Optical Variability Signatures from Massive Black Hole Binaries

NASA Astrophysics Data System (ADS)

Kasliwal, Vishal P.; Frank, Koby Alexander; Lidz, Adam

2017-01-01

The hierarchical merging of dark matter halos and their associated galaxies should lead to a population of supermassive black hole binaries (MBHBs). We consider plausible optical variability signatures from MBHBs at sub-parsec separations and search for these using data from the Catalina Real-Time Transient Survey (CRTS). Specifically, we model the impact of relativistic Doppler beaming on the accretion disk emission from the less massive, secondary black hole. We explore whether this Doppler modulation may be separated from other sources of stochastic variability in the accretion flow around the MBHBs, which we describe as a damped random walk (DRW). In the simple case of a circular orbit, relativistic beaming leads to a series of broad peaks — located at multiples of the orbital frequency — in the fluctuation power spectrum. We extend our analysis to the case of elliptical orbits and discuss the effect of beaming on the flux power spectrum and auto-correlation function using simulations. We present a code to model an observed light curve as a stochastic DRW-type time series modulated by relativistic beaming and apply the code to CRTS data.

Bayesian Lagrangian Data Assimilation and Drifter Deployment Strategies

NASA Astrophysics Data System (ADS)

Dutt, A.; Lermusiaux, P. F. J.

2017-12-01

Ocean currents transport a variety of natural (e.g. water masses, phytoplankton, zooplankton, sediments, etc.) and man-made materials and other objects (e.g. pollutants, floating debris, search and rescue, etc.). Lagrangian Coherent Structures (LCSs) or the most influential/persistent material lines in a flow, provide a robust approach to characterize such Lagrangian transports and organize classic trajectories. Using the flow-map stochastic advection and a dynamically-orthogonal decomposition, we develop uncertainty prediction schemes for both Eulerian and Lagrangian variables. We then extend our Bayesian Gaussian Mixture Model (GMM)-DO filter to a joint Eulerian-Lagrangian Bayesian data assimilation scheme. The resulting nonlinear filter allows the simultaneous non-Gaussian estimation of Eulerian variables (e.g. velocity, temperature, salinity, etc.) and Lagrangian variables (e.g. drifter/float positions, trajectories, LCSs, etc.). Its results are showcased using a double-gyre flow with a random frequency, a stochastic flow past a cylinder, and realistic ocean examples. We further show how our Bayesian mutual information and adaptive sampling equations provide a rigorous efficient methodology to plan optimal drifter deployment strategies and predict the optimal times, locations, and types of measurements to be collected.

Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance

DTIC Science & Technology

2003-07-21

Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance Vincent A. Cicirello CMU-RI-TR-03-27 Submitted in partial fulfillment...AND SUBTITLE Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...lead to the development of a search control framework, called QD-BEACON that uses online -generated statistical models of search performance to

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.

A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

PubMed Central

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature. PMID:24672389

A PSO-based hybrid metaheuristic for permutation flowshop scheduling problems.

PubMed

Zhang, Le; Wu, Jinnan

2014-01-01

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

NASA Astrophysics Data System (ADS)

Allah Taleizadeh, Ata; Aryanezhad, Mir-Bahador; Niaki, Seyed Taghi Akhavan

Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.

The role of beginner’s luck in learning to prefer risky patches by socially foraging house sparrows

PubMed Central

2013-01-01

Although there has been extensive research on the evolution of individual decision making under risk (when facing variable outcomes), little is known on how the evolution of such decision-making mechanisms has been shaped by social learning and exploitation. We presented socially foraging house sparrows with a choice between scattered feeding wells in which millet seeds were hidden under 2 types of colored sand: green sand offering ~80 seeds with a probability of 0.1 (high risk–high reward) and yellow sand offering 1 seed with certainty (low risk–low reward). Although the expected benefit of choosing variable wells was 8 times higher than that of choosing constant wells, only some sparrows developed a preference for variable wells, whereas others developed a significant preference for constant wells. We found that this dichotomy could be explained by stochastic individual differences in sampling success during foraging, rather than by social foraging strategies (active searching vs. joining others). Moreover, preference for variable or constant wells was related to the sparrows’ success during searching, rather than during joining others or when picking exposed seeds (i.e., they learn when actively searching in the sand). Finally, although for many sparrows learning resulted in an apparently maladaptive risk aversion, group living still allowed them to enjoy profitable variable wells by occasionally joining variable-preferring sparrows. PMID:24137046

Stochastic Template Bank for Gravitational Wave Searches for Precessing Neutron Star-Black Hole Coalescence Events

NASA Technical Reports Server (NTRS)

Indik, Nathaniel; Haris, K.; Dal Canton, Tito; Fehrmann, Henning; Krishnan, Badri; Lundgren, Andrew; Nielsen, Alex B.; Pai, Archana

2017-01-01

Gravitational wave searches to date have largely focused on non-precessing systems. Including precession effects greatly increases the number of templates to be searched over. This leads to a corresponding increase in the computational cost and can increase the false alarm rate of a realistic search. On the other hand, there might be astrophysical systems that are entirely missed by non-precessing searches. In this paper we consider the problem of constructing a template bank using stochastic methods for neutron star-black hole binaries allowing for precession, but with the restrictions that the total angular momentum of the binary is pointing toward the detector and that the neutron star spin is negligible relative to that of the black hole. We quantify the number of templates required for the search, and we explicitly construct the template bank. We show that despite the large number of templates, stochastic methods can be adapted to solve the problem. We quantify the parameter space region over which the non-precessing search might miss signals.

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models

PubMed Central

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful. PMID:28626348

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models.

PubMed

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful.

Statistical signatures of a targeted search by bacteria

NASA Astrophysics Data System (ADS)

Jashnsaz, Hossein; Anderson, Gregory G.; Pressé, Steve

2017-12-01

Chemoattractant gradients are rarely well-controlled in nature and recent attention has turned to bacterial chemotaxis toward typical bacterial food sources such as food patches or even bacterial prey. In environments with localized food sources reminiscent of a bacterium’s natural habitat, striking phenomena—such as the volcano effect or banding—have been predicted or expected to emerge from chemotactic models. However, in practice, from limited bacterial trajectory data it is difficult to distinguish targeted searches from an untargeted search strategy for food sources. Here we use a theoretical model to identify statistical signatures of a targeted search toward point food sources, such as prey. Our model is constructed on the basis that bacteria use temporal comparisons to bias their random walk, exhibit finite memory and are subject to random (Brownian) motion as well as signaling noise. The advantage with using a stochastic model-based approach is that a stochastic model may be parametrized from individual stochastic bacterial trajectories but may then be used to generate a very large number of simulated trajectories to explore average behaviors obtained from stochastic search strategies. For example, our model predicts that a bacterium’s diffusion coefficient increases as it approaches the point source and that, in the presence of multiple sources, bacteria may take substantially longer to locate their first source giving the impression of an untargeted search strategy.

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

NASA Astrophysics Data System (ADS)

Chen, Xianshun; Feng, Liang; Ong, Yew Soon

2012-07-01

In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD.

p-adic stochastic hidden variable model

NASA Astrophysics Data System (ADS)

Khrennikov, Andrew

1998-03-01

We propose stochastic hidden variables model in which hidden variables have a p-adic probability distribution ρ(λ) and at the same time conditional probabilistic distributions P(U,λ), U=A,A',B,B', are ordinary probabilities defined on the basis of the Kolmogorov measure-theoretical axiomatics. A frequency definition of p-adic probability is quite similar to the ordinary frequency definition of probability. p-adic frequency probability is defined as the limit of relative frequencies νn but in the p-adic metric. We study a model with p-adic stochastics on the level of the hidden variables description. But, of course, responses of macroapparatuses have to be described by ordinary stochastics. Thus our model describes a mixture of p-adic stochastics of the microworld and ordinary stochastics of macroapparatuses. In this model probabilities for physical observables are the ordinary probabilities. At the same time Bell's inequality is violated.

Boosting association rule mining in large datasets via Gibbs sampling.

PubMed

Qian, Guoqi; Rao, Calyampudi Radhakrishna; Sun, Xiaoying; Wu, Yuehua

2016-05-03

Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling-induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Stochastic effects in EUV lithography: random, local CD variability, and printing failures

NASA Astrophysics Data System (ADS)

De Bisschop, Peter

2017-10-01

Stochastic effects in lithography are usually quantified through local CD variability metrics, such as line-width roughness or local CD uniformity (LCDU), and these quantities have been measured and studied intensively, both in EUV and optical lithography. Next to the CD-variability, stochastic effects can also give rise to local, random printing failures, such as missing contacts or microbridges in spaces. When these occur, there often is no (reliable) CD to be measured locally, and then such failures cannot be quantified with the usual CD-measuring techniques. We have developed algorithms to detect such stochastic printing failures in regular line/space (L/S) or contact- or dot-arrays from SEM images, leading to a stochastic failure metric that we call NOK (not OK), which we consider a complementary metric to the CD-variability metrics. This paper will show how both types of metrics can be used to experimentally quantify dependencies of stochastic effects to, e.g., CD, pitch, resist, exposure dose, etc. As it is also important to be able to predict upfront (in the OPC verification stage of a production-mask tape-out) whether certain structures in the layout are likely to have a high sensitivity to stochastic effects, we look into the feasibility of constructing simple predictors, for both stochastic CD-variability and printing failure, that can be calibrated for the process and exposure conditions used and integrated into the standard OPC verification flow. Finally, we briefly discuss the options to reduce stochastic variability and failure, considering the entire patterning ecosystem.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables

DTIC Science & Technology

2010-08-01

a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. a ...SECURITY CLASSIFICATION OF: This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the...Random Variables Report Title ABSTRACT This study presents a methodology for computing stochastic sensitivities with respect to the design variables

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

Improving the Fitness of High-Dimensional Biomechanical Models via Data-Driven Stochastic Exploration

PubMed Central

Bustamante, Carlos D.; Valero-Cuevas, Francisco J.

2010-01-01

The field of complex biomechanical modeling has begun to rely on Monte Carlo techniques to investigate the effects of parameter variability and measurement uncertainty on model outputs, search for optimal parameter combinations, and define model limitations. However, advanced stochastic methods to perform data-driven explorations, such as Markov chain Monte Carlo (MCMC), become necessary as the number of model parameters increases. Here, we demonstrate the feasibility and, what to our knowledge is, the first use of an MCMC approach to improve the fitness of realistically large biomechanical models. We used a Metropolis–Hastings algorithm to search increasingly complex parameter landscapes (3, 8, 24, and 36 dimensions) to uncover underlying distributions of anatomical parameters of a “truth model” of the human thumb on the basis of simulated kinematic data (thumbnail location, orientation, and linear and angular velocities) polluted by zero-mean, uncorrelated multivariate Gaussian “measurement noise.” Driven by these data, ten Markov chains searched each model parameter space for the subspace that best fit the data (posterior distribution). As expected, the convergence time increased, more local minima were found, and marginal distributions broadened as the parameter space complexity increased. In the 36-D scenario, some chains found local minima but the majority of chains converged to the true posterior distribution (confirmed using a cross-validation dataset), thus demonstrating the feasibility and utility of these methods for realistically large biomechanical problems. PMID:19272906

Output Feedback Stabilization for a Class of Multi-Variable Bilinear Stochastic Systems with Stochastic Coupling Attenuation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Qichun; Zhou, Jinglin; Wang, Hong

In this paper, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.

Search Techniques for Multi-Objective Optimization of Mixed-Variable Systems Having Stochastic Responses

DTIC Science & Technology

2007-09-01

kids . Thanks for all the encouragement and for reminding me that there is more to life than graduate school and that there really is a light at the end...in-law for being a great help with the kids during times of emergency. There are not enough words to thank my husband and sons for their support over...worth doing rarely are. To my beloved sons, hugs and kisses for being such great kids through all of this. You guys are the two best sons a Mommy can

Condition-dependent mate choice: A stochastic dynamic programming approach.

PubMed

Frame, Alicia M; Mills, Alex F

2014-09-01

We study how changing female condition during the mating season and condition-dependent search costs impact female mate choice, and what strategies a female could employ in choosing mates to maximize her own fitness. We address this problem via a stochastic dynamic programming model of mate choice. In the model, a female encounters males sequentially and must choose whether to mate or continue searching. As the female searches, her own condition changes stochastically, and she incurs condition-dependent search costs. The female attempts to maximize the quality of the offspring, which is a function of the female's condition at mating and the quality of the male with whom she mates. The mating strategy that maximizes the female's net expected reward is a quality threshold. We compare the optimal policy with other well-known mate choice strategies, and we use simulations to examine how well the optimal policy fares under imperfect information. Copyright © 2014 Elsevier Inc. All rights reserved.

[Gene method for inconsistent hydrological frequency calculation. I: Inheritance, variability and evolution principles of hydrological genes].

PubMed

Xie, Ping; Wu, Zi Yi; Zhao, Jiang Yan; Sang, Yan Fang; Chen, Jie

2018-04-01

A stochastic hydrological process is influenced by both stochastic and deterministic factors. A hydrological time series contains not only pure random components reflecting its inheri-tance characteristics, but also deterministic components reflecting variability characteristics, such as jump, trend, period, and stochastic dependence. As a result, the stochastic hydrological process presents complicated evolution phenomena and rules. To better understand these complicated phenomena and rules, this study described the inheritance and variability characteristics of an inconsistent hydrological series from two aspects: stochastic process simulation and time series analysis. In addition, several frequency analysis approaches for inconsistent time series were compared to reveal the main problems in inconsistency study. Then, we proposed a new concept of hydrological genes origined from biological genes to describe the inconsistent hydrolocal processes. The hydrologi-cal genes were constructed using moments methods, such as general moments, weight function moments, probability weight moments and L-moments. Meanwhile, the five components, including jump, trend, periodic, dependence and pure random components, of a stochastic hydrological process were defined as five hydrological bases. With this method, the inheritance and variability of inconsistent hydrological time series were synthetically considered and the inheritance, variability and evolution principles were fully described. Our study would contribute to reveal the inheritance, variability and evolution principles in probability distribution of hydrological elements.

Baseline-dependent effect of noise-enhanced insoles on gait variability in healthy elderly walkers.

PubMed

Stephen, Damian G; Wilcox, Bethany J; Niemi, James B; Franz, Jason R; Franz, Jason; Kerrigan, Dr; Kerrigan, D Casey; D'Andrea, Susan E

2012-07-01

The purpose of this study was to determine whether providing subsensory stochastic-resonance mechanical vibration to the foot soles of elderly walkers could decrease gait variability. In a randomized double-blind controlled trial, 29 subjects engaged in treadmill walking while wearing sandals customized with three actuators capable of producing stochastic-resonance mechanical vibration embedded in each sole. For each subject, we determined a subsensory level of vibration stimulation. After a 5-min acclimation period of walking with the footwear, subjects were asked to walk on the treadmill for six trials, each 30s long. Trials were pair-wise random: in three trials, actuators provided subsensory vibration; in the other trials, they did not. Subjects wore reflective markers to track body motion. Stochastic-resonance mechanical stimulation exhibited baseline-dependent effects on spatial stride-to-stride variability in gait, slightly increasing variability in subjects with least baseline variability and providing greater reductions in variability for subjects with greater baseline variability (p<.001). Thus, applying stochastic-resonance mechanical vibrations on the plantar surface of the foot reduces gait variability for subjects with more variable gait. Stochastic-resonance mechanical vibrations may provide an effective intervention for preventing falls in healthy elderly walkers. Published by Elsevier B.V.

Habitat connectivity and in-stream vegetation control temporal variability of benthic invertebrate communities.

PubMed

Huttunen, K-L; Mykrä, H; Oksanen, J; Astorga, A; Paavola, R; Muotka, T

2017-05-03

One of the key challenges to understanding patterns of β diversity is to disentangle deterministic patterns from stochastic ones. Stochastic processes may mask the influence of deterministic factors on community dynamics, hindering identification of the mechanisms causing variation in community composition. We studied temporal β diversity (among-year dissimilarity) of macroinvertebrate communities in near-pristine boreal streams across 14 years. To assess whether the observed β diversity deviates from that expected by chance, and to identify processes (deterministic vs. stochastic) through which different explanatory factors affect community variability, we used a null model approach. We observed that at the majority of sites temporal β diversity was low indicating high community stability. When stochastic variation was unaccounted for, connectivity was the only variable explaining temporal β diversity, with weakly connected sites exhibiting higher community variability through time. After accounting for stochastic effects, connectivity lost importance, suggesting that it was related to temporal β diversity via random colonization processes. Instead, β diversity was best explained by in-stream vegetation, community variability decreasing with increasing bryophyte cover. These results highlight the potential of stochastic factors to dampen the influence of deterministic processes, affecting our ability to understand and predict changes in biological communities through time.

New insights into time series analysis. II - Non-correlated observations

NASA Astrophysics Data System (ADS)

Ferreira Lopes, C. E.; Cross, N. J. G.

2017-08-01

Context. Statistical parameters are used to draw conclusions in a vast number of fields such as finance, weather, industrial, and science. These parameters are also used to identify variability patterns on photometric data to select non-stochastic variations that are indicative of astrophysical effects. New, more efficient, selection methods are mandatory to analyze the huge amount of astronomical data. Aims: We seek to improve the current methods used to select non-stochastic variations on non-correlated data. Methods: We used standard and new data-mining parameters to analyze non-correlated data to find the best way to discriminate between stochastic and non-stochastic variations. A new approach that includes a modified Strateva function was performed to select non-stochastic variations. Monte Carlo simulations and public time-domain data were used to estimate its accuracy and performance. Results: We introduce 16 modified statistical parameters covering different features of statistical distribution such as average, dispersion, and shape parameters. Many dispersion and shape parameters are unbound parameters, I.e. equations that do not require the calculation of average. Unbound parameters are computed with single loop and hence decreasing running time. Moreover, the majority of these parameters have lower errors than previous parameters, which is mainly observed for distributions with few measurements. A set of non-correlated variability indices, sample size corrections, and a new noise model along with tests of different apertures and cut-offs on the data (BAS approach) are introduced. The number of mis-selections are reduced by about 520% using a single waveband and 1200% combining all wavebands. On the other hand, the even-mean also improves the correlated indices introduced in Paper I. The mis-selection rate is reduced by about 18% if the even-mean is used instead of the mean to compute the correlated indices in the WFCAM database. Even-statistics allows us to improve the effectiveness of both correlated and non-correlated indices. Conclusions: The selection of non-stochastic variations is improved by non-correlated indices. The even-averages provide a better estimation of mean and median for almost all statistical distributions analyzed. The correlated variability indices, which are proposed in the first paper of this series, are also improved if the even-mean is used. The even-parameters will also be useful for classifying light curves in the last step of this project. We consider that the first step of this project, where we set new techniques and methods that provide a huge improvement on the efficiency of selection of variable stars, is now complete. Many of these techniques may be useful for a large number of fields. Next, we will commence a new step of this project regarding the analysis of period search methods.

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Agent based reasoning for the non-linear stochastic models of long-range memory

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Gontis, V.

2012-02-01

We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the non-linear stochastic models of long-range memory in financial markets. The agent based model providing matching macroscopic description serves as a microscopic reasoning of the earlier proposed stochastic model exhibiting power law statistics.

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports.

PubMed

Schilde, M; Doerner, K F; Hartl, R F

2011-12-01

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.

Momentum Maps and Stochastic Clebsch Action Principles

NASA Astrophysics Data System (ADS)

Cruzeiro, Ana Bela; Holm, Darryl D.; Ratiu, Tudor S.

2018-01-01

We derive stochastic differential equations whose solutions follow the flow of a stochastic nonlinear Lie algebra operation on a configuration manifold. For this purpose, we develop a stochastic Clebsch action principle, in which the noise couples to the phase space variables through a momentum map. This special coupling simplifies the structure of the resulting stochastic Hamilton equations for the momentum map. In particular, these stochastic Hamilton equations collectivize for Hamiltonians that depend only on the momentum map variable. The Stratonovich equations are derived from the Clebsch variational principle and then converted into Itô form. In comparing the Stratonovich and Itô forms of the stochastic dynamical equations governing the components of the momentum map, we find that the Itô contraction term turns out to be a double Poisson bracket. Finally, we present the stochastic Hamiltonian formulation of the collectivized momentum map dynamics and derive the corresponding Kolmogorov forward and backward equations.

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

NASA Astrophysics Data System (ADS)

Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.

2016-07-01

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.

COMMUNICATION: Stochastic resonance and the evolution of Daphnia foraging strategy

NASA Astrophysics Data System (ADS)

Dees, Nathan D.; Bahar, Sonya; Moss, Frank

2008-12-01

Search strategies are currently of great interest, with reports on foraging ranging from albatrosses and spider monkeys to microzooplankton. Here, we investigate the role of noise in optimizing search strategies. We focus on the zooplankton Daphnia, which move in successive sequences consisting of a hop, a pause and a turn through an angle. Recent experiments have shown that their turning angle distributions (TADs) and underlying noise intensities are similar across species and age groups, suggesting an evolutionary origin of this internal noise. We explore this hypothesis further with a digital simulation (EVO) based solely on the three central Darwinian themes: inheritability, variability and survivability. Separate simulations utilizing stochastic resonance (SR) indicate that foraging success, and hence fitness, is maximized at an optimum TAD noise intensity, which is represented by the distribution's characteristic width, σ. In both the EVO and SR simulations, foraging success is the criterion, and the results are the predicted characteristic widths of the TADs that maximize success. Our results are twofold: (1) the evolving characteristic widths achieve stasis after many generations; (2) as a hop length parameter is changed, variations in the evolved widths generated by EVO parallel those predicted by SR. These findings provide support for the hypotheses that (1) σ is an evolved quantity and that (2) SR plays a role in evolution.

Upper Limits on the Stochastic Gravitational-Wave Background from Advanced LIGO's First Observing Run

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Avila-Alvarez, A.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Beer, C.; Bejger, M.; Belahcene, I.; Belgin, M.; Bell, A. S.; Berger, B. K.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Billman, C. R.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Biscoveanu, A. S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blackman, J.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Boer, M.; Bogaert, G.; Bohe, A.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brown, N. M.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T. A.; Calloni, E.; Camp, J. B.; Campbell, W.; Canepa, M.; Cannon, K. C.; Cao, H.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Cheeseboro, B. D.; Chen, H. Y.; Chen, Y.; Cheng, H.-P.; Chincarini, A.; Chiummo, A.; Chmiel, T.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, A. J. K.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Cocchieri, C.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M.; Conti, L.; Cooper, S. J.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, E.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Covas, P. B.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Cripe, J.; Crowder, S. G.; Cullen, T. J.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Davis, D.; Daw, E. J.; Day, B.; Day, R.; De, S.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Devenson, J.; Devine, R. C.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Essick, R. C.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Fernández Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fong, H.; Forsyth, S. S.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Henry, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Kelley, D. B.; Kennedy, R.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, Whansun; Kim, W.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kirchhoff, R.; Kissel, J. S.; Klein, B.; Kleybolte, L.; Klimenko, S.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lehmann, J.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Liu, J.; Lockerbie, N. A.; Lombardi, A. L.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lovelace, G.; Lück, H.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martynov, D. V.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matas, A.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGrath, C.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, A.; Miller, B. B.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muniz, E. A. M.; Murray, P. G.; Mytidis, A.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nelemans, G.; Nelson, T. J. N.; Neri, M.; Nery, M.; Neunzert, A.; Newport, J. M.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pace, A. E.; Page, J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perez, C. J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Qiu, S.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Rhoades, E.; Ricci, F.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheuer, J.; Schlassa, S.; Schmidt, E.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Setyawati, Y.; Shaddock, D. A.; Shaffer, T. J.; Shahriar, M. S.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S. P.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strigin, S. E.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tao, D.; Tápai, M.; Taracchini, A.; Taylor, R.; Theeg, T.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tippens, T.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tomlinson, C.; Tonelli, M.; Tornasi, Z.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tse, M.; Tso, R.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D. V.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Whittle, C.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Worden, J.; Wright, J. L.; Wu, D. S.; Wu, G.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, Hang; Yu, Haocun; Yvert, M.; ZadroŻny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S. J.; Zhu, X. J.; Zucker, M. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2017-03-01

A wide variety of astrophysical and cosmological sources are expected to contribute to a stochastic gravitational-wave background. Following the observations of GW150914 and GW151226, the rate and mass of coalescing binary black holes appear to be greater than many previous expectations. As a result, the stochastic background from unresolved compact binary coalescences is expected to be particularly loud. We perform a search for the isotropic stochastic gravitational-wave background using data from Advanced Laser Interferometer Gravitational Wave Observatory's (aLIGO) first observing run. The data display no evidence of a stochastic gravitational-wave signal. We constrain the dimensionless energy density of gravitational waves to be Ω0<1.7 ×10-7 with 95% confidence, assuming a flat energy density spectrum in the most sensitive part of the LIGO band (20-86 Hz). This is a factor of ˜33 times more sensitive than previous measurements. We also constrain arbitrary power-law spectra. Finally, we investigate the implications of this search for the background of binary black holes using an astrophysical model for the background.

Upper Limits on the Stochastic Gravitational-Wave Background from Advanced LIGO's First Observing Run.

PubMed

Abbott, B P; Abbott, R; Abbott, T D; Abernathy, M R; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Adya, V B; Affeldt, C; Agathos, M; Agatsuma, K; Aggarwal, N; Aguiar, O D; Aiello, L; Ain, A; Ajith, P; Allen, B; Allocca, A; Altin, P A; Ananyeva, A; Anderson, S B; Anderson, W G; Appert, S; Arai, K; Araya, M C; Areeda, J S; Arnaud, N; Arun, K G; Ascenzi, S; Ashton, G; Ast, M; Aston, S M; Astone, P; Aufmuth, P; Aulbert, C; Avila-Alvarez, A; Babak, S; Bacon, P; Bader, M K M; Baker, P T; Baldaccini, F; Ballardin, G; Ballmer, S W; Barayoga, J C; Barclay, S E; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barta, D; Bartlett, J; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Baune, C; Bavigadda, V; Bazzan, M; Beer, C; Bejger, M; Belahcene, I; Belgin, M; Bell, A S; Berger, B K; Bergmann, G; Berry, C P L; Bersanetti, D; Bertolini, A; Betzwieser, J; Bhagwat, S; Bhandare, R; Bilenko, I A; Billingsley, G; Billman, C R; Birch, J; Birney, R; Birnholtz, O; Biscans, S; Biscoveanu, A S; Bisht, A; Bitossi, M; Biwer, C; Bizouard, M A; Blackburn, J K; Blackman, J; Blair, C D; Blair, D G; Blair, R M; Bloemen, S; Bock, O; Boer, M; Bogaert, G; Bohe, A; Bondu, F; Bonnand, R; Boom, B A; Bork, R; Boschi, V; Bose, S; Bouffanais, Y; Bozzi, A; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Brillet, A; Brinkmann, M; Brisson, V; Brockill, P; Broida, J E; Brooks, A F; Brown, D A; Brown, D D; Brown, N M; Brunett, S; Buchanan, C C; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Buskulic, D; Buy, C; Byer, R L; Cabero, M; Cadonati, L; Cagnoli, G; Cahillane, C; Calderón Bustillo, J; Callister, T A; Calloni, E; Camp, J B; Campbell, W; Canepa, M; Cannon, K C; Cao, H; Cao, J; Capano, C D; Capocasa, E; Carbognani, F; Caride, S; Casanueva Diaz, J; Casentini, C; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C B; Cerboni Baiardi, L; Cerretani, G; Cesarini, E; Chamberlin, S J; Chan, M; Chao, S; Charlton, P; Chassande-Mottin, E; Cheeseboro, B D; Chen, H Y; Chen, Y; Cheng, H-P; Chincarini, A; Chiummo, A; Chmiel, T; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, A J K; Chua, S; Chung, S; Ciani, G; Clara, F; Clark, J A; Cleva, F; Cocchieri, C; Coccia, E; Cohadon, P-F; Colla, A; Collette, C G; Cominsky, L; Constancio, M; Conti, L; Cooper, S J; Corbitt, T R; Cornish, N; Corsi, A; Cortese, S; Costa, C A; Coughlin, E; Coughlin, M W; Coughlin, S B; Coulon, J-P; Countryman, S T; Couvares, P; Covas, P B; Cowan, E E; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Creighton, J D E; Creighton, T D; Cripe, J; Crowder, S G; Cullen, T J; Cumming, A; Cunningham, L; Cuoco, E; Dal Canton, T; Danilishin, S L; D'Antonio, S; Danzmann, K; Dasgupta, A; Da Silva Costa, C F; Dattilo, V; Dave, I; Davier, M; Davies, G S; Davis, D; Daw, E J; Day, B; Day, R; De, S; DeBra, D; Debreczeni, G; Degallaix, J; De Laurentis, M; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Devenson, J; Devine, R C; Dhurandhar, S; Díaz, M C; Di Fiore, L; Di Giovanni, M; Di Girolamo, T; Di Lieto, A; Di Pace, S; Di Palma, I; Di Virgilio, A; Doctor, Z; Dolique, V; Donovan, F; Dooley, K L; Doravari, S; Dorrington, I; Douglas, R; Dovale Álvarez, M; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Ducrot, M; Dwyer, S E; Edo, T B; Edwards, M C; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Essick, R C; Etienne, Z; Etzel, T; Evans, M; Evans, T M; Everett, R; Factourovich, M; Fafone, V; Fair, H; Fairhurst, S; Fan, X; Farinon, S; Farr, B; Farr, W M; Fauchon-Jones, E J; Favata, M; Fays, M; Fehrmann, H; Fejer, M M; Fernández Galiana, A; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Fiori, I; Fiorucci, D; Fisher, R P; Flaminio, R; Fletcher, M; Fong, H; Forsyth, S S; Fournier, J-D; Frasca, S; Frasconi, F; Frei, Z; Freise, A; Frey, R; Frey, V; Fries, E M; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gabbard, H; Gadre, B U; Gaebel, S M; Gair, J R; Gammaitoni, L; Gaonkar, S G; Garufi, F; Gaur, G; Gayathri, V; Gehrels, N; Gemme, G; Genin, E; Gennai, A; George, J; Gergely, L; Germain, V; Ghonge, S; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, K; Glaefke, A; Goetz, E; Goetz, R; Gondan, L; González, G; Gonzalez Castro, J M; Gopakumar, A; Gorodetsky, M L; Gossan, S E; Gosselin, M; Gouaty, R; Grado, A; Graef, C; Granata, M; Grant, A; Gras, S; Gray, C; Greco, G; Green, A C; Groot, P; Grote, H; Grunewald, S; Guidi, G M; Guo, X; Gupta, A; Gupta, M K; Gushwa, K E; Gustafson, E K; Gustafson, R; Hacker, J J; Hall, B R; Hall, E D; Hammond, G; Haney, M; Hanke, M M; Hanks, J; Hanna, C; Hannam, M D; Hanson, J; Hardwick, T; Harms, J; Harry, G M; Harry, I W; Hart, M J; Hartman, M T; Haster, C-J; Haughian, K; Healy, J; Heidmann, A; Heintze, M C; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Hennig, J; Henry, J; Heptonstall, A W; Heurs, M; Hild, S; Hoak, D; Hofman, D; Holt, K; Holz, D E; Hopkins, P; Hough, J; Houston, E A; Howell, E J; Hu, Y M; Huerta, E A; Huet, D; Hughey, B; Husa, S; Huttner, S H; Huynh-Dinh, T; Indik, N; Ingram, D R; Inta, R; Isa, H N; Isac, J-M; Isi, M; Isogai, T; Iyer, B R; Izumi, K; Jacqmin, T; Jani, K; Jaranowski, P; Jawahar, S; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; Junker, J; Kalaghatgi, C V; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karki, S; Karvinen, K S; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, S; Kaur, T; Kawabe, K; Kéfélian, F; Keitel, D; Kelley, D B; Kennedy, R; Key, J S; Khalili, F Y; Khan, I; Khan, S; Khan, Z; Khazanov, E A; Kijbunchoo, N; Kim, Chunglee; Kim, J C; Kim, Whansun; Kim, W; Kim, Y-M; Kimbrell, S J; King, E J; King, P J; Kirchhoff, R; Kissel, J S; Klein, B; Kleybolte, L; Klimenko, S; Koch, P; Koehlenbeck, S M; Koley, S; Kondrashov, V; Kontos, A; Korobko, M; Korth, W Z; Kowalska, I; Kozak, D B; Krämer, C; Kringel, V; Królak, A; Kuehn, G; Kumar, P; Kumar, R; Kuo, L; Kutynia, A; Lackey, B D; Landry, M; Lang, R N; Lange, J; Lantz, B; Lanza, R K; Lartaux-Vollard, A; Lasky, P D; Laxen, M; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lebigot, E O; Lee, C H; Lee, H K; Lee, H M; Lee, K; Lehmann, J; Lenon, A; Leonardi, M; Leong, J R; Leroy, N; Letendre, N; Levin, Y; Li, T G F; Libson, A; Littenberg, T B; Liu, J; Lockerbie, N A; Lombardi, A L; London, L T; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lovelace, G; Lück, H; Lundgren, A P; Lynch, R; Ma, Y; Macfoy, S; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña-Sandoval, F; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A S; Maros, E; Martelli, F; Martellini, L; Martin, I W; Martynov, D V; Mason, K; Masserot, A; Massinger, T J; Masso-Reid, M; Mastrogiovanni, S; Matas, A; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; McCarthy, R; McClelland, D E; McCormick, S; McGrath, C; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McRae, T; McWilliams, S T; Meacher, D; Meadors, G D; Meidam, J; Melatos, A; Mendell, G; Mendoza-Gandara, D; Mercer, R A; Merilh, E L; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Metzdorff, R; Meyers, P M; Mezzani, F; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, A L; Miller, A; Miller, B B; Miller, J; Millhouse, M; Minenkov, Y; Ming, J; Mirshekari, S; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moggi, A; Mohan, M; Mohapatra, S R P; Montani, M; Moore, B C; Moore, C J; Moraru, D; Moreno, G; Morriss, S R; Mours, B; Mow-Lowry, C M; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Muniz, E A M; Murray, P G; Mytidis, A; Napier, K; Nardecchia, I; Naticchioni, L; Nelemans, G; Nelson, T J N; Neri, M; Nery, M; Neunzert, A; Newport, J M; Newton, G; Nguyen, T T; Nielsen, A B; Nissanke, S; Nitz, A; Noack, A; Nocera, F; Nolting, D; Normandin, M E N; Nuttall, L K; Oberling, J; Ochsner, E; Oelker, E; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Overmier, H; Owen, B J; Pace, A E; Page, J; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, H; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Paris, H R; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patricelli, B; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perez, C J; Perreca, A; Perri, L M; Pfeiffer, H P; Phelps, M; Piccinni, O J; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pitkin, M; Poe, M; Poggiani, R; Popolizio, P; Post, A; Powell, J; Prasad, J; Pratt, J W W; Predoi, V; Prestegard, T; Prijatelj, M; Principe, M; Privitera, S; Prodi, G A; Prokhorov, L G; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Qin, J; Qiu, S; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rajan, C; Rakhmanov, M; Rapagnani, P; Raymond, V; Razzano, M; Re, V; Read, J; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Rew, H; Reyes, S D; Rhoades, E; Ricci, F; Riles, K; Rizzo, M; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, J D; Romano, R; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Sakellariadou, M; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sampson, L M; Sanchez, E J; Sandberg, V; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Scheuer, J; Schlassa, S; Schmidt, E; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schutz, B F; Schwalbe, S G; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Setyawati, Y; Shaddock, D A; Shaffer, T J; Shahriar, M S; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sieniawska, M; Sigg, D; Silva, A D; Singer, A; Singer, L P; Singh, A; Singh, R; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, B; Smith, J R; Smith, R J E; Son, E J; Sorazu, B; Sorrentino, F; Souradeep, T; Spencer, A P; Srivastava, A K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stephens, B C; Stevenson, S P; Stone, R; Strain, K A; Straniero, N; Stratta, G; Strigin, S E; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sunil, S; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Talukder, D; Tanner, D B; Tao, D; Tápai, M; Taracchini, A; Taylor, R; Theeg, T; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thrane, E; Tippens, T; Tiwari, S; Tiwari, V; Tokmakov, K V; Toland, K; Tomlinson, C; Tonelli, M; Tornasi, Z; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trifirò, D; Trinastic, J; Tringali, M C; Trozzo, L; Tse, M; Tso, R; Turconi, M; Tuyenbayev, D; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Varma, V; Vass, S; Vasúth, M; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Venugopalan, G; Verkindt, D; Vetrano, F; Viceré, A; Viets, A D; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Voss, D V; Vousden, W D; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, M; Wang, Y; Ward, R L; Warner, J; Was, M; Watchi, J; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Wen, L; Weßels, P; Westphal, T; Wette, K; Whelan, J T; Whiting, B F; Whittle, C; Williams, D; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Woehler, J; Worden, J; Wright, J L; Wu, D S; Wu, G; Yam, W; Yamamoto, H; Yancey, C C; Yap, M J; Yu, Hang; Yu, Haocun; Yvert, M; Zadrożny, A; Zangrando, L; Zanolin, M; Zendri, J-P; Zevin, M; Zhang, L; Zhang, M; Zhang, T; Zhang, Y; Zhao, C; Zhou, M; Zhou, Z; Zhu, S J; Zhu, X J; Zucker, M E; Zweizig, J

2017-03-24

A wide variety of astrophysical and cosmological sources are expected to contribute to a stochastic gravitational-wave background. Following the observations of GW150914 and GW151226, the rate and mass of coalescing binary black holes appear to be greater than many previous expectations. As a result, the stochastic background from unresolved compact binary coalescences is expected to be particularly loud. We perform a search for the isotropic stochastic gravitational-wave background using data from Advanced Laser Interferometer Gravitational Wave Observatory's (aLIGO) first observing run. The data display no evidence of a stochastic gravitational-wave signal. We constrain the dimensionless energy density of gravitational waves to be Ω_{0}<1.7×10^{-7} with 95% confidence, assuming a flat energy density spectrum in the most sensitive part of the LIGO band (20-86 Hz). This is a factor of ∼33 times more sensitive than previous measurements. We also constrain arbitrary power-law spectra. Finally, we investigate the implications of this search for the background of binary black holes using an astrophysical model for the background.

