanickam

    2008-04-01

    We generalize the idea of multiple-stochasticity in chemical reaction systems to gene expression. Using Chemical Langevin Equation approach we investigate how this multiple-stochasticity can influence the overall molecular number fluctuations. We show that the main sources of this multiple-stochasticity in gene expression could be the randomness in transcription and translation initiation times which in turn originates from the underlying bio-macromolecular recognition processes such as the site-specific DNA-protein interactions and therefore can be internally regulated by the supra-molecular structural factors such as the condensation/super-coiling of DNA. Our theory predicts that (1) in case of gene expression system, the variances ( φ) introduced by the randomness in transcription and translation initiation-times approximately scales with the degree of condensation ( s) of DNA or mRNA as φ ∝ s -6. From the theoretical analysis of the Fano factor as well as coefficient of variation associated with the protein number fluctuations we predict that (2) unlike the singly-stochastic case where the Fano factor has been shown to be a monotonous function of translation rate, in case of multiple-stochastic gene expression the Fano factor is a turn over function with a definite minimum. This in turn suggests that the multiple-stochastic processes can also be well tuned to behave like a singly-stochastic point processes by adjusting the rate parameters.

  11. A multilevel stochastic collocation method for SPDEs

    SciTech Connect

    Gunzburger, Max; Jantsch, Peter; Teckentrup, Aretha; Webster, Clayton

    2015-03-10

    We present a multilevel stochastic collocation method that, as do multilevel Monte Carlo methods, uses a hierarchy of spatial approximations to reduce the overall computational complexity when solving partial differential equations with random inputs. For approximation in parameter space, a hierarchy of multi-dimensional interpolants of increasing fidelity are used. Rigorous convergence and computational cost estimates for the new multilevel stochastic collocation method are derived and used to demonstrate its advantages compared to standard single-level stochastic collocation approximations as well as multilevel Monte Carlo methods.

  12. Stochastic blind motion deblurring.

    PubMed

    Xiao, Lei; Gregson, James; Heide, Felix; Heidrich, Wolfgang

    2015-10-01

    Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can, therefore, only be obtained with the help of prior information in the form of (often nonconvex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with Peak Signal-to-Noise Ratio (PSNR) values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms. PMID:25974941

  13. Ergodic crossover in partially self-avoiding stochastic walks

    NASA Astrophysics Data System (ADS)

    Berbert, Juliana M.; González, Rodrigo Silva; Martinez, Alexandre Souto

    2013-09-01

    Consider a one-dimensional environment with N randomly distributed sites. An agent explores this random medium moving deterministically with a spatial memory μ. A crossover from local to global exploration occurs in one dimension at a well-defined memory value μ1=log2N. In its stochastic version, the dynamics is ruled by the memory and by temperature T, which affects the hopping displacement. This dynamics also shows a crossover in one dimension, obtained computationally, between exploration schemes, characterized yet by the trajectory size (Np) (aging effect). In this paper we provide an analytical approach considering the modified stochastic version where the parameter T plays the role of a maximum hopping distance. This modification allows us to obtain a general analytical expression for the crossover, as a function of the parameters μ, T, and Np. Differently from what has been proposed by previous studies, we find that the crossover occurs in any dimension d. These results have been validated by numerical experiments and may be of great value for fixing optimal parameters in search algorithms.

  14. Ergodic crossover in partially self-avoiding stochastic walks.

    PubMed

    Berbert, Juliana M; González, Rodrigo Silva; Martinez, Alexandre Souto

    2013-09-01

    Consider a one-dimensional environment with N randomly distributed sites. An agent explores this random medium moving deterministically with a spatial memory μ. A crossover from local to global exploration occurs in one dimension at a well-defined memory value μ_{1}=log_{2}N. In its stochastic version, the dynamics is ruled by the memory and by temperature T, which affects the hopping displacement. This dynamics also shows a crossover in one dimension, obtained computationally, between exploration schemes, characterized yet by the trajectory size (N_{p}) (aging effect). In this paper we provide an analytical approach considering the modified stochastic version where the parameter T plays the role of a maximum hopping distance. This modification allows us to obtain a general analytical expression for the crossover, as a function of the parameters μ, T, and N_{p}. Differently from what has been proposed by previous studies, we find that the crossover occurs in any dimension d. These results have been validated by numerical experiments and may be of great value for fixing optimal parameters in search algorithms. PMID:24125225

  15. Random-iteration algorithm-based optical parallel architecture for fractal-image decoding by use of iterated-function system codes.

    PubMed

    Chang, H T; Kuo, C J

    1998-03-10

    An optical parallel architecture for the random-iteration algorithm to decode a fractal image by use of iterated-function system (IFS) codes is proposed. The code value is first converted into transmittance in film or a spatial light modulator in the optical part of the system. With an optical-to-electrical converter, electrical-to-optical converter, and some electronic circuits for addition and delay, we can perform the contractive affine transformation (CAT) denoted in IFS codes. In the proposed decoding architecture all CAT's generate points (image pixels) in parallel, and these points then are joined for display purposes. Therefore the decoding speed is improved greatly compared with existing serial-decoding architectures. In addition, an error and stability analysis that considers nonperfect elements is presented for the proposed optical system. Finally, simulation results are given to validate the proposed architecture. PMID:18268718

  16. Simulated annealing and metaheuristic for randomized priority search algorithms for the aerial refuelling parallel machine scheduling problem with due date-to-deadline windows and release times

    NASA Astrophysics Data System (ADS)

    Kaplan, Sezgin; Rabadi, Ghaith

    2013-01-01

    This article addresses the aerial refuelling scheduling problem (ARSP), where a set of fighter jets (jobs) with certain ready times must be refuelled from tankers (machines) by their due dates; otherwise, they reach a low fuel level (deadline) incurring a high cost. ARSP is an identical parallel machine scheduling problem with release times and due date-to-deadline windows to minimize the total weighted tardiness. A simulated annealing (SA) and metaheuristic for randomized priority search (Meta-RaPS) with the newly introduced composite dispatching rule, apparent piecewise tardiness cost with ready times (APTCR), are applied to the problem. Computational experiments compared the algorithms' solutions to optimal solutions for small problems and to each other for larger problems. To obtain optimal solutions, a mixed integer program with a piecewise weighted tardiness objective function was solved for up to 12 jobs. The results show that Meta-RaPS performs better in terms of average relative error but SA is more efficient.

  17. Structural factoring approach for analyzing stochastic networks

    NASA Technical Reports Server (NTRS)

    Hayhurst, Kelly J.; Shier, Douglas R.

    1991-01-01

    The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

  18. Peculiarities of stochastic regime of Arctic ice cover time evolution over 1987-2014 from microwave satellite sounding on the basis of NASA team 2 algorithm

    NASA Astrophysics Data System (ADS)

    Raev, M. D.; Sharkov, E. A.; Tikhonov, V. V.; Repina, I. A.; Komarova, N. Yu.

    2015-12-01

    The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08-F17 satellites; it is permanently supplemented with new data on the Earth's exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987-2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95-98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect.

  19. Digital simulation and modeling of nonlinear stochastic systems

    SciTech Connect

    Richardson, J M; Rowland, J R

    1981-04-01

    Digitally generated solutions of nonlinear stochastic systems are not unique but depend critically on the numerical integration algorithm used. Some theoretical and practical implications of this dependence are examined. The Ito-Stratonovich controversy concerning the solution of nonlinear stochastic systems is shown to be more than a theoretical debate on maintaining Markov properties as opposed to utilizing the computational rules of ordinary calculus. The theoretical arguments give rise to practical considerations in the formation and solution of discrete models from continuous stochastic systems. Well-known numerical integration algorithms are shown not only to provide different solutions for the same stochastic system but also to correspond to different stochastic integral definitions. These correspondences are proved by considering first and second moments of solutions that result from different integration algorithms and then comparing the moments to those arising from various stochastic integral definitions. This algorithm-dependence of solutions is in sharp contrast to the deterministic and linear stochastic cases in which unique solutions are determined by any convergent numerical algorithm. Consequences of the relationship between stochastic system solutions and simulation procedures are presented for a nonlinear filtering example. Monte Carlo simulations and statistical tests are applied to the example to illustrate the determining role which computational procedures play in generating solutions.

  20. Sample controllability of impulsive differential systems with random coefficients

    NASA Astrophysics Data System (ADS)

    Zhang, Shuorui; Sun, Jitao

    2016-07-01

    In this paper, we investigate the controllability of impulsive differential systems with random coefficients. Impulsive differential systems with random coefficients are a different stochastic model from stochastic differential equations. Sufficient conditions of sample controllability for impulsive differential systems with random coefficients are obtained by using random Sadovskii's fixed-point theorem. Finally, an example is given to illustrate our results.

  1. AESS: Accelerated Exact Stochastic Simulation

    NASA Astrophysics Data System (ADS)

    Jenkins, David D.; Peterson, Gregory D.

    2011-12-01

    The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summaryProgram title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution

  2. Surface plasmon enhanced cell microscopy with blocked random spatial activation

    NASA Astrophysics Data System (ADS)

    Son, Taehwang; Oh, Youngjin; Lee, Wonju; Yang, Heejin; Kim, Donghyun

    2016-03-01

    We present surface plasmon enhanced fluorescence microscopy with random spatial sampling using patterned block of silver nanoislands. Rigorous coupled wave analysis was performed to confirm near-field localization on nanoislands. Random nanoislands were fabricated in silver by temperature annealing. By analyzing random near-field distribution, average size of localized fields was found to be on the order of 135 nm. Randomly localized near-fields were used to spatially sample F-actin of J774 cells (mouse macrophage cell-line). Image deconvolution algorithm based on linear imaging theory was established for stochastic estimation of fluorescent molecular distribution. The alignment between near-field distribution and raw image was performed by the patterned block. The achieved resolution is dependent upon factors including the size of localized fields and estimated to be 100-150 nm.

  3. Stochastic Averaging of Duhem Hysteretic Systems

    NASA Astrophysics Data System (ADS)

    YING, Z. G.; ZHU, W. Q.; NI, Y. Q.; KO, J. M.

    2002-06-01

    The response of Duhem hysteretic system to externally and/or parametrically non-white random excitations is investigated by using the stochastic averaging method. A class of integrable Duhem hysteresis models covering many existing hysteresis models is identified and the potential energy and dissipated energy of Duhem hysteretic component are determined. The Duhem hysteretic system under random excitations is replaced equivalently by a non-hysteretic non-linear random system. The averaged Ito's stochastic differential equation for the total energy is derived and the Fokker-Planck-Kolmogorov equation associated with the averaged Ito's equation is solved to yield stationary probability density of total energy, from which the statistics of system response can be evaluated. It is observed that the numerical results by using the stochastic averaging method is in good agreement with that from digital simulation.

  4. Quantum algorithms

    NASA Astrophysics Data System (ADS)

    Abrams, Daniel S.

    This thesis describes several new quantum algorithms. These include a polynomial time algorithm that uses a quantum fast Fourier transform to find eigenvalues and eigenvectors of a Hamiltonian operator, and that can be applied in cases (commonly found in ab initio physics and chemistry problems) for which all known classical algorithms require exponential time. Fast algorithms for simulating many body Fermi systems are also provided in both first and second quantized descriptions. An efficient quantum algorithm for anti-symmetrization is given as well as a detailed discussion of a simulation of the Hubbard model. In addition, quantum algorithms that calculate numerical integrals and various characteristics of stochastic processes are described. Two techniques are given, both of which obtain an exponential speed increase in comparison to the fastest known classical deterministic algorithms and a quadratic speed increase in comparison to classical Monte Carlo (probabilistic) methods. I derive a simpler and slightly faster version of Grover's mean algorithm, show how to apply quantum counting to the problem, develop some variations of these algorithms, and show how both (apparently distinct) approaches can be understood from the same unified framework. Finally, the relationship between physics and computation is explored in some more depth, and it is shown that computational complexity theory depends very sensitively on physical laws. In particular, it is shown that nonlinear quantum mechanics allows for the polynomial time solution of NP-complete and #P oracle problems. Using the Weinberg model as a simple example, the explicit construction of the necessary gates is derived from the underlying physics. Nonlinear quantum algorithms are also presented using Polchinski type nonlinearities which do not allow for superluminal communication. (Copies available exclusively from MIT Libraries, Rm. 14- 0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

  5. Solving stochastic epidemiological models using computer algebra

    NASA Astrophysics Data System (ADS)

    Hincapie, Doracelly; Ospina, Juan

    2011-06-01

    Mathematical modeling in Epidemiology is an important tool to understand the ways under which the diseases are transmitted and controlled. The mathematical modeling can be implemented via deterministic or stochastic models. Deterministic models are based on short systems of non-linear ordinary differential equations and the stochastic models are based on very large systems of linear differential equations. Deterministic models admit complete, rigorous and automatic analysis of stability both local and global from which is possible to derive the algebraic expressions for the basic reproductive number and the corresponding epidemic thresholds using computer algebra software. Stochastic models are more difficult to treat and the analysis of their properties requires complicated considerations in statistical mathematics. In this work we propose to use computer algebra software with the aim to solve epidemic stochastic models such as the SIR model and the carrier-borne model. Specifically we use Maple to solve these stochastic models in the case of small groups and we obtain results that do not appear in standard textbooks or in the books updated on stochastic models in epidemiology. From our results we derive expressions which coincide with those obtained in the classical texts using advanced procedures in mathematical statistics. Our algorithms can be extended for other stochastic models in epidemiology and this shows the power of computer algebra software not only for analysis of deterministic models but also for the analysis of stochastic models. We also perform numerical simulations with our algebraic results and we made estimations for the basic parameters as the basic reproductive rate and the stochastic threshold theorem. We claim that our algorithms and results are important tools to control the diseases in a globalized world.

  6. Preoperative overnight parenteral nutrition (TPN) improves skeletal muscle protein metabolism indicated by microarray algorithm analyses in a randomized trial.

    PubMed

    Iresjö, Britt-Marie; Engström, Cecilia; Lundholm, Kent

    2016-06-01

    Loss of muscle mass is associated with increased risk of morbidity and mortality in hospitalized patients. Uncertainties of treatment efficiency by short-term artificial nutrition remain, specifically improvement of protein balance in skeletal muscles. In this study, algorithmic microarray analysis was applied to map cellular changes related to muscle protein metabolism in human skeletal muscle tissue during provision of overnight preoperative total parenteral nutrition (TPN). Twenty-two patients (11/group) scheduled for upper GI surgery due to malignant or benign disease received a continuous peripheral all-in-one TPN infusion (30 kcal/kg/day, 0.16 gN/kg/day) or saline infusion for 12 h prior operation. Biopsies from the rectus abdominis muscle were taken at the start of operation for isolation of muscle RNA RNA expression microarray analyses were performed with Agilent Sureprint G3, 8 × 60K arrays using one-color labeling. 447 mRNAs were differently expressed between study and control patients (P < 0.1). mRNAs related to ribosomal biogenesis, mRNA processing, and translation were upregulated during overnight nutrition; particularly anabolic signaling S6K1 (P < 0.01-0.1). Transcripts of genes associated with lysosomal degradation showed consistently lower expression during TPN while mRNAs for ubiquitin-mediated degradation of proteins as well as transcripts related to intracellular signaling pathways, PI3 kinase/MAPkinase, were either increased or decreased. In conclusion, muscle mRNA alterations during overnight standard TPN infusions at constant rate altered mRNAs associated with mTOR signaling; increased initiation of protein translation; and suppressed autophagy/lysosomal degradation of proteins. This indicates that overnight preoperative parenteral nutrition is effective to promote muscle protein metabolism. PMID:27273879

  7. Automated classification of seismic sources in large database using random forest algorithm: First results at Piton de la Fournaise volcano (La Réunion).

    NASA Astrophysics Data System (ADS)

    Hibert, Clément; Provost, Floriane; Malet, Jean-Philippe; Stumpf, André; Maggi, Alessia; Ferrazzini, Valérie

    2016-04-01

    In the past decades the increasing quality of seismic sensors and capability to transfer remotely large quantity of data led to a fast densification of local, regional and global seismic networks for near real-time monitoring. This technological advance permits the use of seismology to document geological and natural/anthropogenic processes (volcanoes, ice-calving, landslides, snow and rock avalanches, geothermal fields), but also led to an ever-growing quantity of seismic data. This wealth of seismic data makes the construction of complete seismicity catalogs, that include earthquakes but also other sources of seismic waves, more challenging and very time-consuming as this critical pre-processing stage is classically done by human operators. To overcome this issue, the development of automatic methods for the processing of continuous seismic data appears to be a necessity. The classification algorithm should satisfy the need of a method that is robust, precise and versatile enough to be deployed to monitor the seismicity in very different contexts. We propose a multi-class detection method based on the random forests algorithm to automatically classify the source of seismic signals. Random forests is a supervised machine learning technique that is based on the computation of a large number of decision trees. The multiple decision trees are constructed from training sets including each of the target classes. In the case of seismic signals, these attributes may encompass spectral features but also waveform characteristics, multi-stations observations and other relevant information. The Random Forests classifier is used because it provides state-of-the-art performance when compared with other machine learning techniques (e.g. SVM, Neural Networks) and requires no fine tuning. Furthermore it is relatively fast, robust, easy to parallelize, and inherently suitable for multi-class problems. In this work, we present the first results of the classification method applied

  8. Stochastic inverse problems: Models and metrics

    SciTech Connect

    Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.

    2015-03-31

    In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.

  9. Stochastic optimization model for order acceptance with multiple demand classes and uncertain demand/supply

    NASA Astrophysics Data System (ADS)

    Yang, Wen; Fung, Richard Y. K.

    2014-06-01

    This article considers an order acceptance problem in a make-to-stock manufacturing system with multiple demand classes in a finite time horizon. Demands in different periods are random variables and are independent of one another, and replenishments of inventory deviate from the scheduled quantities. The objective of this work is to maximize the expected net profit over the planning horizon by deciding the fraction of the demand that is going to be fulfilled. This article presents a stochastic order acceptance optimization model and analyses the existence of the optimal promising policies. An example of a discrete problem is used to illustrate the policies by applying the dynamic programming method. In order to solve the continuous problems, a heuristic algorithm based on stochastic approximation (HASA) is developed. Finally, the computational results of a case example illustrate the effectiveness and efficiency of the HASA approach, and make the application of the proposed model readily acceptable.

  10. Controlled Nonlinear Stochastic Delay Equations: Part II: Approximations and Pipe-Flow Representations

    SciTech Connect

    Kushner, Harold J.

    2012-08-15

    This is the second part of a work dealing with key issues that have not been addressed in the modeling and numerical optimization of nonlinear stochastic delay systems. We consider new classes of models, such as those with nonlinear functions of several controls (such as products), each with is own delay, controlled random Poisson measure driving terms, admissions control with delayed retrials, and others. Part I was concerned with issues concerning the class of admissible controls and their approximations, since the classical definitions are inadequate for our models. This part is concerned with transportation equation representations and their approximations. Such representations of nonlinear stochastic delay models have been crucial in the development of numerical algorithms with much reduced memory and computational requirements. The representations for the new models are not obvious and are developed. They also provide a template for the adaptation of the Markov chain approximation numerical methods.

  11. Estimation of optical flow in airborne electro-optical sensors by stochastic approximation

    NASA Technical Reports Server (NTRS)

    Merhav, S. J.

    1991-01-01

    The essence of motion or range estimation by passive electrooptical means is the ability to determine the correspondence of picture elements in pairs of image frames and to estimate their coordinates and their disparity (relative shifts) in the image plane of an electrooptical imaging sensor. The disparity can be in successive frames due to self-motion or in simultaneous frames of a stereo pair. A key issue is to provide these estimates on-line. This paper describes the theoretical background of such an interframe shift estimator. It is based on a stochastic gradient algorithm, specifically implementing a form of stochastic approximation, which can achieve rapid convergence of the shift estimate. Analytical and numerical simulation examples for random texture and isolated features validate the feasibility and the effectiveness of the estimator.

  12. A probabilistic graphical model approach to stochastic multiscale partial differential equations

    SciTech Connect

    Wan, Jiang; Zabaras, Nicholas; Center for Applied Mathematics, Cornell University, 657 Frank H.T. Rhodes Hall, Ithaca, NY 14853

    2013-10-01

    We develop a probabilistic graphical model based methodology to efficiently perform uncertainty quantification in the presence of both stochastic input and multiple scales. Both the stochastic input and model responses are treated as random variables in this framework. Their relationships are modeled by graphical models which give explicit factorization of a high-dimensional joint probability distribution. The hyperparameters in the probabilistic model are learned using sequential Monte Carlo (SMC) method, which is superior to standard Markov chain Monte Carlo (MCMC) methods for multi-modal distributions. Finally, we make predictions from the probabilistic graphical model using the belief propagation algorithm. Numerical examples are presented to show the accuracy and efficiency of the predictive capability of the developed graphical model.

  13. Calculation of a double reactive azeotrope using stochastic optimization approaches

    NASA Astrophysics Data System (ADS)

    Mendes Platt, Gustavo; Pinheiro Domingos, Roberto; Oliveira de Andrade, Matheus

    2013-02-01

    An homogeneous reactive azeotrope is a thermodynamic coexistence condition of two phases under chemical and phase equilibrium, where compositions of both phases (in the Ung-Doherty sense) are equal. This kind of nonlinear phenomenon arises from real world situations and has applications in chemical and petrochemical industries. The modeling of reactive azeotrope calculation is represented by a nonlinear algebraic system with phase equilibrium, chemical equilibrium and azeotropy equations. This nonlinear system can exhibit more than one solution, corresponding to a double reactive azeotrope. The robust calculation of reactive azeotropes can be conducted by several approaches, such as interval-Newton/generalized bisection algorithms and hybrid stochastic-deterministic frameworks. In this paper, we investigate the numerical aspects of the calculation of reactive azeotropes using two metaheuristics: the Luus-Jaakola adaptive random search and the Firefly algorithm. Moreover, we present results for a system (with industrial interest) with more than one azeotrope, the system isobutene/methanol/methyl-tert-butyl-ether (MTBE). We present convergence patterns for both algorithms, illustrating - in a bidimensional subdomain - the identification of reactive azeotropes. A strategy for calculation of multiple roots in nonlinear systems is also applied. The results indicate that both algorithms are suitable and robust when applied to reactive azeotrope calculations for this "challenging" nonlinear system.