Distinguishing between stochasticity and determinism: Examples from cell cycle duration variability.

PubMed

Pearl Mizrahi, Sivan; Sandler, Oded; Lande-Diner, Laura; Balaban, Nathalie Q; Simon, Itamar

2016-01-01

We describe a recent approach for distinguishing between stochastic and deterministic sources of variability, focusing on the mammalian cell cycle. Variability between cells is often attributed to stochastic noise, although it may be generated by deterministic components. Interestingly, lineage information can be used to distinguish between variability and determinism. Analysis of correlations within a lineage of the mammalian cell cycle duration revealed its deterministic nature. Here, we discuss the sources of such variability and the possibility that the underlying deterministic process is due to the circadian clock. Finally, we discuss the "kicked cell cycle" model and its implication on the study of the cell cycle in healthy and cancerous tissues. © 2015 WILEY Periodicals, Inc.

Stochastic search, optimization and regression with energy applications

NASA Astrophysics Data System (ADS)

Hannah, Lauren A.

Designing clean energy systems will be an important task over the next few decades. One of the major roadblocks is a lack of mathematical tools to economically evaluate those energy systems. However, solutions to these mathematical problems are also of interest to the operations research and statistical communities in general. This thesis studies three problems that are of interest to the energy community itself or provide support for solution methods: R&D portfolio optimization, nonparametric regression and stochastic search with an observable state variable. First, we consider the one stage R&D portfolio optimization problem to avoid the sequential decision process associated with the multi-stage. The one stage problem is still difficult because of a non-convex, combinatorial decision space and a non-convex objective function. We propose a heuristic solution method that uses marginal project values---which depend on the selected portfolio---to create a linear objective function. In conjunction with the 0-1 decision space, this new problem can be solved as a knapsack linear program. This method scales well to large decision spaces. We also propose an alternate, provably convergent algorithm that does not exploit problem structure. These methods are compared on a solid oxide fuel cell R&D portfolio problem. Next, we propose Dirichlet Process mixtures of Generalized Linear Models (DPGLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate. We also give examples for when those conditions hold, including models for compactly supported continuous distributions and a model with continuous covariates and categorical response. We empirically analyze the properties of the DP-GLM and why it provides better results than existing Dirichlet process mixture regression models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.

Diffusion Processes Satisfying a Conservation Law Constraint

DOE PAGES

Bakosi, J.; Ristorcelli, J. R.

2014-03-04

We investigate coupled stochastic differential equations governing N non-negative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires that a set of fluctuating variables be non-negative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the non-negativity and the unit-sum conservation law constraint are satisfied as the variables evolve in time. We investigate the consequencesmore » of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.« less

Diffusion Processes Satisfying a Conservation Law Constraint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bakosi, J.; Ristorcelli, J. R.

We investigate coupled stochastic differential equations governing N non-negative continuous random variables that satisfy a conservation principle. In various fields a conservation law requires that a set of fluctuating variables be non-negative and (if appropriately normalized) sum to one. As a result, any stochastic differential equation model to be realizable must not produce events outside of the allowed sample space. We develop a set of constraints on the drift and diffusion terms of such stochastic models to ensure that both the non-negativity and the unit-sum conservation law constraint are satisfied as the variables evolve in time. We investigate the consequencesmore » of the developed constraints on the Fokker-Planck equation, the associated system of stochastic differential equations, and the evolution equations of the first four moments of the probability density function. We show that random variables, satisfying a conservation law constraint, represented by stochastic diffusion processes, must have diffusion terms that are coupled and nonlinear. The set of constraints developed enables the development of statistical representations of fluctuating variables satisfying a conservation law. We exemplify the results with the bivariate beta process and the multivariate Wright-Fisher, Dirichlet, and Lochner’s generalized Dirichlet processes.« less

The importance of environmental variability and management control error to optimal harvest policies

USGS Publications Warehouse

Hunter, C.M.; Runge, M.C.

2004-01-01

State-dependent strategies (SDSs) are the most general form of harvest policy because they allow the harvest rate to depend, without constraint, on the state of the system. State-dependent strategies that provide an optimal harvest rate for any system state can be calculated, and stochasticity can be appropriately accommodated in this optimization. Stochasticity poses 2 challenges to harvest policies: (1) the population will never be at the equilibrium state; and (2) stochasticity induces uncertainty about future states. We investigated the effects of 2 types of stochasticity, environmental variability and management control error, on SDS harvest policies for a white-tailed deer (Odocoileus virginianus) model, and contrasted these with a harvest policy based on maximum sustainable yield (MSY). Increasing stochasticity resulted in more conservative SDSs; that is, higher population densities were required to support the same harvest rate, but these effects were generally small. As stochastic effects increased, SDSs performed much better than MSY. Both deterministic and stochastic SDSs maintained maximum mean annual harvest yield (AHY) and optimal equilibrium population size (Neq) in a stochastic environment, whereas an MSY policy could not. We suggest 3 rules of thumb for harvest management of long-lived vertebrates in stochastic systems: (1) an SDS is advantageous over an MSY policy, (2) using an SDS rather than an MSY is more important than whether a deterministic or stochastic SDS is used, and (3) for SDSs, rankings of the variability in management outcomes (e.g., harvest yield) resulting from parameter stochasticity can be predicted by rankings of the deterministic elasticities.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

A Search for Quasi-periodic Oscillations in the Blazar 1ES 1959+650

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiao-Pan; Luo, Yu-Hui; Yang, Hai-Yan

We have searched quasi-periodic oscillations (QPOs) in the 15 GHz light curve of the BL Lac object 1ES 1959+650 monitored by the Owens Valley Radio Observatory 40 m telescope during the period from 2008 January to 2016 February, using the Lomb–Scargle Periodogram, power spectral density (PSD), discrete autocorrelation function, and phase dispersion minimization (PDM) techniques. The red noise background has been established via the PSD method, and no QPO can be derived at the 3 σ confidence level accounting for the impact of the red noise variability. We conclude that the light curve of 1ES 1959+650 can be explained bymore » a stochastic red noise process that contributes greatly to the total observed variability amplitude, dominates the power spectrum, causes spurious bumps and wiggles in the autocorrelation function and can result in the variance of the folded light curve decreasing toward lower temporal frequencies when few-cycle, sinusoid-like patterns are present. Moreover, many early supposed periodicity claims for blazar light curves need to be reevaluated assuming red noise.« less

A chance-constrained stochastic approach to intermodal container routing problems.

PubMed

Zhao, Yi; Liu, Ronghui; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.

A chance-constrained stochastic approach to intermodal container routing problems

PubMed Central

Zhao, Yi; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost. PMID:29438389

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

Stochastic Control Synthesis of Systems with Structured Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

NASA Astrophysics Data System (ADS)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

A stochastic Markov chain model to describe lung cancer growth and metastasis.

PubMed

Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter

2012-01-01

A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.

Optimality, stochasticity, and variability in motor behavior

PubMed Central

Guigon, Emmanuel; Baraduc, Pierre; Desmurget, Michel

2008-01-01

Recent theories of motor control have proposed that the nervous system acts as a stochastically optimal controller, i.e. it plans and executes motor behaviors taking into account the nature and statistics of noise. Detrimental effects of noise are converted into a principled way of controlling movements. Attractive aspects of such theories are their ability to explain not only characteristic features of single motor acts, but also statistical properties of repeated actions. Here, we present a critical analysis of stochastic optimality in motor control which reveals several difficulties with this hypothesis. We show that stochastic control may not be necessary to explain the stochastic nature of motor behavior, and we propose an alternative framework, based on the action of a deterministic controller coupled with an optimal state estimator, which relieves drawbacks of stochastic optimality and appropriately explains movement variability. PMID:18202922

A stochastic tabu search algorithm to align physician schedule with patient flow.

PubMed

Niroumandrad, Nazgol; Lahrichi, Nadia

2018-06-01

In this study, we consider the pretreatment phase for cancer patients. This is defined as the period between the referral to a cancer center and the confirmation of the treatment plan. Physicians have been identified as bottlenecks in this process, and the goal is to determine a weekly cyclic schedule that improves the patient flow and shortens the pretreatment duration. High uncertainty is associated with the arrival day, profile and type of cancer of each patient. We also include physician satisfaction in the objective function. We present a MIP model for the problem and develop a tabu search algorithm, considering both deterministic and stochastic cases. Experiments show that our method compares very well to CPLEX under deterministic conditions. We describe the stochastic approach in detail and present a real application.

Did ASAS-SN Kill the Supermassive Black Hole Binary Candidate PG1302-102?

NASA Astrophysics Data System (ADS)

Liu, Tingting; Gezari, Suvi; Miller, M. Coleman

2018-05-01

Graham et al. reported a periodically varying quasar and supermassive black hole binary candidate, PG1302-102 (hereafter PG1302), which was discovered in the Catalina Real-time Transient Survey (CRTS). Its combined Lincoln Near-Earth Asteroid Research (LINEAR) and CRTS optical light curve is well fitted to a sinusoid of an observed period of ≈1884 days and well modeled by the relativistic Doppler boosting of the secondary mini-disk. However, the LINEAR+CRTS light curve from MJD ≈52,700 to MJD ≈56,400 covers only ∼2 cycles of periodic variation, which is a short baseline that can be highly susceptible to normal, stochastic quasar variability. In this Letter, we present a reanalysis of PG1302 using the latest light curve from the All-sky Automated Survey for Supernovae (ASAS-SN), which extends the observational baseline to the present day (MJD ≈58,200), and adopting a maximum likelihood method that searches for a periodic component in addition to stochastic quasar variability. When the ASAS-SN data are combined with the previous LINEAR+CRTS data, the evidence for periodicity decreases. For genuine periodicity one would expect that additional data would strengthen the evidence, so the decrease in significance may be an indication that the binary model is disfavored.

Analytical results for a stochastic model of gene expression with arbitrary partitioning of proteins

NASA Astrophysics Data System (ADS)

Tschirhart, Hugo; Platini, Thierry

2018-05-01

In biophysics, the search for analytical solutions of stochastic models of cellular processes is often a challenging task. In recent work on models of gene expression, it was shown that a mapping based on partitioning of Poisson arrivals (PPA-mapping) can lead to exact solutions for previously unsolved problems. While the approach can be used in general when the model involves Poisson processes corresponding to creation or degradation, current applications of the method and new results derived using it have been limited to date. In this paper, we present the exact solution of a variation of the two-stage model of gene expression (with time dependent transition rates) describing the arbitrary partitioning of proteins. The methodology proposed makes full use of the PPA-mapping by transforming the original problem into a new process describing the evolution of three biological switches. Based on a succession of transformations, the method leads to a hierarchy of reduced models. We give an integral expression of the time dependent generating function as well as explicit results for the mean, variance, and correlation function. Finally, we discuss how results for time dependent parameters can be extended to the three-stage model and used to make inferences about models with parameter fluctuations induced by hidden stochastic variables.

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Filippas, T. A.

1971-01-01

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Mean Field Analysis of Stochastic Neural Network Models with Synaptic Depression

NASA Astrophysics Data System (ADS)

Yasuhiko Igarashi,; Masafumi Oizumi,; Masato Okada,

2010-08-01

We investigated the effects of synaptic depression on the macroscopic behavior of stochastic neural networks. Dynamical mean field equations were derived for such networks by taking the average of two stochastic variables: a firing-state variable and a synaptic variable. In these equations, the average product of thesevariables is decoupled as the product of their averages because the two stochastic variables are independent. We proved the independence of these two stochastic variables assuming that the synaptic weight Jij is of the order of 1/N with respect to the number of neurons N. Using these equations, we derived macroscopic steady-state equations for a network with uniform connections and for a ring attractor network with Mexican hat type connectivity and investigated the stability of the steady-state solutions. An oscillatory uniform state was observed in the network with uniform connections owing to a Hopf instability. For the ring network, high-frequency perturbations were shown not to affect system stability. Two mechanisms destabilize the inhomogeneous steady state, leading to two oscillatory states. A Turing instability leads to a rotating bump state, while a Hopf instability leads to an oscillatory bump state, which was previously unreported. Various oscillatory states take place in a network with synaptic depression depending on the strength of the interneuron connections.

The scaling of population persistence with carrying capacity does not asymptote in populations of a fish experiencing extreme climate variability.

PubMed

White, Richard S A; Wintle, Brendan A; McHugh, Peter A; Booker, Douglas J; McIntosh, Angus R

2017-06-14

Despite growing concerns regarding increasing frequency of extreme climate events and declining population sizes, the influence of environmental stochasticity on the relationship between population carrying capacity and time-to-extinction has received little empirical attention. While time-to-extinction increases exponentially with carrying capacity in constant environments, theoretical models suggest increasing environmental stochasticity causes asymptotic scaling, thus making minimum viable carrying capacity vastly uncertain in variable environments. Using empirical estimates of environmental stochasticity in fish metapopulations, we showed that increasing environmental stochasticity resulting from extreme droughts was insufficient to create asymptotic scaling of time-to-extinction with carrying capacity in local populations as predicted by theory. Local time-to-extinction increased with carrying capacity due to declining sensitivity to demographic stochasticity, and the slope of this relationship declined significantly as environmental stochasticity increased. However, recent 1 in 25 yr extreme droughts were insufficient to extirpate populations with large carrying capacity. Consequently, large populations may be more resilient to environmental stochasticity than previously thought. The lack of carrying capacity-related asymptotes in persistence under extreme climate variability reveals how small populations affected by habitat loss or overharvesting, may be disproportionately threatened by increases in extreme climate events with global warming. © 2017 The Author(s).

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

Inferring oscillatory modulation in neural spike trains

PubMed Central

Arai, Kensuke; Kass, Robert E.

2017-01-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak. PMID:28985231

A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

NASA Astrophysics Data System (ADS)

Liu, Shuang; Hu, Xiangyun; Liu, Tianyou

2014-07-01

Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.

Stochastic goal-oriented error estimation with memory

NASA Astrophysics Data System (ADS)

Ackmann, Jan; Marotzke, Jochem; Korn, Peter

2017-11-01

We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.

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.

Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.

PubMed

Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O

2016-06-01

Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.

Search Planning Under Incomplete Information Using Stochastic Optimization and Regression

DTIC Science & Technology

2011-09-01

solve since they involve un- certainty and unknown parameters (see for example Shapiro et al., 2009; Wallace & Ziemba , 2005). One application area is...M16130.2E. 43 Wallace, S. W., & Ziemba , W. T. (2005). Applications of stochastic programming. Philadelphia, PA: Society for Industrial and Applied

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports

PubMed Central

Schilde, M.; Doerner, K.F.; Hartl, R.F.

2011-01-01

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances. PMID:23543641

A possible close supermassive black-hole binary in a quasar with optical periodicity.

PubMed

Graham, Matthew J; Djorgovski, S G; Stern, Daniel; Glikman, Eilat; Drake, Andrew J; Mahabal, Ashish A; Donalek, Ciro; Larson, Steve; Christensen, Eric

2015-02-05

Quasars have long been known to be variable sources at all wavelengths. Their optical variability is stochastic and can be due to a variety of physical mechanisms; it is also well-described statistically in terms of a damped random walk model. The recent availability of large collections of astronomical time series of flux measurements (light curves) offers new data sets for a systematic exploration of quasar variability. Here we report the detection of a strong, smooth periodic signal in the optical variability of the quasar PG 1302-102 with a mean observed period of 1,884 ± 88 days. It was identified in a search for periodic variability in a data set of light curves for 247,000 known, spectroscopically confirmed quasars with a temporal baseline of about 9 years. Although the interpretation of this phenomenon is still uncertain, the most plausible mechanisms involve a binary system of two supermassive black holes with a subparsec separation. Such systems are an expected consequence of galaxy mergers and can provide important constraints on models of galaxy formation and evolution.

Understanding extreme quasar optical variability with CRTS - I. Major AGN flares

NASA Astrophysics Data System (ADS)

Graham, Matthew J.; Djorgovski, S. G.; Drake, Andrew J.; Stern, Daniel; Mahabal, Ashish A.; Glikman, Eilat; Larson, Steve; Christensen, Eric

2017-10-01

There is a large degree of variety in the optical variability of quasars and it is unclear whether this is all attributable to a single (set of) physical mechanism(s). We present the results of a systematic search for major flares in active galactic nucleus (AGN) in the Catalina Real-time Transient Survey as part of a broader study into extreme quasar variability. Such flares are defined in a quantitative manner as being atop of the normal, stochastic variability of quasars. We have identified 51 events from over 900 000 known quasars and high-probability quasar candidates, typically lasting 900 d and with a median peak amplitude of Δm = 1.25 mag. Characterizing the flare profile with a Weibull distribution, we find that nine of the sources are well described by a single-point single-lens model. This supports the proposal by Lawrence et al. that microlensing is a plausible physical mechanism for extreme variability. However, we attribute the majority of our events to explosive stellar-related activity in the accretion disc: superluminous supernovae, tidal disruption events and mergers of stellar mass black holes.

Solution of the finite Milne problem in stochastic media with RVT Technique

NASA Astrophysics Data System (ADS)

Slama, Howida; El-Bedwhey, Nabila A.; El-Depsy, Alia; Selim, Mustafa M.

2017-12-01

This paper presents the solution to the Milne problem in the steady state with isotropic scattering phase function. The properties of the medium are considered as stochastic ones with Gaussian or exponential distributions and hence the problem treated as a stochastic integro-differential equation. To get an explicit form for the radiant energy density, the linear extrapolation distance, reflectivity and transmissivity in the deterministic case the problem is solved using the Pomraning-Eddington method. The obtained solution is found to be dependent on the optical space variable and thickness of the medium which are considered as random variables. The random variable transformation (RVT) technique is used to find the first probability density function (1-PDF) of the solution process. Then the stochastic linear extrapolation distance, reflectivity and transmissivity are calculated. For illustration, numerical results with conclusions are provided.

Mixed Effects Modeling Using Stochastic Differential Equations: Illustrated by Pharmacokinetic Data of Nicotinic Acid in Obese Zucker Rats.

PubMed

Leander, Jacob; Almquist, Joachim; Ahlström, Christine; Gabrielsson, Johan; Jirstrand, Mats

2015-05-01

Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.

Langevin dynamics for vector variables driven by multiplicative white noise: A functional formalism

NASA Astrophysics Data System (ADS)

Moreno, Miguel Vera; Arenas, Zochil González; Barci, Daniel G.

2015-04-01

We discuss general multidimensional stochastic processes driven by a system of Langevin equations with multiplicative white noise. In particular, we address the problem of how time reversal diffusion processes are affected by the variety of conventions available to deal with stochastic integrals. We present a functional formalism to build up the generating functional of correlation functions without any type of discretization of the Langevin equations at any intermediate step. The generating functional is characterized by a functional integration over two sets of commuting variables, as well as Grassmann variables. In this representation, time reversal transformation became a linear transformation in the extended variables, simplifying in this way the complexity introduced by the mixture of prescriptions and the associated calculus rules. The stochastic calculus is codified in our formalism in the structure of the Grassmann algebra. We study some examples such as higher order derivative Langevin equations and the functional representation of the micromagnetic stochastic Landau-Lifshitz-Gilbert equation.

Optimisation in radiotherapy. III: Stochastic optimisation algorithms and conclusions.

PubMed

Ebert, M

1997-12-01

This is the final article in a three part examination of optimisation in radiotherapy. Previous articles have established the bases and form of the radiotherapy optimisation problem, and examined certain types of optimisation algorithm, namely, those which perform some form of ordered search of the solution space (mathematical programming), and those which attempt to find the closest feasible solution to the inverse planning problem (deterministic inversion). The current paper examines algorithms which search the space of possible irradiation strategies by stochastic methods. The resulting iterative search methods move about the solution space by sampling random variates, which gradually become more constricted as the algorithm converges upon the optimal solution. This paper also discusses the implementation of optimisation in radiotherapy practice.

SASS: A symmetry adapted stochastic search algorithm exploiting site symmetry

NASA Astrophysics Data System (ADS)

Wheeler, Steven E.; Schleyer, Paul v. R.; Schaefer, Henry F.

2007-03-01

A simple symmetry adapted search algorithm (SASS) exploiting point group symmetry increases the efficiency of systematic explorations of complex quantum mechanical potential energy surfaces. In contrast to previously described stochastic approaches, which do not employ symmetry, candidate structures are generated within simple point groups, such as C2, Cs, and C2v. This facilitates efficient sampling of the 3N-6 Pople's dimensional configuration space and increases the speed and effectiveness of quantum chemical geometry optimizations. Pople's concept of framework groups [J. Am. Chem. Soc. 102, 4615 (1980)] is used to partition the configuration space into structures spanning all possible distributions of sets of symmetry equivalent atoms. This provides an efficient means of computing all structures of a given symmetry with minimum redundancy. This approach also is advantageous for generating initial structures for global optimizations via genetic algorithm and other stochastic global search techniques. Application of the SASS method is illustrated by locating 14 low-lying stationary points on the cc-pwCVDZ ROCCSD(T) potential energy surface of Li5H2. The global minimum structure is identified, along with many unique, nonintuitive, energetically favorable isomers.

Stochastic analysis of multiphase flow in porous media: II. Numerical simulations

NASA Astrophysics Data System (ADS)

Abin, A.; Kalurachchi, J. J.; Kemblowski, M. W.; Chang, C.-M.

1996-08-01

The first paper (Chang et al., 1995b) of this two-part series described the stochastic analysis using spectral/perturbation approach to analyze steady state two-phase (water and oil) flow in a, liquid-unsaturated, three fluid-phase porous medium. In this paper, the results between the numerical simulations and closed-form expressions obtained using the perturbation approach are compared. We present the solution to the one-dimensional, steady-state oil and water flow equations. The stochastic input processes are the spatially correlated logk where k is the intrinsic permeability and the soil retention parameter, α. These solutions are subsequently used in the numerical simulations to estimate the statistical properties of the key output processes. The comparison between the results of the perturbation analysis and numerical simulations showed a good agreement between the two methods over a wide range of logk variability with three different combinations of input stochastic processes of logk and soil parameter α. The results clearly demonstrated the importance of considering the spatial variability of key subsurface properties under a variety of physical scenarios. The variability of both capillary pressure and saturation is affected by the type of input stochastic process used to represent the spatial variability. The results also demonstrated the applicability of perturbation theory in predicting the system variability and defining effective fluid properties through the ergodic assumption.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Optimizing signal recycling for detecting a stochastic gravitational-wave background

NASA Astrophysics Data System (ADS)

Tao, Duo; Christensen, Nelson

2018-06-01

Signal recycling is applied in laser interferometers such as the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO) to increase their sensitivity to gravitational waves. In this study, signal recycling configurations for detecting a stochastic gravitational wave background are optimized based on aLIGO parameters. Optimal transmission of the signal recycling mirror (SRM) and detuning phase of the signal recycling cavity under a fixed laser power and low-frequency cutoff are calculated. Based on the optimal configurations, the compatibility with a binary neutron star (BNS) search is discussed. Then, different laser powers and low-frequency cutoffs are considered. Two models for the dimensionless energy density of gravitational waves , the flat model and the model, are studied. For a stochastic background search, it is found that an interferometer using signal recycling has a better sensitivity than an interferometer not using it. The optimal stochastic search configurations are typically found when both the SRM transmission and the signal recycling detuning phase are low. In this region, the BNS range mostly lies between 160 and 180 Mpc. When a lower laser power is used the optimal signal recycling detuning phase increases, the optimal SRM transmission increases and the optimal sensitivity improves. A reduced low-frequency cutoff gives a better sensitivity limit. For both models of , a typical optimal sensitivity limit on the order of 10‑10 is achieved at a reference frequency of Hz.

Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of Advanced LIGO

NASA Astrophysics Data System (ADS)

Covas, P. B.; Effler, A.; Goetz, E.; Meyers, P. M.; Neunzert, A.; Oliver, M.; Pearlstone, B. L.; Roma, V. J.; Schofield, R. M. S.; Adya, V. B.; Astone, P.; Biscoveanu, S.; Callister, T. A.; Christensen, N.; Colla, A.; Coughlin, E.; Coughlin, M. W.; Crowder, S. G.; Dwyer, S. E.; Eggenstein, H.-B.; Hourihane, S.; Kandhasamy, S.; Liu, W.; Lundgren, A. P.; Matas, A.; McCarthy, R.; McIver, J.; Mendell, G.; Ormiston, R.; Palomba, C.; Papa, M. A.; Piccinni, O. J.; Rao, K.; Riles, K.; Sammut, L.; Schlassa, S.; Sigg, D.; Strauss, N.; Tao, D.; Thorne, K. A.; Thrane, E.; Trembath-Reichert, S.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Adams, C.; Adhikari, R. X.; Ananyeva, A.; Appert, S.; Arai, K.; Aston, S. M.; Austin, C.; Ballmer, S. W.; Barker, D.; Barr, B.; Barsotti, L.; Bartlett, J.; Bartos, I.; Batch, J. C.; Bejger, M.; Bell, A. S.; Betzwieser, J.; Billingsley, G.; Birch, J.; Biscans, S.; Biwer, C.; Blair, C. D.; Blair, R. M.; Bork, R.; Brooks, A. F.; Cao, H.; Ciani, G.; Clara, F.; Clearwater, P.; Cooper, S. J.; Corban, P.; Countryman, S. T.; Cowart, M. J.; Coyne, D. C.; Cumming, A.; Cunningham, L.; Danzmann, K.; Costa, C. F. Da Silva; Daw, E. J.; DeBra, D.; DeRosa, R. T.; DeSalvo, R.; Dooley, K. L.; Doravari, S.; Driggers, J. C.; Edo, T. B.; Etzel, T.; Evans, M.; Evans, T. M.; Factourovich, M.; Fair, H.; Galiana, A. Fernández; Ferreira, E. C.; Fisher, R. P.; Fong, H.; Frey, R.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gateley, B.; Giaime, J. A.; Giardina, K. D.; Goetz, R.; Goncharov, B.; Gras, S.; Gray, C.; Grote, H.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hall, E. D.; Hammond, G.; Hanks, J.; Hanson, J.; Hardwick, T.; Harry, G. M.; Heintze, M. C.; Heptonstall, A. W.; Hough, J.; Inta, R.; Izumi, K.; Jones, R.; Karki, S.; Kasprzack, M.; Kaufer, S.; Kawabe, K.; Kennedy, R.; Kijbunchoo, N.; Kim, W.; King, E. J.; King, P. J.; Kissel, J. S.; Korth, W. Z.; Kuehn, G.; Landry, M.; Lantz, B.; Laxen, M.; Liu, J.; Lockerbie, N. A.; Lormand, M.; MacInnis, M.; Macleod, D. M.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Marsh, P.; Martin, I. W.; Martynov, D. V.; Mason, K.; Massinger, T. J.; Matichard, F.; Mavalvala, N.; McClelland, D. E.; McCormick, S.; McCuller, L.; McIntyre, G.; McRae, T.; Merilh, E. L.; Miller, J.; Mittleman, R.; Mo, G.; Mogushi, K.; Moraru, D.; Moreno, G.; Mueller, G.; Mukund, N.; Mullavey, A.; Munch, J.; Nelson, T. J. N.; Nguyen, P.; Nuttall, L. K.; Oberling, J.; Oktavia, O.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; Ottaway, D. J.; Overmier, H.; Palamos, J. R.; Parker, W.; Pele, A.; Penn, S.; Perez, C. J.; Phelps, M.; Pierro, V.; Pinto, I.; Principe, M.; Prokhorov, L. G.; Puncken, O.; Quetschke, V.; Quintero, E. A.; Radkins, H.; Raffai, P.; Ramirez, K. E.; Reid, S.; Reitze, D. H.; Robertson, N. A.; Rollins, J. G.; Romel, C. L.; Romie, J. H.; Ross, M. P.; Rowan, S.; Ryan, K.; Sadecki, T.; Sanchez, E. J.; Sanchez, L. E.; Sandberg, V.; Savage, R. L.; Sellers, D.; Shaddock, D. A.; Shaffer, T. J.; Shapiro, B.; Shoemaker, D. H.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; Sorazu, B.; Spencer, A. P.; Staley, A.; Strain, K. A.; Sun, L.; Tanner, D. B.; Tasson, J. D.; Taylor, R.; Thomas, M.; Thomas, P.; Toland, K.; Torrie, C. I.; Traylor, G.; Tse, M.; Tuyenbayev, D.; Vajente, G.; Valdes, G.; van Veggel, A. A.; Vecchio, A.; Veitch, P. J.; Venkateswara, K.; Vo, T.; Vorvick, C.; Wade, M.; Walker, M.; Ward, R. L.; Warner, J.; Weaver, B.; Weiss, R.; Weßels, P.; Willke, B.; Wipf, C. C.; Wofford, J.; Worden, J.; Yamamoto, H.; Yancey, C. C.; Yu, Hang; Yu, Haocun; Zhang, L.; Zhu, S.; Zucker, M. E.; Zweizig, J.; LSC Instrument Authors

2018-04-01

Searches are under way in Advanced LIGO and Virgo data for persistent gravitational waves from continuous sources, e.g. rapidly rotating galactic neutron stars, and stochastic sources, e.g. relic gravitational waves from the Big Bang or superposition of distant astrophysical events such as mergers of black holes or neutron stars. These searches can be degraded by the presence of narrow spectral artifacts (lines) due to instrumental or environmental disturbances. We describe a variety of methods used for finding, identifying and mitigating these artifacts, illustrated with particular examples. Results are provided in the form of lists of line artifacts that can safely be treated as non-astrophysical. Such lists are used to improve the efficiencies and sensitivities of continuous and stochastic gravitational wave searches by allowing vetoes of false outliers and permitting data cleaning.

Algebraic, geometric, and stochastic aspects of genetic operators

NASA Technical Reports Server (NTRS)

Foo, N. Y.; Bosworth, J. L.

1972-01-01

Genetic algorithms for function optimization employ genetic operators patterned after those observed in search strategies employed in natural adaptation. Two of these operators, crossover and inversion, are interpreted in terms of their algebraic and geometric properties. Stochastic models of the operators are developed which are employed in Monte Carlo simulations of their behavior.