  14. Numerical analysis of randomly forced glycolitic oscillations

    SciTech Connect

    Ryashko, Lev

    2015-03-10

    Randomly forced glycolytic oscillations in Higgins model are studied both numerically and analytically. Numerical analysis is based on the direct simulation of the solutions of stochastic system. Non-uniformity of the stochastic bundle along the deterministic cycle is shown. For the analytical investigation of the randomly forced Higgins model, the stochastic sensitivity function technique and confidence domains method are applied. Results of the influence of additive noise on the cycle of this model are given.

  15. Stochastic Impulse Control of Non-Markovian Processes

    SciTech Connect

    Djehiche, Boualem; Hamadene, Said Hdhiri, Ibtissam

    2010-02-15

    We consider a class of stochastic impulse control problems of general stochastic processes i.e. not necessarily Markovian. Under fairly general conditions we establish existence of an optimal impulse control. We also prove existence of combined optimal stochastic and impulse control of a fairly general class of diffusions with random coefficients. Unlike, in the Markovian framework, we cannot apply quasi-variational inequalities techniques. We rather derive the main results using techniques involving reflected BSDEs and the Snell envelope.

  16. Quantumness, Randomness and Computability

    NASA Astrophysics Data System (ADS)

    Solis, Aldo; Hirsch, Jorge G.

    2015-06-01

    Randomness plays a central role in the quantum mechanical description of our interactions. We review the relationship between the violation of Bell inequalities, non signaling and randomness. We discuss the challenge in defining a random string, and show that algorithmic information theory provides a necessary condition for randomness using Borel normality. We close with a view on incomputablity and its implications in physics.

  17. Stochastic resonance with a mesoscopic reaction-diffusion system.

    PubMed

    Mahara, Hitoshi; Yamaguchi, Tomohiko; Parmananda, P

    2014-06-01

    In a mesoscopic reaction-diffusion system with an Oregonator reaction model, we show that intrinsic noise can drive a resonant stable pattern in the presence of the initial subthreshold perturbations. Both spatially periodic and aperiodic stochastic resonances are demonstrated by employing the Gillespies stochastic simulation algorithm. The mechanisms for these phenomena are discussed. PMID:25019857

  18. On Scaling Modes and Balancing Stochastic, Discretization, and Modeling Error

    NASA Astrophysics Data System (ADS)

    Brown, J.

    2015-12-01

    We consider accuracy-cost tradeoffs and the problem of finding Pareto optimal configurations for stochastic forward and inverse problems. As the target accuracy is changed, we should use different physical models, stochastic models, discretizations, and solution algorithms. In this spectrum, we see different scientifically-relevant scaling modes, thus different opportunities and limitations on parallel computers and emerging architectures.

  19. Binomial distribution based τ-leap accelerated stochastic simulation

    NASA Astrophysics Data System (ADS)

    Chatterjee, Abhijit; Vlachos, Dionisios G.; Katsoulakis, Markos A.

    2005-01-01

    Recently, Gillespie introduced the τ-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution τ-leap algorithm (abbreviated as BD-τ method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original τ-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-τ method is more accurate than the original method for comparable coarse-graining in time.

  20. Mechanical Autonomous Stochastic Heat Engine.

    PubMed

    Serra-Garcia, Marc; Foehr, André; Molerón, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara

    2016-07-01

    Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using, e.g., thermal cycles implemented in optical traps. However, recent experimental demonstrations of classical stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle and consume more energy than they produce. We present a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir. PMID:27419553

  1. Mechanical Autonomous Stochastic Heat Engine

    NASA Astrophysics Data System (ADS)

    Serra-Garcia, Marc; Foehr, André; Molerón, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara

    2016-07-01

    Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using, e.g., thermal cycles implemented in optical traps. However, recent experimental demonstrations of classical stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle and consume more energy than they produce. We present a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir.

  2. Resolution analysis by random probing

    NASA Astrophysics Data System (ADS)

    Simutė, S.; Fichtner, A.; van Leeuwen, T.

    2015-12-01

    We develop and apply methods for resolution analysis in tomography, based on stochastic probing of the Hessian or resolution operators. Key properties of our methods are (i) low algorithmic complexity and easy implementation, (ii) applicability to any tomographic technique, including full-waveform inversion and linearized ray tomography, (iii) applicability in any spatial dimension and to inversions with a large number of model parameters, (iv) low computational costs that are mostly a fraction of those required for synthetic recovery tests, and (v) the ability to quantify both spatial resolution and inter-parameter trade-offs. Using synthetic full-waveform inversions as benchmarks, we demonstrate that auto-correlations of random-model applications to the Hessian yield various resolution measures, including direction- and position-dependent resolution lengths, and the strength of inter-parameter mappings. We observe that the required number of random test models is around 5 in one, two and three dimensions. This means that the proposed resolution analyses are not only more meaningful than recovery tests but also computationally less expensive. We demonstrate the applicability of our method in 3D real-data full-waveform inversions for the western Mediterranean and Japan. In addition to tomographic problems, resolution analysis by random probing may be used in other inverse methods that constrain continuously distributed properties, including electromagnetic and potential-field inversions, as well as recently emerging geodynamic data assimilation.

  3. Resolution analysis by random probing

    NASA Astrophysics Data System (ADS)

    Fichtner, Andreas; Leeuwen, Tristan van

    2015-08-01

    We develop and apply methods for resolution analysis in tomography, based on stochastic probing of the Hessian or resolution operators. Key properties of our methods are (i) low algorithmic complexity and easy implementation, (ii) applicability to any tomographic technique, including full-waveform inversion and linearized ray tomography, (iii) applicability in any spatial dimension and to inversions with a large number of model parameters, (iv) low computational costs that are mostly a fraction of those required for synthetic recovery tests, and (v) the ability to quantify both spatial resolution and interparameter trade-offs. Using synthetic full-waveform inversions as benchmarks, we demonstrate that autocorrelations of random-model applications to the Hessian yield various resolution measures, including direction- and position-dependent resolution lengths and the strength of interparameter mappings. We observe that the required number of random test models is around five in one, two, and three dimensions. This means that the proposed resolution analyses are not only more meaningful than recovery tests but also computationally less expensive. We demonstrate the applicability of our method in a 3-D real-data full-waveform inversion for the western Mediterranean. In addition to tomographic problems, resolution analysis by random probing may be used in other inverse methods that constrain continuously distributed properties, including electromagnetic and potential-field inversions, as well as recently emerging geodynamic data assimilation.

  4. A termination criterion for parameter estimation in stochastic models in systems biology.

    PubMed

    Zimmer, Christoph; Sahle, Sven

    2015-11-01

    Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model. PMID:26360409

  5. Stochastic many-body perturbation theory for anharmonic molecular vibrations

    NASA Astrophysics Data System (ADS)

    Hermes, Matthew R.; Hirata, So

    2014-08-01

    A new quantum Monte Carlo (QMC) method for anharmonic vibrational zero-point energies and transition frequencies is developed, which combines the diagrammatic vibrational many-body perturbation theory based on the Dyson equation with Monte Carlo integration. The infinite sums of the diagrammatic and thus size-consistent first- and second-order anharmonic corrections to the energy and self-energy are expressed as sums of a few m- or 2m-dimensional integrals of wave functions and a potential energy surface (PES) (m is the vibrational degrees of freedom). Each of these integrals is computed as the integrand (including the value of the PES) divided by the value of a judiciously chosen weight function evaluated on demand at geometries distributed randomly but according to the weight function via the Metropolis algorithm. In this way, the method completely avoids cumbersome evaluation and storage of high-order force constants necessary in the original formulation of the vibrational perturbation theory; it furthermore allows even higher-order force constants essentially up to an infinite order to be taken into account in a scalable, memory-efficient algorithm. The diagrammatic contributions to the frequency-dependent self-energies that are stochastically evaluated at discrete frequencies can be reliably interpolated, allowing the self-consistent solutions to the Dyson equation to be obtained. This method, therefore, can compute directly and stochastically the transition frequencies of fundamentals and overtones as well as their relative intensities as pole strengths, without fixed-node errors that plague some QMC. It is shown that, for an identical PES, the new method reproduces the correct deterministic values of the energies and frequencies within a few cm-1 and pole strengths within a few thousandths. With the values of a PES evaluated on the fly at random geometries, the new method captures a noticeably greater proportion of anharmonic effects.

  6. Stochastic many-body perturbation theory for anharmonic molecular vibrations

    SciTech Connect

    Hermes, Matthew R.; Hirata, So

    2014-08-28

    A new quantum Monte Carlo (QMC) method for anharmonic vibrational zero-point energies and transition frequencies is developed, which combines the diagrammatic vibrational many-body perturbation theory based on the Dyson equation with Monte Carlo integration. The infinite sums of the diagrammatic and thus size-consistent first- and second-order anharmonic corrections to the energy and self-energy are expressed as sums of a few m- or 2m-dimensional integrals of wave functions and a potential energy surface (PES) (m is the vibrational degrees of freedom). Each of these integrals is computed as the integrand (including the value of the PES) divided by the value of a judiciously chosen weight function evaluated on demand at geometries distributed randomly but according to the weight function via the Metropolis algorithm. In this way, the method completely avoids cumbersome evaluation and storage of high-order force constants necessary in the original formulation of the vibrational perturbation theory; it furthermore allows even higher-order force constants essentially up to an infinite order to be taken into account in a scalable, memory-efficient algorithm. The diagrammatic contributions to the frequency-dependent self-energies that are stochastically evaluated at discrete frequencies can be reliably interpolated, allowing the self-consistent solutions to the Dyson equation to be obtained. This method, therefore, can compute directly and stochastically the transition frequencies of fundamentals and overtones as well as their relative intensities as pole strengths, without fixed-node errors that plague some QMC. It is shown that, for an identical PES, the new method reproduces the correct deterministic values of the energies and frequencies within a few cm{sup −1} and pole strengths within a few thousandths. With the values of a PES evaluated on the fly at random geometries, the new method captures a noticeably greater proportion of anharmonic effects.

  7. Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Meiling; Liu, Xiangnan; Li, Jin; Ding, Chao; Jiang, Jiale

    2014-12-01

    Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and HV and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising.

  8. Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm

    NASA Astrophysics Data System (ADS)

    Yan, Dong; de Beurs, Kirsten M.

    2016-05-01

    The objective of this paper is to demonstrate a new method to map the distributions of C3 and C4 grasses at 30 m resolution and over a 25-year period of time (1988-2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C3 and C4 grasses in our study area such as elevation, soil pH, soil organic matter and soil texture.

  9. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

    NASA Astrophysics Data System (ADS)

    Ahmed, Oumer S.; Franklin, Steven E.; Wulder, Michael A.; White, Joanne C.