Stochastic Local Search for Core Membership Checking in Hedonic Games

NASA Astrophysics Data System (ADS)

Keinänen, Helena

Hedonic games have emerged as an important tool in economics and show promise as a useful formalism to model multi-agent coalition formation in AI as well as group formation in social networks. We consider a coNP-complete problem of core membership checking in hedonic coalition formation games. No previous algorithms to tackle the problem have been presented. In this work, we overcome this by developing two stochastic local search algorithms for core membership checking in hedonic games. We demonstrate the usefulness of the algorithms by showing experimentally that they find solutions efficiently, particularly for large agent societies.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Afrough, M.; Agarwal, B.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allen, G.; Allocca, A.; Altin, P. A.; Amato, A.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Angelova, S. V.; Antier, S.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arnaud, N.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Atallah, D. V.; Aufmuth, P.; Aulbert, C.; AultONeal, K.; Austin, C.; Avila-Alvarez, A.; Babak, S.; Bacon, P.; Bader, M. K. M.; Bae, S.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Banagiri, S.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barkett, K.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Bawaj, M.; Bayley, J. C.; Bazzan, M.; Bécsy, B.; Beer, C.; Bejger, M.; Belahcene, I.; Bell, A. S.; Berger, B. K.; Bergmann, G.; Bero, J. J.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Billman, C. R.; Birch, J.; Birney, R.; Birnholtz, O.; Biscans, S.; Biscoveanu, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blackman, J.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bode, N.; Boer, M.; Bogaert, G.; Bohe, A.; Bondu, F.; Bonilla, E.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bossie, K.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T. A.; Calloni, E.; Camp, J. B.; Canepa, M.; Canizares, P.; Cannon, K. C.; Cao, H.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Carney, M. F.; Diaz, J. Casanueva; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerdá-Durán, P.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chase, E.; Chassande-Mottin, E.; Chatterjee, D.; Cheeseboro, B. D.; Chen, H. Y.; Chen, X.; Chen, Y.; Cheng, H.-P.; Chia, H.; Chincarini, A.; Chiummo, A.; Chmiel, T.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, A. J. K.; Chua, S.; Chung, A. K. W.; Chung, S.; Ciani, G.; Ciolfi, R.; Cirelli, C. E.; Cirone, A.; Clara, F.; Clark, J. A.; Clearwater, P.; Cleva, F.; Cocchieri, C.; Coccia, E.; Cohadon, P.-F.; Cohen, D.; Colla, A.; Collette, C. G.; Cominsky, L. R.; Constancio, M.; Conti, L.; Cooper, S. J.; Corban, P.; Corbitt, T. R.; Cordero-Carrión, I.; Corley, K. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, E.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Covas, P. B.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Cripe, J.; Crowder, S. G.; Cullen, T. J.; Cumming, A.; Cunningham, L.; Cuoco, E.; Canton, T. Dal; Dálya, G.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davis, D.; Daw, E. J.; Day, B.; De, S.; DeBra, D.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Demos, N.; Denker, T.; Dent, T.; De Pietri, R.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; De Rossi, C.; DeSalvo, R.; de Varona, O.; Devenson, J.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Renzo, F.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Dreissigacker, C.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dupej, P.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Eisenstein, R. A.; Essick, R. C.; Estevez, D.; Etienne, Z. B.; Etzel, T.; Evans, M.; Evans, T. M.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fee, C.; Fehrmann, H.; Feicht, J.; Fejer, M. M.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Finstad, D.; Fiori, I.; Fiorucci, D.; Fishbach, M.; Fisher, R. P.; Fitz-Axen, M.; Flaminio, R.; Fletcher, M.; Fong, H.; Font, J. A.; Forsyth, P. W. F.; Forsyth, S. S.; Fournier, J.-D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Ganija, M. R.; Gaonkar, S. G.; Garcia-Quiros, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, D.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glover, L.; Goetz, E.; Goetz, R.; Gomes, S.; Goncharov, B.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Gretarsson, E. M.; Groot, P.; Grote, H.; Grunewald, S.; Gruning, P.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Halim, O.; Hall, B. R.; Hall, E. D.; Hamilton, E. Z.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hannuksela, O. A.; Hanson, J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Haster, C.-J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hinderer, T.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Horst, C.; Hough, J.; Houston, E. A.; Howell, E. J.; Hreibi, A.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Inta, R.; Intini, G.; Isa, H. N.; Isac, J.-M.; Isi, M.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kamai, B.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kapadia, S. J.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Katolik, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Kemball, A. J.; Kennedy, R.; Kent, C.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, K.; Kim, W.; Kim, W. S.; Kim, Y.-M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Knowles, T. D.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kumar, S.; Kuo, L.; Kutynia, A.; Kwang, S.; Lackey, B. D.; Lai, K. H.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, H. W.; Lee, K.; Lehmann, J.; Lenon, A.; Leonardi, M.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G. F.; Linker, S. D.; Littenberg, T. B.; Liu, J.; Lo, R. K. L.; Lockerbie, N. A.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lousto, C. O.; Lovelace, G.; Lück, H.; Lumaca, D.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macas, R.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña Hernandez, I.; Magaña-Sandoval, F.; Magaña Zertuche, L.; Magee, R. M.; Majorana, E.; Maksimovic, I.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markakis, C.; Markosyan, A. S.; Markowitz, A.; Maros, E.; Marquina, A.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Mason, K.; Massera, E.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matas, A.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McCuller, L.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McNeill, L.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Mejuto-Villa, E.; Melatos, A.; Mendell, G.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, B. B.; Miller, J.; Millhouse, M.; Milovich-Goff, M. C.; Minazzoli, O.; Minenkov, Y.; Ming, J.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moffa, D.; Moggi, A.; Mogushi, K.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muñiz, E. A.; Muratore, M.; Murray, P. G.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Neilson, J.; Nelemans, G.; Nelson, T. J. N.; Nery, M.; Neunzert, A.; Nevin, L.; Newport, J. M.; Newton, G.; Ng, K. K. Y.; Nguyen, T. T.; Nichols, D.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; North, C.; Nuttall, L. K.; Oberling, J.; O'Dea, G. D.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Okada, M. A.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; Ormiston, R.; Ortega, L. F.; O'Shaughnessy, R.; Ossokine, S.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pace, A. E.; Page, J.; Page, M. A.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, Howard; Pan, Huang-Wei; Pang, B.; Pang, P. T. H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Parida, A.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patil, M.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perez, C. J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pirello, M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Porter, E. K.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Pratten, G.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rajbhandari, B.; Rakhmanov, M.; Ramirez, K. E.; Ramos-Buades, A.; Rapagnani, P.; Raymond, V.; Razzano, M.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Ren, W.; Reyes, S. D.; Ricci, F.; Ricker, P. M.; Rieger, S.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romel, C. L.; Romie, J. H.; Rosińska, D.; Ross, M. P.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Rutins, G.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sanchez, L. E.; Sanchis-Gual, N.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheel, M.; Scheuer, J.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schulte, B. W.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, S. M.; Seidel, E.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D. A.; Shaffer, T. J.; Shah, A. A.; Shahriar, M. S.; Shaner, M. B.; Shao, L.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, L. P.; Singh, A.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, J. R.; Smith, R. J. E.; Somala, S.; Son, E. J.; Sonnenberg, J. A.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staats, K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stevenson, S. P.; Stone, R.; Stops, D. J.; Strain, K. A.; Stratta, G.; Strigin, S. E.; Strunk, A.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Suresh, J.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Tait, S. C.; Talbot, C.; Talukder, D.; Tanner, D. B.; Tao, D.; Tápai, M.; Taracchini, A.; Tasson, J. D.; Taylor, J. A.; Taylor, R.; Tewari, S. V.; Theeg, T.; Thies, F.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tonelli, M.; Tornasi, Z.; Torres-Forné, A.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tsang, K. W.; Tse, M.; Tso, R.; Tsukada, L.; Tsuna, D.; Tuyenbayev, D.; Ueno, K.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walet, R.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, J. Z.; Wang, W. H.; Wang, Y. F.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Wessel, E. K.; Weßels, P.; Westerweck, J.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Whittle, C.; Wilken, D.; Williams, D.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Wofford, J.; Wong, K. W. K.; Worden, J.; Wright, J. L.; Wu, D. S.; Wysocki, D. M.; Xiao, S.; Yamamoto, H.; Yancey, C. C.; Yang, L.; Yap, M. J.; Yazback, M.; Yu, Hang; Yu, Haocun; Yvert, M.; ZadroŻny, A.; Zanolin, M.; Zelenova, T.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.-H.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S. J.; Zhu, X. J.; Zucker, M. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2018-05-01

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω0T<5.58 ×10-8 , Ω0V<6.35 ×10-8 , and Ω0S<1.08 ×10-7 at a reference frequency f0=25 Hz .

Importance of vesicle release stochasticity in neuro-spike communication.

PubMed

Ramezani, Hamideh; Akan, Ozgur B

2017-07-01

Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

PubMed

Adalsteinsson, David; McMillen, David; Elston, Timothy C

2004-03-08

Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds

PubMed Central

Virshup, Aaron M.; Contreras-García, Julia; Wipf, Peter; Yang, Weitao; Beratan, David N.

2013-01-01

The “small molecule universe” (SMU), the set of all synthetically feasible organic molecules of 500 Daltons molecular weight or less, is estimated to contain over 1060 structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a “representative universal library” spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds. PMID:23548177

Gene selection heuristic algorithm for nutrigenomics studies.

PubMed

Valour, D; Hue, I; Grimard, B; Valour, B

2013-07-15

Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.

Implementation of Monte Carlo Tree Search (MCTS) Algorithm in COMBATXXI using JDAFS

DTIC Science & Technology

2014-07-31

ManyStochasticDuelModelforaMountainBattle.pdf. [10] M Kress and I Talmor. “A new look at the 3: 1 rule of combat through Markov stochastic Lanchester ...00000007/2600758. [11] FW Lanchester . Aircraft in warfare: The dawn of the fourth arm. London: Constable, 1916. url: http://books.google.com/books?hl=en\\&lr

Real time selective harmonic minimization for multilevel inverters using genetic algorithm and artifical neural network angle generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Filho, Faete J; Tolbert, Leon M; Ozpineci, Burak

2012-01-01

The work developed here proposes a methodology for calculating switching angles for varying DC sources in a multilevel cascaded H-bridges converter. In this approach the required fundamental is achieved, the lower harmonics are minimized, and the system can be implemented in real time with low memory requirements. Genetic algorithm (GA) is the stochastic search method to find the solution for the set of equations where the input voltages are the known variables and the switching angles are the unknown variables. With the dataset generated by GA, an artificial neural network (ANN) is trained to store the solutions without excessive memorymore » storage requirements. This trained ANN then senses the voltage of each cell and produces the switching angles in order to regulate the fundamental at 120 V and eliminate or minimize the low order harmonics while operating in real time.« less

Individual power density spectra of Swift gamma-ray bursts

NASA Astrophysics Data System (ADS)

Guidorzi, C.; Dichiara, S.; Amati, L.

2016-05-01

Context. Timing analysis can be a powerful tool with which to shed light on the still obscure emission physics and geometry of the prompt emission of gamma-ray bursts (GRBs). Fourier power density spectra (PDS) characterise time series as stochastic processes and can be used to search for coherent pulsations and, more in general, to investigate the dominant variability timescales in astrophysical sources. Because of the limited duration and of the statistical properties involved, modelling the PDS of individual GRBs is challenging, and only average PDS of large samples have been discussed in the literature thus far. Aims: We aim at characterising the individual PDS of GRBs to describe their variability in terms of a stochastic process, to explore their variety, and to carry out for the first time a systematic search for periodic signals and for a link between PDS properties and other GRB observables. Methods: We present a Bayesian procedure that uses a Markov chain Monte Carlo technique and apply it to study the individual PDS of 215 bright long GRBs detected with the Swift Burst Alert Telescope in the 15-150 keV band from January 2005 to May 2015. The PDS are modelled with a power-law either with or without a break. Results: Two classes of GRBs emerge: with or without a unique dominant timescale. A comparison with active galactic nuclei (AGNs) reveals similar distributions of PDS slopes. Unexpectedly, GRBs with subsecond-dominant timescales and duration longer than a few tens of seconds in the source frame appear to be either very rare or altogether absent. Three GRBs are found with possible evidence for a periodic signal at 3.0-3.2σ (Gaussian) significance, corresponding to a multi-trial chance probability of ~1%. Thus, we found no compelling evidence for periodic signal in GRBs. Conclusions: The analogy between the PDS of GRBs and of AGNs could tentatively indicate similar stochastic processes that rule BH accretion across different BH mass scales and objects. In addition, we find evidence that short dominant timescales and duration are not completely independent of each other, in contrast with commonly accepted paradigms. Full Tables 1-4 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/589/A98

A Stochastic Diffusion Process for the Dirichlet Distribution

DOE PAGES

Bakosi, J.; Ristorcelli, J. R.

2013-03-01

The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability ofNcoupled stochastic variables with the Dirichlet distribution as its asymptotic solution. To ensure a bounded sample space, a coupled nonlinear diffusion process is required: the Wiener processes in the equivalent system of stochastic differential equations are multiplicative with coefficients dependent on all the stochastic variables. Individual samples of a discrete ensemble, obtained from the stochastic process, satisfy a unit-sum constraint at all times. The process may be used to represent realizations of a fluctuating ensemble ofNvariables subject to a conservation principle.more » Similar to the multivariate Wright-Fisher process, whose invariant is also Dirichlet, the univariate case yields a process whose invariant is the beta distribution. As a test of the results, Monte Carlo simulations are used to evolve numerical ensembles toward the invariant Dirichlet distribution.« less

A multi-site stochastic weather generator of daily precipitation and temperature

USDA-ARS?s Scientific Manuscript database

Stochastic weather generators are used to generate time series of climate variables that have statistical properties similar to those of observed data. Most stochastic weather generators work for a single site, and can only generate climate data at a single point, or independent time series at sever...

Hybrid Stochastic Search Technique based Suboptimal AGC Regulator Design for Power System using Constrained Feedback Control Strategy

NASA Astrophysics Data System (ADS)

Ibraheem, Omveer, Hasan, N.

2010-10-01

A new hybrid stochastic search technique is proposed to design of suboptimal AGC regulator for a two area interconnected non reheat thermal power system incorporating DC link in parallel with AC tie-line. In this technique, we are proposing the hybrid form of Genetic Algorithm (GA) and simulated annealing (SA) based regulator. GASA has been successfully applied to constrained feedback control problems where other PI based techniques have often failed. The main idea in this scheme is to seek a feasible PI based suboptimal solution at each sampling time. The feasible solution decreases the cost function rather than minimizing the cost function.

Tests of oceanic stochastic parameterisation in a seasonal forecast system.

NASA Astrophysics Data System (ADS)

Cooper, Fenwick; Andrejczuk, Miroslaw; Juricke, Stephan; Zanna, Laure; Palmer, Tim

2015-04-01

Over seasonal time scales, our aim is to compare the relative impact of ocean initial condition and model uncertainty, upon the ocean forecast skill and reliability. Over seasonal timescales we compare four oceanic stochastic parameterisation schemes applied in a 1x1 degree ocean model (NEMO) with a fully coupled T159 atmosphere (ECMWF IFS). The relative impacts upon the ocean of the resulting eddy induced activity, wind forcing and typical initial condition perturbations are quantified. Following the historical success of stochastic parameterisation in the atmosphere, two of the parameterisations tested were multiplicitave in nature: A stochastic variation of the Gent-McWilliams scheme and a stochastic diffusion scheme. We also consider a surface flux parameterisation (similar to that introduced by Williams, 2012), and stochastic perturbation of the equation of state (similar to that introduced by Brankart, 2013). The amplitude of the stochastic term in the Williams (2012) scheme was set to the physically reasonable amplitude considered in that paper. The amplitude of the stochastic term in each of the other schemes was increased to the limits of model stability. As expected, variability was increased. Up to 1 month after initialisation, ensemble spread induced by stochastic parameterisation is greater than that induced by the atmosphere, whilst being smaller than the initial condition perturbations currently used at ECMWF. After 1 month, the wind forcing becomes the dominant source of model ocean variability, even at depth.

Hybrid geometric-random template-placement algorithm for gravitational wave searches from compact binary coalescences

NASA Astrophysics Data System (ADS)

Roy, Soumen; Sengupta, Anand S.; Thakor, Nilay

2017-05-01

Astrophysical compact binary systems consisting of neutron stars and black holes are an important class of gravitational wave (GW) sources for advanced LIGO detectors. Accurate theoretical waveform models from the inspiral, merger, and ringdown phases of such systems are used to filter detector data under the template-based matched-filtering paradigm. An efficient grid over the parameter space at a fixed minimal match has a direct impact on the overall time taken by these searches. We present a new hybrid geometric-random template placement algorithm for signals described by parameters of two masses and one spin magnitude. Such template banks could potentially be used in GW searches from binary neutron stars and neutron star-black hole systems. The template placement is robust and is able to automatically accommodate curvature and boundary effects with no fine-tuning. We also compare these banks against vanilla stochastic template banks and show that while both are equally efficient in the fitting-factor sense, the bank sizes are ˜25 % larger in the stochastic method. Further, we show that the generation of the proposed hybrid banks can be sped up by nearly an order of magnitude over the stochastic bank. Generic issues related to optimal implementation are discussed in detail. These improvements are expected to directly reduce the computational cost of gravitational wave searches.

The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability

PubMed Central

Reich, Steven

2014-01-01

Neuronal variability plays a central role in neural coding and impacts the dynamics of neuronal networks. Unreliability of synaptic transmission is a major source of neural variability: synaptic neurotransmitter vesicles are released probabilistically in response to presynaptic action potentials and are recovered stochastically in time. The dynamics of this process of vesicle release and recovery interacts with variability in the arrival times of presynaptic spikes to shape the variability of the postsynaptic response. We use continuous time Markov chain methods to analyze a model of short term synaptic depression with stochastic vesicle dynamics coupled with three different models of presynaptic spiking: one model in which the timing of presynaptic action potentials are modeled as a Poisson process, one in which action potentials occur more regularly than a Poisson process (sub-Poisson) and one in which action potentials occur more irregularly (super-Poisson). We use this analysis to investigate how variability in a presynaptic spike train is transformed by short term depression and stochastic vesicle dynamics to determine the variability of the postsynaptic response. We find that sub-Poisson presynaptic spiking increases the average rate at which vesicles are released, that the number of vesicles released over a time window is more variable for smaller time windows than larger time windows and that fast presynaptic spiking gives rise to Poisson-like variability of the postsynaptic response even when presynaptic spike times are non-Poisson. Our results complement and extend previously reported theoretical results and provide possible explanations for some trends observed in recorded data. PMID:23354693

Stochastic analysis of uncertain thermal parameters for random thermal regime of frozen soil around a single freezing pipe

NASA Astrophysics Data System (ADS)

Wang, Tao; Zhou, Guoqing; Wang, Jianzhou; Zhou, Lei

2018-03-01

The artificial ground freezing method (AGF) is widely used in civil and mining engineering, and the thermal regime of frozen soil around the freezing pipe affects the safety of design and construction. The thermal parameters can be truly random due to heterogeneity of the soil properties, which lead to the randomness of thermal regime of frozen soil around the freezing pipe. The purpose of this paper is to study the one-dimensional (1D) random thermal regime problem on the basis of a stochastic analysis model and the Monte Carlo (MC) method. Considering the uncertain thermal parameters of frozen soil as random variables, stochastic processes and random fields, the corresponding stochastic thermal regime of frozen soil around a single freezing pipe are obtained and analyzed. Taking the variability of each stochastic parameter into account individually, the influences of each stochastic thermal parameter on stochastic thermal regime are investigated. The results show that the mean temperatures of frozen soil around the single freezing pipe with three analogy method are the same while the standard deviations are different. The distributions of standard deviation have a great difference at different radial coordinate location and the larger standard deviations are mainly at the phase change area. The computed data with random variable method and stochastic process method have a great difference from the measured data while the computed data with random field method well agree with the measured data. Each uncertain thermal parameter has a different effect on the standard deviation of frozen soil temperature around the single freezing pipe. These results can provide a theoretical basis for the design and construction of AGF.

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Northern Hemisphere glaciation and the evolution of Plio-Pleistocene climate noise

NASA Astrophysics Data System (ADS)

Meyers, Stephen R.; Hinnov, Linda A.

2010-08-01

Deterministic orbital controls on climate variability are commonly inferred to dominate across timescales of 104-106 years, although some studies have suggested that stochastic processes may be of equal or greater importance. Here we explicitly quantify changes in deterministic orbital processes (forcing and/or pacing) versus stochastic climate processes during the Plio-Pleistocene, via time-frequency analysis of two prominent foraminifera oxygen isotopic stacks. Our results indicate that development of the Northern Hemisphere ice sheet is paralleled by an overall amplification of both deterministic and stochastic climate energy, but their relative dominance is variable. The progression from a more stochastic early Pliocene to a strongly deterministic late Pleistocene is primarily accommodated during two transitory phases of Northern Hemisphere ice sheet growth. This long-term trend is punctuated by “stochastic events,” which we interpret as evidence for abrupt reorganization of the climate system at the initiation and termination of the mid-Pleistocene transition and at the onset of Northern Hemisphere glaciation. In addition to highlighting a complex interplay between deterministic and stochastic climate change during the Plio-Pleistocene, our results support an early onset for Northern Hemisphere glaciation (between 3.5 and 3.7 Ma) and reveal some new characteristics of the orbital signal response, such as the puzzling emergence of 100 ka and 400 ka cyclic climate variability during theoretical eccentricity nodes.

Impacts of Considering Climate Variability on Investment Decisions in Ethiopia

NASA Astrophysics Data System (ADS)

Strzepek, K.; Block, P.; Rosegrant, M.; Diao, X.

2005-12-01

In Ethiopia, climate extremes, inducing droughts or floods, are not unusual. Monitoring the effects of these extremes, and climate variability in general, is critical for economic prediction and assessment of the country's future welfare. The focus of this study involves adding climate variability to a deterministic, mean climate-driven agro-economic model, in an attempt to understand its effects and degree of influence on general economic prediction indicators for Ethiopia. Four simulations are examined, including a baseline simulation and three investment strategies: simulations of irrigation investment, roads investment, and a combination investment of both irrigation and roads. The deterministic model is transformed into a stochastic model by dynamically adding year-to-year climate variability through climate-yield factors. Nine sets of actual, historic, variable climate data are individually assembled and implemented into the 12-year stochastic model simulation, producing an ensemble of economic prediction indicators. This ensemble allows for a probabilistic approach to planning and policy making, allowing decision makers to consider risk. The economic indicators from the deterministic and stochastic approaches, including rates of return to investments, are significantly different. The predictions of the deterministic model appreciably overestimate the future welfare of Ethiopia; the predictions of the stochastic model, utilizing actual climate data, tend to give a better semblance of what may be expected. Inclusion of climate variability is vital for proper analysis of the predictor values from this agro-economic model.

Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background.

PubMed

Abbott, B P; Abbott, R; Abbott, T D; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Adya, V B; Affeldt, C; Afrough, M; Agarwal, B; Agathos, M; Agatsuma, K; Aggarwal, N; Aguiar, O D; Aiello, L; Ain, A; Ajith, P; Allen, B; Allen, G; Allocca, A; Altin, P A; Amato, A; Ananyeva, A; Anderson, S B; Anderson, W G; Angelova, S V; Antier, S; Appert, S; Arai, K; Araya, M C; Areeda, J S; Arnaud, N; Ascenzi, S; Ashton, G; Ast, M; Aston, S M; Astone, P; Atallah, D V; Aufmuth, P; Aulbert, C; AultONeal, K; Austin, C; Avila-Alvarez, A; Babak, S; Bacon, P; Bader, M K M; Bae, S; Baker, P T; Baldaccini, F; Ballardin, G; Ballmer, S W; Banagiri, S; Barayoga, J C; Barclay, S E; Barish, B C; Barker, D; Barkett, K; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barta, D; Bartlett, J; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bawaj, M; Bayley, J C; Bazzan, M; Bécsy, B; Beer, C; Bejger, M; Belahcene, I; Bell, A S; Berger, B K; Bergmann, G; Bero, J J; Berry, C P L; Bersanetti, D; Bertolini, A; Betzwieser, J; Bhagwat, S; Bhandare, R; Bilenko, I A; Billingsley, G; Billman, C R; Birch, J; Birney, R; Birnholtz, O; Biscans, S; Biscoveanu, S; Bisht, A; Bitossi, M; Biwer, C; Bizouard, M A; Blackburn, J K; Blackman, J; Blair, C D; Blair, D G; Blair, R M; Bloemen, S; Bock, O; Bode, N; Boer, M; Bogaert, G; Bohe, A; Bondu, F; Bonilla, E; Bonnand, R; Boom, B A; Bork, R; Boschi, V; Bose, S; Bossie, K; Bouffanais, Y; Bozzi, A; Bradaschia, C; Brady, P R; Branchesi, M; Brau, J E; Briant, T; Brillet, A; Brinkmann, M; Brisson, V; Brockill, P; Broida, J E; Brooks, A F; Brown, D A; Brown, D D; Brunett, S; Buchanan, C C; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Buskulic, D; Buy, C; Byer, R L; Cabero, M; Cadonati, L; Cagnoli, G; Cahillane, C; Calderón Bustillo, J; Callister, T A; Calloni, E; Camp, J B; Canepa, M; Canizares, P; Cannon, K C; Cao, H; Cao, J; Capano, C D; Capocasa, E; Carbognani, F; Caride, S; Carney, M F; Diaz, J Casanueva; Casentini, C; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C B; Cerdá-Durán, P; Cerretani, G; Cesarini, E; Chamberlin, S J; Chan, M; Chao, S; Charlton, P; Chase, E; Chassande-Mottin, E; Chatterjee, D; Cheeseboro, B D; Chen, H Y; Chen, X; Chen, Y; Cheng, H-P; Chia, H; Chincarini, A; Chiummo, A; Chmiel, T; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, A J K; Chua, S; Chung, A K W; Chung, S; Ciani, G; Ciolfi, R; Cirelli, C E; Cirone, A; Clara, F; Clark, J A; Clearwater, P; Cleva, F; Cocchieri, C; Coccia, E; Cohadon, P-F; Cohen, D; Colla, A; Collette, C G; Cominsky, L R; Constancio, M; Conti, L; Cooper, S J; Corban, P; Corbitt, T R; Cordero-Carrión, I; Corley, K R; Cornish, N; Corsi, A; Cortese, S; Costa, C A; Coughlin, E; Coughlin, M W; Coughlin, S B; Coulon, J-P; Countryman, S T; Couvares, P; Covas, P B; Cowan, E E; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Creighton, J D E; Creighton, T D; Cripe, J; Crowder, S G; Cullen, T J; Cumming, A; Cunningham, L; Cuoco, E; Canton, T Dal; Dálya, G; Danilishin, S L; D'Antonio, S; Danzmann, K; Dasgupta, A; Da Silva Costa, C F; Dattilo, V; Dave, I; Davier, M; Davis, D; Daw, E J; Day, B; De, S; DeBra, D; Degallaix, J; De Laurentis, M; Deléglise, S; Del Pozzo, W; Demos, N; Denker, T; Dent, T; De Pietri, R; Dergachev, V; De Rosa, R; DeRosa, R T; De Rossi, C; DeSalvo, R; de Varona, O; Devenson, J; Dhurandhar, S; Díaz, M C; Di Fiore, L; Di Giovanni, M; Di Girolamo, T; Di Lieto, A; Di Pace, S; Di Palma, I; Di Renzo, F; Doctor, Z; Dolique, V; Donovan, F; Dooley, K L; Doravari, S; Dorrington, I; Douglas, R; Dovale Álvarez, M; Downes, T P; Drago, M; Dreissigacker, C; Driggers, J C; Du, Z; Ducrot, M; Dupej, P; Dwyer, S E; Edo, T B; Edwards, M C; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Eisenstein, R A; Essick, R C; Estevez, D; Etienne, Z B; Etzel, T; Evans, M; Evans, T M; Factourovich, M; Fafone, V; Fair, H; Fairhurst, S; Fan, X; Farinon, S; Farr, B; Farr, W M; Fauchon-Jones, E J; Favata, M; Fays, M; Fee, C; Fehrmann, H; Feicht, J; Fejer, M M; Fernandez-Galiana, A; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Finstad, D; Fiori, I; Fiorucci, D; Fishbach, M; Fisher, R P; Fitz-Axen, M; Flaminio, R; Fletcher, M; Fong, H; Font, J A; Forsyth, P W F; Forsyth, S S; Fournier, J-D; Frasca, S; Frasconi, F; Frei, Z; Freise, A; Frey, R; Frey, V; Fries, E M; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gabbard, H; Gadre, B U; Gaebel, S M; Gair, J R; Gammaitoni, L; Ganija, M R; Gaonkar, S G; Garcia-Quiros, C; Garufi, F; Gateley, B; Gaudio, S; Gaur, G; Gayathri, V; Gehrels, N; Gemme, G; Genin, E; Gennai, A; George, D; George, J; Gergely, L; Germain, V; Ghonge, S; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, K; Glover, L; Goetz, E; Goetz, R; Gomes, S; Goncharov, B; González, G; Gonzalez Castro, J M; Gopakumar, A; Gorodetsky, M L; Gossan, S E; Gosselin, M; Gouaty, R; Grado, A; Graef, C; Granata, M; Grant, A; Gras, S; Gray, C; Greco, G; Green, A C; Gretarsson, E M; Groot, P; Grote, H; Grunewald, S; Gruning, P; Guidi, G M; Guo, X; Gupta, A; Gupta, M K; Gushwa, K E; Gustafson, E K; Gustafson, R; Halim, O; Hall, B R; Hall, E D; Hamilton, E Z; Hammond, G; Haney, M; Hanke, M M; Hanks, J; Hanna, C; Hannam, M D; Hannuksela, O A; Hanson, J; Hardwick, T; Harms, J; Harry, G M; Harry, I W; Hart, M J; Haster, C-J; Haughian, K; Healy, J; Heidmann, A; Heintze, M C; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Hennig, J; Heptonstall, A W; Heurs, M; Hild, S; Hinderer, T; Hoak, D; Hofman, D; Holt, K; Holz, D E; Hopkins, P; Horst, C; Hough, J; Houston, E A; Howell, E J; Hreibi, A; Hu, Y M; Huerta, E A; Huet, D; Hughey, B; Husa, S; Huttner, S H; Huynh-Dinh, T; Indik, N; Inta, R; Intini, G; Isa, H N; Isac, J-M; Isi, M; Iyer, B R; Izumi, K; Jacqmin, T; Jani, K; Jaranowski, P; Jawahar, S; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; Junker, J; Kalaghatgi, C V; Kalogera, V; Kamai, B; Kandhasamy, S; Kang, G; Kanner, J B; Kapadia, S J; Karki, S; Karvinen, K S; Kasprzack, M; Katolik, M; Katsavounidis, E; Katzman, W; Kaufer, S; Kawabe, K; Kéfélian, F; Keitel, D; Kemball, A J; Kennedy, R; Kent, C; Key, J S; Khalili, F Y; Khan, I; Khan, S; Khan, Z; Khazanov, E A; Kijbunchoo, N; Kim, Chunglee; Kim, J C; Kim, K; Kim, W; Kim, W S; Kim, Y-M; Kimbrell, S J; King, E J; King, P J; Kinley-Hanlon, M; Kirchhoff, R; Kissel, J S; Kleybolte, L; Klimenko, S; Knowles, T D; Koch, P; Koehlenbeck, S M; Koley, S; Kondrashov, V; Kontos, A; Korobko, M; Korth, W Z; Kowalska, I; Kozak, D B; Krämer, C; Kringel, V; Królak, A; Kuehn, G; Kumar, P; Kumar, R; Kumar, S; Kuo, L; Kutynia, A; Kwang, S; Lackey, B D; Lai, K H; Landry, M; Lang, R N; Lange, J; Lantz, B; Lanza, R K; Lartaux-Vollard, A; Lasky, P D; Laxen, M; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lee, C H; Lee, H K; Lee, H M; Lee, H W; Lee, K; Lehmann, J; Lenon, A; Leonardi, M; Leroy, N; Letendre, N; Levin, Y; Li, T G F; Linker, S D; Littenberg, T B; Liu, J; Lo, R K L; Lockerbie, N A; London, L T; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lousto, C O; Lovelace, G; Lück, H; Lumaca, D; Lundgren, A P; Lynch, R; Ma, Y; Macas, R; Macfoy, S; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña Hernandez, I; Magaña-Sandoval, F; Magaña Zertuche, L; Magee, R M; Majorana, E; Maksimovic, I; Man, N; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markakis, C; Markosyan, A S; Markowitz, A; Maros, E; Marquina, A; Martelli, F; Martellini, L; Martin, I W; Martin, R M; Martynov, D V; Mason, K; Massera, E; Masserot, A; Massinger, T J; Masso-Reid, M; Mastrogiovanni, S; Matas, A; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; McCarthy, R; McClelland, D E; McCormick, S; McCuller, L; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McNeill, L; McRae, T; McWilliams, S T; Meacher, D; Meadors, G D; Mehmet, M; Meidam, J; Mejuto-Villa, E; Melatos, A; Mendell, G; Mercer, R A; Merilh, E L; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Metzdorff, R; Meyers, P M; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, A L; Miller, B B; Miller, J; Millhouse, M; Milovich-Goff, M C; Minazzoli, O; Minenkov, Y; Ming, J; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moffa, D; Moggi, A; Mogushi, K; Mohan, M; Mohapatra, S R P; Montani, M; Moore, C J; Moraru, D; Moreno, G; Morriss, S R; Mours, B; Mow-Lowry, C M; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Muñiz, E A; Muratore, M; Murray, P G; Napier, K; Nardecchia, I; Naticchioni, L; Nayak, R K; Neilson, J; Nelemans, G; Nelson, T J N; Nery, M; Neunzert, A; Nevin, L; Newport, J M; Newton, G; Ng, K K Y; Nguyen, T T; Nichols, D; Nielsen, A B; Nissanke, S; Nitz, A; Noack, A; Nocera, F; Nolting, D; North, C; Nuttall, L K; Oberling, J; O'Dea, G D; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Okada, M A; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; Ormiston, R; Ortega, L F; O'Shaughnessy, R; Ossokine, S; Ottaway, D J; Overmier, H; Owen, B J; Pace, A E; Page, J; Page, M A; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, Howard; Pan, Huang-Wei; Pang, B; Pang, P T H; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Parida, A; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patil, M; Patricelli, B; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perez, C J; Perreca, A; Perri, L M; Pfeiffer, H P; Phelps, M; Piccinni, O J; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pirello, M; Pitkin, M; Poe, M; Poggiani, R; Popolizio, P; Porter, E K; Post, A; Powell, J; Prasad, J; Pratt, J W W; Pratten, G; Predoi, V; Prestegard, T; Prijatelj, M; Principe, M; Privitera, S; Prodi, G A; Prokhorov, L G; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rajan, C; Rajbhandari, B; Rakhmanov, M; Ramirez, K E; Ramos-Buades, A; Rapagnani, P; Raymond, V; Razzano, M; Read, J; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Ren, W; Reyes, S D; Ricci, F; Ricker, P M; Rieger, S; Riles, K; Rizzo, M; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, J D; Romano, R; Romel, C L; Romie, J H; Rosińska, D; Ross, M P; Rowan, S; Rüdiger, A; Ruggi, P; Rutins, G; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Sakellariadou, M; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sampson, L M; Sanchez, E J; Sanchez, L E; Sanchis-Gual, N; Sandberg, V; Sanders, J R; Sassolas, B; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Scheel, M; Scheuer, J; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schulte, B W; Schutz, B F; Schwalbe, S G; Scott, J; Scott, S M; Seidel, E; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Shaddock, D A; Shaffer, T J; Shah, A A; Shahriar, M S; Shaner, M B; Shao, L; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sieniawska, M; Sigg, D; Silva, A D; Singer, L P; Singh, A; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, B; Smith, J R; Smith, R J E; Somala, S; Son, E J; Sonnenberg, J A; Sorazu, B; Sorrentino, F; Souradeep, T; Spencer, A P; Srivastava, A K; Staats, K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stevenson, S P; Stone, R; Stops, D J; Strain, K A; Stratta, G; Strigin, S E; Strunk, A; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sunil, S; Suresh, J; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Tait, S C; Talbot, C; Talukder, D; Tanner, D B; Tao, D; Tápai, M; Taracchini, A; Tasson, J D; Taylor, J A; Taylor, R; Tewari, S V; Theeg, T; Thies, F; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Toland, K; Tonelli, M; Tornasi, Z; Torres-Forné, A; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trinastic, J; Tringali, M C; Trozzo, L; Tsang, K W; Tse, M; Tso, R; Tsukada, L; Tsuna, D; Tuyenbayev, D; Ueno, K; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Varma, V; Vass, S; Vasúth, M; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Venugopalan, G; Verkindt, D; Vetrano, F; Viceré, A; Viets, A D; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walet, R; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, J Z; Wang, W H; Wang, Y F; Ward, R L; Warner, J; Was, M; Watchi, J; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Wen, L; Wessel, E K; Weßels, P; Westerweck, J; Westphal, T; Wette, K; Whelan, J T; Whiting, B F; Whittle, C; Wilken, D; Williams, D; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Woehler, J; Wofford, J; Wong, K W K; Worden, J; Wright, J L; Wu, D S; Wysocki, D M; Xiao, S; Yamamoto, H; Yancey, C C; Yang, L; Yap, M J; Yazback, M; Yu, Hang; Yu, Haocun; Yvert, M; Zadrożny, A; Zanolin, M; Zelenova, T; Zendri, J-P; Zevin, M; Zhang, L; Zhang, M; Zhang, T; Zhang, Y-H; Zhao, C; Zhou, M; Zhou, Z; Zhu, S J; Zhu, X J; Zucker, M E; Zweizig, J

2018-05-18

The detection of gravitational waves with Advanced LIGO and Advanced Virgo has enabled novel tests of general relativity, including direct study of the polarization of gravitational waves. While general relativity allows for only two tensor gravitational-wave polarizations, general metric theories can additionally predict two vector and two scalar polarizations. The polarization of gravitational waves is encoded in the spectral shape of the stochastic gravitational-wave background, formed by the superposition of cosmological and individually unresolved astrophysical sources. Using data recorded by Advanced LIGO during its first observing run, we search for a stochastic background of generically polarized gravitational waves. We find no evidence for a background of any polarization, and place the first direct bounds on the contributions of vector and scalar polarizations to the stochastic background. Under log-uniform priors for the energy in each polarization, we limit the energy densities of tensor, vector, and scalar modes at 95% credibility to Ω_{0}^{T}<5.58×10^{-8}, Ω_{0}^{V}<6.35×10^{-8}, and Ω_{0}^{S}<1.08×10^{-7} at a reference frequency f_{0}=25  Hz.

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

NASA Astrophysics Data System (ADS)

Romano, Joseph D.; Cornish, Neil. J.

2017-04-01

We review detection methods that are currently in use or have been proposed to search for a stochastic background of gravitational radiation. We consider both Bayesian and frequentist searches using ground-based and space-based laser interferometers, spacecraft Doppler tracking, and pulsar timing arrays; and we allow for anisotropy, non-Gaussianity, and non-standard polarization states. Our focus is on relevant data analysis issues, and not on the particular astrophysical or early Universe sources that might give rise to such backgrounds. We provide a unified treatment of these searches at the level of detector response functions, detection sensitivity curves, and, more generally, at the level of the likelihood function, since the choice of signal and noise models and prior probability distributions are actually what define the search. Pedagogical examples are given whenever possible to compare and contrast different approaches. We have tried to make the article as self-contained and comprehensive as possible, targeting graduate students and new researchers looking to enter this field.

Reducing the width of confidence intervals for the difference between two population means by inverting adaptive tests.

PubMed

O'Gorman, Thomas W

2018-05-01

In the last decade, it has been shown that an adaptive testing method could be used, along with the Robbins-Monro search procedure, to obtain confidence intervals that are often narrower than traditional confidence intervals. However, these confidence interval limits require a great deal of computation and some familiarity with stochastic search methods. We propose a method for estimating the limits of confidence intervals that uses only a few tests of significance. We compare these limits to those obtained by a lengthy Robbins-Monro stochastic search and find that the proposed method is nearly as accurate as the Robbins-Monro search. Adaptive confidence intervals that are produced by the proposed method are often narrower than traditional confidence intervals when the distributions are long-tailed, skewed, or bimodal. Moreover, the proposed method of estimating confidence interval limits is easy to understand, because it is based solely on the p-values from a few tests of significance.

Slow-fast stochastic diffusion dynamics and quasi-stationarity for diploid populations with varying size.

PubMed

Coron, Camille

2016-01-01

We are interested in the long-time behavior of a diploid population with sexual reproduction and randomly varying population size, characterized by its genotype composition at one bi-allelic locus. The population is modeled by a 3-dimensional birth-and-death process with competition, weak cooperation and Mendelian reproduction. This stochastic process is indexed by a scaling parameter K that goes to infinity, following a large population assumption. When the individual birth and natural death rates are of order K, the sequence of stochastic processes indexed by K converges toward a new slow-fast dynamics with variable population size. We indeed prove the convergence toward 0 of a fast variable giving the deviation of the population from quasi Hardy-Weinberg equilibrium, while the sequence of slow variables giving the respective numbers of occurrences of each allele converges toward a 2-dimensional diffusion process that reaches (0,0) almost surely in finite time. The population size and the proportion of a given allele converge toward a Wright-Fisher diffusion with stochastically varying population size and diploid selection. We insist on differences between haploid and diploid populations due to population size stochastic variability. Using a non trivial change of variables, we study the absorption of this diffusion and its long time behavior conditioned on non-extinction. In particular we prove that this diffusion starting from any non-trivial state and conditioned on not hitting (0,0) admits a unique quasi-stationary distribution. We give numerical approximations of this quasi-stationary behavior in three biologically relevant cases: neutrality, overdominance, and separate niches.

Impact of a Stochastic Parameterization Scheme on El Nino-Southern Oscillation in the Community Climate System Model

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Berner, J.; Sardeshmukh, P. D.

2017-12-01

Stochastic parameterizations have been used for more than a decade in atmospheric models. They provide a way to represent model uncertainty through representing the variability of unresolved sub-grid processes, and have been shown to have a beneficial effect on the spread and mean state for medium- and extended-range forecasts. There is increasing evidence that stochastic parameterization of unresolved processes can improve the bias in mean and variability, e.g. by introducing a noise-induced drift (nonlinear rectification), and by changing the residence time and structure of flow regimes. We present results showing the impact of including the Stochastically Perturbed Parameterization Tendencies scheme (SPPT) in coupled runs of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4) with historical forcing. SPPT results in a significant improvement in the representation of the El Nino-Southern Oscillation in CAM4, improving the power spectrum, as well as both the inter- and intra-annual variability of tropical pacific sea surface temperatures. We use a Linear Inverse Modelling framework to gain insight into the mechanisms by which SPPT has improved ENSO-variability.

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.

Environmental stochasticity controls soil erosion variability

PubMed Central

Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone

2016-01-01

Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. Improved understanding of the underlying processes and their interactions are needed to infer scaling properties of soil loss and better inform predictive methods. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. The reduction of variability of the geomorphic response is attributed to a ‘compensation effect’: temporal alternation of events that exhibit either source-limited or transport-limited regimes. The rate of reduction is related to environment stochasticity and a novel index is derived to reflect the level of variability of intra- and inter-event hydrometeorologic conditions. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments. PMID:26925542

Portfolios in Stochastic Local Search: Efficiently Computing Most Probable Explanations in Bayesian Networks

NASA Technical Reports Server (NTRS)

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

2001-01-01

Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function - i.e. a ratio of two polynomials - of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGSs performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa.

Diffusive transport in the presence of stochastically gated absorption

NASA Astrophysics Data System (ADS)

Bressloff, Paul C.; Karamched, Bhargav R.; Lawley, Sean D.; Levien, Ethan

2017-08-01

We analyze a population of Brownian particles moving in a spatially uniform environment with stochastically gated absorption. The state of the environment at time t is represented by a discrete stochastic variable k (t )∈{0 ,1 } such that the rate of absorption is γ [1 -k (t )] , with γ a positive constant. The variable k (t ) evolves according to a two-state Markov chain. We focus on how stochastic gating affects the attenuation of particle absorption with distance from a localized source in a one-dimensional domain. In the static case (no gating), the steady-state attenuation is given by an exponential with length constant √{D /γ }, where D is the diffusivity. We show that gating leads to slower, nonexponential attenuation. We also explore statistical correlations between particles due to the fact that they all diffuse in the same switching environment. Such correlations can be determined in terms of moments of the solution to a corresponding stochastic Fokker-Planck equation.

Towards a Stochastic Predictive Understanding of Ecosystem Functioning and Resilience to Environmental Changes

NASA Astrophysics Data System (ADS)

Pappas, C.

2017-12-01

Terrestrial ecosystem processes respond differently to hydrometeorological variability across timescales, and so does our scientific understanding of the underlying mechanisms. Process-based modeling of ecosystem functioning is therefore challenging, especially when long-term predictions are envisioned. Here we analyze the statistical properties of hydrometeorological and ecosystem variability, i.e., the variability of ecosystem process related to vegetation carbon dynamics, from hourly to decadal timescales. 23 extra-tropical forest sites, covering different climatic zones and vegetation characteristics, are examined. Micrometeorological and reanalysis data of precipitation, air temperature, shortwave radiation and vapor pressure deficit are used to describe hydrometeorological variability. Ecosystem variability is quantified using long-term eddy covariance flux data of hourly net ecosystem exchange of CO2 between land surface and atmosphere, monthly remote sensing vegetation indices, annual tree-ring widths and above-ground biomass increment estimates. We find that across sites and timescales ecosystem variability is confined within a hydrometeorological envelope that describes the range of variability of the available resources, i.e., water and energy. Furthermore, ecosystem variability demonstrates long-term persistence, highlighting ecological memory and slow ecosystem recovery rates after disturbances. We derive an analytical model, combining deterministic harmonics and stochastic processes, that represents major mechanisms and uncertainties and mimics the observed pattern of hydrometeorological and ecosystem variability. This stochastic framework offers a parsimonious and mathematically tractable approach for modelling ecosystem functioning and for understanding its response and resilience to environmental changes. Furthermore, this framework reflects well the observed ecological memory, an inherent property of ecosystem functioning that is currently not captured by simulation results with process-based models. Our analysis offers a perspective for terrestrial ecosystem modelling, combining current process understanding with stochastic methods, and paves the way for new model-data integration opportunities in Earth system sciences.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ismail, Zuhaimy, E-mail: zuhaimyi@yahoo.com, E-mail: irhamahn@yahoo.com; Irhamah, E-mail: zuhaimyi@yahoo.com, E-mail: irhamahn@yahoo.com

2010-11-11

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 aremore » its robustness and better solution qualities.« less

Normal forms for reduced stochastic climate models

PubMed Central

Majda, Andrew J.; Franzke, Christian; Crommelin, Daan

2009-01-01

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen–Loéve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability. PMID:19228943

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).

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob A.

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASA's Dawn mission. The Dawn trajectory was designed with the DDP-based Static/Dynamic Optimal Control algorithm used in the Mystic software.1 Another recently developed method, Hybrid Differential Dynamic Programming (HDDP),2, 3 is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

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

Reducing the number of templates for aligned-spin compact binary coalescence gravitational wave searches using metric-agnostic template nudging

NASA Astrophysics Data System (ADS)

Indik, Nathaniel; Fehrmann, Henning; Harke, Franz; Krishnan, Badri; Nielsen, Alex B.

2018-06-01

Efficient multidimensional template placement is crucial in computationally intensive matched-filtering searches for gravitational waves (GWs). Here, we implement the neighboring cell algorithm (NCA) to improve the detection volume of an existing compact binary coalescence (CBC) template bank. This algorithm has already been successfully applied for a binary millisecond pulsar search in data from the Fermi satellite. It repositions templates from overdense regions to underdense regions and reduces the number of templates that would have been required by a stochastic method to achieve the same detection volume. Our method is readily generalizable to other CBC parameter spaces. Here we apply this method to the aligned-single-spin neutron star-black hole binary coalescence inspiral-merger-ringdown gravitational wave parameter space. We show that the template nudging algorithm can attain the equivalent effectualness of the stochastic method with 12% fewer templates.

SPIKE: AI scheduling techniques for Hubble Space Telescope

NASA Astrophysics Data System (ADS)

Johnston, Mark D.

1991-09-01

AI (Artificial Intelligence) scheduling techniques for HST are presented in the form of the viewgraphs. The following subject areas are covered: domain; HST constraint timescales; HTS scheduling; SPIKE overview; SPIKE architecture; constraint representation and reasoning; use of suitability functions by scheduling agent; SPIKE screen example; advantages of suitability function framework; limiting search and constraint propagation; scheduling search; stochastic search; repair methods; implementation; and status.

CSI 2264: CHARACTERIZING YOUNG STARS IN NGC 2264 WITH STOCHASTICALLY VARYING LIGHT CURVES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stauffer, John; Rebull, Luisa; Carey, Sean

2016-03-15

We provide CoRoT and Spitzer light curves and other supporting data for 17 classical T Tauri stars in NGC 2264 whose CoRoT light curves exemplify the “stochastic” light curve class as defined in 2014 by Cody et al. The most probable physical mechanism to explain the optical variability within this light curve class is time-dependent mass accretion onto the stellar photosphere, producing transient hot spots. Where we have appropriate spectral data, we show that the veiling variability in these stars is consistent in both amplitude and timescale with the optical light curve morphology. The veiling variability is also well-correlated with the strengthmore » of the He i 6678 Å emission line, predicted by models to arise in accretion shocks on or near the stellar photosphere. Stars with accretion burst light curve morphology also have variable mass accretion. The stochastic and accretion burst light curves can both be explained by a simple model of randomly occurring flux bursts, with the stochastic light curve class having a higher frequency of lower amplitude events. Members of the stochastic light curve class have only moderate mass accretion rates. Their Hα profiles usually have blueshifted absorption features, probably originating in a disk wind. The lack of periodic signatures in the light curves suggests that little of the variability is due to long-lived hot spots rotating into or out of our line of sight; instead, the primary driver of the observed photometric variability is likely to be instabilities in the inner disk that lead to variable mass accretion.« less

Fusion of Hard and Soft Information in Nonparametric Density Estimation

DTIC Science & Technology

2015-06-10

and stochastic optimization models, in analysis of simulation output, and when instantiating probability models. We adopt a constrained maximum...particular, density estimation is needed for generation of input densities to simulation and stochastic optimization models, in analysis of simulation output...an essential step in simulation analysis and stochastic optimization is the generation of probability densities for input random variables; see for

Model reduction for slow–fast stochastic systems with metastable behaviour

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruna, Maria, E-mail: bruna@maths.ox.ac.uk; Computational Science Laboratory, Microsoft Research, Cambridge CB1 2FB; Chapman, S. Jonathan

2014-05-07

The quasi-steady-state approximation (or stochastic averaging principle) is a useful tool in the study of multiscale stochastic systems, giving a practical method by which to reduce the number of degrees of freedom in a model. The method is extended here to slow–fast systems in which the fast variables exhibit metastable behaviour. The key parameter that determines the form of the reduced model is the ratio of the timescale for the switching of the fast variables between metastable states to the timescale for the evolution of the slow variables. The method is illustrated with two examples: one from biochemistry (a fast-species-mediatedmore » chemical switch coupled to a slower varying species), and one from ecology (a predator–prey system). Numerical simulations of each model reduction are compared with those of the full system.« less

Optimal regulation in systems with stochastic time sampling

NASA Technical Reports Server (NTRS)

Montgomery, R. C.; Lee, P. S.

1980-01-01

An optimal control theory that accounts for stochastic variable time sampling in a distributed microprocessor based flight control system is presented. The theory is developed by using a linear process model for the airplane dynamics and the information distribution process is modeled as a variable time increment process where, at the time that information is supplied to the control effectors, the control effectors know the time of the next information update only in a stochastic sense. An optimal control problem is formulated and solved for the control law that minimizes the expected value of a quadratic cost function. The optimal cost obtained with a variable time increment Markov information update process where the control effectors know only the past information update intervals and the Markov transition mechanism is almost identical to that obtained with a known and uniform information update interval.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

Design and Construction of Multi-Variable Vortex-Ring Bubble Generator for Use in Interactive Exhibit

DTIC Science & Technology

2013-12-01

providing the opportunity to teach complex subjects related to stable and unstable equilibrium, stochastic systems, and conservation laws. The...bubbles through adjustment of three variables. The seal pressure, actuating pressure, and cycle time of the triggering solenoid valve each contribute to...stable and unstable equilibrium, stochastic systems, and conservation laws. The diaphragm valve designed in this thesis provides the centerpiece for

Final Report for Dynamic Models for Causal Analysis of Panel Data. Models for Change in Quantitative Variables, Part II Scholastic Models. Part II, Chapter 4.

ERIC Educational Resources Information Center

Hannan, Michael T.

This document is part of a series of chapters described in SO 011 759. Stochastic models for the sociological analysis of change and the change process in quantitative variables are presented. The author lays groundwork for the statistical treatment of simple stochastic differential equations (SDEs) and discusses some of the continuities of…

A Primary Care Workload Production Model for Estimating Relative Value Unit Output

DTIC Science & Technology

2011-03-01

for Medicare and Medicaid Services, Office of the Actuary , National Health Statistics Group; and U.S. Department of Commerce, Bureau of Economic...The systematic variation in a relationship can be represented by a mathematical expression, whereas stochastic variation cannot. Further, stochastic...expressed mathematically as an equation, whereby a response variable Y is fitted to a function of “regressor variables and parameters” (SAS©, 2010). A

Optimization under variability and uncertainty: a case study for NOx emissions control for a gasification system.

PubMed

Chen, Jianjun; Frey, H Christopher

2004-12-15

Methods for optimization of process technologies considering the distinction between variability and uncertainty are developed and applied to case studies of NOx control for Integrated Gasification Combined Cycle systems. Existing methods of stochastic optimization (SO) and stochastic programming (SP) are demonstrated. A comparison of SO and SP results provides the value of collecting additional information to reduce uncertainty. For example, an expected annual benefit of 240,000 dollars is estimated if uncertainty can be reduced before a final design is chosen. SO and SP are typically applied to uncertainty. However, when applied to variability, the benefit of dynamic process control is obtained. For example, an annual savings of 1 million dollars could be achieved if the system is adjusted to changes in process conditions. When variability and uncertainty are treated distinctively, a coupled stochastic optimization and programming method and a two-dimensional stochastic programming method are demonstrated via a case study. For the case study, the mean annual benefit of dynamic process control is estimated to be 700,000 dollars, with a 95% confidence range of 500,000 dollars to 940,000 dollars. These methods are expected to be of greatest utility for problems involving a large commitment of resources, for which small differences in designs can produce large cost savings.

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.

multiUQ: An intrusive uncertainty quantification tool for gas-liquid multiphase flows

NASA Astrophysics Data System (ADS)

Turnquist, Brian; Owkes, Mark

2017-11-01

Uncertainty quantification (UQ) can improve our understanding of the sensitivity of gas-liquid multiphase flows to variability about inflow conditions and fluid properties, creating a valuable tool for engineers. While non-intrusive UQ methods (e.g., Monte Carlo) are simple and robust, the cost associated with these techniques can render them unrealistic. In contrast, intrusive UQ techniques modify the governing equations by replacing deterministic variables with stochastic variables, adding complexity, but making UQ cost effective. Our numerical framework, called multiUQ, introduces an intrusive UQ approach for gas-liquid flows, leveraging a polynomial chaos expansion of the stochastic variables: density, momentum, pressure, viscosity, and surface tension. The gas-liquid interface is captured using a conservative level set approach, including a modified reinitialization equation which is robust and quadrature free. A least-squares method is leveraged to compute the stochastic interface normal and curvature needed in the continuum surface force method for surface tension. The solver is tested by applying uncertainty to one or two variables and verifying results against the Monte Carlo approach. NSF Grant #1511325.

Hybrid genetic algorithm with an adaptive penalty function for fitting multimodal experimental data: application to exchange-coupled non-Kramers binuclear iron active sites.

PubMed

Beaser, Eric; Schwartz, Jennifer K; Bell, Caleb B; Solomon, Edward I

2011-09-26

A Genetic Algorithm (GA) is a stochastic optimization technique based on the mechanisms of biological evolution. These algorithms have been successfully applied in many fields to solve a variety of complex nonlinear problems. While they have been used with some success in chemical problems such as fitting spectroscopic and kinetic data, many have avoided their use due to the unconstrained nature of the fitting process. In engineering, this problem is now being addressed through incorporation of adaptive penalty functions, but their transfer to other fields has been slow. This study updates the Nanakorrn Adaptive Penalty function theory, expanding its validity beyond maximization problems to minimization as well. The expanded theory, using a hybrid genetic algorithm with an adaptive penalty function, was applied to analyze variable temperature variable field magnetic circular dichroism (VTVH MCD) spectroscopic data collected on exchange coupled Fe(II)Fe(II) enzyme active sites. The data obtained are described by a complex nonlinear multimodal solution space with at least 6 to 13 interdependent variables and are costly to search efficiently. The use of the hybrid GA is shown to improve the probability of detecting the global optimum. It also provides large gains in computational and user efficiency. This method allows a full search of a multimodal solution space, greatly improving the quality and confidence in the final solution obtained, and can be applied to other complex systems such as fitting of other spectroscopic or kinetics data.

Spatio-Temporal Process Variability in Watershed Scale Wetland Restoration Planning

NASA Astrophysics Data System (ADS)

Evenson, G. R.

2012-12-01

Watershed scale restoration decision making processes are increasingly informed by quantitative methodologies providing site-specific restoration recommendations - sometimes referred to as "systematic planning." The more advanced of these methodologies are characterized by a coupling of search algorithms and ecological models to discover restoration plans that optimize environmental outcomes. Yet while these methods have exhibited clear utility as decision support toolsets, they may be critiqued for flawed evaluations of spatio-temporally variable processes fundamental to watershed scale restoration. Hydrologic and non-hydrologic mediated process connectivity along with post-restoration habitat dynamics, for example, are commonly ignored yet known to appreciably affect restoration outcomes. This talk will present a methodology to evaluate such spatio-temporally complex processes in the production of watershed scale wetland restoration plans. Using the Tuscarawas Watershed in Eastern Ohio as a case study, a genetic algorithm will be coupled with the Soil and Water Assessment Tool (SWAT) to reveal optimal wetland restoration plans as measured by their capacity to maximize nutrient reductions. Then, a so-called "graphical" representation of the optimization problem will be implemented in-parallel to promote hydrologic and non-hydrologic mediated connectivity amongst existing wetlands and sites selected for restoration. Further, various search algorithm mechanisms will be discussed as a means of accounting for temporal complexities such as post-restoration habitat dynamics. Finally, generalized patterns of restoration plan optimality will be discussed as an alternative and possibly superior decision support toolset given the complexity and stochastic nature of spatio-temporal process variability.

Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics.

PubMed

Lin, Yen Ting; Chylek, Lily A; Lemons, Nathan W; Hlavacek, William S

2018-06-21

The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.

Simulation of multivariate stationary stochastic processes using dimension-reduction representation methods

NASA Astrophysics Data System (ADS)

Liu, Zhangjun; Liu, Zenghui; Peng, Yongbo

2018-03-01

In view of the Fourier-Stieltjes integral formula of multivariate stationary stochastic processes, a unified formulation accommodating spectral representation method (SRM) and proper orthogonal decomposition (POD) is deduced. By introducing random functions as constraints correlating the orthogonal random variables involved in the unified formulation, the dimension-reduction spectral representation method (DR-SRM) and the dimension-reduction proper orthogonal decomposition (DR-POD) are addressed. The proposed schemes are capable of representing the multivariate stationary stochastic process with a few elementary random variables, bypassing the challenges of high-dimensional random variables inherent in the conventional Monte Carlo methods. In order to accelerate the numerical simulation, the technique of Fast Fourier Transform (FFT) is integrated with the proposed schemes. For illustrative purposes, the simulation of horizontal wind velocity field along the deck of a large-span bridge is proceeded using the proposed methods containing 2 and 3 elementary random variables. Numerical simulation reveals the usefulness of the dimension-reduction representation methods.

Optimal Search for an Astrophysical Gravitational-Wave Background

NASA Astrophysics Data System (ADS)

Smith, Rory; Thrane, Eric

2018-04-01

Roughly every 2-10 min, a pair of stellar-mass black holes merge somewhere in the Universe. A small fraction of these mergers are detected as individually resolvable gravitational-wave events by advanced detectors such as LIGO and Virgo. The rest contribute to a stochastic background. We derive the statistically optimal search strategy (producing minimum credible intervals) for a background of unresolved binaries. Our method applies Bayesian parameter estimation to all available data. Using Monte Carlo simulations, we demonstrate that the search is both "safe" and effective: it is not fooled by instrumental artifacts such as glitches and it recovers simulated stochastic signals without bias. Given realistic assumptions, we estimate that the search can detect the binary black hole background with about 1 day of design sensitivity data versus ≈40 months using the traditional cross-correlation search. This framework independently constrains the merger rate and black hole mass distribution, breaking a degeneracy present in the cross-correlation approach. The search provides a unified framework for population studies of compact binaries, which is cast in terms of hyperparameter estimation. We discuss a number of extensions and generalizations, including application to other sources (such as binary neutron stars and continuous-wave sources), simultaneous estimation of a continuous Gaussian background, and applications to pulsar timing.

Stochastic industrial source detection using lower cost methods

EPA Science Inventory

Hazardous air pollutants (HAPs) can be emitted from a variety of sources in industrial facilities, energy production, and commercial operations. Stochastic industrial sources (SISs) represent a subcategory of emissions from fugitive leaks, variable area sources, malfunctioning p...

Hybrid stochastic and deterministic simulations of calcium blips.

PubMed

Rüdiger, S; Shuai, J W; Huisinga, W; Nagaiah, C; Warnecke, G; Parker, I; Falcke, M

2007-09-15

Intracellular calcium release is a prime example for the role of stochastic effects in cellular systems. Recent models consist of deterministic reaction-diffusion equations coupled to stochastic transitions of calcium channels. The resulting dynamics is of multiple time and spatial scales, which complicates far-reaching computer simulations. In this article, we introduce a novel hybrid scheme that is especially tailored to accurately trace events with essential stochastic variations, while deterministic concentration variables are efficiently and accurately traced at the same time. We use finite elements to efficiently resolve the extreme spatial gradients of concentration variables close to a channel. We describe the algorithmic approach and we demonstrate its efficiency compared to conventional methods. Our single-channel model matches experimental data and results in intriguing dynamics if calcium is used as charge carrier. Random openings of the channel accumulate in bursts of calcium blips that may be central for the understanding of cellular calcium dynamics.

A note on: "A Gaussian-product stochastic Gent-McWilliams parameterization"

NASA Astrophysics Data System (ADS)

Jansen, Malte F.

2017-02-01

This note builds on a recent article by Grooms (2016), which introduces a new stochastic parameterization for eddy buoyancy fluxes. The closure proposed by Grooms accounts for the fact that eddy fluxes arise as the product of two approximately Gaussian variables, which in turn leads to a distinctly non-Gaussian distribution. The directionality of the stochastic eddy fluxes, however, remains somewhat ad-hoc and depends on the reference frame of the chosen coordinate system. This note presents a modification of the approach proposed by Grooms, which eliminates this shortcoming. Eddy fluxes are computed based on a stochastic mixing length model, which leads to a frame invariant formulation. As in the original closure proposed by Grooms, eddy fluxes are proportional to the product of two Gaussian variables, and the parameterization reduces to the Gent and McWilliams parameterization for the mean buyoancy fluxes.

Effects of stochastic interest rates in decision making under risk: A Markov decision process model for forest management

Treesearch

Mo Zhou; Joseph Buongiorno

2011-01-01

Most economic studies of forest decision making under risk assume a fixed interest rate. This paper investigated some implications of this stochastic nature of interest rates. Markov decision process (MDP) models, used previously to integrate stochastic stand growth and prices, can be extended to include variable interest rates as well. This method was applied to...

Multi-period natural gas market modeling Applications, stochastic extensions and solution approaches

NASA Astrophysics Data System (ADS)

Egging, Rudolf Gerardus

This dissertation develops deterministic and stochastic multi-period mixed complementarity problems (MCP) for the global natural gas market, as well as solution approaches for large-scale stochastic MCP. The deterministic model is unique in the combination of the level of detail of the actors in the natural gas markets and the transport options, the detailed regional and global coverage, the multi-period approach with endogenous capacity expansions for transportation and storage infrastructure, the seasonal variation in demand and the representation of market power according to Nash-Cournot theory. The model is applied to several scenarios for the natural gas market that cover the formation of a cartel by the members of the Gas Exporting Countries Forum, a low availability of unconventional gas in the United States, and cost reductions in long-distance gas transportation. 1 The results provide insights in how different regions are affected by various developments, in terms of production, consumption, traded volumes, prices and profits of market participants. The stochastic MCP is developed and applied to a global natural gas market problem with four scenarios for a time horizon until 2050 with nineteen regions and containing 78,768 variables. The scenarios vary in the possibility of a gas market cartel formation and varying depletion rates of gas reserves in the major gas importing regions. Outcomes for hedging decisions of market participants show some significant shifts in the timing and location of infrastructure investments, thereby affecting local market situations. A first application of Benders decomposition (BD) is presented to solve a large-scale stochastic MCP for the global gas market with many hundreds of first-stage capacity expansion variables and market players exerting various levels of market power. The largest problem solved successfully using BD contained 47,373 variables of which 763 first-stage variables, however using BD did not result in shorter solution times relative to solving the extensive-forms. Larger problems, up to 117,481 variables, were solved in extensive-form, but not when applying BD due to numerical issues. It is discussed how BD could significantly reduce the solution time of large-scale stochastic models, but various challenges remain and more research is needed to assess the potential of Benders decomposition for solving large-scale stochastic MCP. 1 www.gecforum.org

Memristor-based neural networks: Synaptic versus neuronal stochasticity

NASA Astrophysics Data System (ADS)

Naous, Rawan; AlShedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled Nabil

2016-11-01

In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

Gene regulation and noise reduction by coupling of stochastic processes

NASA Astrophysics Data System (ADS)

Ramos, Alexandre F.; Hornos, José Eduardo M.; Reinitz, John

2015-02-01

Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.

Gene regulation and noise reduction by coupling of stochastic processes

PubMed Central

Hornos, José Eduardo M.; Reinitz, John

2015-01-01

Here we characterize the low noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the the two gene states depends on protein number. This fact has a very important implication: there exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction. PMID:25768447

Gene regulation and noise reduction by coupling of stochastic processes.

PubMed

Ramos, Alexandre F; Hornos, José Eduardo M; Reinitz, John

2015-02-01

Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

PubMed

Kalantzis, Georgios

2009-06-01

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

Stochastic Simulation Tool for Aerospace Structural Analysis

NASA Technical Reports Server (NTRS)

Knight, Norman F.; Moore, David F.

2006-01-01

Stochastic simulation refers to incorporating the effects of design tolerances and uncertainties into the design analysis model and then determining their influence on the design. A high-level evaluation of one such stochastic simulation tool, the MSC.Robust Design tool by MSC.Software Corporation, has been conducted. This stochastic simulation tool provides structural analysts with a tool to interrogate their structural design based on their mathematical description of the design problem using finite element analysis methods. This tool leverages the analyst's prior investment in finite element model development of a particular design. The original finite element model is treated as the baseline structural analysis model for the stochastic simulations that are to be performed. A Monte Carlo approach is used by MSC.Robust Design to determine the effects of scatter in design input variables on response output parameters. The tool was not designed to provide a probabilistic assessment, but to assist engineers in understanding cause and effect. It is driven by a graphical-user interface and retains the engineer-in-the-loop strategy for design evaluation and improvement. The application problem for the evaluation is chosen to be a two-dimensional shell finite element model of a Space Shuttle wing leading-edge panel under re-entry aerodynamic loading. MSC.Robust Design adds value to the analysis effort by rapidly being able to identify design input variables whose variability causes the most influence in response output parameters.

A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks

NASA Astrophysics Data System (ADS)

Zubaidi, Salah L.; Dooley, Jayne; Alkhaddar, Rafid M.; Abdellatif, Mawada; Al-Bugharbee, Hussein; Ortega-Martorell, Sandra

2018-06-01

Valid and dependable water demand prediction is a major element of the effective and sustainable expansion of municipal water infrastructures. This study provides a novel approach to quantifying water demand through the assessment of climatic factors, using a combination of a pretreatment signal technique, a hybrid particle swarm optimisation algorithm and an artificial neural network (PSO-ANN). The Singular Spectrum Analysis (SSA) technique was adopted to decompose and reconstruct water consumption in relation to six weather variables, to create a seasonal and stochastic time series. The results revealed that SSA is a powerful technique, capable of decomposing the original time series into many independent components including trend, oscillatory behaviours and noise. In addition, the PSO-ANN algorithm was shown to be a reliable prediction model, outperforming the hybrid Backtracking Search Algorithm BSA-ANN in terms of fitness function (RMSE). The findings of this study also support the view that water demand is driven by climatological variables.

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.

An operational search and rescue model for the Norwegian Sea and the North Sea

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Allen, Arthur A.

A new operational, ensemble-based search and rescue model for the Norwegian Sea and the North Sea is presented. The stochastic trajectory model computes the net motion of a range of search and rescue objects. A new, robust formulation for the relation between the wind and the motion of the drifting object (termed the leeway of the object) is employed. Empirically derived coefficients for 63 categories of search objects compiled by the US Coast Guard are ingested to estimate the leeway of the drifting objects. A Monte Carlo technique is employed to generate an ensemble that accounts for the uncertainties in forcing fields (wind and current), leeway drift properties, and the initial position of the search object. The ensemble yields an estimate of the time-evolving probability density function of the location of the search object, and its envelope defines the search area. Forcing fields from the operational oceanic and atmospheric forecast system of The Norwegian Meteorological Institute are used as input to the trajectory model. This allows for the first time high-resolution wind and current fields to be used to forecast search areas up to 60 h into the future. A limited set of field exercises show good agreement between model trajectories, search areas, and observed trajectories for life rafts and other search objects. Comparison with older methods shows that search areas expand much more slowly using the new ensemble method with high resolution forcing fields and the new leeway formulation. It is found that going to higher-order stochastic trajectory models will not significantly improve the forecast skill and the rate of expansion of search areas.

Methodology for the AutoRegressive Planet Search (ARPS) Project

NASA Astrophysics Data System (ADS)

Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration

2018-01-01

The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.

Modeling heart rate variability by stochastic feedback

NASA Technical Reports Server (NTRS)

Amaral, L. A.; Goldberger, A. L.; Stanley, H. E.

1999-01-01

We consider the question of how the cardiac rhythm spontaneously self-regulates and propose a new mechanism as a possible answer. We model the neuroautonomic regulation of the heart rate as a stochastic feedback system and find that the model successfully accounts for key characteristics of cardiac variability, including the 1/f power spectrum, the functional form and scaling of the distribution of variations of the interbeat intervals, and the correlations in the Fourier phases which indicate nonlinear dynamics.

Climate SPHINX: High-resolution present-day and future climate simulations with an improved representation of small-scale variability

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Subramanian, Aneesh; Weisheimer, Antje; Christensen, Hannah; Juricke, Stephan; Palmer, Tim

2016-04-01

The PRACE Climate SPHINX project investigates the sensitivity of climate simulations to model resolution and stochastic parameterization. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in 30-years climate integrations as a function of model resolution (from 80km up to 16km for the atmosphere). The experiments include more than 70 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), using RCP8.5 CMIP5 forcing. A total amount of 20 million core hours will be used at end of the project (March 2016) and about 150 TBytes of post-processed data will be available to the climate community. Preliminary results show a clear improvement in the representation of climate variability over the Euro-Atlantic following resolution increase. More specifically, the well-known atmospheric blocking negative bias over Europe is definitely resolved. High resolution runs also show improved fidelity in representation of tropical variability - such as the MJO and its propagation - over the low resolution simulations. It is shown that including stochastic parameterization in the low resolution runs help to improve some of the aspects of the MJO propagation further. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).

Molecular analyses of the principal components of response strength.

PubMed Central

Killeen, Peter R; Hall, Scott S; Reilly, Mark P; Kettle, Lauren C

2002-01-01

Killeen and Hall (2001) showed that a common factor called strength underlies the key dependent variables of response probability, latency, and rate, and that overall response rate is a good predictor of strength. In a search for the mechanisms that underlie those correlations, this article shows that (a) the probability of responding on a trial is a two-state Markov process; (b) latency and rate of responding can be described in terms of the probability and period of stochastic machines called clocked Bernoulli modules, and (c) one such machine, the refractory Poisson process, provides a functional relation between the probability of observing a response during any epoch and the rate of responding. This relation is one of proportionality at low rates and curvilinearity at higher rates. PMID:12216975

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia.