    2015-03-01

    Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = -0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = -0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

  10. Numerical Stochastic Homogenization Method and Multiscale Stochastic Finite Element Method - A Paradigm for Multiscale Computation of Stochastic PDEs

    SciTech Connect

    X. Frank Xu

    2010-03-30

    Multiscale modeling of stochastic systems, or uncertainty quantization of multiscale modeling is becoming an emerging research frontier, with rapidly growing engineering applications in nanotechnology, biotechnology, advanced materials, and geo-systems, etc. While tremendous efforts have been devoted to either stochastic methods or multiscale methods, little combined work had been done on integration of multiscale and stochastic methods, and there was no method formally available to tackle multiscale problems involving uncertainties. By developing an innovative Multiscale Stochastic Finite Element Method (MSFEM), this research has made a ground-breaking contribution to the emerging field of Multiscale Stochastic Modeling (MSM) (Fig 1). The theory of MSFEM basically decomposes a boundary value problem of random microstructure into a slow scale deterministic problem and a fast scale stochastic one. The slow scale problem corresponds to common engineering modeling practices where fine-scale microstructure is approximated by certain effective constitutive constants, which can be solved by using standard numerical solvers. The fast scale problem evaluates fluctuations of local quantities due to random microstructure, which is important for scale-coupling systems and particularly those involving failure mechanisms. The Green-function-based fast-scale solver developed in this research overcomes the curse-of-dimensionality commonly met in conventional approaches, by proposing a random field-based orthogonal expansion approach. The MSFEM formulated in this project paves the way to deliver the first computational tool/software on uncertainty quantification of multiscale systems. The applications of MSFEM on engineering problems will directly enhance our modeling capability on materials science (composite materials, nanostructures), geophysics (porous media, earthquake), biological systems (biological tissues, bones, protein folding). Continuous development of MSFEM will

  11. Discrete analysis of stochastic NMR.II

    NASA Astrophysics Data System (ADS)

    Wong, S. T. S.; Rods, M. S.; Newmark, R. D.; Budinger, T. F.

    Stochastic NMR is an efficient technique for high-field in vivo imaging and spectroscopic studies where the peak RF power required may be prohibitively high for conventional pulsed NMR techniques. A stochastic NMR experiment excites the spin system with a sequence of RF pulses where the flip angles or the phases of the pulses are samples of a discrete stochastic process. In a previous paper the stochastic experiment was analyzed and analytic expressions for the input-output cross-correlations, average signal power, and signal spectral density were obtained for a general stochastic RF excitation. In this paper specific cases of excitation with random phase, fixed flip angle, and excitation with two random components in quadrature are analyzed. The input-output cross-correlation for these two types of excitations is shown to be Lorentzian. Line broadening is the only spectral distortion as the RF excitation power is increased. The systematic noise power is inversely proportional to the number of data points N used in the spectral reconstruction. The use of a complete maximum length sequence (MLS) may improve the signal-to-systematic-noise ratio by 20 dB relative to random binary excitation, but peculiar features in the higher-order autocorrelations of MLS cause noise-like distortion in the reconstructed spectra when the excitation power is high. The amount of noise-like distortion depends on the choice of the MLS generator.

  12. The Dropout Learning Algorithm

    PubMed Central

    Baldi, Pierre; Sadowski, Peter

    2014-01-01

    Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879

  13. Least expected time paths in stochastic, time-varying transportation networks

    SciTech Connect

    Miller-Hooks, E.D.; Mahmassani, H.S.

    1999-06-01

    The authors consider stochastic, time-varying transportation networks, where the arc weights (arc travel times) are random variables with probability distribution functions that vary with time. Efficient procedures are widely available for determining least time paths in deterministic networks. In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic, time-varying networks. Two procedures are presented. The first procedure determines the a priori least expected time paths from all origins to a single destination for each departure time in the peak period. The second procedure determines lower bounds on the expected times of these a priori least expected time paths. This procedure determines an exact solution for the problem where the driver is permitted to react to revealed travel times on traveled links en route, i.e. in a time-adaptive route choice framework. Modifications to each of these procedures for determining least expected cost (where cost is not necessarily travel time) paths and lower bounds on the expected costs of these paths are given. Extensive numerical tests are conducted to illustrate the algorithms` computational performance as well as the properties of the solution.

  14. Stochastic cooling in RHIC

    SciTech Connect

    Brennan,J.M.; Blaskiewicz, M. M.; Severino, F.

    2009-05-04

    After the success of longitudinal stochastic cooling of bunched heavy ion beam in RHIC, transverse stochastic cooling in the vertical plane of Yellow ring was installed and is being commissioned with proton beam. This report presents the status of the effort and gives an estimate, based on simulation, of the RHIC luminosity with stochastic cooling in all planes.

  15. Stochastic scanning multiphoton multifocal microscopy.

    PubMed

    Jureller, Justin E; Kim, Hee Y; Scherer, Norbert F

    2006-04-17

    Multiparticle tracking with scanning confocal and multiphoton fluorescence imaging is increasingly important for elucidating biological function, as in the transport of intracellular cargo-carrying vesicles. We demonstrate a simple rapid-sampling stochastic scanning multifocal multiphoton microscopy (SS-MMM) fluorescence imaging technique that enables multiparticle tracking without specialized hardware at rates 1,000 times greater than conventional single point raster scanning. Stochastic scanning of a diffractive optic generated 10x10 hexagonal array of foci with a white noise driven galvanometer yields a scan pattern that is random yet space-filling. SS-MMM creates a more uniformly sampled image with fewer spatio-temporal artifacts than obtained by conventional or multibeam raster scanning. SS-MMM is verified by simulation and experimentally demonstrated by tracking microsphere diffusion in solution. PMID:19516485

  16. Stochastic Models of Quantum Decoherence

    NASA Astrophysics Data System (ADS)

    Kennerly, Sam

    Suppose a single qubit is repeatedly prepared and evolved under imperfectly-controlled conditions. A drunk model represents uncontrolled interactions on each experimental trial as random or stochastic terms in the qubit's Hamiltonian operator. Time evolution of states is generated by a stochastic differential equation whose sample paths evolve according to the Schrodinger equation. For models with Gaussian white noise which is independent of the qubit's state, the expectation value of the solution obeys a master equation which is identical to the high-temperature limit of the Bloch equation. Drunk models predict that experimental data can appear consistent with decoherence even if qubit states evolve by unitary transformations. Examples are shown in which reversible evolution appears to cause irreversible information loss. This paradox is resolved by distinguishing between the true state of a system and the estimated state inferred from an experimental dataset.

  17. Stochastic differential equations for non-linear hydrodynamics

    NASA Astrophysics Data System (ADS)

    Español, Pep

    1998-02-01

    We formulate the stochastic differential equations for non-linear hydrodynamic fluctuations. The equations incorporate the random forces through a random stres tensor and random heat flux as in the Landau and Lifshitz theory. However, the equations are non-linear and the random forces are non-Gaussian. We provide explicit expressions for these random quantities in terms of the well-defined increments of the Wienner process.

  18. Investigation for improving Global Positioning System (GPS) orbits using a discrete sequential estimator and stochastic models of selected physical processes

    NASA Technical Reports Server (NTRS)

    Goad, Clyde C.; Chadwell, C. David

    1993-01-01

    GEODYNII is a conventional batch least-squares differential corrector computer program with deterministic models of the physical environment. Conventional algorithms were used to process differenced phase and pseudorange data to determine eight-day Global Positioning system (GPS) orbits with several meter accuracy. However, random physical processes drive the errors whose magnitudes prevent improving the GPS orbit accuracy. To improve the orbit accuracy, these random processes should be modeled stochastically. The conventional batch least-squares algorithm cannot accommodate stochastic models, only a stochastic estimation algorithm is suitable, such as a sequential filter/smoother. Also, GEODYNII cannot currently model the correlation among data values. Differenced pseudorange, and especially differenced phase, are precise data types that can be used to improve the GPS orbit precision. To overcome these limitations and improve the accuracy of GPS orbits computed using GEODYNII, we proposed to develop a sequential stochastic filter/smoother processor by using GEODYNII as a type of trajectory preprocessor. Our proposed processor is now completed. It contains a correlated double difference range processing capability, first order Gauss Markov models for the solar radiation pressure scale coefficient and y-bias acceleration, and a random walk model for the tropospheric refraction correction. The development approach was to interface the standard GEODYNII output files (measurement partials and variationals) with software modules containing the stochastic estimator, the stochastic models, and a double differenced phase range processing routine. Thus, no modifications to the original GEODYNII software were required. A schematic of the development is shown. The observational data are edited in the preprocessor and the data are passed to GEODYNII as one of its standard data types. A reference orbit is determined using GEODYNII as a batch least-squares processor and the

  19. Analysis of stochastically forced quasi-periodic attractors

    SciTech Connect

    Ryashko, Lev

    2015-11-30

    A problem of the analysis of stochastically forced quasi-periodic auto-oscillations of nonlinear dynamic systems is considered. A stationary distribution of random trajectories in the neighborhood of the corresponding deterministic attractor (torus) is studied. A parametric description of quadratic approximation of the quasipotential based on the stochastic sensitivity functions (SSF) technique is given. Using this technique, we analyse a dispersion of stochastic flows near the torus. For the case of two-torus in three-dimensional space, the stochastic sensitivity function is constructed.

  20. Non-random structures in universal compression and the Fermi paradox

    NASA Astrophysics Data System (ADS)

    Gurzadyan, A. V.; Allahverdyan, A. E.

    2016-02-01

    We study the hypothesis of information panspermia assigned recently among possible solutions of the Fermi paradox ("where are the aliens?"). It suggests that the expenses of alien signaling can be significantly reduced, if their messages contained compressed information. To this end we consider universal compression and decoding mechanisms ( e.g. the Lempel-Ziv-Welch algorithm) that can reveal non-random structures in compressed bit strings. The efficiency of the Kolmogorov stochasticity parameter for detection of non-randomness is illustrated, along with the Zipf's law. The universality of these methods, i.e. independence from data details, can be principal in searching for intelligent messages.

  1. Stochastic resonance: A residence time approach

    SciTech Connect

    Gammaitoni, L. |; Marchesoni, F. |; Menichella Saetta, E.; Santucci, S.

    1996-06-01

    The Stochastic Resonance phenomenon is described as a synchronization process between periodic signals and the random response in bistable systems. The residence time approach as a useful tool in characterizing hidden periodicities is discussed. {copyright} {ital 1996 American Institute of Physics.}

  2. Stochastic Differential Games with Asymmetric Information

    SciTech Connect

    Cardaliaguet, Pierre Rainer, Catherine

    2009-02-15

    We investigate a two-player zero-sum stochastic differential game in which the players have an asymmetric information on the random payoff. We prove that the game has a value and characterize this value in terms of dual viscosity solutions of some second order Hamilton-Jacobi equation.

  3. Stochastic solution to quantum dynamics

    NASA Technical Reports Server (NTRS)

    John, Sarah; Wilson, John W.

    1994-01-01

    The quantum Liouville equation in the Wigner representation is solved numerically by using Monte Carlo methods. For incremental time steps, the propagation is implemented as a classical evolution in phase space modified by a quantum correction. The correction, which is a momentum jump function, is simulated in the quasi-classical approximation via a stochastic process. The technique, which is developed and validated in two- and three- dimensional momentum space, extends an earlier one-dimensional work. Also, by developing a new algorithm, the application to bound state motion in an anharmonic quartic potential shows better agreement with exact solutions in two-dimensional phase space.

  4. Constrained Stochastic Extended Redundancy Analysis.

    PubMed

    DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco

    2015-06-01

    We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA). PMID:24327066

  5. Planning under uncertainty solving large-scale stochastic linear programs

    SciTech Connect

    Infanger, G. . Dept. of Operations Research Technische Univ., Vienna . Inst. fuer Energiewirtschaft)

    1992-12-01

    For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.

  6. A stochastic method for stand-alone photovoltaic system sizing

    SciTech Connect

    Cabral, Claudia Valeria Tavora; Filho, Delly Oliveira; Martins, Jose Helvecio; Toledo, Olga Moraes

    2010-09-15

    Photovoltaic systems utilize solar energy to generate electrical energy to meet load demands. Optimal sizing of these systems includes the characterization of solar radiation. Solar radiation at the Earth's surface has random characteristics and has been the focus of various academic studies. The objective of this study was to stochastically analyze parameters involved in the sizing of photovoltaic generators and develop a methodology for sizing of stand-alone photovoltaic systems. Energy storage for isolated systems and solar radiation were analyzed stochastically due to their random behavior. For the development of the methodology proposed stochastic analysis were studied including the Markov chain and beta probability density function. The obtained results were compared with those for sizing of stand-alone using from the Sandia method (deterministic), in which the stochastic model presented more reliable values. Both models present advantages and disadvantages; however, the stochastic one is more complex and provides more reliable and realistic results. (author)

  7. Stability of solutions to stochastic partial differential equations

    NASA Astrophysics Data System (ADS)

    Gess, Benjamin; Tölle, Jonas M.

    2016-03-01

    We provide a general framework for the stability of solutions to stochastic partial differential equations with respect to perturbations of the drift. More precisely, we consider stochastic partial differential equations with drift given as the subdifferential of a convex function and prove continuous dependence of the solutions with regard to random Mosco convergence of the convex potentials. In particular, we identify the concept of stochastic variational inequalities (SVI) as a well-suited framework to study such stability properties. The generality of the developed framework is then laid out by deducing Trotter type and homogenization results for stochastic fast diffusion and stochastic singular p-Laplace equations. In addition, we provide an SVI treatment for stochastic nonlocal p-Laplace equations and prove their convergence to the respective local models.

  8. Method to describe stochastic dynamics using an optimal coordinate.

    PubMed

    Krivov, Sergei V

    2013-12-01

    A general method to describe the stochastic dynamics of Markov processes is suggested. The method aims to solve three related problems: the determination of an optimal coordinate for the description of stochastic dynamics; the reconstruction of time from an ensemble of stochastic trajectories; and the decomposition of stationary stochastic dynamics into eigenmodes which do not decay exponentially with time. The problems are solved by introducing additive eigenvectors which are transformed by a stochastic matrix in a simple way - every component is translated by a constant distance. Such solutions have peculiar properties. For example, an optimal coordinate for stochastic dynamics with detailed balance is a multivalued function. An optimal coordinate for a random walk on a line corresponds to the conventional eigenvector of the one-dimensional Dirac equation. The equation for the optimal coordinate in a slowly varying potential reduces to the Hamilton-Jacobi equation for the action function. PMID:24483410

  9. Adaptive path planning: Algorithm and analysis

    SciTech Connect

    Chen, Pang C.

    1995-03-01

    To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions difficult tasks. From these solutions, an evolving sparse work of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a framework in which a slow but effective planner may be improved both cost-wise and capability-wise by a faster but less effective planner coupled with experience. We analyze algorithm by formalizing the concept of improvability and deriving conditions under which a planner can be improved within the framework. The analysis is based on two stochastic models, one pessimistic (on task complexity), the other randomized (on experience utility). Using these models, we derive quantitative bounds to predict the learning behavior. We use these estimation tools to characterize the situations in which the algorithm is useful and to provide bounds on the training time. In particular, we show how to predict the maximum achievable speedup. Additionally, our analysis techniques are elementary and should be useful for studying other types of probabilistic learning as well.

  10. Stochastic solution of population balance equations for reactor networks

    NASA Astrophysics Data System (ADS)

    Menz, William J.; Akroyd, Jethro; Kraft, Markus

    2014-01-01

    This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images.

  11. The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC.

    PubMed

    Inghelbrecht, Veronique; Verhaevert, Jo; van Hecke, Tanja; Rogier, Hendrik

    2014-01-01

    Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case. PMID:25393783

  12. The Influence of Random Element Displacement on DOA Estimates Obtained with (Khatri–Rao-)Root-MUSIC

    PubMed Central

    Inghelbrecht, Veronique; Verhaevert, Jo; van Hecke, Tanja; Rogier, Hendrik

    2014-01-01

    Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case. PMID:25393783

  13. Random walk particle tracking simulations of non-Fickian transport in heterogeneous media

    SciTech Connect

    Srinivasan, G. Tartakovsky, D.M. Dentz, M. Viswanathan, H.; Berkowitz, B.; Robinson, B.A.

    2010-06-01

    Derivations of continuum nonlocal models of non-Fickian (anomalous) transport require assumptions that might limit their applicability. We present a particle-based algorithm, which obviates the need for many of these assumptions by allowing stochastic processes that represent spatial and temporal random increments to be correlated in space and time, be stationary or non-stationary, and to have arbitrary distributions. The approach treats a particle trajectory as a subordinated stochastic process that is described by a set of Langevin equations, which represent a continuous time random walk (CTRW). Convolution-based particle tracking (CBPT) is used to increase the computational efficiency and accuracy of these particle-based simulations. The combined CTRW-CBPT approach enables one to convert any particle tracking legacy code into a simulator capable of handling non-Fickian transport.

  14. Multivariable integration method for estimating sea surface salinity in coastal waters from in situ data and remotely sensed data using random forest algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Meiling; Liu, Xiangnan; Liu, Da; Ding, Chao; Jiang, Jiale

    2015-02-01

    A random forest (RF) model was created to estimate sea surface salinity (SSS) in the Hong Kong Sea, China, by integrating in situ and remotely sensed data. Optical remotely sensed data from China's HJ-1 satellite and in situ data were collected. The prediction model of salinity was developed by in situ environmental variables in the ocean, namely sea surface temperature (SST), pH, total inorganic nitrogen (TIN) and Chl-a, which are strongly related to SSS according to Pearson's correlation analysis. The large-scale SSS was estimated using the established salinity model with the same input parameters. The ordinary kriging interpolation using in situ data and the retrieval model based on remotely sensed data were developed to obtain the large-scale input parameters of the model. The different number of trees in the forest (ntree) and the number of features at each node (mtry) were adjusted in the RF model. The results showed that an optimum RF model was obtained with mtry=32 and ntree=2000, and the most important variable of the model for SSS prediction was SST, followed by TIN, Chl-a and pH. Such an RF model was successful in evaluating the temporal-spatial distribution of SSS and had a relatively low estimation error. The root mean square error (RMSE) was less than 2.0 psu, the mean absolute error (MAE) was below 1.5 psu, and the absolute percent error (APE) was lower than 5%. The final RF salinity model was then compared with a multiple linear regression model (MLR), a back-propagation artificial neural network model, and a classification and regression trees (CART) model. The RF had a lower estimation error than the other three models. In addition, the RF model was used extensively under different periods and could be universal. This demonstrated that the RF algorithm has the capability to estimate SSS in coastal waters by integrating in situ and remotely sensed data.

  15. Variance-based sensitivity indices for stochastic models with correlated inputs

    SciTech Connect

    Kala, Zdeněk

    2015-03-10

    The goal of this article is the formulation of the principles of one of the possible strategies in implementing correlation between input random variables so as to be usable for algorithm development and the evaluation of Sobol’s sensitivity analysis. With regard to the types of stochastic computational models, which are commonly found in structural mechanics, an algorithm was designed for effective use in conjunction with Monte Carlo methods. Sensitivity indices are evaluated for all possible permutations of the decorrelation procedures for input parameters. The evaluation of Sobol’s sensitivity coefficients is illustrated on an example in which a computational model was used for the analysis of the resistance of a steel bar in tension with statistically dependent input geometric characteristics.

  16. Stochastic reduced order models for inverse problems under uncertainty

    PubMed Central

    Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

    2014-01-01

    This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

  17. Fluctuations as stochastic deformation.

    PubMed

    Kazinski, P O

    2008-04-01

    A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium. PMID:18517590

  18. Fluctuations as stochastic deformation

    NASA Astrophysics Data System (ADS)

    Kazinski, P. O.

    2008-04-01

    A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium.

  19. Experimental evidence of quantum randomness incomputability

    SciTech Connect

    Calude, Cristian S.; Dinneen, Michael J.; Dumitrescu, Monica; Svozil, Karl

    2010-08-15

    In contrast with software-generated randomness (called pseudo-randomness), quantum randomness can be proven incomputable; that is, it is not exactly reproducible by any algorithm. We provide experimental evidence of incomputability--an asymptotic property--of quantum randomness by performing finite tests of randomness inspired by algorithmic information theory.

  20. Stochastic solution of population balance equations for reactor networks

    SciTech Connect

    Menz, William J.; Akroyd, Jethro; Kraft, Markus

    2014-01-01

    This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images. Highlights: •An algorithm is presented to solve reactor networks with a population balance model. •A stochastic method is used to solve the population balance equations. •The convergence and efficiency of the reported algorithms are evaluated. •The algorithm is applied to simulate silicon nanoparticle synthesis in a 1D reactor. •Particle structure is reported as a function of reactor length and time.

  1. ON NONSTATIONARY STOCHASTIC MODELS FOR EARTHQUAKES.

    USGS Publications Warehouse

    Safak, Erdal; Boore, David M.

    1986-01-01

    A seismological stochastic model for earthquake ground-motion description is presented. Seismological models are based on the physical properties of the source and the medium and have significant advantages over the widely used empirical models. The model discussed here provides a convenient form for estimating structural response by using random vibration theory. A commonly used random process for ground acceleration, filtered white-noise multiplied by an envelope function, introduces some errors in response calculations for structures whose periods are longer than the faulting duration. An alternate random process, filtered shot-noise process, eliminates these errors.