PubMed

Landau, Dan A; Clement, Kendell; Ziller, Michael J; Boyle, Patrick; Fan, Jean; Gu, Hongcang; Stevenson, Kristen; Sougnez, Carrie; Wang, Lili; Li, Shuqiang; Kotliar, Dylan; Zhang, Wandi; Ghandi, Mahmoud; Garraway, Levi; Fernandes, Stacey M; Livak, Kenneth J; Gabriel, Stacey; Gnirke, Andreas; Lander, Eric S; Brown, Jennifer R; Neuberg, Donna; Kharchenko, Peter V; Hacohen, Nir; Getz, Gad; Meissner, Alexander; Wu, Catherine J

2014-12-08

Intratumoral heterogeneity plays a critical role in tumor evolution. To define the contribution of DNA methylation to heterogeneity within tumors, we performed genome-scale bisulfite sequencing of 104 primary chronic lymphocytic leukemias (CLLs). Compared with 26 normal B cell samples, CLLs consistently displayed higher intrasample variability of DNA methylation patterns across the genome, which appears to arise from stochastically disordered methylation in malignant cells. Transcriptome analysis of bulk and single CLL cells revealed that methylation disorder was linked to low-level expression. Disordered methylation was further associated with adverse clinical outcome. We therefore propose that disordered methylation plays a similar role to that of genetic instability, enhancing the ability of cancer cells to search for superior evolutionary trajectories. Copyright © 2014 Elsevier Inc. All rights reserved.

Locally disordered methylation forms the basis of intra-tumor methylome variation in chronic lymphocytic leukemia

PubMed Central

Landau, Dan A.; Clement, Kendell; Ziller, Michael J.; Boyle, Patrick; Fan, Jean; Gu, Hongcang; Stevenson, Kristen; Sougnez, Carrie; Wang, Lili; Li, Shuqiang; Kotliar, Dylan; Zhang, Wandi; Ghandi, Mahmoud; Garraway, Levi; Fernandes, Stacey M.; Livak, Kenneth J.; Gabriel, Stacey; Gnirke, Andreas; Lander, Eric S.; Brown, Jennifer R.; Neuberg, Donna; Kharchenko, Peter V.; Hacohen, Nir; Getz, Gad; Meissner, Alexander; Wu, Catherine J.

2014-01-01

SUMMARY Intra-tumoral heterogeneity plays a critical role in tumor evolution. To define the contribution of DNA methylation to heterogeneity within tumors, we performed genome-scale bisulfite sequencing of 104 primary chronic lymphocytic leukemias (CLL). Compared to 26 normal B cell samples, CLLs consistently displayed higher intra-sample variability of DNA methylation patterns across the genome, which appears to arise from stochastically disordered methylation in malignant cells. Transcriptome analysis of bulk and single CLL cells revealed that methylation disorder was linked to low-level expression. Disordered methylation was further associated with adverse clinical outcome. We therefore propose that disordered methylation plays a similar role to genetic instability, enhancing the ability of cancer cells to search for superior evolutionary trajectories. PMID:25490447

Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process

NASA Astrophysics Data System (ADS)

Turner, Douglas C.; Ladde, Gangaram S.

2018-03-01

Analytical solutions, discretization schemes and simulation results are presented for the time delay deterministic differential equation model of the solar dynamo presented by Wilmot-Smith et al. In addition, this model is extended under stochastic Gaussian white noise parametric fluctuations. The introduction of stochastic fluctuations incorporates variables affecting the dynamo process in the solar interior, estimation error of parameters, and uncertainty of the α-effect mechanism. Simulation results are presented and analyzed to exhibit the effects of stochastic parametric volatility-dependent perturbations. The results generalize and extend the work of Hazra et al. In fact, some of these results exhibit the oscillatory dynamic behavior generated by the stochastic parametric additative perturbations in the absence of time delay. In addition, the simulation results of the modified stochastic models influence the change in behavior of the very recently developed stochastic model of Hazra et al.

Stochastic foundations of undulatory transport phenomena: generalized Poisson-Kac processes—part III extensions and applications to kinetic theory and transport

NASA Astrophysics Data System (ADS)

Giona, Massimiliano; Brasiello, Antonio; Crescitelli, Silvestro

2017-08-01

This third part extends the theory of Generalized Poisson-Kac (GPK) processes to nonlinear stochastic models and to a continuum of states. Nonlinearity is treated in two ways: (i) as a dependence of the parameters (intensity of the stochastic velocity, transition rates) of the stochastic perturbation on the state variable, similarly to the case of nonlinear Langevin equations, and (ii) as the dependence of the stochastic microdynamic equations of motion on the statistical description of the process itself (nonlinear Fokker-Planck-Kac models). Several numerical and physical examples illustrate the theory. Gathering nonlinearity and a continuum of states, GPK theory provides a stochastic derivation of the nonlinear Boltzmann equation, furnishing a positive answer to the Kac’s program in kinetic theory. The transition from stochastic microdynamics to transport theory within the framework of the GPK paradigm is also addressed.

Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

NASA Astrophysics Data System (ADS)

Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

2006-06-01

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Stochastic and Centrifuge Modelling of Jointed Rock. Volume 3. Stochastic and Topological Fracture Geometry Model

DTIC Science & Technology

1990-08-31

IPATRr 3DOFN) = 0 110 CONTINUEI CCTMX(IPATR) = 0. CCTMY(IPATR) = 0. 120 CONTINUE C START SEARCHING :1ST IF ( XCONN(1,ICOLl) .EQ.0. )GO TO 300 NCTM (,1...EQ.ICOL1.OR.ICOL4.EQ.1COL2 ) GO TO 270 NCTMX(4,1) = ICOL3 NCTM ~X(4,2) = ICOL4I CCTMX(4) = XCONN(ICOL3,ICOL4) CCTMY(4) = YCONN(ICOL3,ICOL4) IT ( CCTMX(4

Stochastic modelling of turbulent combustion for design optimization of gas turbine combustors

NASA Astrophysics Data System (ADS)

Mehanna Ismail, Mohammed Ali

The present work covers the development and the implementation of an efficient algorithm for the design optimization of gas turbine combustors. The purpose is to explore the possibilities and indicate constructive suggestions for optimization techniques as alternative methods for designing gas turbine combustors. The algorithm is general to the extent that no constraints are imposed on the combustion phenomena or on the combustor configuration. The optimization problem is broken down into two elementary problems: the first is the optimum search algorithm, and the second is the turbulent combustion model used to determine the combustor performance parameters. These performance parameters constitute the objective and physical constraints in the optimization problem formulation. The examination of both turbulent combustion phenomena and the gas turbine design process suggests that the turbulent combustion model represents a crucial part of the optimization algorithm. The basic requirements needed for a turbulent combustion model to be successfully used in a practical optimization algorithm are discussed. In principle, the combustion model should comply with the conflicting requirements of high fidelity, robustness and computational efficiency. To that end, the problem of turbulent combustion is discussed and the current state of the art of turbulent combustion modelling is reviewed. According to this review, turbulent combustion models based on the composition PDF transport equation are found to be good candidates for application in the present context. However, these models are computationally expensive. To overcome this difficulty, two different models based on the composition PDF transport equation were developed: an improved Lagrangian Monte Carlo composition PDF algorithm and the generalized stochastic reactor model. Improvements in the Lagrangian Monte Carlo composition PDF model performance and its computational efficiency were achieved through the implementation of time splitting, variable stochastic fluid particle mass control, and a second order time accurate (predictor-corrector) scheme used for solving the stochastic differential equations governing the particles evolution. The model compared well against experimental data found in the literature for two different configurations: bluff body and swirl stabilized combustors. The generalized stochastic reactor is a newly developed model. This model relies on the generalization of the concept of the classical stochastic reactor theory in the sense that it accounts for both finite micro- and macro-mixing processes. (Abstract shortened by UMI.)

Bubonic plague: a metapopulation model of a zoonosis.

PubMed Central

Keeling, M J; Gilligan, C A

2000-01-01

Bubonic plague (Yersinia pestis) is generally thought of as a historical disease; however, it is still responsible for around 1000-3000 deaths each year worldwide. This paper expands the analysis of a model for bubonic plague that encompasses the disease dynamics in rat, flea and human populations. Some key variables of the deterministic model, including the force of infection to humans, are shown to be robust to changes in the basic parameters, although variation in the flea searching efficiency, and the movement rates of rats and fleas will be considered throughout the paper. The stochastic behaviour of the corresponding metapopulation model is discussed, with attention focused on the dynamics of rats and the force of infection at the local spatial scale. Short-lived local epidemics in rats govern the invasion of the disease and produce an irregular pattern of human cases similar to those observed. However, the endemic behaviour in a few rat subpopulations allows the disease to persist for many years. This spatial stochastic model is also used to identify the criteria for the spread to human populations in terms of the rat density. Finally, the full stochastic model is reduced to the form of a probabilistic cellular automaton, which allows the analysis of a large number of replicated epidemics in large populations. This simplified model enables us to analyse the spatial properties of rat epidemics and the effects of movement rates, and also to test whether the emergent metapopulation behaviour is a property of the local dynamics rather than the precise details of the model. PMID:11413636

Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes

PubMed Central

Soltani, Mohammad; Vargas-Garcia, Cesar A.; Antunes, Duarte; Singh, Abhyudai

2016-01-01

Inside individual cells, expression of genes is inherently stochastic and manifests as cell-to-cell variability or noise in protein copy numbers. Since proteins half-lives can be comparable to the cell-cycle length, randomness in cell-division times generates additional intercellular variability in protein levels. Moreover, as many mRNA/protein species are expressed at low-copy numbers, errors incurred in partitioning of molecules between two daughter cells are significant. We derive analytical formulas for the total noise in protein levels when the cell-cycle duration follows a general class of probability distributions. Using a novel hybrid approach the total noise is decomposed into components arising from i) stochastic expression; ii) partitioning errors at the time of cell division and iii) random cell-division events. These formulas reveal that random cell-division times not only generate additional extrinsic noise, but also critically affect the mean protein copy numbers and intrinsic noise components. Counter intuitively, in some parameter regimes, noise in protein levels can decrease as cell-division times become more stochastic. Computations are extended to consider genome duplication, where transcription rate is increased at a random point in the cell cycle. We systematically investigate how the timing of genome duplication influences different protein noise components. Intriguingly, results show that noise contribution from stochastic expression is minimized at an optimal genome-duplication time. Our theoretical results motivate new experimental methods for decomposing protein noise levels from synchronized and asynchronized single-cell expression data. Characterizing the contributions of individual noise mechanisms will lead to precise estimates of gene expression parameters and techniques for altering stochasticity to change phenotype of individual cells. PMID:27536771

The Stochastic X-Ray Variability of the Accreting Millisecond Pulsar MAXI J0911-655

NASA Technical Reports Server (NTRS)

Bult, Peter

2017-01-01

In this work, I report on the stochastic X-ray variability of the 340 hertz accreting millisecond pulsar MAXI J0911-655. Analyzing pointed observations of the XMM-Newton and NuSTAR observatories, I find that the source shows broad band-limited stochastic variability in the 0.01-10 hertz range with a total fractional variability of approximately 24 percent root mean square timing residuals in the 0.4 to 3 kiloelectronvolt energy band that increases to approximately 40 percent root mean square timing residuals in the 3 to 10 kiloelectronvolt band. Additionally, a pair of harmonically related quasi-periodic oscillations (QPOs) are discovered. The fundamental frequency of this harmonic pair is observed between frequencies of 62 and 146 megahertz. Like the band-limited noise, the amplitudes of the QPOs show a steep increase as a function of energy; this suggests that they share a similar origin, likely the inner accretion flow. Based on their energy dependence and frequency relation with respect to the noise terms, the QPOs are identified as low-frequency oscillations and discussed in terms of the Lense-Thirring precession model.

Stochastic loss and gain of symmetric divisions in the C. elegans epidermis perturbs robustness of stem cell number

PubMed Central

Katsanos, Dimitris; Koneru, Sneha L.; Mestek Boukhibar, Lamia; Gritti, Nicola; Ghose, Ritobrata; Appleford, Peter J.; Doitsidou, Maria; Woollard, Alison; van Zon, Jeroen S.; Poole, Richard J.

2017-01-01

Biological systems are subject to inherent stochasticity. Nevertheless, development is remarkably robust, ensuring the consistency of key phenotypic traits such as correct cell numbers in a certain tissue. It is currently unclear which genes modulate phenotypic variability, what their relationship is to core components of developmental gene networks, and what is the developmental basis of variable phenotypes. Here, we start addressing these questions using the robust number of Caenorhabditis elegans epidermal stem cells, known as seam cells, as a readout. We employ genetics, cell lineage tracing, and single molecule imaging to show that mutations in lin-22, a Hes-related basic helix-loop-helix (bHLH) transcription factor, increase seam cell number variability. We show that the increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisions to asymmetric and vice versa during development, which affect the terminal seam cell number in opposing directions. We demonstrate that LIN-22 acts within the epidermal gene network to antagonise the Wnt signalling pathway. However, lin-22 mutants exhibit cell-to-cell variability in Wnt pathway activation, which correlates with and may drive phenotypic variability. Our study demonstrates the feasibility to study phenotypic trait variance in tractable model organisms using unbiased mutagenesis screens. PMID:29108019

Model reduction method using variable-separation for stochastic saddle point problems

NASA Astrophysics Data System (ADS)

Jiang, Lijian; Li, Qiuqi

2018-02-01

In this paper, we consider a variable-separation (VS) method to solve the stochastic saddle point (SSP) problems. The VS method is applied to obtain the solution in tensor product structure for stochastic partial differential equations (SPDEs) in a mixed formulation. The aim of such a technique is to construct a reduced basis approximation of the solution of the SSP problems. The VS method attempts to get a low rank separated representation of the solution for SSP in a systematic enrichment manner. No iteration is performed at each enrichment step. In order to satisfy the inf-sup condition in the mixed formulation, we enrich the separated terms for the primal system variable at each enrichment step. For the SSP problems by regularization or penalty, we propose a more efficient variable-separation (VS) method, i.e., the variable-separation by penalty method. This can avoid further enrichment of the separated terms in the original mixed formulation. The computation of the variable-separation method decomposes into offline phase and online phase. Sparse low rank tensor approximation method is used to significantly improve the online computation efficiency when the number of separated terms is large. For the applications of SSP problems, we present three numerical examples to illustrate the performance of the proposed methods.

Dynamical signatures of isometric force control as a function of age, expertise, and task constraints.

PubMed

Vieluf, Solveig; Sleimen-Malkoun, Rita; Voelcker-Rehage, Claudia; Jirsa, Viktor; Reuter, Eva-Maria; Godde, Ben; Temprado, Jean-Jacques; Huys, Raoul

2017-07-01

From the conceptual and methodological framework of the dynamical systems approach, force control results from complex interactions of various subsystems yielding observable behavioral fluctuations, which comprise both deterministic (predictable) and stochastic (noise-like) dynamical components. Here, we investigated these components contributing to the observed variability in force control in groups of participants differing in age and expertise level. To this aim, young (18-25 yr) as well as late middle-aged (55-65 yr) novices and experts (precision mechanics) performed a force maintenance and a force modulation task. Results showed that whereas the amplitude of force variability did not differ across groups in the maintenance tasks, in the modulation task it was higher for late middle-aged novices than for experts and higher for both these groups than for young participants. Within both tasks and for all groups, stochastic fluctuations were lowest where the deterministic influence was smallest. However, although all groups showed similar dynamics underlying force control in the maintenance task, a group effect was found for deterministic and stochastic fluctuations in the modulation task. The latter findings imply that both components were involved in the observed group differences in the variability of force fluctuations in the modulation task. These findings suggest that between groups the general characteristics of the dynamics do not differ in either task and that force control is more affected by age than by expertise. However, expertise seems to counteract some of the age effects. NEW & NOTEWORTHY Stochastic and deterministic dynamical components contribute to force production. Dynamical signatures differ between force maintenance and cyclic force modulation tasks but hardly between age and expertise groups. Differences in both stochastic and deterministic components are associated with group differences in behavioral variability, and observed behavioral variability is more strongly task dependent than person dependent. Copyright © 2017 the American Physiological Society.

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yunlong; Wang, Aiping; Guo, Lei

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

Investigation of the stochastic nature of solar radiation for renewable resources management

NASA Astrophysics Data System (ADS)

Koudouris, Giannis; Dimitriadis, Panayiotis; Iliopoulou, Theano; Mamasis, Nikos; Koutsoyiannis, Demetris

2017-04-01

A detailed investigation of the variability of solar radiation can be proven useful towards more efficient and sustainable design of renewable resources systems. This variability is mainly caused from the regular seasonal and diurnal variation, as well as its stochastic nature of the atmospheric processes, i.e. sunshine duration. In this context, we analyze numerous observations in Greece (Hellenic National Meteorological Service; http://www.hnms.gr/) and around the globe (NASA SSE - Surface meteorology and Solar Energy; http://www.soda-pro.com/web-services/radiation/nasa-sse) and we investigate the long-term behaviour and double periodicity of the solar radiation process. Also, we apply a parsimonious double-cyclostationary stochastic model to a theoretical scenario of solar energy production for an island in the Aegean Sea. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

Casein Kinase II Regulation of the Hot1 Transcription Factor Promotes Stochastic Gene Expression*

PubMed Central

Burns, Laura T.; Wente, Susan R.

2014-01-01

In Saccharomyces cerevisiae, Hog1 MAPK is activated and induces a transcriptional program in response to hyperosmotic stress. Several Hog1-responsive genes exhibit stochastic transcription, resulting in cell-to-cell variability in mRNA and protein levels. However, the mechanisms governing stochastic gene activity are not fully defined. Here we uncover a novel role for casein kinase II (CK2) in the cellular response to hyperosmotic stress. CK2 interacts with and phosphorylates the Hot1 transcription factor; however, Hot1 phosphorylation is not sufficient for controlling the stochastic response. The CK2 protein itself is required to negatively regulate mRNA expression of Hot1-responsive genes and Hot1 enrichment at target promoters. Single-cell gene expression analysis reveals altered activation of Hot1-targeted STL1 in ck2 mutants, resulting in a bimodal to unimodal shift in expression. Together, this work reveals a novel CK2 function during the hyperosmotic stress response that promotes cell-to-cell variability in gene expression. PMID:24817120

Analysis of degree of nonlinearity and stochastic nature of HRV signal during meditation using delay vector variance method.

PubMed

Reddy, L Ram Gopal; Kuntamalla, Srinivas

2011-01-01

Heart rate variability analysis is fast gaining acceptance as a potential non-invasive means of autonomic nervous system assessment in research as well as clinical domains. In this study, a new nonlinear analysis method is used to detect the degree of nonlinearity and stochastic nature of heart rate variability signals during two forms of meditation (Chi and Kundalini). The data obtained from an online and widely used public database (i.e., MIT/BIH physionet database), is used in this study. The method used is the delay vector variance (DVV) method, which is a unified method for detecting the presence of determinism and nonlinearity in a time series and is based upon the examination of local predictability of a signal. From the results it is clear that there is a significant change in the nonlinearity and stochastic nature of the signal before and during the meditation (p value > 0.01). During Chi meditation there is a increase in stochastic nature and decrease in nonlinear nature of the signal. There is a significant decrease in the degree of nonlinearity and stochastic nature during Kundalini meditation.

Inflow forecasting model construction with stochastic time series for coordinated dam operation

NASA Astrophysics Data System (ADS)

Kim, T.; Jung, Y.; Kim, H.; Heo, J. H.

2014-12-01

Dam inflow forecasting is one of the most important tasks in dam operation for an effective water resources management and control. In general, dam inflow forecasting with stochastic time series model is possible to apply when the data is stationary because most of stochastic process based on stationarity. However, recent hydrological data cannot be satisfied the stationarity anymore because of climate change. Therefore a stochastic time series model, which can consider seasonality and trend in the data series, named SARIMAX(Seasonal Autoregressive Integrated Average with eXternal variable) model were constructed in this study. This SARIMAX model could increase the performance of stochastic time series model by considering the nonstationarity components and external variable such as precipitation. For application, the models were constructed for four coordinated dams on Han river in South Korea with monthly time series data. As a result, the models of each dam have similar performance and it would be possible to use the model for coordinated dam operation.Acknowledgement This research was supported by a grant 'Establishing Active Disaster Management System of Flood Control Structures by using 3D BIM Technique' [NEMA-NH-12-57] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of Korea.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization.

PubMed

Hartenfeller, Markus; Proschak, Ewgenij; Schüller, Andreas; Schneider, Gisbert

2008-07-01

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Fast smooth second-order sliding mode control for stochastic systems with enumerable coloured noises

NASA Astrophysics Data System (ADS)

Yang, Peng-fei; Fang, Yang-wang; Wu, You-li; Zhang, Dan-xu; Xu, Yang

2018-01-01

A fast smooth second-order sliding mode control is presented for a class of stochastic systems driven by enumerable Ornstein-Uhlenbeck coloured noises with time-varying coefficients. Instead of treating the noise as bounded disturbance, the stochastic control techniques are incorporated into the design of the control. The finite-time mean-square practical stability and finite-time mean-square practical reachability are first introduced. Then the prescribed sliding variable dynamic is presented. The sufficient condition guaranteeing its finite-time convergence is given and proved using stochastic Lyapunov-like techniques. The proposed sliding mode controller is applied to a second-order nonlinear stochastic system. Simulation results are given comparing with smooth second-order sliding mode control to validate the analysis.

Understanding Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.

PubMed

Nguyen, A; Yosinski, J; Clune, J

2016-01-01

The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search mitigates this problem by encouraging exploration in all interesting directions by replacing the performance objective with a reward for novel behaviors. This reward for novel behaviors has traditionally required a human-crafted, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a DNN-based novelty search in the image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g., churches, mosques, obelisks, etc.). Here, we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: for example, producing intelligent software, robot controllers, optimized physical components, and art.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2016-04-01

In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.

FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.

PubMed

Li, Pu; Chen, Bing

2011-04-01

Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

Forecasting the stochastic demand for inpatient care: the case of the Greek national health system.

PubMed

Boutsioli, Zoe

2010-08-01

The aim of this study is to estimate the unexpected demand of Greek public hospitals. A multivariate model with four explanatory variables is used. These are as follows: the weekend effect, the duty effect, the summer holiday and the official holiday. The method of the ordinary least squares is used to estimate the impact of these variables on the daily hospital emergency admissions series. The forecasted residuals of hospital regressions for each year give the estimated stochastic demand. Daily emergency admissions decline during weekends, summer months and official holidays, and increase on duty hospital days. Stochastic hospital demand varies both among hospitals and over the five-year time period under investigation. Variations among hospitals are larger than time variations. Hospital managers and health policy-makers can be availed by forecasting the future flows of emergent patients. The benefit can be both at managerial and economical level. More advanced models including additional daily variables such as the weather forecasts could provide more accurate estimations.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks.

PubMed

Navarro Jimenez, M; Le Maître, O P; Knio, O M

2016-12-28

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Random function representation of stationary stochastic vector processes for probability density evolution analysis of wind-induced structures

NASA Astrophysics Data System (ADS)

Liu, Zhangjun; Liu, Zenghui

2018-06-01

This paper develops a hybrid approach of spectral representation and random function for simulating stationary stochastic vector processes. In the proposed approach, the high-dimensional random variables, included in the original spectral representation (OSR) formula, could be effectively reduced to only two elementary random variables by introducing the random functions that serve as random constraints. Based on this, a satisfactory simulation accuracy can be guaranteed by selecting a small representative point set of the elementary random variables. The probability information of the stochastic excitations can be fully emerged through just several hundred of sample functions generated by the proposed approach. Therefore, combined with the probability density evolution method (PDEM), it could be able to implement dynamic response analysis and reliability assessment of engineering structures. For illustrative purposes, a stochastic turbulence wind velocity field acting on a frame-shear-wall structure is simulated by constructing three types of random functions to demonstrate the accuracy and efficiency of the proposed approach. Careful and in-depth studies concerning the probability density evolution analysis of the wind-induced structure have been conducted so as to better illustrate the application prospects of the proposed approach. Numerical examples also show that the proposed approach possesses a good robustness.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

DOE PAGES

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-23

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes thatmore » the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. Here, a sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.« less

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

NASA Astrophysics Data System (ADS)

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-01

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Stochastic variation in avian survival rates: Life-history predictions, population consequences, and the potential responses to human perturbations and climate change

USGS Publications Warehouse

Schmutz, Joel A.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Stochastic variation in survival rates is expected to decrease long-term population growth rates. This expectation influences both life-history theory and the conservation of species. From this expectation, Pfister (1998) developed the important life-history prediction that natural selection will have minimized variability in those elements of the annual life cycle (such as adult survival rate) with high sensitivity. This prediction has not been rigorously evaluated for bird populations, in part due to statistical difficulties related to variance estimation. I here overcome these difficulties, and in an analysis of 62 populations, I confirm her prediction by showing a negative relationship between the proportional sensitivity (elasticity) of adult survival and the proportional variance (CV) of adult survival. However, several species deviated significantly from this expectation, with more process variance in survival than predicted. For instance, projecting the magnitude of process variance in annual survival for American redstarts (Setophaga ruticilla) for 25 years resulted in a 44% decline in abundance without assuming any change in mean survival rate. For most of these species with high process variance, recent changes in harvest, habitats, or changes in climate patterns are the likely sources of environmental variability causing this variability in survival. Because of climate change, environmental variability is increasing on regional and global scales, which is expected to increase stochasticity in vital rates of species. Increased stochasticity in survival will depress population growth rates, and this result will magnify the conservation challenges we face.

Stochastic E2F activation and reconciliation of phenomenological cell-cycle models.

PubMed

Lee, Tae J; Yao, Guang; Bennett, Dorothy C; Nevins, Joseph R; You, Lingchong

2010-09-21

The transition of the mammalian cell from quiescence to proliferation is a highly variable process. Over the last four decades, two lines of apparently contradictory, phenomenological models have been proposed to account for such temporal variability. These include various forms of the transition probability (TP) model and the growth control (GC) model, which lack mechanistic details. The GC model was further proposed as an alternative explanation for the concept of the restriction point, which we recently demonstrated as being controlled by a bistable Rb-E2F switch. Here, through a combination of modeling and experiments, we show that these different lines of models in essence reflect different aspects of stochastic dynamics in cell cycle entry. In particular, we show that the variable activation of E2F can be described by stochastic activation of the bistable Rb-E2F switch, which in turn may account for the temporal variability in cell cycle entry. Moreover, we show that temporal dynamics of E2F activation can be recast into the frameworks of both the TP model and the GC model via parameter mapping. This mapping suggests that the two lines of phenomenological models can be reconciled through the stochastic dynamics of the Rb-E2F switch. It also suggests a potential utility of the TP or GC models in defining concise, quantitative phenotypes of cell physiology. This may have implications in classifying cell types or states.

Nine Hundred Years of Weekly Streamflows: Stochastic Downscaling of Ensemble Tree-Ring Reconstructions

NASA Astrophysics Data System (ADS)

Sauchyn, David; Ilich, Nesa

2017-11-01

We combined the methods and advantages of stochastic hydrology and paleohydrology to estimate 900 years of weekly flows for the North and South Saskatchewan Rivers at Edmonton and Medicine Hat, Alberta, respectively. Regression models of water-year streamflow were constructed using historical naturalized flow data and a pool of 196 tree-ring (earlywood, latewood, and annual) ring-width chronologies from 76 sites. The tree-ring models accounted for up to 80% of the interannual variability in historical naturalized flows. We developed a new algorithm for generating stochastic time series of weekly flows constrained by the statistical properties of both the historical record and proxy streamflow data, and by the necessary condition that weekly flows correlate between the end of a year and the start of the next. A second innovation, enabled by the density of our tree-ring network, is to derive the paleohydrology from an ensemble of 100 statistically significant reconstructions at each gauge. Using paleoclimatic data to generate long series of weekly flow estimates augments the short historical record with an expanded range of hydrologic variability, including sequences of wet and dry years of greater length and severity. This unique hydrometric time series will enable evaluation of the reliability of current water supply and management systems given the range of hydroclimatic variability and extremes contained in the stochastic paleohydrology. It also could inform evaluation of the uncertainty in climate model projections, given that internal hydroclimatic variability is the dominant source of uncertainty.

Stochastic Approximation Methods for Latent Regression Item Response Models

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2010-01-01

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…

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 ...

Individualism in plant populations: using stochastic differential equations to model individual neighbourhood-dependent plant growth.

PubMed

Lv, Qiming; Schneider, Manuel K; Pitchford, Jonathan W

2008-08-01

We study individual plant growth and size hierarchy formation in an experimental population of Arabidopsis thaliana, within an integrated analysis that explicitly accounts for size-dependent growth, size- and space-dependent competition, and environmental stochasticity. It is shown that a Gompertz-type stochastic differential equation (SDE) model, involving asymmetric competition kernels and a stochastic term which decreases with the logarithm of plant weight, efficiently describes individual plant growth, competition, and variability in the studied population. The model is evaluated within a Bayesian framework and compared to its deterministic counterpart, and to several simplified stochastic models, using distributional validation. We show that stochasticity is an important determinant of size hierarchy and that SDE models outperform the deterministic model if and only if structural components of competition (asymmetry; size- and space-dependence) are accounted for. Implications of these results are discussed in the context of plant ecology and in more general modelling situations.

Nonholonomic relativistic diffusion and exact solutions for stochastic Einstein spaces

NASA Astrophysics Data System (ADS)

Vacaru, S. I.

2012-03-01

We develop an approach to the theory of nonholonomic relativistic stochastic processes in curved spaces. The Itô and Stratonovich calculus are formulated for spaces with conventional horizontal (holonomic) and vertical (nonholonomic) splitting defined by nonlinear connection structures. Geometric models of the relativistic diffusion theory are elaborated for nonholonomic (pseudo) Riemannian manifolds and phase velocity spaces. Applying the anholonomic deformation method, the field equations in Einstein's gravity and various modifications are formally integrated in general forms, with generic off-diagonal metrics depending on some classes of generating and integration functions. Choosing random generating functions we can construct various classes of stochastic Einstein manifolds. We show how stochastic gravitational interactions with mixed holonomic/nonholonomic and random variables can be modelled in explicit form and study their main geometric and stochastic properties. Finally, the conditions when non-random classical gravitational processes transform into stochastic ones and inversely are analyzed.

Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book.

PubMed

Sadygov, Rovshan G; Cociorva, Daniel; Yates, John R

2004-12-01

Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability-based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.

Stochastic Calculus and Differential Equations for Physics and Finance

NASA Astrophysics Data System (ADS)

McCauley, Joseph L.

2013-02-01

1. Random variables and probability distributions; 2. Martingales, Markov, and nonstationarity; 3. Stochastic calculus; 4. Ito processes and Fokker-Planck equations; 5. Selfsimilar Ito processes; 6. Fractional Brownian motion; 7. Kolmogorov's PDEs and Chapman-Kolmogorov; 8. Non Markov Ito processes; 9. Black-Scholes, martingales, and Feynman-Katz; 10. Stochastic calculus with martingales; 11. Statistical physics and finance, a brief history of both; 12. Introduction to new financial economics; 13. Statistical ensembles and time series analysis; 14. Econometrics; 15. Semimartingales; References; Index.

Optimal Search Strategy for the Definition of a DNAPL Source

DTIC Science & Technology

2009-08-01

29. Flow field results for stochastic model (colored contours) and potentiometric map created by hydrogeologist using well water level measurements...potentiometric map created by hydrogeologist using well water level measurements (black contours). 5.1.3. Source search algorithm Figure 30 shows the 15...and C. D. Tankersley, “Forecasting piezometric head levels in the Floridian aquifer: A Kalman filtering approach”, Water Resources Research, 29(11

Stochastic dynamics and combinatorial optimization

NASA Astrophysics Data System (ADS)

Ovchinnikov, Igor V.; Wang, Kang L.

2017-11-01

Natural dynamics is often dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, etc., that exhibit scale-free statistics much in the spirit of the logarithmic Ritcher scale for earthquake magnitudes. On phase diagrams, stochastic dynamical systems (DSs) exhibiting this type of dynamics belong to the finite-width phase (N-phase for brevity) that precedes ordinary chaotic behavior and that is known under such names as noise-induced chaos, self-organized criticality, dynamical complexity, etc. Within the recently proposed supersymmetric theory of stochastic dynamics, the N-phase can be roughly interpreted as the noise-induced “overlap” between integrable and chaotic deterministic dynamics. As a result, the N-phase dynamics inherits the properties of the both. Here, we analyze this unique set of properties and conclude that the N-phase DSs must naturally be the most efficient optimizers: on one hand, N-phase DSs have integrable flows with well-defined attractors that can be associated with candidate solutions and, on the other hand, the noise-induced attractor-to-attractor dynamics in the N-phase is effectively chaotic or aperiodic so that a DS must avoid revisiting solutions/attractors thus accelerating the search for the best solution. Based on this understanding, we propose a method for stochastic dynamical optimization using the N-phase DSs. This method can be viewed as a hybrid of the simulated and chaotic annealing methods. Our proposition can result in a new generation of hardware devices for efficient solution of various search and/or combinatorial optimization problems.

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.

Stochastic sensitivity analysis of the variability of dynamics and transition to chaos in the business cycles model

NASA Astrophysics Data System (ADS)

Bashkirtseva, Irina; Ryashko, Lev; Ryazanova, Tatyana

2018-01-01

A problem of mathematical modeling of complex stochastic processes in macroeconomics is discussed. For the description of dynamics of income and capital stock, the well-known Kaldor model of business cycles is used as a basic example. The aim of the paper is to give an overview of the variety of stochastic phenomena which occur in Kaldor model forced by additive and parametric random noise. We study a generation of small- and large-amplitude stochastic oscillations, and their mixed-mode intermittency. To analyze these phenomena, we suggest a constructive approach combining the study of the peculiarities of deterministic phase portrait, and stochastic sensitivity of attractors. We show how parametric noise can stabilize the unstable equilibrium and transform dynamics of Kaldor system from order to chaos.

Influence of Microphysical Variability on Stochastic Condensation in Turbulent Clouds

NASA Astrophysics Data System (ADS)

Desai, N.; Chandrakar, K. K.; Chang, K.; Glienke, S.; Cantrell, W. H.; Fugal, J. P.; Shaw, R. A.

2017-12-01

We investigate the influence of variability in droplet number concentration and radius on the evolution of cloud droplet size distributions. Measurements are made on the centimeter scale using digitial inline holography, both in a controlled laboratory setting and in the field using HOLODEC measurements from CSET. We created steady state cloud conditions in the laboratory Pi Chamber, in which a turbulent cloud can be sustained for long periods of time. Using holographic imaging, we directly observe the variations in local number concentration and droplet size distribution and, thereby, the integral radius. We interpret the measurements in the context of stochastic condensation theory to determine how fluctuations in integral radius contribute to droplet growth. We find that the variability in integral radius is primarily driven by variations in the droplet number concentration and not the droplet radius. This variability does not contribute significantly to the mean droplet growth rate, but contributes significantly to the rate of increase of the size distribution width. We compare these results with in-situ measurements and find evidence for microphysical signatures of stochastic condensation. The results suggest that supersaturation fluctuations lead to broader size distributions and allow droplets to reach the collision-coalescence stage.