  2. Stochastic Convection Parameterizations

    NASA Technical Reports Server (NTRS)

    Teixeira, Joao; Reynolds, Carolyn; Suselj, Kay; Matheou, Georgios

    2012-01-01

    computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts

  3. A Stochastic Employment Problem

    ERIC Educational Resources Information Center

    Wu, Teng

    2013-01-01

    The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…

  4. Derivatives of the Stochastic Growth Rate

    PubMed Central

    Steinsaltz, David; Tuljapurkar, Shripad; Horvitz, Carol

    2011-01-01

    We consider stochastic matrix models for population driven by random environments which form a Markov chain. The top Lyapunov exponent a, which describes the long-term growth rate, depends smoothly on the demographic parameters (represented as matrix entries) and on the parameters that define the stochastic matrix of the driving Markov chain. The derivatives of a — the “stochastic elasticities” — with respect to changes in the demographic parameters were derived by Tuljapurkar (1990). These results are here extended to a formula for the derivatives with respect to changes in the Markov chain driving the environments. We supplement these formulas with rigorous bounds on computational estimation errors, and with rigorous derivations of both the new and the old formulas. PMID:21463645

  5. On co-design of filter and fault estimator against randomly occurring nonlinearities and randomly occurring deception attacks

    NASA Astrophysics Data System (ADS)

    Hu, Jun; Liu, Steven; Ji, Donghai; Li, Shanqiang

    2016-07-01

    In this paper, the co-design problem of filter and fault estimator is studied for a class of time-varying non-linear stochastic systems subject to randomly occurring nonlinearities and randomly occurring deception attacks. Two mutually independent random variables obeying the Bernoulli distribution are employed to characterize the phenomena of the randomly occurring nonlinearities and randomly occurring deception attacks, respectively. By using the augmentation approach, the co-design problem of the robust filter and fault estimator is converted into the recursive filter design problem. A new compensation scheme is proposed such that, for both randomly occurring nonlinearities and randomly occurring deception attacks, an upper bound of the filtering error covariance is obtained and such an upper bound is minimized by properly designing the filter gain at each sampling instant. Moreover, the explicit form of the filter gain is given based on the solution to two Riccati-like difference equations. It is shown that the proposed co-design algorithm is of a recursive form that is suitable for online computation. Finally, a simulation example is given to illustrate the usefulness of the developed filtering approach.

  6. Algorithms and Algorithmic Languages.

    ERIC Educational Resources Information Center

    Veselov, V. M.; Koprov, V. M.

    This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…

  7. Encoded Expansion: An Efficient Algorithm to Discover Identical String Motifs

    PubMed Central

    Azmi, Aqil M.; Al-Ssulami, Abdulrakeeb

    2014-01-01

    A major task in computational biology is the discovery of short recurring string patterns known as motifs. Most of the schemes to discover motifs are either stochastic or combinatorial in nature. Stochastic approaches do not guarantee finding the correct motifs, while the combinatorial schemes tend to have an exponential time complexity with respect to motif length. To alleviate the cost, the combinatorial approach exploits dynamic data structures such as trees or graphs. Recently (Karci (2009) Efficient automatic exact motif discovery algorithms for biological sequences, Expert Systems with Applications 36:7952–7963) devised a deterministic algorithm that finds all the identical copies of string motifs of all sizes in theoretical time complexity of and a space complexity of where is the length of the input sequence and is the length of the longest possible string motif. In this paper, we present a significant improvement on Karci's original algorithm. The algorithm that we propose reports all identical string motifs of sizes that occur at least times. Our algorithm starts with string motifs of size 2, and at each iteration it expands the candidate string motifs by one symbol throwing out those that occur less than times in the entire input sequence. We use a simple array and data encoding to achieve theoretical worst-case time complexity of and a space complexity of Encoding of the substrings can speed up the process of comparison between string motifs. Experimental results on random and real biological sequences confirm that our algorithm has indeed a linear time complexity and it is more scalable in terms of sequence length than the existing algorithms. PMID:24871320

  8. A heterogeneous stochastic FEM framework for elliptic PDEs

    SciTech Connect

    Hou, Thomas Y. Liu, Pengfei

    2015-01-15

    We introduce a new concept of sparsity for the stochastic elliptic operator −div(a(x,ω)∇(⋅)), which reflects the compactness of its inverse operator in the stochastic direction and allows for spatially heterogeneous stochastic structure. This new concept of sparsity motivates a heterogeneous stochastic finite element method (HSFEM) framework for linear elliptic equations, which discretizes the equations using the heterogeneous coupling of spatial basis with local stochastic basis to exploit the local stochastic structure of the solution space. We also provide a sampling method to construct the local stochastic basis for this framework using the randomized range finding techniques. The resulting HSFEM involves two stages and suits the multi-query setting: in the offline stage, the local stochastic structure of the solution space is identified; in the online stage, the equation can be efficiently solved for multiple forcing functions. An online error estimation and correction procedure through Monte Carlo sampling is given. Numerical results for several problems with high dimensional stochastic input are presented to demonstrate the efficiency of the HSFEM in the online stage.

  9. Stochastic Flow Modeling for Resin Transfer Moulding

    NASA Astrophysics Data System (ADS)

    Desplentere, Frederik; Verpoest, Ignaas; Lomov, Stepan

    2009-07-01

    Liquid moulding processes suffer from inherently present scatter in the textile reinforcement properties. This variability can lead to unwanted filling patterns within the mould resulting in bad parts. If thermoplastic resins are used with the in-situ polymerisation technique, an additional difficulty appears. The time window to inject the material is small if industrial processing parameters are used (<5 minutes). To model the stochastic nature of RTM, Darcy's description of the mould filling process has been used with the permeability distribution of the preform given as a random field. The random field of the permeability is constructed as a correlated field with an exponential correlation function. Optical microscopy and X-ray micro-CT have been used to study the stochastic parameters of the geometry for 2D and 3D woven textile preforms. The parameters describing the random permeability field (average, standard deviation and correlation length) are identified based on the stochastic parameters of the geometry for the preforms, analytical estimations and CFD modelling of the permeability. In order to implement the random field for the permeability and the variability for the resin viscosity, an add-on to the mould filling simulation software PAM-RTM™ has been developed. This analysis has been validated on case studies.

  10. Estimating stepwise debromination pathways of polybrominated diphenyl ethers with an analogue Markov Chain Monte Carlo algorithm.

    PubMed

    Zou, Yonghong; Christensen, Erik R; Zheng, Wei; Wei, Hua; Li, An

    2014-11-01

    A stochastic process was developed to simulate the stepwise debromination pathways for polybrominated diphenyl ethers (PBDEs). The stochastic process uses an analogue Markov Chain Monte Carlo (AMCMC) algorithm to generate PBDE debromination profiles. The acceptance or rejection of the randomly drawn stepwise debromination reactions was determined by a maximum likelihood function. The experimental observations at certain time points were used as target profiles; therefore, the stochastic processes are capable of presenting the effects of reaction conditions on the selection of debromination pathways. The application of the model is illustrated by adopting the experimental results of decabromodiphenyl ether (BDE209) in hexane exposed to sunlight. Inferences that were not obvious from experimental data were suggested by model simulations. For example, BDE206 has much higher accumulation at the first 30 min of sunlight exposure. By contrast, model simulation suggests that, BDE206 and BDE207 had comparable yields from BDE209. The reason for the higher BDE206 level is that BDE207 has the highest depletion in producing octa products. Compared to a previous version of the stochastic model based on stochastic reaction sequences (SRS), the AMCMC approach was determined to be more efficient and robust. Due to the feature of only requiring experimental observations as input, the AMCMC model is expected to be applicable to a wide range of PBDE debromination processes, e.g. microbial, photolytic, or joint effects in natural environments. PMID:25113201

  11. A non-linear dimension reduction methodology for generating data-driven stochastic input models

    SciTech Connect

    Ganapathysubramanian, Baskar; Zabaras, Nicholas

    2008-06-20

    Stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R{sup n}. An isometric mapping F from M to a low-dimensional, compact, connected set A is contained in R{sup d}(d<stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the

  12. Improving the detection sensitivity of chromatography by stochastic resonance.

    PubMed

    Zhang, Wei; Guo, Jianru; Xiang, Bingren; Fan, Hongyan; Xu, Fengguo

    2014-05-01

    Improving the detection sensitivity of analytical instruments has been a challenging task for chemometricians since undetectability has been almost unavoidable in trace analysis, even under optimized experimental conditions and with the use of modern instruments. Various chemometrics methods have been developed which attempt to address this detection problem but with limited success (e.g., fast Fourier transform and wavelet transform). However, the application of stochastic resonance (SR) creates an entirely new and effective methodology. Stochastic resonance is a phenomenon which is manifested in non-linear systems where a weak signal can be amplified and optimized with the assistance of noise. In this review, we summarize the use of basic SR, optimization of parameters and its modifications, including periodic modulation stochastic resonance (PSRA), linear modulation stochastic resonance (LSRA), single-well potential stochastic resonance (SSR) and the Duffing oscillator algorithm (DOA) for amplifying sub-threshold small signals. We also review the advantages and the disadvantages of various SR procedures. PMID:24622614

  13. Performance study of LMS based adaptive algorithms for unknown system identification

    SciTech Connect

    Javed, Shazia; Ahmad, Noor Atinah

    2014-07-10

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

  14. Performance study of LMS based adaptive algorithms for unknown system identification

    NASA Astrophysics Data System (ADS)

    Javed, Shazia; Ahmad, Noor Atinah

    2014-07-01

    Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

  15. Identification and stochastic control of helicopter dynamic modes

    NASA Technical Reports Server (NTRS)

    Molusis, J. A.; Bar-Shalom, Y.

    1983-01-01

    A general treatment of parameter identification and stochastic control for use on helicopter dynamic systems is presented. Rotor dynamic models, including specific applications to rotor blade flapping and the helicopter ground resonance problem are emphasized. Dynamic systems which are governed by periodic coefficients as well as constant coefficient models are addressed. The dynamic systems are modeled by linear state variable equations which are used in the identification and stochastic control formulation. The pure identification problem as well as the stochastic control problem which includes combined identification and control for dynamic systems is addressed. The stochastic control problem includes the effect of parameter uncertainty on the solution and the concept of learning and how this is affected by the control's duel effect. The identification formulation requires algorithms suitable for on line use and thus recursive identification algorithms are considered. The applications presented use the recursive extended kalman filter for parameter identification which has excellent convergence for systems without process noise.

  16. Anisotropic diffusion of neutral particles in stochastic media

    NASA Astrophysics Data System (ADS)

    Vasques, Richard

    This work introduces a new homogenization theory for the transport of particles in stochastic media. This theory utilizes a nonclassical form of the Boltzmann equation in which the locations of the scattering centers in the system are correlated and the distance-to-collision is not exponentially distributed. We take the diffusion limit of this equation and derive an anisotropic diffusion equation. (The diffusion is anisotropic because the mean and mean square distances between collisions in the horizontal and vertical directions are slightly different.) We then generate different possible realizations of modeled 2-D and 3-D Pebble-Bed Reactor cores, divided into crystal (honeycomb in 2-D, face-centered in 3-D) and random structures. (To generate the random structures, we developed 2-D and 3-D ballistic deposition algorithms.) We apply Monte Carlo codes (which we also developed) in these structures to simulate neutron transport in both 2-D and 3-D systems; results from these simulations are presented. We show that the results predicted using the new theory more closely agree with the numerical experiments than the atomic mix results and its corrections, and that the new theory can accurately predict small anisotropic effects detected in the simulations. We conclude by discussing the general anisotropic behavior of particles that are born close to the wall of the core, and by showing that the new theory can be used to accurately estimate this effect.

  17. Stochastic reasoning, free energy, and information geometry.

    PubMed

    Ikeda, Shiro; Tanaka, Toshiyuki; Amari, Shun-ichi

    2004-09-01

    Belief propagation (BP) is a universal method of stochastic reasoning. It gives exact inference for stochastic models with tree interactions and works surprisingly well even if the models have loopy interactions. Its performance has been analyzed separately in many fields, such as AI, statistical physics, information theory, and information geometry. This article gives a unified framework for understanding BP and related methods and summarizes the results obtained in many fields. In particular, BP and its variants, including tree reparameterization and concave-convex procedure, are reformulated with information-geometrical terms, and their relations to the free energy function are elucidated from an information-geometrical viewpoint. We then propose a family of new algorithms. The stabilities of the algorithms are analyzed, and methods to accelerate them are investigated. PMID:15265322

  18. StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes

    PubMed Central

    Maarleveld, Timo R.; Olivier, Brett G.; Bruggeman, Frank J.

    2013-01-01

    Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python), which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool. PMID:24260203

  19. Optimizing Constrained Single Period Problem under Random Fuzzy Demand

    NASA Astrophysics Data System (ADS)

    Taleizadeh, Ata Allah; Shavandi, Hassan; Riazi, Afshin

    2008-09-01

    In this paper, we consider the multi-product multi-constraint newsboy problem with random fuzzy demands and total discount. The demand of the products is often stochastic in the real word but the estimation of the parameters of distribution function may be done by fuzzy manner. So an appropriate option to modeling the demand of products is using the random fuzzy variable. The objective function of proposed model is to maximize the expected profit of newsboy. We consider the constraints such as warehouse space and restriction on quantity order for products, and restriction on budget. We also consider the batch size for products order. Finally we introduce a random fuzzy multi-product multi-constraint newsboy problem (RFM-PM-CNP) and it is changed to a multi-objective mixed integer nonlinear programming model. Furthermore, a hybrid intelligent algorithm based on genetic algorithm, Pareto and TOPSIS is presented for the developed model. Finally an illustrative example is presented to show the performance of the developed model and algorithm.

  20. Stochastic Galerkin methods for the steady-state Navier-Stokes equations

    NASA Astrophysics Data System (ADS)

    Sousedík, Bedřich; Elman, Howard C.

    2016-07-01

    We study the steady-state Navier-Stokes equations in the context of stochastic finite element discretizations. Specifically, we assume that the viscosity is a random field given in the form of a generalized polynomial chaos expansion. For the resulting stochastic problem, we formulate the model and linearization schemes using Picard and Newton iterations in the framework of the stochastic Galerkin method, and we explore properties of the resulting stochastic solutions. We also propose a preconditioner for solving the linear systems of equations arising at each step of the stochastic (Galerkin) nonlinear iteration and demonstrate its effectiveness for solving a set of benchmark problems.

  1. Planning with Continuous Resources in Stochastic Domains

    NASA Technical Reports Server (NTRS)

    Mausam, Mausau; Benazera, Emmanuel; Brafman, Roneu; Hansen, Eric

    2005-01-01

    We consider the problem of optimal planning in stochastic domains with metric resource constraints. Our goal is to generate a policy whose expected sum of rewards is maximized for a given initial state. We consider a general formulation motivated by our application domain--planetary exploration--in which the choice of an action at each step may depend on the current resource levels. We adapt the forward search algorithm AO* to handle our continuous state space efficiently.

  2. Consistency of stochastic context-free grammars and application to stochastic parsing of GMTI tracker data

    NASA Astrophysics Data System (ADS)

    Balaji, Bhashyam

    2012-06-01

    Conventional trackers provide the human operator with estimated target tracks. It is desirable to make higher level inference of the target behaviour/intent (e.g., trajectory inference) in an automated manner. One such approach is to use stochastic context-free grammars and the Earley-Stoelcke parsing algorithm. The problem of inference is reformulated as one of parsing. In this paper, the consistency of stochastic context-free grammars is reviewed. Some examples illustrating the constraints on SCFGs due to consistency are presented, including a toy SCFG that has been used to successfully parse real GMTI radar data.

  3. Primal and Dual Integrated Force Methods Used for Stochastic Analysis

    NASA Technical Reports Server (NTRS)

    Patnaik, Surya N.

    2005-01-01

    At the NASA Glenn Research Center, the primal and dual integrated force methods are being extended for the stochastic analysis of structures. The stochastic simulation can be used to quantify the consequence of scatter in stress and displacement response because of a specified variation in input parameters such as load (mechanical, thermal, and support settling loads), material properties (strength, modulus, density, etc.), and sizing design variables (depth, thickness, etc.). All the parameters are modeled as random variables with given probability distributions, means, and covariances. The stochastic response is formulated through a quadratic perturbation theory, and it is verified through a Monte Carlo simulation.

  4. Stochastic behavior of nanoscale dielectric wall buckling

    NASA Astrophysics Data System (ADS)

    Friedman, Lawrence H.; Levin, Igor; Cook, Robert F.

    2016-03-01

    The random buckling patterns of nanoscale dielectric walls are analyzed using a nonlinear multi-scale stochastic method that combines experimental measurements with simulations. The dielectric walls, approximately 200 nm tall and 20 nm wide, consist of compliant, low dielectric constant (low-k) fins capped with stiff, compressively stressed TiN lines that provide the driving force for buckling. The deflections of the buckled lines exhibit sinusoidal pseudoperiodicity with amplitude fluctuation and phase decorrelation arising from stochastic variations in wall geometry, properties, and stress state at length scales shorter than the characteristic deflection wavelength of about 1000 nm. The buckling patterns are analyzed and modeled at two length scales: a longer scale (up to 5000 nm) that treats randomness as a longer-scale measurable quantity, and a shorter-scale (down to 20 nm) that treats buckling as a deterministic phenomenon. Statistical simulation is used to join the two length scales. Through this approach, the buckling model is validated and material properties and stress states are inferred. In particular, the stress state of TiN lines in three different systems is determined, along with the elastic moduli of low-k fins and the amplitudes of the small-scale random fluctuations in wall properties—all in the as-processed state. The important case of stochastic effects giving rise to buckling in a deterministically sub-critical buckling state is demonstrated. The nonlinear multiscale stochastic analysis provides guidance for design of low-k structures with acceptable buckling behavior and serves as a template for how randomness that is common to nanoscale phenomena might be measured and analyzed in other contexts.

  5. Stochastic behavior of nanoscale dielectric wall buckling

    PubMed Central

    Friedman, Lawrence H.; Levin, Igor; Cook, Robert F.

    2016-01-01

    The random buckling patterns of nanoscale dielectric walls are analyzed using a nonlinear multi-scale stochastic method that combines experimental measurements with simulations. The dielectric walls, approximately 200 nm tall and 20 nm wide, consist of compliant, low dielectric constant (low-k) fins capped with stiff, compressively stressed TiN lines that provide the driving force for buckling. The deflections of the buckled lines exhibit sinusoidal pseudoperiodicity with amplitude fluctuation and phase decorrelation arising from stochastic variations in wall geometry, properties, and stress state at length scales shorter than the characteristic deflection wavelength of about 1000 nm. The buckling patterns are analyzed and modeled at two length scales: a longer scale (up to 5000 nm) that treats randomness as a longer-scale measurable quantity, and a shorter-scale (down to 20 nm) that treats buckling as a deterministic phenomenon. Statistical simulation is used to join the two length scales. Through this approach, the buckling model is validated and material properties and stress states are inferred. In particular, the stress state of TiN lines in three different systems is determined, along with the elastic moduli of low-k fins and the amplitudes of the small-scale random fluctuations in wall properties—all in the as-processed state. The important case of stochastic effects giving rise to buckling in a deterministically sub-critical buckling state is demonstrated. The nonlinear multiscale stochastic analysis provides guidance for design of low-k structures with acceptable buckling behavior and serves as a template for how randomness that is common to nanoscale phenomena might be measured and analyzed in other contexts. PMID:27330220

  6. Quantum Stochastic Processes

    SciTech Connect

    Spring, William Joseph

    2009-04-13

    We consider quantum analogues of n-parameter stochastic processes, associated integrals and martingale properties extending classical results obtained in [1, 2, 3], and quantum results in [4, 5, 6, 7, 8, 9, 10].

  7. Dynamics of Double Stochastic Operators

    NASA Astrophysics Data System (ADS)

    Saburov, Mansoor

    2016-03-01

    A double stochastic operator is a generalization of a double stochastic matrix. In this paper, we study the dynamics of double stochastic operators. We give a criterion for a regularity of a double stochastic operator in terms of absences of its periodic points. We provide some examples to insure that, in general, a trajectory of a double stochastic operator may converge to any interior point of the simplex.

  8. Subcell resolution in simplex stochastic collocation for spatial discontinuities

    NASA Astrophysics Data System (ADS)

    Witteveen, Jeroen A. S.; Iaccarino, Gianluca

    2013-10-01

    Subcell resolution has been used in the Finite Volume Method (FVM) to obtain accurate approximations of discontinuities in the physical space. Stochastic methods are usually based on local adaptivity for resolving discontinuities in the stochastic dimensions. However, the adaptive refinement in the probability space is ineffective in the non-intrusive uncertainty quantification framework, if the stochastic discontinuity is caused by a discontinuity in the physical space with a random location. The dependence of the discontinuity location in the probability space on the spatial coordinates then results in a staircase approximation of the statistics, which leads to first-order error convergence and an underprediction of the maximum standard deviation. To avoid these problems, we introduce subcell resolution into the Simplex Stochastic Collocation (SSC) method for obtaining a truly discontinuous representation of random spatial discontinuities in the interior of the cells discretizing the probability space. The presented SSC-SR method is based on resolving the discontinuity location in the probability space explicitly as function of the spatial coordinates and extending the stochastic response surface approximations up to the predicted discontinuity location. The applications to a linear advection problem, the inviscid Burgers' equation, a shock tube problem, and the transonic flow over the RAE 2822 airfoil show that SSC-SR resolves random spatial discontinuities with multiple stochastic and spatial dimensions accurately using a minimal number of samples.

  9. Subcell resolution in simplex stochastic collocation for spatial discontinuities

    SciTech Connect

    Witteveen, Jeroen A.S.; Iaccarino, Gianluca

    2013-10-15

    Subcell resolution has been used in the Finite Volume Method (FVM) to obtain accurate approximations of discontinuities in the physical space. Stochastic methods are usually based on local adaptivity for resolving discontinuities in the stochastic dimensions. However, the adaptive refinement in the probability space is ineffective in the non-intrusive uncertainty quantification framework, if the stochastic discontinuity is caused by a discontinuity in the physical space with a random location. The dependence of the discontinuity location in the probability space on the spatial coordinates then results in a staircase approximation of the statistics, which leads to first-order error convergence and an underprediction of the maximum standard deviation. To avoid these problems, we introduce subcell resolution into the Simplex Stochastic Collocation (SSC) method for obtaining a truly discontinuous representation of random spatial discontinuities in the interior of the cells discretizing the probability space. The presented SSC–SR method is based on resolving the discontinuity location in the probability space explicitly as function of the spatial coordinates and extending the stochastic response surface approximations up to the predicted discontinuity location. The applications to a linear advection problem, the inviscid Burgers’ equation, a shock tube problem, and the transonic flow over the RAE 2822 airfoil show that SSC–SR resolves random spatial discontinuities with multiple stochastic and spatial dimensions accurately using a minimal number of samples.