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lei; Fang, Hongwei; Xu, Xingya

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). We formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions, tomore » obtain statistical descriptions of the P concentration and retention in the TGR. The correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. This study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.« less

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lei; Fang, Hongwei; Xu, Xingya

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). Here, we formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions,more » to obtain statistical descriptions of the P concentration and retention in the TGR. Furthermore, the correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. Our study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.« less

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

DOE PAGES

Huang, Lei; Fang, Hongwei; Xu, Xingya; ...

2017-08-01

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). Here, we formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions,more » to obtain statistical descriptions of the P concentration and retention in the TGR. Furthermore, the correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. Our study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.« less

Hamiltonian chaos acts like a finite energy reservoir: accuracy of the Fokker-Planck approximation.

PubMed

Riegert, Anja; Baba, Nilüfer; Gelfert, Katrin; Just, Wolfram; Kantz, Holger

2005-02-11

The Hamiltonian dynamics of slow variables coupled to fast degrees of freedom is modeled by an effective stochastic differential equation. Formal perturbation expansions, involving a Markov approximation, yield a Fokker-Planck equation in the slow subspace which respects conservation of energy. A detailed numerical and analytical analysis of suitable model systems demonstrates the feasibility of obtaining the system specific drift and diffusion terms and the accuracy of the stochastic approximation on all time scales. Non-Markovian and non-Gaussian features of the fast variables are negligible.

Optical EVPA rotations in blazars: testing a stochastic variability model with RoboPol data

NASA Astrophysics Data System (ADS)

Kiehlmann, S.; Blinov, D.; Pearson, T. J.; Liodakis, I.

2017-12-01

We identify rotations of the polarization angle in a sample of blazars observed for three seasons with the RoboPol instrument. A simplistic stochastic variability model is tested against this sample of rotation events. The model is capable of producing samples of rotations with parameters similar to the observed ones, but fails to reproduce the polarization fraction at the same time. Even though we can neither accept nor conclusively reject the model, we point out various aspects of the observations that are fully consistent with a random walk process.

Effect of multiplicative noise on stationary stochastic process

NASA Astrophysics Data System (ADS)

Kargovsky, A. V.; Chikishev, A. Yu.; Chichigina, O. A.

2018-03-01

An open system that can be analyzed using the Langevin equation with multiplicative noise is considered. The stationary state of the system results from a balance of deterministic damping and random pumping simulated as noise with controlled periodicity. The dependence of statistical moments of the variable that characterizes the system on parameters of the problem is studied. A nontrivial decrease in the mean value of the main variable with an increase in noise stochasticity is revealed. Applications of the results in several physical, chemical, biological, and technical problems of natural and humanitarian sciences are discussed.

Computationally efficient stochastic optimization using multiple realizations

NASA Astrophysics Data System (ADS)

Bayer, P.; Bürger, C. M.; Finkel, M.

2008-02-01

The presented study is concerned with computationally efficient methods for solving stochastic optimization problems involving multiple equally probable realizations of uncertain parameters. A new and straightforward technique is introduced that is based on dynamically ordering the stack of realizations during the search procedure. The rationale is that a small number of critical realizations govern the output of a reliability-based objective function. By utilizing a problem, which is typical to designing a water supply well field, several variants of this "stack ordering" approach are tested. The results are statistically assessed, in terms of optimality and nominal reliability. This study demonstrates that the simple ordering of a given number of 500 realizations while applying an evolutionary search algorithm can save about half of the model runs without compromising the optimization procedure. More advanced variants of stack ordering can, if properly configured, save up to more than 97% of the computational effort that would be required if the entire number of realizations were considered. The findings herein are promising for similar problems of water management and reliability-based design in general, and particularly for non-convex problems that require heuristic search techniques.

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

A stochastic diffusion process for Lochner's generalized Dirichlet distribution

DOE PAGES

Bakosi, J.; Ristorcelli, J. R.

2013-10-01

The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability of N stochastic variables with Lochner’s generalized Dirichlet distribution as its asymptotic solution. Individual samples of a discrete ensemble, obtained from the system of stochastic differential equations, equivalent to the Fokker-Planck equation developed here, satisfy a unit-sum constraint at all times and ensure a bounded sample space, similarly to the process developed in for the Dirichlet distribution. Consequently, the generalized Dirichlet diffusion process may be used to represent realizations of a fluctuating ensemble of N variables subject to a conservation principle.more » Compared to the Dirichlet distribution and process, the additional parameters of the generalized Dirichlet distribution allow a more general class of physical processes to be modeled with a more general covariance matrix.« less

Time Evolution of the Dynamical Variables of a Stochastic System.

ERIC Educational Resources Information Center

de la Pena, L.

1980-01-01

By using the method of moments, it is shown that several important and apparently unrelated theorems describing average properties of stochastic systems are in fact particular cases of a general law; this method is applied to generalize the virial theorem and the fluctuation-dissipation theorem to the time-dependent case. (Author/SK)

Seed availability constrains plant species sorting along a soil fertility gradient

Treesearch

Bryan L. Foster; Erin J. Questad; Cathy D. Collins; Cheryl A. Murphy; Timothy L. Dickson; Val H. Smith

2011-01-01

1. Spatial variation in species composition within and among communities may be caused by deterministic, niche-based species sorting in response to underlying environmental heterogeneity as well as by stochastic factors such as dispersal limitation and variable species pools. An important goal in ecology is to reconcile deterministic and stochastic perspectives of...

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...

Intrusive Method for Uncertainty Quantification in a Multiphase Flow Solver

NASA Astrophysics Data System (ADS)

Turnquist, Brian; Owkes, Mark

2016-11-01

Uncertainty quantification (UQ) is a necessary, interesting, and often neglected aspect of fluid flow simulations. To determine the significance of uncertain initial and boundary conditions, a multiphase flow solver is being created which extends a single phase, intrusive, polynomial chaos scheme into multiphase flows. Reliably estimating the impact of input uncertainty on design criteria can help identify and minimize unwanted variability in critical areas, and has the potential to help advance knowledge in atomizing jets, jet engines, pharmaceuticals, and food processing. Use of an intrusive polynomial chaos method has been shown to significantly reduce computational cost over non-intrusive collocation methods such as Monte-Carlo. This method requires transforming the model equations into a weak form through substitution of stochastic (random) variables. Ultimately, the model deploys a stochastic Navier Stokes equation, a stochastic conservative level set approach including reinitialization, as well as stochastic normals and curvature. By implementing these approaches together in one framework, basic problems may be investigated which shed light on model expansion, uncertainty theory, and fluid flow in general. NSF Grant Number 1511325.

Supermassive Black Hole Binary Candidates from the Pan-STARRS1 Medium Deep Survey

NASA Astrophysics Data System (ADS)

Liu, Tingting; Gezari, Suvi

2018-01-01

Supermassive black hole binaries (SMBHBs) should be a common product of the hierarchal growth of galaxies and gravitational wave sources at nano-Hz frequencies. We have performed a systematic search in the Pan-STARRS1 Medium Deep Survey for periodically varying quasars, which are predicted manifestations of SMBHBs, and identified 26 candidates that are periodically varying on the timescale of ~300-1000 days over the 4-year baseline of MDS. We continue to monitor them with the Discovery Channel Telescope and the LCO network telescopes and thus are able to extend the baseline to 3-8 cycles and break false positive signals due to stochastic, normal quasar variability. From our imaging campaign, five candidates show persistent periodic variability and remain strong SMBHB candidates for follow-up observations. We calculate the cumulative number rate of SMBHBs and compare with previous work. We also compare the gravitational wave strain amplitudes of the candidates with the capability of pulsar timing arrays and discuss the future capabilities to detect periodic quasar and SMBHB candidates with the Large Synoptic Survey Telescope.

Fast smooth second-order sliding mode control for systems with additive colored noises.

PubMed

Yang, Pengfei; Fang, Yangwang; Wu, Youli; Liu, Yunxia; Zhang, Danxu

2017-01-01

In this paper, a fast smooth second-order sliding mode control is presented for a class of stochastic systems with enumerable Ornstein-Uhlenbeck colored noises. The finite-time mean-square practical stability and finite-time mean-square practical reachability are first introduced. Instead of treating the noise as bounded disturbance, the stochastic control techniques are incorporated into the design of the controller. The finite-time convergence of the prescribed sliding variable dynamics system is proved by using stochastic Lyapunov-like techniques. Then the proposed sliding mode controller is applied to a second-order nonlinear stochastic system. Simulation results are presented comparing with smooth second-order sliding mode control to validate the analysis.

Doubly stochastic radial basis function methods

NASA Astrophysics Data System (ADS)

Yang, Fenglian; Yan, Liang; Ling, Leevan

2018-06-01

We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).

Evolution in health and medicine Sackler colloquium: Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease.

PubMed

Feinberg, Andrew P; Irizarry, Rafael A

2010-01-26

Neo-Darwinian evolutionary theory is based on exquisite selection of phenotypes caused by small genetic variations, which is the basis of quantitative trait contribution to phenotype and disease. Epigenetics is the study of nonsequence-based changes, such as DNA methylation, heritable during cell division. Previous attempts to incorporate epigenetics into evolutionary thinking have focused on Lamarckian inheritance, that is, environmentally directed epigenetic changes. Here, we propose a new non-Lamarckian theory for a role of epigenetics in evolution. We suggest that genetic variants that do not change the mean phenotype could change the variability of phenotype; and this could be mediated epigenetically. This inherited stochastic variation model would provide a mechanism to explain an epigenetic role of developmental biology in selectable phenotypic variation, as well as the largely unexplained heritable genetic variation underlying common complex disease. We provide two experimental results as proof of principle. The first result is direct evidence for stochastic epigenetic variation, identifying highly variably DNA-methylated regions in mouse and human liver and mouse brain, associated with development and morphogenesis. The second is a heritable genetic mechanism for variable methylation, namely the loss or gain of CpG dinucleotides over evolutionary time. Finally, we model genetically inherited stochastic variation in evolution, showing that it provides a powerful mechanism for evolutionary adaptation in changing environments that can be mediated epigenetically. These data suggest that genetically inherited propensity to phenotypic variability, even with no change in the mean phenotype, substantially increases fitness while increasing the disease susceptibility of a population with a changing environment.

Population viability of Pediocactus bradyi (Cactaceae) in a changing climate.

PubMed

Shryock, Daniel F; Esque, Todd C; Hughes, Lee

2014-11-01

A key question concerns the vulnerability of desert species adapted to harsh, variable climates to future climate change. Evaluating this requires coupling long-term demographic models with information on past and projected future climates. We investigated climatic drivers of population growth using a 22-yr demographic model for Pediocactus bradyi, an endangered cactus in northern Arizona. We used a matrix model to calculate stochastic population growth rates (λs) and the relative influences of life-cycle transitions on population growth. Regression models linked population growth with climatic variability, while stochastic simulations were used to (1) understand how predicted increases in drought frequency and extreme precipitation would affect λs, and (2) quantify variability in λs based on temporal replication of data. Overall λs was below unity (0.961). Population growth was equally influenced by fecundity and survival and significantly correlated with increased annual precipitation and higher winter temperatures. Stochastic simulations increasing the probability of drought and extreme precipitation reduced λs, but less than simulations increasing the probability of drought alone. Simulations varying the temporal replication of data suggested 14 yr were required for accurate λs estimates. Pediocactus bradyi may be vulnerable to increases in the frequency and intensity of extreme climatic events, particularly drought. Biotic interactions resulting in low survival during drought years outweighed increased seedling establishment following heavy precipitation. Climatic extremes beyond historical ranges of variability may threaten rare desert species with low population growth rates and therefore high susceptibility to stochastic events. © 2014 Botanical Society of America, Inc.

Sensitivity curves for searches for gravitational-wave backgrounds

NASA Astrophysics Data System (ADS)

Thrane, Eric; Romano, Joseph D.

2013-12-01

We propose a graphical representation of detector sensitivity curves for stochastic gravitational-wave backgrounds that takes into account the increase in sensitivity that comes from integrating over frequency in addition to integrating over time. This method is valid for backgrounds that have a power-law spectrum in the analysis band. We call these graphs “power-law integrated curves.” For simplicity, we consider cross-correlation searches for unpolarized and isotropic stochastic backgrounds using two or more detectors. We apply our method to construct power-law integrated sensitivity curves for second-generation ground-based detectors such as Advanced LIGO, space-based detectors such as LISA and the Big Bang Observer, and timing residuals from a pulsar timing array. The code used to produce these plots is available at https://dcc.ligo.org/LIGO-P1300115/public for researchers interested in constructing similar sensitivity curves.

Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Moroz, I.; Palmer, T.

2015-12-01

It is now acknowledged that representing model uncertainty in atmospheric simulators is essential for the production of reliable probabilistic ensemble forecasts, and a number of different techniques have been proposed for this purpose. Stochastic convection parameterization schemes use random numbers to represent the difference between a deterministic parameterization scheme and the true atmosphere, accounting for the unresolved sub grid-scale variability associated with convective clouds. An alternative approach varies the values of poorly constrained physical parameters in the model to represent the uncertainty in these parameters. This study presents new perturbed parameter schemes for use in the European Centre for Medium Range Weather Forecasts (ECMWF) convection scheme. Two types of scheme are developed and implemented. Both schemes represent the joint uncertainty in four of the parameters in the convection parametrisation scheme, which was estimated using the Ensemble Prediction and Parameter Estimation System (EPPES). The first scheme developed is a fixed perturbed parameter scheme, where the values of uncertain parameters are changed between ensemble members, but held constant over the duration of the forecast. The second is a stochastically varying perturbed parameter scheme. The performance of these schemes was compared to the ECMWF operational stochastic scheme, Stochastically Perturbed Parametrisation Tendencies (SPPT), and to a model which does not represent uncertainty in convection. The skill of probabilistic forecasts made using the different models was evaluated. While the perturbed parameter schemes improve on the stochastic parametrisation in some regards, the SPPT scheme outperforms the perturbed parameter approaches when considering forecast variables that are particularly sensitive to convection. Overall, SPPT schemes are the most skilful representations of model uncertainty due to convection parametrisation. Reference: H. M. Christensen, I. M. Moroz, and T. N. Palmer, 2015: Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization. J. Atmos. Sci., 72, 2525-2544.

Uncertainty quantification for personalized analyses of human proximal femurs.

PubMed

Wille, Hagen; Ruess, Martin; Rank, Ernst; Yosibash, Zohar

2016-02-29

Computational models for the personalized analysis of human femurs contain uncertainties in bone material properties and loads, which affect the simulation results. To quantify the influence we developed a probabilistic framework based on polynomial chaos (PC) that propagates stochastic input variables through any computational model. We considered a stochastic E-ρ relationship and a stochastic hip contact force, representing realistic variability of experimental data. Their influence on the prediction of principal strains (ϵ1 and ϵ3) was quantified for one human proximal femur, including sensitivity and reliability analysis. Large variabilities in the principal strain predictions were found in the cortical shell of the femoral neck, with coefficients of variation of ≈40%. Between 60 and 80% of the variance in ϵ1 and ϵ3 are attributable to the uncertainty in the E-ρ relationship, while ≈10% are caused by the load magnitude and 5-30% by the load direction. Principal strain directions were unaffected by material and loading uncertainties. The antero-superior and medial inferior sides of the neck exhibited the largest probabilities for tensile and compression failure, however all were very small (pf<0.001). In summary, uncertainty quantification with PC has been demonstrated to efficiently and accurately describe the influence of very different stochastic inputs, which increases the credibility and explanatory power of personalized analyses of human proximal femurs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Upper Limits on a Stochastic Gravitational-Wave Background Using LIGO and Virgo Interferometers at 600-1000 Hz

NASA Technical Reports Server (NTRS)

Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adhikari, R.; Affeldt, C.;

Upper limits on a stochastic gravitational-wave background using LIGO and Virgo interferometers at 600-1000 Hz

NASA Astrophysics Data System (ADS)

Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M.; Accadia, T.; Acernese, F.; Adams, C.; Adhikari, R.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Ajith, P.; Allen, B.; Amador Ceron, E.; Amariutei, D.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Arain, M. A.; Araya, M. C.; Aston, S. M.; Astone, P.; Atkinson, D.; Aufmuth, P.; Aulbert, C.; Aylott, B. E.; Babak, S.; Baker, P.; Ballardin, G.; Ballmer, S.; Barayoga, J. C. B.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Bastarrika, M.; Basti, A.; Batch, J.; Bauchrowitz, J.; Bauer, Th. S.; Bebronne, M.; Beck, D.; Behnke, B.; Bejger, M.; Beker, M. G.; Bell, A. S.; Belletoile, A.; Belopolski, I.; Benacquista, M.; Berliner, J. M.; Bertolini, A.; Betzwieser, J.; Beveridge, N.; Beyersdorf, P. T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biswas, R.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bland, B.; Blom, M.; Bock, O.; Bodiya, T. P.; Bogan, C.; Bondarescu, R.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, S.; Bosi, L.; Bouhou, B.; Braccini, S.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Breyer, J.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Britzger, M.; Brooks, A. F.; Brown, D. A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burguet–Castell, J.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Calloni, E.; Camp, J. B.; Campsie, P.; Cannizzo, J.; Cannon, K.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cesarini, E.; Chaibi, O.; Chalermsongsak, T.; Charlton, P.; Chassande-Mottin, E.; Chelkowski, S.; Chen, W.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chua, S. S. Y.; Chung, C. T. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, D. E.; Clark, J.; Clayton, J. H.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colacino, C. N.; Colas, J.; Colla, A.; Colombini, M.; Conte, A.; Conte, R.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corsi, A.; Costa, C. A.; Coughlin, M.; Coulon, J.-P.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Creighton, J. D. E.; Creighton, T. D.; Cruise, A. M.; Cumming, A.; Cunningham, L.; Cuoco, E.; Cutler, R. M.; Dahl, K.; Danilishin, S. L.; Dannenberg, R.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daudert, B.; Daveloza, H.; Davier, M.; Daw, E. J.; Day, R.; Dayanga, T.; De Rosa, R.; DeBra, D.; Debreczeni, G.; Del Pozzo, W.; del Prete, M.; Dent, T.; Dergachev, V.; DeRosa, R.; DeSalvo, R.; Dhurandhar, S.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Paolo Emilio, M.; Di Virgilio, A.; Díaz, M.; Dietz, A.; Donovan, F.; Dooley, K. L.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dumas, J.-C.; Dwyer, S.; Eberle, T.; Edgar, M.; Edwards, M.; Effler, A.; Ehrens, P.; Endrőczi, G.; Engel, R.; Etzel, T.; Evans, K.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Fan, Y.; Farr, B. F.; Fazi, D.; Fehrmann, H.; Feldbaum, D.; Feroz, F.; Ferrante, I.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Flanigan, M.; Foley, S.; Forsi, E.; Forte, L. A.; Fotopoulos, N.; Fournier, J.-D.; Franc, J.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, M.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Friedrich, D.; Fritschel, P.; Frolov, V. V.; Fujimoto, M.-K.; Fulda, P. J.; Fyffe, M.; Gair, J.; Galimberti, M.; Gammaitoni, L.; Garcia, J.; Garufi, F.; Gáspár, M. E.; Gemme, G.; Geng, R.; Genin, E.; Gennai, A.; Gergely, L. Á.; Ghosh, S.; Giaime, J. A.; Giampanis, S.; Giardina, K. D.; Giazotto, A.; Gil-Casanova, S.; Gill, C.; Gleason, J.; Goetz, E.; Goggin, L. M.; González, G.; Gorodetsky, M. L.; Goßler, S.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Gray, N.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Greverie, C.; Grosso, R.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guido, C..; Gupta, R.; Gustafson, E. K.; Gustafson, R.; Ha, T.; Hallam, J. M.; Hammer, D.; Hammond, G.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hartman, M. T.; Haughian, K.; Hayama, K.; Hayau, J.-F.; Heefner, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hendry, M. A.; Heng, I. S.; Heptonstall, A. W.; Herrera, V.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Holtrop, M.; Hong, T.; Hooper, S.; Hosken, D. J.; Hough, J.; Howell, E. J.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Izumi, K.; Jacobson, M.; James, E.; Jang, Y. J.; Jaranowski, P.; Jesse, E.; Johnson, W. W.; Jones, D. I.; Jones, G.; Jones, R.; Ju, L.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kasturi, R.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kawabe, K.; Kawamura, S.; Kawazoe, F.; Kelley, D.; Kells, W.; Keppel, D. G.; Keresztes, Z.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, B. K.; Kim, C.; Kim, H.; Kim, K.; Kim, N.; Kim, Y. M.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D.; Kranz, O.; Kringel, V.; Krishnamurthy, S.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, R.; Kwee, P.; Lam, P. K.; Landry, M.; Lantz, B.; Lastzka, N.; Lawrie, C.; Lazzarini, A.; Leaci, P.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Leong, J. R.; Leonor, I.; Leroy, N.; Letendre, N.; Li, J.; Li, T. G. F.; Liguori, N.; Lindquist, P. E.; Liu, Y.; Liu, Z.; Lockerbie, N. A.; Lodhia, D.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Luan, J.; Lubinski, M.; Lück, H.; Lundgren, A. P.; Macdonald, E.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Mageswaran, M.; Mailand, K.; Majorana, E.; Maksimovic, I.; Man, N.; Mandel, I.; Mandic, V.; Mantovani, M.; Marandi, A.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Marx, J. N.; Mason, K.; Masserot, A.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; McKechan, D. J. A.; McWilliams, S.; Meadors, G. D.; Mehmet, M.; Meier, T.; Melatos, A.; Melissinos, A. C.; Mendell, G.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyer, M. S.; Miao, H.; Michel, C.; Milano, L.; Miller, J.; Minenkov, Y.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Miyakawa, O.; Moe, B.; Mohan, M.; Mohanty, S. D.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morgia, A.; Mori, T.; Morriss, S. R.; Mosca, S.; Mossavi, K.; Mours, B.; Mow–Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Müller-Ebhardt, H.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nash, T.; Naticchioni, L.; Necula, V.; Nelson, J.; Neri, I.; Newton, G.; Nguyen, T.; Nishizawa, A.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ott, C. D.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Page, A.; Pagliaroli, G.; Palladino, L.; Palomba, C.; Pan, Y.; Pankow, C.; Paoletti, F.; Papa, M. A.; Parisi, M.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patel, P.; Pedraza, M.; Peiris, P.; Pekowsky, L.; Penn, S.; Perreca, A.; Persichetti, G.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pietka, M.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Pletsch, H. J.; Plissi, M. V.; Poggiani, R.; Pöld, J.; Postiglione, F.; Prato, M.; Predoi, V.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prix, R.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Quetschke, V.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Rakhmanov, M.; Rankins, B.; Rapagnani, P.; Raymond, V.; Re, V.; Redwine, K.; Reed, C. M.; Reed, T.; Regimbau, T.; Reid, S.; Reitze, D. H.; Ricci, F.; Riesen, R.; Riles, K.; Robertson, N. A.; Robinet, F.; Robinson, C.; Robinson, E. L.; Rocchi, A.; Roddy, S.; Rodriguez, C.; Rodruck, M.; Rolland, L.; Rollins, J. G.; Romano, J. D.; Romano, R.; Romie, J. H.; Rosińska, D.; Röver, C.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sainathan, P.; Salemi, F.; Sammut, L.; Sandberg, V.; Sannibale, V.; Santamaría, L.; Santiago-Prieto, I.; Santostasi, G.; Sassolas, B.; Sathyaprakash, B. S.; Sato, S.; Saulson, P. R.; Savage, R. L.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schreiber, E.; Schulz, B.; Schutz, B. F.; Schwinberg, P.; Scott, J.; Scott, S. M.; Seifert, F.; Sellers, D.; Sentenac, D.; Sergeev, A.; Shaddock, D. A.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sibley, A.; Siemens, X.; Sigg, D.; Singer, A.; Singer, L.; Sintes, A. M.; Skelton, G. R.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M. R.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Somiya, K.; Sorazu, B.; Soto, J.; Speirits, F. C.; Sperandio, L.; Stefszky, M.; Stein, A. J.; Stein, L. C.; Steinert, E.; Steinlechner, J.; Steinlechner, S.; Steplewski, S.; Stochino, A.; Stone, R.; Strain, K. A.; Strigin, S. E.; Stroeer, A. S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sung, M.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Tacca, M.; Taffarello, L.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, J. R.; Taylor, R.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Thüring, A.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C.; Torrie, C. I.; Tournefier, E.; Travasso, F.; Traylor, G.; Tseng, K.; Ugolini, D.; Vahlbruch, H.; Vajente, G.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van Veggel, A. A.; Vass, S.; Vasuth, M.; Vaulin, R.; Vavoulidis, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Veltkamp, C.; Verkindt, D.; Vetrano, F.; Viceré, A.; Villar, A. E.; Vinet, J.-Y.; Vitale, S.; Vocca, H.; Vorvick, C.; Vyatchanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Waldman, S. J.; Wallace, L.; Wan, Y.; Wang, M.; Wang, X.; Wang, Z.; Wanner, A.; Ward, R. L.; Was, M.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; Whitcomb, S. E.; White, D. J.; Whiting, B. F.; Wilkinson, C.; Willems, P. A.; Williams, L.; Williams, R.; Willke, B.; Winkelmann, L.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Wooley, R.; Worden, J.; Yakushin, I.; Yamamoto, H.; Yamamoto, K.; Yancey, C. C.; Yang, H.; Yeaton-Massey, D.; Yoshida, S.; Yu, P.; Yvert, M.; Zadroźny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, F.; Zhang, L.; Zhang, W.; Zhao, C.; Zotov, N.; Zucker, M. E.; Zweizig, J.

2012-06-01

A stochastic background of gravitational waves is expected to arise from a superposition of many incoherent sources of gravitational waves, of either cosmological or astrophysical origin. This background is a target for the current generation of ground-based detectors. In this article we present the first joint search for a stochastic background using data from the LIGO and Virgo interferometers. In a frequency band of 600-1000 Hz, we obtained a 95% upper limit on the amplitude of ΩGW(f)=Ω3(f/900Hz)3, of Ω3<0.32, assuming a value of the Hubble parameter of h100=0.71. These new limits are a factor of seven better than the previous best in this frequency band.

Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes

NASA Astrophysics Data System (ADS)

Ross, Brian; Imada, Janine

Genetic programming is used to automatically construct stochastic processes written in the stochastic π-calculus. Grammar-guided genetic programming constrains search to useful process algebra structures. The time-series behaviour of a target process is denoted with a suitable selection of statistical feature tests. Feature tests can permit complex process behaviours to be effectively evaluated. However, they must be selected with care, in order to accurately characterize the desired process behaviour. Multi-objective evaluation is shown to be appropriate for this application, since it permits heterogeneous statistical feature tests to reside as independent objectives. Multiple undominated solutions can be saved and evaluated after a run, for determination of those that are most appropriate. Since there can be a vast number of candidate solutions, however, strategies for filtering and analyzing this set are required.

Two-stage stochastic unit commitment model including non-generation resources with conditional value-at-risk constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Yuping; Zheng, Qipeng P.; Wang, Jianhui

2014-11-01

tThis paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including riskconstraints to balance between cost and system reliability due to the fluctuation of variable genera-tion such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to modelrisks associated with the decisions in a stochastic environment. In contrast to chance-constrained modelsrequiring extra binary variables, risk constraints based on CVaR only involve linear constraints and con-tinuous variables, making it more computationally attractive. The proposed models with risk constraintsare able to avoid over-conservative solutions butmore » still ensure system reliability represented by loss ofloads. Then numerical experiments are conducted to study the effects of non-generation resources ongenerator schedules and the difference of total expected generation costs with risk consideration. Sen-sitivity analysis based on reliability parameters is also performed to test the decision preferences ofconfidence levels and load-shedding loss allowances on generation cost reduction.« less

Statistical Compression of Wind Speed Data

NASA Astrophysics Data System (ADS)

Tagle, F.; Castruccio, S.; Crippa, P.; Genton, M.

2017-12-01

In this work we introduce a lossy compression approach that utilizes a stochastic wind generator based on a non-Gaussian distribution to reproduce the internal climate variability of daily wind speed as represented by the CESM Large Ensemble over Saudi Arabia. Stochastic wind generators, and stochastic weather generators more generally, are statistical models that aim to match certain statistical properties of the data on which they are trained. They have been used extensively in applications ranging from agricultural models to climate impact studies. In this novel context, the parameters of the fitted model can be interpreted as encoding the information contained in the original uncompressed data. The statistical model is fit to only 3 of the 30 ensemble members and it adequately captures the variability of the ensemble in terms of seasonal internannual variability of daily wind speed. To deal with such a large spatial domain, it is partitioned into 9 region, and the model is fit independently to each of these. We further discuss a recent refinement of the model, which relaxes this assumption of regional independence, by introducing a large-scale component that interacts with the fine-scale regional effects.

Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

PubMed Central

2012-01-01

Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.

PubMed

Chebouba, Lokmane; Boughaci, Dalila; Guziolowski, Carito

2018-06-04

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2013-04-01

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Using stochastic models to incorporate spatial and temporal variability [Exercise 14

Treesearch

Carolyn Hull Sieg; Rudy M. King; Fred Van Dyke

2003-01-01

To this point, our analysis of population processes and viability in the western prairie fringed orchid has used only deterministic models. In this exercise, we conduct a similar analysis, using a stochastic model instead. This distinction is of great importance to population biology in general and to conservation biology in particular. In deterministic models,...

Stochastic Approximation Methods for Latent Regression Item Response Models. Research Report. ETS RR-09-09

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2009-01-01

This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the…

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2017-04-01

In climate simulations, the impacts of the subgrid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the subgrid variability in a computationally inexpensive manner. This study shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a nonzero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference Williams PD, Howe NJ, Gregory JM, Smith RS, and Joshi MM (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, 29, 8763-8781. http://dx.doi.org/10.1175/JCLI-D-15-0746.1

Variable classification in the LSST era: exploring a model for quasi-periodic light curves

NASA Astrophysics Data System (ADS)

Zinn, J. C.; Kochanek, C. S.; Kozłowski, S.; Udalski, A.; Szymański, M. K.; Soszyński, I.; Wyrzykowski, Ł.; Ulaczyk, K.; Poleski, R.; Pietrukowicz, P.; Skowron, J.; Mróz, P.; Pawlak, M.

2017-06-01

The Large Synoptic Survey Telescope (LSST) is expected to yield ˜107 light curves over the course of its mission, which will require a concerted effort in automated classification. Stochastic processes provide one means of quantitatively describing variability with the potential advantage over simple light-curve statistics that the parameters may be physically meaningful. Here, we survey a large sample of periodic, quasi-periodic and stochastic Optical Gravitational Lensing Experiment-III variables using the damped random walk (DRW; CARMA(1,0)) and quasi-periodic oscillation (QPO; CARMA(2,1)) stochastic process models. The QPO model is described by an amplitude, a period and a coherence time-scale, while the DRW has only an amplitude and a time-scale. We find that the periodic and quasi-periodic stellar variables are generally better described by a QPO than a DRW, while quasars are better described by the DRW model. There are ambiguities in interpreting the QPO coherence time due to non-sinusoidal light-curve shapes, signal-to-noise ratio, error mischaracterizations and cadence. Higher order implementations of the QPO model that better capture light-curve shapes are necessary for the coherence time to have its implied physical meaning. Independent of physical meaning, the extra parameter of the QPO model successfully distinguishes most of the classes of periodic and quasi-periodic variables we consider.

Binomial leap methods for simulating stochastic chemical kinetics.

PubMed

Tian, Tianhai; Burrage, Kevin

2004-12-01

This paper discusses efficient simulation methods for stochastic chemical kinetics. Based on the tau-leap and midpoint tau-leap methods of Gillespie [D. T. Gillespie, J. Chem. Phys. 115, 1716 (2001)], binomial random variables are used in these leap methods rather than Poisson random variables. The motivation for this approach is to improve the efficiency of the Poisson leap methods by using larger stepsizes. Unlike Poisson random variables whose range of sample values is from zero to infinity, binomial random variables have a finite range of sample values. This probabilistic property has been used to restrict possible reaction numbers and to avoid negative molecular numbers in stochastic simulations when larger stepsize is used. In this approach a binomial random variable is defined for a single reaction channel in order to keep the reaction number of this channel below the numbers of molecules that undergo this reaction channel. A sampling technique is also designed for the total reaction number of a reactant species that undergoes two or more reaction channels. Samples for the total reaction number are not greater than the molecular number of this species. In addition, probability properties of the binomial random variables provide stepsize conditions for restricting reaction numbers in a chosen time interval. These stepsize conditions are important properties of robust leap control strategies. Numerical results indicate that the proposed binomial leap methods can be applied to a wide range of chemical reaction systems with very good accuracy and significant improvement on efficiency over existing approaches. (c) 2004 American Institute of Physics.

Learning abstract visual concepts via probabilistic program induction in a Language of Thought.

PubMed

Overlan, Matthew C; Jacobs, Robert A; Piantadosi, Steven T

2017-11-01

The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization. Copyright © 2017 Elsevier B.V. All rights reserved.

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

Approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays: a robust stability problem.

PubMed

Pandiselvi, S; Raja, R; Cao, Jinde; Rajchakit, G; Ahmad, Bashir

2018-01-01

This work predominantly labels the problem of approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays. Here we design a linear estimator in such a way that the absorption of mRNA and protein can be approximated via known measurement outputs. By utilizing a Lyapunov-Krasovskii functional and some stochastic analysis execution, we obtain the stability formula of the estimation error systems in the structure of linear matrix inequalities under which the estimation error dynamics is robustly exponentially stable. Further, the obtained conditions (in the form of LMIs) can be effortlessly solved by some available software packages. Moreover, the specific expression of the desired estimator is also shown in the main section. Finally, two mathematical illustrative examples are accorded to show the advantage of the proposed conceptual results.

Final Technical Report for DOE Award SC0006616

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, Andrew

2015-08-01

This report summarizes research carried out by the project "Collaborative Research, Type 1: Decadal Prediction and Stochastic Simulation of Hydroclimate Over Monsoonal Asia. This collaborative project brought together climate dynamicists (UCLA, IRI), dendroclimatologists (LDEO Tree Ring Laboratory), computer scientists (UCI), and hydrologists (Columbia Water Center, CWC), together with applied scientists in climate risk management (IRI) to create new scientific approaches to quantify and exploit the role of climate variability and change in the growing water crisis across southern and eastern Asia. This project developed new tree-ring based streamflow reconstructions for rivers in monsoonal Asia; improved understanding of hydrologic spatio-temporal modesmore » of variability over monsoonal Asia on interannual-to-centennial time scales; assessed decadal predictability of hydrologic spatio-temporal modes; developed stochastic simulation tools for creating downscaled future climate scenarios based on historical/proxy data and GCM climate change; and developed stochastic reservoir simulation and optimization for scheduling hydropower, irrigation and navigation releases.« less

Lévy-taxis: a novel search strategy for finding odor plumes in turbulent flow-dominated environments

NASA Astrophysics Data System (ADS)

Pasternak, Zohar; Bartumeus, Frederic; Grasso, Frank W.

2009-10-01

Locating chemical plumes in aquatic or terrestrial environments is important for many economic, conservation, security and health related human activities. The localization process is composed mainly of two phases: finding the chemical plume and then tracking it to its source. Plume tracking has been the subject of considerable study whereas plume finding has received little attention. We address here the latter issue, where the searching agent must find the plume in a region often many times larger than the plume and devoid of the relevant chemical cues. The probability of detecting the plume not only depends on the movements of the searching agent but also on the fluid mechanical regime, shaping plume intermittency in space and time; this is a basic, general problem when exploring for ephemeral resources (e.g. moving and/or concealing targets). Here we present a bio-inspired search strategy named Lévy-taxis that, under certain conditions, located odor plumes significantly faster and with a better success rate than other search strategies such as Lévy walks (LW), correlated random walks (CRW) and systematic zig-zag. These results are based on computer simulations which contain, for the first time ever, digitalized real-world water flow and chemical plume instead of their theoretical model approximations. Combining elements of LW and CRW, Lévy-taxis is particularly efficient for searching in flow-dominated environments: it adaptively controls the stochastic search pattern using environmental information (i.e. flow) that is available throughout the course of the search and shows correlation with the source providing the cues. This strategy finds natural application in real-world search missions, both by humans and autonomous robots, since it accomodates the stochastic nature of chemical mixing in turbulent flows. In addition, it may prove useful in the field of behavioral ecology, explaining and predicting the movement patterns of various animals searching for food or mates.

The structure of red-infrared scattergrams of semivegetated landscapes

NASA Technical Reports Server (NTRS)

Jasinski, Michael F.; Eagleson, Peter S.

1988-01-01

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated and semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

The structure of red-infrared scattergrams of semivegetated landscapes

NASA Technical Reports Server (NTRS)

Jasinski, Michael F.; Eagleson, Peter S.

1989-01-01

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation.

PubMed

Sturrock, Marc; Hellander, Andreas; Matzavinos, Anastasios; Chaplain, Mark A J

2013-03-06

Individual mouse embryonic stem cells have been found to exhibit highly variable differentiation responses under the same environmental conditions. The noisy cyclic expression of Hes1 and its downstream genes are known to be responsible for this, but the mechanism underlying this variability in expression is not well understood. In this paper, we show that the observed experimental data and diverse differentiation responses can be explained by a spatial stochastic model of the Hes1 gene regulatory network. We also propose experiments to control the precise differentiation response using drug treatment.

Stochastic Time Models of Syllable Structure

PubMed Central

Shaw, Jason A.; Gafos, Adamantios I.

2015-01-01

Drawing on phonology research within the generative linguistics tradition, stochastic methods, and notions from complex systems, we develop a modelling paradigm linking phonological structure, expressed in terms of syllables, to speech movement data acquired with 3D electromagnetic articulography and X-ray microbeam methods. The essential variable in the models is syllable structure. When mapped to discrete coordination topologies, syllabic organization imposes systematic patterns of variability on the temporal dynamics of speech articulation. We simulated these dynamics under different syllabic parses and evaluated simulations against experimental data from Arabic and English, two languages claimed to parse similar strings of segments into different syllabic structures. Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid. More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability. Prospects for extensions of our modelling paradigm to acoustic data are also discussed. PMID:25996153

Contribution of tropical instability waves to ENSO irregularity

NASA Astrophysics Data System (ADS)

Holmes, Ryan M.; McGregor, Shayne; Santoso, Agus; England, Matthew H.

2018-05-01

Tropical instability waves (TIWs) are a major source of internally-generated oceanic variability in the equatorial Pacific Ocean. These non-linear phenomena play an important role in the sea surface temperature (SST) budget in a region critical for low-frequency modes of variability such as the El Niño-Southern Oscillation (ENSO). However, the direct contribution of TIW-driven stochastic variability to ENSO has received little attention. Here, we investigate the influence of TIWs on ENSO using a 1/4° ocean model coupled to a simple atmosphere. The use of a simple atmosphere removes complex intrinsic atmospheric variability while allowing the dominant mode of air-sea coupling to be represented as a statistical relationship between SST and wind stress anomalies. Using this hybrid coupled model, we perform a suite of coupled ensemble forecast experiments initiated with wind bursts in the western Pacific, where individual ensemble members differ only due to internal oceanic variability. We find that TIWs can induce a spread in the forecast amplitude of the Niño 3 SST anomaly 6-months after a given sequence of WWBs of approximately ± 45% the size of the ensemble mean anomaly. Further, when various estimates of stochastic atmospheric forcing are added, oceanic internal variability is found to contribute between about 20% and 70% of the ensemble forecast spread, with the remainder attributable to the atmospheric variability. While the oceanic contribution to ENSO stochastic forcing requires further quantification beyond the idealized approach used here, our results nevertheless suggest that TIWs may impact ENSO irregularity and predictability. This has implications for ENSO representation in low-resolution coupled models.

An upper limit on the stochastic gravitational-wave background of cosmological origin.

PubMed

Abbott, B P; Abbott, R; Acernese, F; Adhikari, R; Ajith, P; Allen, B; Allen, G; Alshourbagy, M; Amin, R S; Anderson, S B; Anderson, W G; Antonucci, F; Aoudia, S; Arain, M A; Araya, M; Armandula, H; Armor, P; Arun, K G; Aso, Y; Aston, S; Astone, P; Aufmuth, P; Aulbert, C; Babak, S; Baker, P; Ballardin, G; Ballmer, S; Barker, C; Barker, D; Barone, F; Barr, B; Barriga, P; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Bastarrika, M; Bauer, Th S; Behnke, B; Beker, M; Benacquista, M; Betzwieser, J; Beyersdorf, P T; Bigotta, S; Bilenko, I A; Billingsley, G; Birindelli, S; Biswas, R; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bland, B; Boccara, C; Bodiya, T P; Bogue, L; Bondu, F; Bonelli, L; Bork, R; Boschi, V; Bose, S; Bosi, L; Braccini, S; Bradaschia, C; Brady, P R; Braginsky, V B; Brand, J F J van den; Brau, J E; Bridges, D O; Brillet, A; Brinkmann, M; Brisson, V; Van Den Broeck, C; Brooks, A F; Brown, D A; Brummit, A; Brunet, G; Bullington, A; Bulten, H J; Buonanno, A; Burmeister, O; Buskulic, D; Byer, R L; Cadonati, L; Cagnoli, G; Calloni, E; Camp, J B; Campagna, E; Cannizzo, J; Cannon, K C; Canuel, B; Cao, J; Carbognani, F; Cardenas, L; Caride, S; Castaldi, G; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C; Cesarini, E; Chalermsongsak, T; Chalkley, E; Charlton, P; Chassande-Mottin, E; Chatterji, S; Chelkowski, S; Chen, Y; Christensen, N; Chung, C T Y; Clark, D; Clark, J; Clayton, J H; Cleva, F; Coccia, E; Cokelaer, T; Colacino, C N; Colas, J; Colla, A; Colombini, M; Conte, R; Cook, D; Corbitt, T R C; Corda, C; Cornish, N; Corsi, A; Coulon, J-P; Coward, D; Coyne, D C; Creighton, J D E; Creighton, T D; Cruise, A M; Culter, R M; Cumming, A; Cunningham, L; Cuoco, E; Danilishin, S L; D'Antonio, S; Danzmann, K; Dari, A; Dattilo, V; Daudert, B; Davier, M; Davies, G; Daw, E J; Day, R; De Rosa, R; Debra, D; Degallaix, J; Del Prete, M; Dergachev, V; Desai, S; Desalvo, R; Dhurandhar, S; Di Fiore, L; Di Lieto, A; Di Paolo Emilio, M; Di Virgilio, A; Díaz, M; Dietz, A; Donovan, F; Dooley, K L; Doomes, E E; Drago, M; Drever, R W P; Dueck, J; Duke, I; Dumas, J-C; Dwyer, J G; Echols, C; Edgar, M; Effler, A; Ehrens, P; Ely, G; Espinoza, E; Etzel, T; Evans, M; Evans, T; Fafone, V; Fairhurst, S; Faltas, Y; Fan, Y; Fazi, D; Fehrmann, H; Ferrante, I; Fidecaro, F; Finn, L S; Fiori, I; Flaminio, R; Flasch, K; Foley, S; Forrest, C; Fotopoulos, N; Fournier, J-D; Franc, J; Franzen, A; Frasca, S; Frasconi, F; Frede, M; Frei, M; Frei, Z; Freise, A; Frey, R; Fricke, T; Fritschel, P; Frolov, V V; Fyffe, M; Galdi, V; Gammaitoni, L; Garofoli, J A; Garufi, F; Genin, E; Gennai, A; Gholami, I; Giaime, J A; Giampanis, S; Giardina, K D; Giazotto, A; Goda, K; Goetz, E; Goggin, L M; González, G; Gorodetsky, M L; Gobler, S; Gouaty, R; Granata, M; Granata, V; Grant, A; Gras, S; Gray, C; Gray, M; Greenhalgh, R J S; Gretarsson, A M; Greverie, C; Grimaldi, F; Grosso, R; Grote, H; Grunewald, S; Guenther, M; Guidi, G; Gustafson, E K; Gustafson, R; Hage, B; Hallam, J M; Hammer, D; Hammond, G D; Hanna, C; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Haughian, K; Hayama, K; Heefner, J; Heitmann, H; Hello, P; Heng, I S; Heptonstall, A; Hewitson, M; Hild, S; Hirose, E; Hoak, D; Hodge, K A; Holt, K; Hosken, D J; Hough, J; Hoyland, D; Huet, D; Hughey, B; Huttner, S H; Ingram, D R; Isogai, T; Ito, M; Ivanov, A; Johnson, B; Johnson, W W; Jones, D I; Jones, G; Jones, R; Sancho de la Jordana, L; Ju, L; Kalmus, P; Kalogera, V; Kandhasamy, S; Kanner, J; Kasprzyk, D; Katsavounidis, E; Kawabe, K; Kawamura, S; Kawazoe, F; Kells, W; Keppel, D G; Khalaidovski, A; Khalili, F Y; Khan, R; Khazanov, E; King, P; Kissel, J S; Klimenko, S; Kokeyama, K; Kondrashov, V; Kopparapu, R; Koranda, S; Kozak, D; Krishnan, B; Kumar, R; Kwee, P; La Penna, P; Lam, P K; Landry, M; Lantz, B; Laval, M; Lazzarini, A; Lei, H; Lei, M; Leindecker, N; Leonor, I; Leroy, N; Letendre, N; Li, C; Lin, H; Lindquist, P E; Littenberg, T B; Lockerbie, N A; Lodhia, D; Longo, M; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lu, P; Lubinski, M; Lucianetti, A; Lück, H; Machenschalk, B; Macinnis, M; Mackowski, J-M; Mageswaran, M; Mailand, K; Majorana, E; Man, N; Mandel, I; Mandic, V; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A; Markowitz, J; Maros, E; Marque, J; Martelli, F; Martin, I W; Martin, R M; Marx, J N; Mason, K; Masserot, A; Matichard, F; Matone, L; Matzner, R A; Mavalvala, N; McCarthy, R; McClelland, D E; McGuire, S C; McHugh, M; McIntyre, G; McKechan, D J A; McKenzie, K; Mehmet, M; Melatos, A; Melissinos, A C; Mendell, G; Menéndez, D F; Menzinger, F; Mercer, R A; Meshkov, S; Messenger, C; Meyer, M S; Michel, C; Milano, L; Miller, J; Minelli, J; Minenkov, Y; Mino, Y; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Miyakawa, O; Moe, B; Mohan, M; Mohanty, S D; Mohapatra, S R P; Moreau, J; Moreno, G; Morgado, N; Morgia, A; Morioka, T; Mors, K; Mosca, S; Mossavi, K; Mours, B; Mowlowry, C; Mueller, G; Muhammad, D; Mühlen, H Zur; Mukherjee, S; Mukhopadhyay, H; Mullavey, A; Müller-Ebhardt, H; Munch, J; Murray, P G; Myers, E; Myers, J; Nash, T; Nelson, J; Neri, I; Newton, G; Nishizawa, A; Nocera, F; Numata, K; Ochsner, E; O'Dell, J; Ogin, G H; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Pagliaroli, G; Palomba, C; Pan, Y; Pankow, C; Paoletti, F; Papa, M A; Parameshwaraiah, V; Pardi, S; Pasqualetti, A; Passaquieti, R; Passuello, D; Patel, P; Pedraza, M; Penn, S; Perreca, A; Persichetti, G; Pichot, M; Piergiovanni, F; Pierro, V; Pinard, L; Pinto, I M; Pitkin, M; Pletsch, H J; Plissi, M V; Poggiani, R; Postiglione, F; Principe, M; Prix, R; Prodi, G A; Prokhorov, L; Punken, O; Punturo, M; Puppo, P; Putten, S van der; Quetschke, V; Raab, F J; Rabaste, O; Rabeling, D S; Radkins, H; Raffai, P; Raics, Z; Rainer, N; Rakhmanov, M; Rapagnani, P; Raymond, V; Re, V; Reed, C M; Reed, T; Regimbau, T; Rehbein, H; Reid, S; Reitze, D H; Ricci, F; Riesen, R; Riles, K; Rivera, B; Roberts, P; Robertson, N A; Robinet, F; Robinson, C; Robinson, E L; Rocchi, A; Roddy, S; Rolland, L; Rollins, J; Romano, J D; Romano, R; Romie, J H; Röver, C; Rowan, S; Rüdiger, A; Ruggi, P; Russell, P; Ryan, K; Sakata, S; Salemi, F; Sandberg, V; Sannibale, V; Santamaría, L; Saraf, S; Sarin, P; Sassolas, B; Sathyaprakash, B S; Sato, S; Satterthwaite, M; Saulson, P R; Savage, R; Savov, P; Scanlan, M; Schilling, R; Schnabel, R; Schofield, R; Schulz, B; Schutz, B F; Schwinberg, P; Scott, J; Scott, S M; Searle, A C; Sears, B; Seifert, F; Sellers, D; Sengupta, A S; Sentenac, D; Sergeev, A; Shapiro, B; Shawhan, P; Shoemaker, D H; Sibley, A; Siemens, X; Sigg, D; Sinha, S; Sintes, A M; Slagmolen, B J J; Slutsky, J; van der Sluys, M V; Smith, J R; Smith, M R; Smith, N D; Somiya, K; Sorazu, B; Stein, A; Stein, L C; Steplewski, S; Stochino, A; Stone, R; Strain, K A; Strigin, S; Stroeer, A; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, K-X; Sung, M; Sutton, P J; Swinkels, B L; Szokoly, G P; Talukder, D; Tang, L; Tanner, D B; Tarabrin, S P; Taylor, J R; Taylor, R; Terenzi, R; Thacker, J; Thorne, K A; Thorne, K S; Thüring, A; Tokmakov, K V; Toncelli, A; Tonelli, M; Torres, C; Torrie, C; Tournefier, E; Travasso, F; Traylor, G; Trias, M; Trummer, J; Ugolini, D; Ulmen, J; Urbanek, K; Vahlbruch, H; Vajente, G; Vallisneri, M; Vass, S; Vaulin, R; Vavoulidis, M; Vecchio, A; Vedovato, G; van Veggel, A A; Veitch, J; Veitch, P; Veltkamp, C; Verkindt, D; Vetrano, F; Viceré, A; Villar, A; Vinet, J-Y; Vocca, H; Vorvick, C; Vyachanin, S P; Waldman, S J; Wallace, L; Ward, H; Ward, R L; Was, M; Weidner, A; Weinert, M; Weinstein, A J; Weiss, R; Wen, L; Wen, S; Wette, K; Whelan, J T; Whitcomb, S E; Whiting, B F; Wilkinson, C; Willems, P A; Williams, H R; Williams, L; Willke, B; Wilmut, I; Winkelmann, L; Winkler, W; Wipf, C C; Wiseman, A G; Woan, G; Wooley, R; Worden, J; Wu, W; Yakushin, I; Yamamoto, H; Yan, Z; Yoshida, S; Yvert, M; Zanolin, M; Zhang, J; Zhang, L; Zhao, C; Zotov, N; Zucker, M E; Zweizig, J

2009-08-20

A stochastic background of gravitational waves is expected to arise from a superposition of a large number of unresolved gravitational-wave sources of astrophysical and cosmological origin. It should carry unique signatures from the earliest epochs in the evolution of the Universe, inaccessible to standard astrophysical observations. Direct measurements of the amplitude of this background are therefore of fundamental importance for understanding the evolution of the Universe when it was younger than one minute. Here we report limits on the amplitude of the stochastic gravitational-wave background using the data from a two-year science run of the Laser Interferometer Gravitational-wave Observatory (LIGO). Our result constrains the energy density of the stochastic gravitational-wave background normalized by the critical energy density of the Universe, in the frequency band around 100 Hz, to be <6.9 x 10(-6) at 95% confidence. The data rule out models of early Universe evolution with relatively large equation-of-state parameter, as well as cosmic (super)string models with relatively small string tension that are favoured in some string theory models. This search for the stochastic background improves on the indirect limits from Big Bang nucleosynthesis and cosmic microwave background at 100 Hz.

Balancing Exploration, Uncertainty Representation and Computational Time in Many-Objective Reservoir Policy Optimization

NASA Astrophysics Data System (ADS)

Zatarain-Salazar, J.; Reed, P. M.; Quinn, J.; Giuliani, M.; Castelletti, A.

2016-12-01

As we confront the challenges of managing river basin systems with a large number of reservoirs and increasingly uncertain tradeoffs impacting their operations (due to, e.g. climate change, changing energy markets, population pressures, ecosystem services, etc.), evolutionary many-objective direct policy search (EMODPS) solution strategies will need to address the computational demands associated with simulating more uncertainties and therefore optimizing over increasingly noisy objective evaluations. Diagnostic assessments of state-of-the-art many-objective evolutionary algorithms (MOEAs) to support EMODPS have highlighted that search time (or number of function evaluations) and auto-adaptive search are key features for successful optimization. Furthermore, auto-adaptive MOEA search operators are themselves sensitive to having a sufficient number of function evaluations to learn successful strategies for exploring complex spaces and for escaping from local optima when stagnation is detected. Fortunately, recent parallel developments allow coordinated runs that enhance auto-adaptive algorithmic learning and can handle scalable and reliable search with limited wall-clock time, but at the expense of the total number of function evaluations. In this study, we analyze this tradeoff between parallel coordination and depth of search using different parallelization schemes of the Multi-Master Borg on a many-objective stochastic control problem. We also consider the tradeoff between better representing uncertainty in the stochastic optimization, and simplifying this representation to shorten the function evaluation time and allow for greater search. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple competing objectives for hydropower production, urban water supply, recreation and environmental flows need to be balanced. Our results provide guidance for balancing exploration, uncertainty, and computational demands when using the EMODPS framework to discover key tradeoffs within the LSRB system.

An advanced stochastic weather generator for simulating 2-D high-resolution climate variables

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2017-07-01

A new stochastic weather generator, Advanced WEather GENerator for a two-dimensional grid (AWE-GEN-2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near-surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near-surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE-GEN-2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high-resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE-GEN-2d with a level of accuracy that is sufficient for many practical applications.

Rethinking "normal": The role of stochasticity in the phenology of a synchronously breeding seabird.

PubMed

Youngflesh, Casey; Jenouvrier, Stephanie; Hinke, Jefferson T; DuBois, Lauren; St Leger, Judy; Trivelpiece, Wayne Z; Trivelpiece, Susan G; Lynch, Heather J

2018-05-01

Phenological changes have been observed in a variety of systems over the past century. There is concern that, as a consequence, ecological interactions are becoming increasingly mismatched in time, with negative consequences for ecological function. Significant spatial heterogeneity (inter-site) and temporal variability (inter-annual) can make it difficult to separate intrinsic, extrinsic and stochastic drivers of phenological variability. The goal of this study was to understand the timing and variability in breeding phenology of Adélie penguins under fixed environmental conditions and to use those data to identify a "null model" appropriate for disentangling the sources of variation in wild populations. Data on clutch initiation were collected from both wild and captive populations of Adélie penguins. Clutch initiation in the captive population was modelled as a function of year, individual and age to better understand phenological patterns observed in the wild population. Captive populations displayed as much inter-annual variability in breeding phenology as wild populations, suggesting that variability in breeding phenology is the norm and thus may be an unreliable indicator of environmental forcing. The distribution of clutch initiation dates was found to be moderately asymmetric (right skewed) both in the wild and in captivity, consistent with the pattern expected under social facilitation. The role of stochasticity in phenological processes has heretofore been largely ignored. However, these results suggest that inter-annual variability in breeding phenology can arise independent of any environmental or demographic drivers and that synchronous breeding can enhance inherent stochasticity. This complicates efforts to relate phenological variation to environmental variability in the wild. Accordingly, we must be careful to consider random forcing in phenological processes, lest we fit models to data dominated by random noise. This is particularly true for colonial species where breeding synchrony may outweigh each individual's effort to time breeding with optimal environmental conditions. Our study highlights the importance of identifying appropriate null models for studying phenology. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Density, Velocity and Ionization Structure in Accretion-Disc Winds

NASA Technical Reports Server (NTRS)

Sonneborn, George (Technical Monitor); Long, Knox

2004-01-01

This was a project to exploit the unique capabilities of FUSE to monitor variations in the wind- formed spectral lines of the luminous, low-inclination, cataclysmic variables(CV) -- RW Sex. (The original proposal contained two additional objects but these were not approved.) These observations were intended to allow us to determine the relative roles of density and ionization state changes in the outflow and to search for spectroscopic signatures of stochastic small-scale structure and shocked gas. By monitoring the temporal behavior of blue-ward extended absorption lines with a wide range of ionization potentials and excitation energies, we proposed to track the changing physical conditions in the outflow. We planned to use a new Monte Carlo code to calculate the ionization structure of and radiative transfer through the CV wind. The analysis therefore was intended to establish the wind geometry, kinematics and ionization state, both in a time-averaged sense and as a function of time.

Debates—Stochastic subsurface hydrology from theory to practice: The relevance of stochastic subsurface hydrology to practical problems of contaminant transport and remediation. What is characterization and stochastic theory good for?

NASA Astrophysics Data System (ADS)

Fiori, A.; Cvetkovic, V.; Dagan, G.; Attinger, S.; Bellin, A.; Dietrich, P.; Zech, A.; Teutsch, G.

2016-12-01

The emergence of stochastic subsurface hydrology stemmed from the realization that the random spatial variability of aquifer properties has a profound impact on solute transport. The last four decades witnessed a tremendous expansion of the discipline, many fundamental processes and principal mechanisms being identified. However, the research findings have not impacted significantly the application in practice, for several reasons which are discussed. The paper discusses the current status of stochastic subsurface hydrology, the relevance of the scientific results for applications and it also provides a perspective to a few possible future directions. In particular, we discuss how the transfer of knowledge can be facilitated by identifying clear goals for characterization and modeling application, relying on recent recent advances in research in these areas.

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.

Stochastic Watershed Models for Risk Based Decision Making

NASA Astrophysics Data System (ADS)

Vogel, R. M.

2017-12-01

Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

REVISITING EVIDENCE OF CHAOS IN X-RAY LIGHT CURVES: THE CASE OF GRS 1915+105

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mannattil, Manu; Gupta, Himanshu; Chakraborty, Sagar, E-mail: mmanu@iitk.ac.in, E-mail: hiugupta@iitk.ac.in, E-mail: sagarc@iitk.ac.in

2016-12-20

Nonlinear time series analysis has been widely used to search for signatures of low-dimensional chaos in light curves emanating from astrophysical bodies. A particularly popular example is the microquasar GRS 1915+105, whose irregular but systematic X-ray variability has been well studied using data acquired by the Rossi X-ray Timing Explorer . With a view to building simpler models of X-ray variability, attempts have been made to classify the light curves of GRS 1915+105 as chaotic or stochastic. Contrary to some of the earlier suggestions, after careful analysis, we find no evidence for chaos or determinism in any of the GRS 1915+105 classes. Themore » dearth of long and stationary data sets representing all the different variability classes of GRS 1915+105 makes it a poor candidate for analysis using nonlinear time series techniques. We conclude that either very exhaustive data analysis with sufficiently long and stationary light curves should be performed, keeping all the pitfalls of nonlinear time series analysis in mind, or alternative schemes of classifying the light curves should be adopted. The generic limitations of the techniques that we point out in the context of GRS 1915+105 affect all similar investigations of light curves from other astrophysical sources.« less

Research in Stochastic Processes.

DTIC Science & Technology

1983-10-01

increases. A more detailed investigation for the exceedances themselves (rather than Just the cluster centers) was undertaken, together with J. HUsler and...J. HUsler and M.R. Leadbetter, Compoung Poisson limit theorems for high level exceedances by stationary sequences, Center for Stochastic Processes...stability by a random linear operator. C.D. Hardin, General (asymmetric) stable variables and processes. T. Hsing, J. HUsler and M.R. Leadbetter, Compound

Grassmann phase space methods for fermions. I. Mode theory

NASA Astrophysics Data System (ADS)

Dalton, B. J.; Jeffers, J.; Barnett, S. M.

2016-07-01

In both quantum optics and cold atom physics, the behaviour of bosonic photons and atoms is often treated using phase space methods, where mode annihilation and creation operators are represented by c-number phase space variables, with the density operator equivalent to a distribution function of these variables. The anti-commutation rules for fermion annihilation, creation operators suggest the possibility of using anti-commuting Grassmann variables to represent these operators. However, in spite of the seminal work by Cahill and Glauber and a few applications, the use of Grassmann phase space methods in quantum-atom optics to treat fermionic systems is rather rare, though fermion coherent states using Grassmann variables are widely used in particle physics. The theory of Grassmann phase space methods for fermions based on separate modes is developed, showing how the distribution function is defined and used to determine quantum correlation functions, Fock state populations and coherences via Grassmann phase space integrals, how the Fokker-Planck equations are obtained and then converted into equivalent Ito equations for stochastic Grassmann variables. The fermion distribution function is an even Grassmann function, and is unique. The number of c-number Wiener increments involved is 2n2, if there are n modes. The situation is somewhat different to the bosonic c-number case where only 2 n Wiener increments are involved, the sign of the drift term in the Ito equation is reversed and the diffusion matrix in the Fokker-Planck equation is anti-symmetric rather than symmetric. The un-normalised B distribution is of particular importance for determining Fock state populations and coherences, and as pointed out by Plimak, Collett and Olsen, the drift vector in its Fokker-Planck equation only depends linearly on the Grassmann variables. Using this key feature we show how the Ito stochastic equations can be solved numerically for finite times in terms of c-number stochastic quantities. Averages of products of Grassmann stochastic variables at the initial time are also involved, but these are determined from the initial conditions for the quantum state. The detailed approach to the numerics is outlined, showing that (apart from standard issues in such numerics) numerical calculations for Grassmann phase space theories of fermion systems could be carried out without needing to represent Grassmann phase space variables on the computer, and only involving processes using c-numbers. We compare our approach to that of Plimak, Collett and Olsen and show that the two approaches differ. As a simple test case we apply the B distribution theory and solve the Ito stochastic equations to demonstrate coupling between degenerate Cooper pairs in a four mode fermionic system involving spin conserving interactions between the spin 1 / 2 fermions, where modes with momenta - k , + k-each associated with spin up, spin down states, are involved.

Ensemble modeling of stochastic unsteady open-channel flow in terms of its time-space evolutionary probability distribution - Part 2: numerical application

NASA Astrophysics Data System (ADS)

Dib, Alain; Kavvas, M. Levent

2018-03-01

The characteristic form of the Saint-Venant equations is solved in a stochastic setting by using a newly proposed Fokker-Planck Equation (FPE) methodology. This methodology computes the ensemble behavior and variability of the unsteady flow in open channels by directly solving for the flow variables' time-space evolutionary probability distribution. The new methodology is tested on a stochastic unsteady open-channel flow problem, with an uncertainty arising from the channel's roughness coefficient. The computed statistical descriptions of the flow variables are compared to the results obtained through Monte Carlo (MC) simulations in order to evaluate the performance of the FPE methodology. The comparisons show that the proposed methodology can adequately predict the results of the considered stochastic flow problem, including the ensemble averages, variances, and probability density functions in time and space. Unlike the large number of simulations performed by the MC approach, only one simulation is required by the FPE methodology. Moreover, the total computational time of the FPE methodology is smaller than that of the MC approach, which could prove to be a particularly crucial advantage in systems with a large number of uncertain parameters. As such, the results obtained in this study indicate that the proposed FPE methodology is a powerful and time-efficient approach for predicting the ensemble average and variance behavior, in both space and time, for an open-channel flow process under an uncertain roughness coefficient.

BBPH: Using progressive hedging within branch and bound to solve multi-stage stochastic mixed integer programs

DOE PAGES

Barnett, Jason; Watson, Jean -Paul; Woodruff, David L.

2016-11-27

Progressive hedging, though an effective heuristic for solving stochastic mixed integer programs (SMIPs), is not guaranteed to converge in this case. Here, we describe BBPH, a branch and bound algorithm that uses PH at each node in the search tree such that, given sufficient time, it will always converge to a globally optimal solution. Additionally, to providing a theoretically convergent “wrapper” for PH applied to SMIPs, computational results demonstrate that for some difficult problem instances branch and bound can find improved solutions after exploring only a few nodes.

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Fluctuation theorem: A critical review

NASA Astrophysics Data System (ADS)

Malek Mansour, M.; Baras, F.

2017-10-01

Fluctuation theorem for entropy production is revisited in the framework of stochastic processes. The applicability of the fluctuation theorem to physico-chemical systems and the resulting stochastic thermodynamics were analyzed. Some unexpected limitations are highlighted in the context of jump Markov processes. We have shown that these limitations handicap the ability of the resulting stochastic thermodynamics to correctly describe the state of non-equilibrium systems in terms of the thermodynamic properties of individual processes therein. Finally, we considered the case of diffusion processes and proved that the fluctuation theorem for entropy production becomes irrelevant at the stationary state in the case of one variable systems.

Provably unbounded memory advantage in stochastic simulation using quantum mechanics

NASA Astrophysics Data System (ADS)

Garner, Andrew J. P.; Liu, Qing; Thompson, Jayne; Vedral, Vlatko; Gu, mile

2017-10-01

Simulating the stochastic evolution of real quantities on a digital computer requires a trade-off between the precision to which these quantities are approximated, and the memory required to store them. The statistical accuracy of the simulation is thus generally limited by the internal memory available to the simulator. Here, using tools from computational mechanics, we show that quantum processors with a fixed finite memory can simulate stochastic processes of real variables to arbitrarily high precision. This demonstrates a provable, unbounded memory advantage that a quantum simulator can exhibit over its best possible classical counterpart.

Heart rate variability as determinism with jump stochastic parameters.

PubMed

Zheng, Jiongxuan; Skufca, Joseph D; Bollt, Erik M

2013-08-01

We use measured heart rate information (RR intervals) to develop a one-dimensional nonlinear map that describes short term deterministic behavior in the data. Our study suggests that there is a stochastic parameter with persistence which causes the heart rate and rhythm system to wander about a bifurcation point. We propose a modified circle map with a jump process noise term as a model which can qualitatively capture such this behavior of low dimensional transient determinism with occasional (stochastically defined) jumps from one deterministic system to another within a one parameter family of deterministic systems.

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.

Path-integral methods for analyzing the effects of fluctuations in stochastic hybrid neural networks.

PubMed

Bressloff, Paul C

2015-01-01

We consider applications of path-integral methods to the analysis of a stochastic hybrid model representing a network of synaptically coupled spiking neuronal populations. The state of each local population is described in terms of two stochastic variables, a continuous synaptic variable and a discrete activity variable. The synaptic variables evolve according to piecewise-deterministic dynamics describing, at the population level, synapses driven by spiking activity. The dynamical equations for the synaptic currents are only valid between jumps in spiking activity, and the latter are described by a jump Markov process whose transition rates depend on the synaptic variables. We assume a separation of time scales between fast spiking dynamics with time constant [Formula: see text] and slower synaptic dynamics with time constant τ. This naturally introduces a small positive parameter [Formula: see text], which can be used to develop various asymptotic expansions of the corresponding path-integral representation of the stochastic dynamics. First, we derive a variational principle for maximum-likelihood paths of escape from a metastable state (large deviations in the small noise limit [Formula: see text]). We then show how the path integral provides an efficient method for obtaining a diffusion approximation of the hybrid system for small ϵ. The resulting Langevin equation can be used to analyze the effects of fluctuations within the basin of attraction of a metastable state, that is, ignoring the effects of large deviations. We illustrate this by using the Langevin approximation to analyze the effects of intrinsic noise on pattern formation in a spatially structured hybrid network. In particular, we show how noise enlarges the parameter regime over which patterns occur, in an analogous fashion to PDEs. Finally, we carry out a [Formula: see text]-loop expansion of the path integral, and use this to derive corrections to voltage-based mean-field equations, analogous to the modified activity-based equations generated from a neural master equation.

Stochastic and Simulation Models of Maritime Intercept Operations Capabilities

DTIC Science & Technology

2005-12-01

methods of operations research. 2 Furusawa , Tadahiko, “On Territorial Defense – Policing Sea Area...searches a rectangle region AOI with area x yM M ⋅ . The MPA travels the AOI at a speed v . The radar coverage or radar footprint is assumed to be a...Borges, Jose Manuel, “Radar Search and Detection with the CASA 212 S43 Aircraft,” Naval Postgraduate School, (2004) 17 whole AOI is ( ) 2/x yM M f

Schumann resonance transients and the search for gravitational waves

NASA Astrophysics Data System (ADS)

Silagadze, Z. K.

2018-02-01

Schumann resonance transients which propagate around the globe can potentially generate a correlated background in widely separated gravitational-wave detectors. We show that due to the distribution of lightning hotspots around the globe, these transients have characteristic time lags, and this feature can be useful to further suppress such a background, especially in searches of the stochastic gravitational-wave background. A brief review of the corresponding literature on Schumann resonances and lightnings is also given.

Conformational sampling with stochastic proximity embedding and self-organizing superimposition: establishing reasonable parameters for their practical use.

PubMed

Tresadern, Gary; Agrafiotis, Dimitris K

2009-12-01

Stochastic proximity embedding (SPE) and self-organizing superimposition (SOS) are two recently introduced methods for conformational sampling that have shown great promise in several application domains. Our previous validation studies aimed at exploring the limits of these methods and have involved rather exhaustive conformational searches producing a large number of conformations. However, from a practical point of view, such searches have become the exception rather than the norm. The increasing popularity of virtual screening has created a need for 3D conformational search methods that produce meaningful answers in a relatively short period of time and work effectively on a large scale. In this work, we examine the performance of these algorithms and the effects of different parameter settings at varying levels of sampling. Our goal is to identify search protocols that can produce a diverse set of chemically sensible conformations and have a reasonable probability of sampling biologically active space within a small number of trials. Our results suggest that both SPE and SOS are extremely competitive in this regard and produce very satisfactory results with as few as 500 conformations per molecule. The results improve even further when the raw conformations are minimized with a molecular mechanics force field to remove minor imperfections and any residual strain. These findings provide additional evidence that these methods are suitable for many everyday modeling tasks, both high- and low-throughput.