  10. Numerical tests of stochastic tomography

    NASA Astrophysics Data System (ADS)

    Ru-Shan, Wu; Xiao-Bi, Xie

    1991-05-01

    The method of stochastic tomography proposed by Wu is tested numerically. This method reconstructs the heterospectra (power spectra of heterogeneities) at all depths of a non-uniform random medium using measured joint transverse-angular coherence functions (JTACF) of transmission fluctuations on an array. The inversion method is based on a constrained least-squares inversion implemented via the singular value decomposition. The inversion is also applicable to reconstructions using transverse coherence functions (TCF) or angular coherence functions (ACF); these are merely special cases of JTACF. Through the analysis of sampling functions and singular values, and through numerical examples of reconstruction using theoretically generated coherence functions, we compare the resolution and robustness of reconstructions using TCF, ACF and JTACF. The JTACF can `focus' the coherence analysis at different depths and therefore has a better depth resolution than TCF and ACF. In addition, the JTACF contains much more information than the sum of TCF and ACF, and has much better noise resistance properties than TCF and ACF. Inversion of JTACF can give a reliable reconstruction of heterospectra at different depths even for data with 20% noise contamination. This demonstrates the feasibility of stochastic tomography using JTACF.

  11. Evaluating uncertainty in stochastic simulation models

    SciTech Connect

    McKay, M.D.

    1998-02-01

    This paper discusses fundamental concepts of uncertainty analysis relevant to both stochastic simulation models and deterministic models. A stochastic simulation model, called a simulation model, is a stochastic mathematical model that incorporates random numbers in the calculation of the model prediction. Queuing models are familiar simulation models in which random numbers are used for sampling interarrival and service times. Another example of simulation models is found in probabilistic risk assessments where atmospheric dispersion submodels are used to calculate movement of material. For these models, randomness comes not from the sampling of times but from the sampling of weather conditions, which are described by a frequency distribution of atmospheric variables like wind speed and direction as a function of height above ground. A common characteristic of simulation models is that single predictions, based on one interarrival time or one weather condition, for example, are not nearly as informative as the probability distribution of possible predictions induced by sampling the simulation variables like time and weather condition. The language of model analysis is often general and vague, with terms having mostly intuitive meaning. The definition and motivations for some of the commonly used terms and phrases offered in this paper lead to an analysis procedure based on prediction variance. In the following mathematical abstraction the authors present a setting for model analysis, relate practical objectives to mathematical terms, and show how two reasonable premises lead to a viable analysis strategy.

  12. Rupture Propagation for Stochastic Fault Models

    NASA Astrophysics Data System (ADS)

    Favreau, P.; Lavallee, D.; Archuleta, R.

    2003-12-01

    The inversion of strong motion data of large earhquakes give the spatial distribution of pre-stress on the ruptured faults and it can be partially reproduced by stochastic models, but a fundamental question remains: how rupture propagates, constrained by the presence of spatial heterogeneity? For this purpose we investigate how the underlying random variables, that control the pre-stress spatial variability, condition the propagation of the rupture. Two stochastic models of prestress distributions are considered, respectively based on Cauchy and Gaussian random variables. The parameters of the two stochastic models have values corresponding to the slip distribution of the 1979 Imperial Valley earthquake. We use a finite difference code to simulate the spontaneous propagation of shear rupture on a flat fault in a 3D continuum elastic body. The friction law is the slip dependent friction law. The simulations show that the propagation of the rupture front is more complex, incoherent or snake-like for a prestress distribution based on Cauchy random variables. This may be related to the presence of a higher number of asperities in this case. These simulations suggest that directivity is stronger in the Cauchy scenario, compared to the smoother rupture of the Gauss scenario.

  13. Is random access memory random?

    NASA Technical Reports Server (NTRS)

    Denning, P. J.

    1986-01-01

    Most software is contructed on the assumption that the programs and data are stored in random access memory (RAM). Physical limitations on the relative speeds of processor and memory elements lead to a variety of memory organizations that match processor addressing rate with memory service rate. These include interleaved and cached memory. A very high fraction of a processor's address requests can be satified from the cache without reference to the main memory. The cache requests information from main memory in blocks that can be transferred at the full memory speed. Programmers who organize algorithms for locality can realize the highest performance from these computers.

  14. Control of Finite-State, Finite Memory Stochastic Systems

    NASA Technical Reports Server (NTRS)

    Sandell, Nils R.

    1974-01-01

    A generalized problem of stochastic control is discussed in which multiple controllers with different data bases are present. The vehicle for the investigation is the finite state, finite memory (FSFM) stochastic control problem. Optimality conditions are obtained by deriving an equivalent deterministic optimal control problem. A FSFM minimum principle is obtained via the equivalent deterministic problem. The minimum principle suggests the development of a numerical optimization algorithm, the min-H algorithm. The relationship between the sufficiency of the minimum principle and the informational properties of the problem are investigated. A problem of hypothesis testing with 1-bit memory is investigated to illustrate the application of control theoretic techniques to information processing problems.

  15. Pushing the envelope: Planning, propositional logic, and stochastic search

    SciTech Connect

    Kautz, H.; Selman, B.

    1996-12-31

    Planning is a notoriously hard combinatorial. search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning systems. Although stochastic methods have been shown to be very effective on a wide range of scheduling problems, this is the first demonstration of its power on truly challenging classical planning instances. This work also provides a new perspective on representational issues in planning.

  16. Rare-event Simulation for Stochastic Korteweg-de Vries Equation

    SciTech Connect

    Xu, Gongjun; Lin, Guang; Liu, Jingchen

    2014-01-01

    An asymptotic analysis of the tail probabilities for the dynamics of a soliton wave $U(x,t)$ under a stochastic time-dependent force is developed. The dynamics of the soliton wave $U(x,t)$ is described by the Korteweg-de Vries Equation with homogeneous Dirichlet boundary conditions under a stochastic time-dependent force, which is modeled as a time-dependent Gaussian noise with amplitude $\\epsilon$. The tail probability we considered is $w(b) :=P(\\sup_{t\\in [0,T]} U(x,t) > b ),$ as $b\\rightarrow \\infty,$ for some constant $T>0$ and a fixed $x$, which can be interpreted as tail probability of the amplitude of water wave on shallow surface of a fluid or long internal wave in a density-stratified ocean. Our goal is to characterize the asymptotic behaviors of $w(b)$ and to evaluate the tail probability of the event that the soliton wave exceeds a certain threshold value under a random force term. Such rare-event calculation of $w(b)$ is very useful for fast estimation of the risk of the potential damage that could caused by the water wave in a density-stratified ocean modeled by the stochastic KdV equation. In this work, the asymptotic approximation of the probability that the soliton wave exceeds a high-level $b$ is derived. In addition, we develop a provably efficient rare-event simulation algorithm to compute $w(b)$. The efficiency of the algorithm only requires mild conditions and therefore it is applicable to a general class of Gaussian processes and many diverse applications.

  17. Parallel Environment for the Creation of Stochastics 1.0

    Energy Science and Technology Software Center (ESTSC)

    2011-01-06

    PECOS is a computational library for creating and manipulating realizations of stochastic quantities, including scalar uncertain variables, random fields, and stochastic processes. It offers a unified interface to univariate and multivariate polynomial approximations using either orthogonal or interpolation polynomials; numerical integration drivers for Latin hypercube sampling, quadrature, cubature, and sparse grids; and fast Fourier transforms using third party libraries. The PECOS core also offers statistical utilities and transformations between various representations of stochastic uncertainty. PECOSmore » provides a C++ API through which users can generate and transform realizations of stochastic quantities. It is currently used by Sandia’s DAKOTA, Stokhos, and Encore software packages for uncertainty quantification and verification. PECOS generates random sample sets and multi-dimensional integration grids, typically used in forward propagation of scalar uncertainty in computational models (uncertainty quantification (UQ)). PECOS also generates samples of random fields (RFs) and stochastic processes (SPs) from a set of user-defined power spectral densities (PSDs). The RF/SP may be either Gaussian or non-Gaussian and either stationary or nonstationary, and the resulting sample is intended for run-time query by parallel finite element simulation codes. Finally, PECOS supports nonlinear transformations of random variables via the Nataf transformation and extensions.« less

  18. Parallel Environment for the Creation of Stochastics 1.0

    SciTech Connect

    2011-01-06

    PECOS is a computational library for creating and manipulating realizations of stochastic quantities, including scalar uncertain variables, random fields, and stochastic processes. It offers a unified interface to univariate and multivariate polynomial approximations using either orthogonal or interpolation polynomials; numerical integration drivers for Latin hypercube sampling, quadrature, cubature, and sparse grids; and fast Fourier transforms using third party libraries. The PECOS core also offers statistical utilities and transformations between various representations of stochastic uncertainty. PECOS provides a C++ API through which users can generate and transform realizations of stochastic quantities. It is currently used by Sandia’s DAKOTA, Stokhos, and Encore software packages for uncertainty quantification and verification. PECOS generates random sample sets and multi-dimensional integration grids, typically used in forward propagation of scalar uncertainty in computational models (uncertainty quantification (UQ)). PECOS also generates samples of random fields (RFs) and stochastic processes (SPs) from a set of user-defined power spectral densities (PSDs). The RF/SP may be either Gaussian or non-Gaussian and either stationary or nonstationary, and the resulting sample is intended for run-time query by parallel finite element simulation codes. Finally, PECOS supports nonlinear transformations of random variables via the Nataf transformation and extensions.

  19. Angular Distribution of Particles Emerging from a Diffusive Region and its Implications for the Fleck-Canfield Random Walk Algorithm for Implicit Monte Carlo Radiation Transport

    SciTech Connect

    Cooper, M.A.

    2000-07-03

    We present various approximations for the angular distribution of particles emerging from an optically thick, purely isotropically scattering region into a vacuum. Our motivation is to use such a distribution for the Fleck-Canfield random walk method [1] for implicit Monte Carlo (IMC) [2] radiation transport problems. We demonstrate that the cosine distribution recommended in the original random walk paper [1] is a poor approximation to the angular distribution predicted by transport theory. Then we examine other approximations that more closely match the transport angular distribution.

  20. Random broadcast on random geometric graphs

    SciTech Connect

    Bradonjic, Milan; Elsasser, Robert; Friedrich, Tobias

    2009-01-01

    In this work, we consider the random broadcast time on random geometric graphs (RGGs). The classic random broadcast model, also known as push algorithm, is defined as: starting with one informed node, in each succeeding round every informed node chooses one of its neighbors uniformly at random and informs it. We consider the random broadcast time on RGGs, when with high probability: (i) RGG is connected, (ii) when there exists the giant component in RGG. We show that the random broadcast time is bounded by {Omicron}({radical} n + diam(component)), where diam(component) is a diameter of the entire graph, or the giant component, for the regimes (i), or (ii), respectively. In other words, for both regimes, we derive the broadcast time to be {Theta}(diam(G)), which is asymptotically optimal.