Developmental mechanisms underlying variable, invariant and plastic phenotypes

PubMed Central

Abley, Katie; Locke, James C. W.; Leyser, H. M. Ottoline

2016-01-01

Background Discussions of phenotypic robustness often consider scenarios where invariant phenotypes are optimal and assume that developmental mechanisms have evolved to buffer the phenotypes of specific traits against stochastic and environmental perturbations. However, plastic plant phenotypes that vary between environments or variable phenotypes that vary stochastically within an environment may also be advantageous in some scenarios. Scope Here the conditions under which invariant, plastic and variable phenotypes of specific traits may confer a selective advantage in plants are examined. Drawing on work from microbes and multicellular organisms, the mechanisms that may give rise to each type of phenotype are discussed. Conclusion In contrast to the view of robustness as being the ability of a genotype to produce a single, invariant phenotype, changes in a phenotype in response to the environment, or phenotypic variability within an environment, may also be delivered consistently (i.e. robustly). Thus, for some plant traits, mechanisms have probably evolved to produce plasticity or variability in a reliable manner. PMID:27072645

The Effects of a Change in the Variability of Irrigation Water

NASA Astrophysics Data System (ADS)

Lyon, Kenneth S.

1983-10-01

This paper examines the short-run effects upon several variables of an increase in the variability of an input. The measure of an increase in the variability is the "mean preserving spread" suggested by Rothschild and Stiglitz (1970). The variables examined are real income (utility), expected profits, expected output, the quantity used of the controllable input, and the shadow price of the stochastic input. Four striking features of the results follow: (1) The concepts that have been useful in summarizing deterministic comparative static results are nearly absent when an input is stochastic. (2) Most of the signs of the partial derivatives depend upon more than concavity of the utility and production functions. (3) If the utility function is not "too" risk averse, then the risk-neutral results hold for the risk-aversion case. (4) If the production function is Cobb-Douglas, then definite results are achieved if the utility function is linear or if the "degree of risk-aversion" is "small."

Identification of independent storm events: Seasonal and spatial variability of times between storms in Alpine area

NASA Astrophysics Data System (ADS)

Iadanzaa, Carla; Rianna, Maura; Orlando, Dario; Ubertini, Lucio; Napolitano, Francesco

2013-10-01

The aim of the paper is the identification of rain events that trigger landslides through the use of an exponential method to separate stochastic independent events. This activity is carried out within the definition of empirical rainfall thresholds for debris flows and shallow landslides. The study area is the Trento district, which is located in the northeast zone of an Alpine area. The work evaluates the factors that affect the variability in space and time of the critical duration of each rain gauge, defined as the minimum dry period duration that separates two rainy periods that are stochastically independent.

Spatial stochastic modelling of the Hes1 gene regulatory network: intrinsic noise can explain heterogeneity in embryonic stem cell differentiation

PubMed Central

Sturrock, Marc; Hellander, Andreas; Matzavinos, Anastasios; Chaplain, Mark A. J.

2013-01-01

Individual mouse embryonic stem cells have been found to exhibit highly variable differentiation responses under the same environmental conditions. The noisy cyclic expression of Hes1 and its downstream genes are known to be responsible for this, but the mechanism underlying this variability in expression is not well understood. In this paper, we show that the observed experimental data and diverse differentiation responses can be explained by a spatial stochastic model of the Hes1 gene regulatory network. We also propose experiments to control the precise differentiation response using drug treatment. PMID:23325756

Determination of relative phase permeabilities in stochastic model of pore channel distribution by diameter

NASA Astrophysics Data System (ADS)

Zemenkova, M. Y.; Shabarov, A.; Shatalov, A.; Puldas, L.

2018-05-01

The problem of the pore space description and the calculation of relative phase permeabilities (RPP) for two-phase filtration is considered. A technique for constructing a pore-network structure for constant and variable channel diameters is proposed. A description of the design model of RPP based on the capillary pressure curves is presented taking into account the variability of diameters along the length of pore channels. By the example of the calculation analysis for the core samples of the Urnenskoye and Verkhnechonskoye deposits, the possibilities of calculating RPP are shown when using the stochastic distribution of pores by diameters and medium-flow diameters.

Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB.

PubMed

Ullah, M; Schmidt, H; Cho, K H; Wolkenhauer, O

2006-03-01

The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) with probabilities as variables. This is to generate counts of molecules for chemical species as realisations of random variables drawn from the probability distribution described by the CMEs. Although there are numerous tools available, many of them free, the modelling and simulation environment MATLAB is widely used in the physical and engineering sciences. We describe a collection of MATLAB functions to construct and solve ODEs for deterministic simulation and to implement realisations of CMEs for stochastic simulation using advanced MATLAB coding (Release 14). The program was successfully applied to pathway models from the literature for both cases. The results were compared to implementations using alternative tools for dynamic modelling and simulation of biochemical networks. The aim is to provide a concise set of MATLAB functions that encourage the experimentation with systems biology models. All the script files are available from www.sbi.uni-rostock.de/ publications_matlab-paper.html.

Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization

NASA Astrophysics Data System (ADS)

Lee, Kyungbook; Song, Seok Goo

2017-09-01

Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events ( M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.

Simplified rotor load models and fatigue damage estimates for offshore wind turbines.

PubMed

Muskulus, M

2015-02-28

The aim of rotor load models is to characterize and generate the thrust loads acting on an offshore wind turbine. Ideally, the rotor simulation can be replaced by time series from a model with a few parameters and state variables only. Such models are used extensively in control system design and, as a potentially new application area, structural optimization of support structures. Different rotor load models are here evaluated for a jacket support structure in terms of fatigue lifetimes of relevant structural variables. All models were found to be lacking in accuracy, with differences of more than 20% in fatigue load estimates. The most accurate models were the use of an effective thrust coefficient determined from a regression analysis of dynamic thrust loads, and a novel stochastic model in state-space form. The stochastic model explicitly models the quasi-periodic components obtained from rotational sampling of turbulent fluctuations. Its state variables follow a mean-reverting Ornstein-Uhlenbeck process. Although promising, more work is needed on how to determine the parameters of the stochastic model and before accurate lifetime predictions can be obtained without comprehensive rotor simulations. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Chaotic simulated annealing by a neural network with a variable delay: design and application.

PubMed

Chen, Shyan-Shiou

2011-10-01

In this paper, we have three goals: the first is to delineate the advantages of a variably delayed system, the second is to find a more intuitive Lyapunov function for a delayed neural network, and the third is to design a delayed neural network for a quadratic cost function. For delayed neural networks, most researchers construct a Lyapunov function based on the linear matrix inequality (LMI) approach. However, that approach is not intuitive. We provide a alternative candidate Lyapunov function for a delayed neural network. On the other hand, if we are first given a quadratic cost function, we can construct a delayed neural network by suitably dividing the second-order term into two parts: a self-feedback connection weight and a delayed connection weight. To demonstrate the advantage of a variably delayed neural network, we propose a transiently chaotic neural network with variable delay and show numerically that the model should possess a better searching ability than Chen-Aihara's model, Wang's model, and Zhao's model. We discuss both the chaotic and the convergent phases. During the chaotic phase, we simply present bifurcation diagrams for a single neuron with a constant delay and with a variable delay. We show that the variably delayed model possesses the stochastic property and chaotic wandering. During the convergent phase, we not only provide a novel Lyapunov function for neural networks with a delay (the Lyapunov function is independent of the LMI approach) but also establish a correlation between the Lyapunov function for a delayed neural network and an objective function for the traveling salesman problem. © 2011 IEEE

Searching for stochastic gravitational waves using data from the two colocated LIGO Hanford detectors

NASA Astrophysics Data System (ADS)

Aasi, J.; Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T.; Abernathy, M. R.; Accadia, T.; Acernese, F.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Affeldt, C.; Agathos, M.; Aggarwal, N.; Aguiar, O. D.; Ajith, P.; Allen, B.; Allocca, A.; Amador Ceron, E.; Amariutei, D.; Anderson, R. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C.; Areeda, J.; Ast, S.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Austin, L.; Aylott, B. E.; Babak, S.; Baker, P. T.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barker, D.; Barnum, S. H.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J.; Bauchrowitz, J.; Bauer, Th. S.; Bebronne, M.; Behnke, B.; Bejger, M.; Beker, M. G.; Bell, A. S.; Bell, C.; Belopolski, I.; Bergmann, G.; Berliner, J. M.; Bersanetti, D.; Bertolini, A.; Bessis, D.; Betzwieser, J.; Beyersdorf, P. T.; Bhadbhade, T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biscans, S.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Blom, M.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogan, C.; Bond, C.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, S.; Bosi, L.; Bowers, J.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brannen, C. A.; Brau, J. E.; Breyer, J.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Britzger, M.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brückner, F.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Calderón Bustillo, J.; Calloni, E.; Camp, J. B.; Campsie, P.; Cannon, K. C.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Castiglia, A.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chu, Q.; Chua, S. S. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, D. E.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Colombini, M.; Constancio, M.; Conte, A.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corsi, A.; Costa, C. A.; Coughlin, M. W.; Coulon, J.-P.; Countryman, S.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Craig, K.; Creighton, J. D. E.; Creighton, T. D.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dahl, K.; Dal Canton, T.; Damjanic, M.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daudert, B.; Daveloza, H.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; Dayanga, T.; Debreczeni, G.; Degallaix, J.; Deleeuw, E.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; DeRosa, R. T.; De Rosa, R.; DeSalvo, R.; Dhurandhar, S.; Díaz, M.; Dietz, A.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Virgilio, A.; Dmitry, K.; Donovan, F.; Dooley, K. L.; Doravari, S.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dumas, J.-C.; Dwyer, S.; Eberle, T.; Edwards, M.; Effler, A.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Endrőczi, G.; Essick, R.; Etzel, T.; Evans, K.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Fang, Q.; Farr, B.; Farr, W.; Favata, M.; Fazi, D.; Fehrmann, H.; Feldbaum, D.; Ferrante, I.; Ferrini, F.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R.; Flaminio, R.; Foley, E.; Foley, S.; Forsi, E.; Fotopoulos, N.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, M.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fujimoto, M.-K.; Fulda, P.; Fyffe, M.; Gair, J.; Gammaitoni, L.; Garcia, J.; Garufi, F.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; Gergely, L.; Ghosh, S.; Giaime, J. A.; Giampanis, S.; Giardina, K. D.; Giazotto, A.; Gil-Casanova, S.; Gill, C.; Gleason, J.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gordon, N.; Gorodetsky, M. L.; Gossan, S.; Goßler, S.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Griffo, C.; Grote, H.; Grover, K.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hall, B.; Hall, E.; Hammer, D.; Hammond, G.; Hanke, M.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hartman, M. T.; Haughian, K.; Hayama, K.; Heefner, J.; Heidmann, A.; Heintze, M.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Hong, T.; Hooper, S.; Horrom, T.; Hosken, D. J.; Hough, J.; Howell, E. J.; Hu, Y.; Hua, Z.; Huang, V.; Huerta, E. A.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh, M.; Huynh-Dinh, T.; Iafrate, J.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Iyer, B. R.; Izumi, K.; Jacobson, M.; James, E.; Jang, H.; Jang, Y. J.; Jaranowski, P.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, D.; Jones, R.; Jonker, R. J. G.; Ju, L.; Haris, K.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Kasprzack, M.; Kasturi, R.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kaufman, K.; Kawabe, K.; Kawamura, S.; Kawazoe, F.; Kéfélian, F.; Keitel, D.; Kelley, D. B.; Kells, W.; Keppel, D. G.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, B. K.; Kim, C.; Kim, K.; Kim, N.; Kim, W.; Kim, Y.-M.; King, E.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kline, J.; Koehlenbeck, S.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D.; Kremin, A.; Kringel, V.; Krishnan, B.; Królak, A.; Kucharczyk, C.; Kudla, S.; Kuehn, G.; Kumar, A.; Kumar, D. Nanda; Kumar, P.; Kumar, R.; Kurdyumov, R.; Kwee, P.; Landry, M.; Lantz, B.; Larson, S.; Lasky, P. D.; Lawrie, C.; Lazzarini, A.; Leaci, P.; Lebigot, E. O.; Lee, C.-H.; Lee, H. K.; Lee, H. M.; Lee, J. J.; Lee, J.; Leonardi, M.; Leong, J. R.; Le Roux, A.; Leroy, N.; Letendre, N.; Levine, B.; Lewis, J. B.; Lhuillier, V.; Li, T. G. F.; Lin, A. C.; Littenberg, T. B.; Litvine, V.; Liu, F.; Liu, H.; Liu, Y.; Liu, Z.; Lloyd, D.; Lockerbie, N. A.; Lockett, V.; Lodhia, D.; Loew, K.; Logue, J.; Lombardi, A. L.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Luan, J.; Lubinski, M. J.; Lück, H.; Lundgren, A. P.; Macarthur, J.; Macdonald, E.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magana-Sandoval, F.; Mageswaran, M.; Mailand, K.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Manca, G. M.; Mandel, I.; Mandic, V.; Mangano, V.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martini, G.; Martynov, D.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; May, G.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Meier, T.; Melatos, A.; Mendell, G.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyer, M. S.; Miao, H.; Michel, C.; Mikhailov, E.; Milano, L.; Miller, J.; Minenkov, Y.; Mingarelli, C. M. F.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moe, B.; Mohan, M.; Mohapatra, S. R. P.; Mokler, F.; Moraru, D.; Moreno, G.; Morgado, N.; Mori, T.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nagy, M. F.; Nardecchia, I.; Nash, T.; Naticchioni, L.; Nayak, R.; Necula, V.; Neri, I.; Neri, M.; Newton, G.; Nguyen, T.; Nishida, E.; Nishizawa, A.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E.; Nuttall, L. K.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oppermann, P.; O'Reilly, B.; Ortega Larcher, W.; O'Shaughnessy, R.; Osthelder, C.; Ottaway, D. J.; Ottens, R. S.; Ou, J.; Overmier, H.; Owen, B. J.; Padilla, C.; Pai, A.; Palomba, C.; Pan, Y.; Pankow, C.; Paoletti, F.; Paoletti, R.; Paris, H.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Pedraza, M.; Peiris, P.; Penn, S.; Perreca, A.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pierro, V.; Pinard, L.; Pindor, B.; Pinto, I. M.; Pitkin, M.; Poeld, J.; Poggiani, R.; Poole, V.; Postiglione, F.; Poux, C.; Predoi, V.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Quetschke, V.; Quintero, E.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Raja, S.; Rajalakshmi, G.; Rakhmanov, M.; Ramet, C.; Rapagnani, P.; Raymond, V.; Re, V.; Reed, C. M.; Reed, T.; Regimbau, T.; Reid, S.; Reitze, D. H.; Ricci, F.; Riesen, R.; Riles, K.; Robertson, N. A.; Robinet, F.; Rocchi, A.; Roddy, S.; Rodriguez, C.; Rodruck, M.; Roever, C.; Rolland, L.; Rollins, J. G.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Salemi, F.; Sammut, L.; Sandberg, V.; Sanders, J.; Sannibale, V.; Santiago-Prieto, I.; Saracco, E.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Savage, R.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schreiber, E.; Schuette, D.; Schulz, B.; Schutz, B. F.; Schwinberg, P.; Scott, J.; Scott, S. M.; Seifert, F.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sidery, T. L.; Siellez, K.; Siemens, X.; Sigg, D.; Simakov, D.; Singer, A.; Singer, L.; Sintes, A. M.; Skelton, G. R.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M. R.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Soden, K.; Son, E. J.; Sorazu, B.; Souradeep, T.; Sperandio, L.; Staley, A.; Steinert, E.; Steinlechner, J.; Steinlechner, S.; Steplewski, S.; Stevens, D.; Stochino, A.; Stone, R.; Strain, K. A.; Straniero, N.; Strigin, S.; Stroeer, A. S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Szeifert, G.; Tacca, M.; Talukder, D.; Tang, L.; Tanner, D. B.; Tarabrin, S. P.; Taylor, R.; ter Braack, A. P. M.; Thirugnanasambandam, M. P.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C. V.; Torrie, C. I.; Travasso, F.; Traylor, G.; Tse, M.; Ugolini, D.; Unnikrishnan, C. S.; Vahlbruch, H.; Vajente, G.; Vallisneri, M.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van der Sluys, M. V.; van Heijningen, J.; van Veggel, A. A.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, P. J.; Veitch, J.; Venkateswara, K.; Verkindt, D.; Verma, S.; Vetrano, F.; Viceré, A.; Vincent-Finley, R.; Vinet, J.-Y.; Vitale, S.; Vitale, S.; Vlcek, B.; Vo, T.; Vocca, H.; Vorvick, C.; Vousden, W. D.; Vrinceanu, D.; Vyachanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Waldman, S. J.; Walker, M.; Wallace, L.; Wan, Y.; Wang, J.; Wang, M.; Wang, X.; Wanner, A.; Ward, R. L.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Wibowo, S.; Wiesner, K.; Wilkinson, C.; Williams, L.; Williams, R.; Williams, T.; Willis, J. L.; Willke, B.; Wimmer, M.; Winkelmann, L.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yancey, C. C.; Yang, H.; Yeaton-Massey, D.; Yoshida, S.; Yum, H.; Yvert, M.; ZadroŻny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, F.; Zhang, L.; Zhao, C.; Zhu, H.; Zhu, X. J.; Zotov, N.; Zucker, M. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2015-01-01

Searches for a stochastic gravitational-wave background (SGWB) using terrestrial detectors typically involve cross-correlating data from pairs of detectors. The sensitivity of such cross-correlation analyses depends, among other things, on the separation between the two detectors: the smaller the separation, the better the sensitivity. Hence, a colocated detector pair is more sensitive to a gravitational-wave background than a noncolocated detector pair. However, colocated detectors are also expected to suffer from correlated noise from instrumental and environmental effects that could contaminate the measurement of the background. Hence, methods to identify and mitigate the effects of correlated noise are necessary to achieve the potential increase in sensitivity of colocated detectors. Here we report on the first SGWB analysis using the two LIGO Hanford detectors and address the complications arising from correlated environmental noise. We apply correlated noise identification and mitigation techniques to data taken by the two LIGO Hanford detectors, H1 and H2, during LIGO's fifth science run. At low frequencies, 40-460 Hz, we are unable to sufficiently mitigate the correlated noise to a level where we may confidently measure or bound the stochastic gravitational-wave signal. However, at high frequencies, 460-1000 Hz, these techniques are sufficient to set a 95% confidence level upper limit on the gravitational-wave energy density of Ω (f )<7.7 ×1 0-4(f /900 Hz )3 , which improves on the previous upper limit by a factor of ˜180 . In doing so, we demonstrate techniques that will be useful for future searches using advanced detectors, where correlated noise (e.g., from global magnetic fields) may affect even widely separated detectors.

Searching for Stochastic Gravitational Waves Using Data from the Two Co-Located LIGO Hanford Detectors

NASA Technical Reports Server (NTRS)

Aasi, J.; Abadie, J.; Abbott, B. P.; Abbott, R.; Abbott, T.; Abernathy, M. R.; Accadia, T.; Acernese, F.; Adams, C.; Adams, T.;

Coupled Effects of non-Newtonian Rheology and Aperture Variability on Flow in a Single Fracture

NASA Astrophysics Data System (ADS)

Di Federico, V.; Felisa, G.; Lauriola, I.; Longo, S.

2017-12-01

Modeling of non-Newtonian flow in fractured media is essential in hydraulic fracturing and drilling operations, EOR, environmental remediation, and to understand magma intrusions. An important step in the modeling effort is a detailed understanding of flow in a single fracture, as the fracture aperture is spatially variable. A large bibliography exists on Newtonian and non-Newtonian flow in variable aperture fractures. Ultimately, stochastic or deterministic modeling leads to the flowrate under a given pressure gradient as a function of the parameters describing the aperture variability and the fluid rheology. Typically, analytical or numerical studies are performed adopting a power-law (Oswald-de Waele) model. Yet the power-law model, routinely used e.g. for hydro-fracturing modeling, does not characterize real fluids at low and high shear rates. A more appropriate rheological model is provided by e.g. the four-parameter Carreau constitutive equation, which is in turn approximated by the more tractable truncated power-law model. Moreover, fluids of interest may exhibit yield stress, which requires the Bingham or Herschel-Bulkely model. This study employs different rheological models in the context of flow in variable aperture fractures, with the aim of understanding the coupled effect of rheology and aperture spatial variability with a simplified model. The aperture variation, modeled within a stochastic or deterministic framework, is taken to be one-dimensional and i) perpendicular; ii) parallel to the flow direction; for stochastic modeling, the influence of different distribution functions is examined. Results for the different rheological models are compared with those obtained for the pure power-law. The adoption of the latter model leads to overestimation of the flowrate, more so for large aperture variability. The presence of yield stress also induces significant changes in the resulting flowrate for assigned external pressure gradient.

Treatment of constraints in the stochastic quantization method and covariantized Langevin equation

NASA Astrophysics Data System (ADS)

Ikegami, Kenji; Kimura, Tadahiko; Mochizuki, Riuji

1993-04-01

We study the treatment of the constraints in the stochastic quantization method. We improve the treatment of the stochastic consistency condition proposed by Namiki et al. by suitably taking into account the Ito calculus. Then we obtain an improved Langevi equation and the Fokker-Planck equation which naturally leads to the correct path integral quantization of the constrained system as the stochastic equilibrium state. This treatment is applied to an O( N) non-linear α model and it is shown that singular terms appearing in the improved Langevin equation cancel out the σ n(O) divergences in one loop order. We also ascertain that the above Langevin equation, rewritten in terms of idependent variables, is actually equivalent to the one in the general-coordinate transformation covariant and vielbein-rotation invariant formalish.

Dynamics of stochastic SEIS epidemic model with varying population size

NASA Astrophysics Data System (ADS)

Liu, Jiamin; Wei, Fengying

2016-12-01

We introduce the stochasticity into a deterministic model which has state variables susceptible-exposed-infected with varying population size in this paper. The infected individuals could return into susceptible compartment after recovering. We show that the stochastic model possesses a unique global solution under building up a suitable Lyapunov function and using generalized Itô's formula. The densities of the exposed and infected tend to extinction when some conditions are being valid. Moreover, the conditions of persistence to a global solution are derived when the parameters are subject to some simple criteria. The stochastic model admits a stationary distribution around the endemic equilibrium, which means that the disease will prevail. To check the validity of the main results, numerical simulations are demonstrated as end of this contribution.

Beller Lectureship: Stochasticity and robustness in growth and morphogenesis

NASA Astrophysics Data System (ADS)

Boudaoud, Arezki

How do organisms cope with natural variability to achieve well-defined morphologies and architectures? We addressed this question by combining experiments with live plants and analyses of stochastic models that integrate cell-cell communication and tissue mechanics. This led us to counterintuitive results on the role of noise in development, whereby noise is either filtered or enhanced according to the level at which it is acting.

Investigation of advanced UQ for CRUD prediction with VIPRE.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eldred, Michael Scott

2011-09-01

This document summarizes the results from a level 3 milestone study within the CASL VUQ effort. It demonstrates the application of 'advanced UQ,' in particular dimension-adaptive p-refinement for polynomial chaos and stochastic collocation. The study calculates statistics for several quantities of interest that are indicators for the formation of CRUD (Chalk River unidentified deposit), which can lead to CIPS (CRUD induced power shift). Stochastic expansion methods are attractive methods for uncertainty quantification due to their fast convergence properties. For smooth functions (i.e., analytic, infinitely-differentiable) in L{sup 2} (i.e., possessing finite variance), exponential convergence rates can be obtained under order refinementmore » for integrated statistical quantities of interest such as mean, variance, and probability. Two stochastic expansion methods are of interest: nonintrusive polynomial chaos expansion (PCE), which computes coefficients for a known basis of multivariate orthogonal polynomials, and stochastic collocation (SC), which forms multivariate interpolation polynomials for known coefficients. Within the DAKOTA project, recent research in stochastic expansion methods has focused on automated polynomial order refinement ('p-refinement') of expansions to support scalability to higher dimensional random input spaces [4, 3]. By preferentially refining only in the most important dimensions of the input space, the applicability of these methods can be extended from O(10{sup 0})-O(10{sup 1}) random variables to O(10{sup 2}) and beyond, depending on the degree of anisotropy (i.e., the extent to which randominput variables have differing degrees of influence on the statistical quantities of interest (QOIs)). Thus, the purpose of this study is to investigate the application of these adaptive stochastic expansion methods to the analysis of CRUD using the VIPRE simulation tools for two different plant models of differing random dimension, anisotropy, and smoothness.« less

Intermittent search strategies

NASA Astrophysics Data System (ADS)

Bénichou, O.; Loverdo, C.; Moreau, M.; Voituriez, R.

2011-01-01

This review examines intermittent target search strategies, which combine phases of slow motion, allowing the searcher to detect the target, and phases of fast motion during which targets cannot be detected. It is first shown that intermittent search strategies are actually widely observed at various scales. At the macroscopic scale, this is, for example, the case of animals looking for food; at the microscopic scale, intermittent transport patterns are involved in a reaction pathway of DNA-binding proteins as well as in intracellular transport. Second, generic stochastic models are introduced, which show that intermittent strategies are efficient strategies that enable the minimization of search time. This suggests that the intrinsic efficiency of intermittent search strategies could justify their frequent observation in nature. Last, beyond these modeling aspects, it is proposed that intermittent strategies could also be used in a broader context to design and accelerate search processes.

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.

Evolution of the Climate Continuum from the Mid-Miocene Climatic Optimum to the Present

NASA Astrophysics Data System (ADS)

Aswasereelert, W.; Meyers, S. R.; Hinnov, L. A.; Kelly, D.

2011-12-01

The recognition of orbital rhythms in paleoclimate data has led to a rich understanding of climate evolution during the Neogene and Quaternary. In contrast, changes in stochastic variability associated with the transition from unipolar to bipolar glaciation have received less attention, although the stochastic component likely preserves key insights about climate. In this study, we seek to evaluate the dominance and character of stochastic climate energy since the Middle Miocene Climatic Optimum (~17 Ma). These analyses extend a previous study that suggested diagnostic stochastic responses associated with Northern Hemisphere ice sheet development during the Plio-Pleistocene (Meyers and Hinnov, 2010). A critical and challenging step necessary to conduct the work is the conversion of depth data to time data. We investigate climate proxy datasets using multiple time scale hypotheses, including depth-derived time scales, sedimentologic/geochemical "tuning", minimal orbital tuning, and comprehensive orbital tuning. To extract the stochastic component of climate, and also explore potential relationships between the orbital parameters and paleoclimate response, a number of approaches rooted in Thomson's (1982) multi-taper spectral method (MTM) are applied. Importantly, the MTM technique is capable of separating the spectral "continuum" - a measure of stochastic variability - from the deterministic periodic orbital signals (spectral "lines") preserved in proxy data. Time series analysis of the proxy records using different chronologic approaches allows us to evaluate the sensitivity of our conclusion about stochastic and deterministic orbital processes during the Middle Miocene to present. Moreover, comparison of individual records permits examination of the spatial dependence of the identified climate responses. Meyers, S.R., and Hinnov, L.A. (2010), Northern Hemisphere glaciation and the evolution of Plio-Pleistocene climate noise: Paleoceanography, 25, PA3207, doi:10.1029/2009PA001834. Thomson, D.J. (1982), Spectrum estimation and harmonic analysis: IEEE Proceedings, v. 70, p. 1055-1096.

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

NASA Astrophysics Data System (ADS)

López, Cristian; Zhong, Wei; Lu, Siliang; Cong, Feiyun; Cortese, Ignacio

2017-12-01

Vibration signals are widely used for bearing fault detection and diagnosis. When signals are acquired in the field, usually, the faulty periodic signal is weak and is concealed by noise. Various de-noising methods have been developed to extract the target signal from the raw signal. Stochastic resonance (SR) is a technique that changed the traditional denoising process, in which the weak periodic fault signal can be identified by adding an expression, the potential, to the raw signal and solving a differential equation problem. However, current SR methods have some deficiencies such us limited filtering performance, low frequency input signal and sequential search for optimum parameters. Consequently, in this study, we explore the application of SR based on the FitzHug-Nagumo (FHN) potential in rolling bearing vibration signals. Besides, we improve the search of the SR optimum parameters by the use of particle swarm optimization (PSO). The effectiveness of the proposed method is verified by using both simulated and real bearing data sets.

Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu\\xA0search

NASA Astrophysics Data System (ADS)

Yeh, Cheng-Ta; Lin, Yi-Kuei; Yang, Jo-Yun

2018-07-01

Network reliability is an important performance index for many real-life systems, such as electric power systems, computer systems and transportation systems. These systems can be modelled as stochastic-flow networks (SFNs) composed of arcs and nodes. Most system supervisors respect the network reliability maximization by finding the optimal multi-state resource assignment, which is one resource to each arc. However, a disaster may cause correlated failures for the assigned resources, affecting the network reliability. This article focuses on determining the optimal resource assignment with maximal network reliability for SFNs. To solve the problem, this study proposes a hybrid algorithm integrating the genetic algorithm and tabu search to determine the optimal assignment, called the hybrid GA-TS algorithm (HGTA), and integrates minimal paths, recursive sum of disjoint products and the correlated binomial distribution to calculate network reliability. Several practical numerical experiments are adopted to demonstrate that HGTA has better computational quality than several popular soft computing algorithms.

Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly

NASA Astrophysics Data System (ADS)

Eugène, Sarah; Xue, Wei-Feng; Robert, Philippe; Doumic, Marie

2016-05-01

Self-assembly of proteins into amyloid aggregates is an important biological phenomenon associated with human diseases such as Alzheimer's disease. Amyloid fibrils also have potential applications in nano-engineering of biomaterials. The kinetics of amyloid assembly show an exponential growth phase preceded by a lag phase, variable in duration as seen in bulk experiments and experiments that mimic the small volumes of cells. Here, to investigate the origins and the properties of the observed variability in the lag phase of amyloid assembly currently not accounted for by deterministic nucleation dependent mechanisms, we formulate a new stochastic minimal model that is capable of describing the characteristics of amyloid growth curves despite its simplicity. We then solve the stochastic differential equations of our model and give mathematical proof of a central limit theorem for the sample growth trajectories of the nucleated aggregation process. These results give an asymptotic description for our simple model, from which closed form analytical results capable of describing and predicting the variability of nucleated amyloid assembly were derived. We also demonstrate the application of our results to inform experiments in a conceptually friendly and clear fashion. Our model offers a new perspective and paves the way for a new and efficient approach on extracting vital information regarding the key initial events of amyloid formation.

Stochastical analysis of surfactant-enhanced remediation of denser-than-water nonaqueous phase liquid (DNAPL)-contaminated soils.

PubMed

Zhang, Renduo; Wood, A Lynn; Enfield, Carl G; Jeong, Seung-Woo

2003-01-01

Stochastical analysis was performed to assess the effect of soil spatial variability and heterogeneity on the recovery of denser-than-water nonaqueous phase liquids (DNAPL) during the process of surfactant-enhanced remediation. UTCHEM, a three-dimensional, multicomponent, multiphase, compositional model, was used to simulate water flow and chemical transport processes in heterogeneous soils. Soil spatial variability and heterogeneity were accounted for by considering the soil permeability as a spatial random variable and a geostatistical method was used to generate random distributions of the permeability. The randomly generated permeability fields were incorporated into UTCHEM to simulate DNAPL transport in heterogeneous media and stochastical analysis was conducted based on the simulated results. From the analysis, an exponential relationship between average DNAPL recovery and soil heterogeneity (defined as the standard deviation of log of permeability) was established with a coefficient of determination (r2) of 0.991, which indicated that DNAPL recovery decreased exponentially with increasing soil heterogeneity. Temporal and spatial distributions of relative saturations in the water phase, DNAPL, and microemulsion in heterogeneous soils were compared with those in homogeneous soils and related to soil heterogeneity. Cleanup time and uncertainty to determine DNAPL distributions in heterogeneous soils were also quantified. The study would provide useful information to design strategies for the characterization and remediation of nonaqueous phase liquid-contaminated soils with spatial variability and heterogeneity.

An observational method for fast stochastic X-ray polarimetry timing

NASA Astrophysics Data System (ADS)

Ingram, Adam R.; Maccarone, Thomas J.

2017-11-01

The upcoming launch of the first space based X-ray polarimeter in ˜40 yr will provide powerful new diagnostic information to study accreting compact objects. In particular, analysis of rapid variability of the polarization degree and angle will provide the opportunity to probe the relativistic motions of material in the strong gravitational fields close to the compact objects, and enable new methods to measure black hole and neutron star parameters. However, polarization properties are measured in a statistical sense, and a statistically significant polarization detection requires a fairly long exposure, even for the brightest objects. Therefore, the sub-minute time-scales of interest are not accessible using a direct time-resolved analysis of polarization degree and angle. Phase-folding can be used for coherent pulsations, but not for stochastic variability such as quasi-periodic oscillations. Here, we introduce a Fourier method that enables statistically robust detection of stochastic polarization variability for arbitrarily short variability time-scales. Our method is analogous to commonly used spectral-timing techniques. We find that it should be possible in the near future to detect the quasi-periodic swings in polarization angle predicted by Lense-Thirring precession of the inner accretion flow. This is contingent on the mean polarization degree of the source being greater than ˜4-5 per cent, which is consistent with the best current constraints on Cygnus X-1 from the late 1970s.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

PubMed

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization

PubMed Central

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Chen, Chun-Hung

2017-01-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort. PMID:29170617

A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

PubMed

Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju

2004-10-01

Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

Modeling the Webgraph: How Far We Are

NASA Astrophysics Data System (ADS)

Donato, Debora; Laura, Luigi; Leonardi, Stefano; Millozzi, Stefano

The following sections are included: * Introduction * Preliminaries * WebBase * In-degree and out-degree * PageRank * Bipartite cliques * Strongly connected components * Stochastic models of the webgraph * Models of the webgraph * A multi-layer model * Large scale simulation * Algorithmic techniques for generating and measuring webgraphs * Data representation and multifiles * Generating webgraphs * Traversal with two bits for each node * Semi-external breadth first search * Semi-external depth first search * Computation of the SCCs * Computation of the bow-tie regions * Disjoint bipartite cliques * PageRank * Summary and outlook

3D aquifer characterization using stochastic streamline calibration

NASA Astrophysics Data System (ADS)

Jang, Minchul

2007-03-01

In this study, a new inverse approach, stochastic streamline calibration is proposed. Using both a streamline concept and a stochastic technique, stochastic streamline calibration optimizes an identified field to fit in given observation data in a exceptionally fast and stable fashion. In the stochastic streamline calibration, streamlines are adopted as basic elements not only for describing fluid flow but also for identifying the permeability distribution. Based on the streamline-based inversion by Agarwal et al. [Agarwal B, Blunt MJ. Streamline-based method with full-physics forward simulation for history matching performance data of a North sea field. SPE J 2003;8(2):171-80], Wang and Kovscek [Wang Y, Kovscek AR. Streamline approach for history matching production data. SPE J 2000;5(4):353-62], permeability is modified rather along streamlines than at the individual gridblocks. Permeabilities in the gridblocks which a streamline passes are adjusted by being multiplied by some factor such that we can match flow and transport properties of the streamline. This enables the inverse process to achieve fast convergence. In addition, equipped with a stochastic module, the proposed technique supportively calibrates the identified field in a stochastic manner, while incorporating spatial information into the field. This prevents the inverse process from being stuck in local minima and helps search for a globally optimized solution. Simulation results indicate that stochastic streamline calibration identifies an unknown permeability exceptionally quickly. More notably, the identified permeability distribution reflected realistic geological features, which had not been achieved in the original work by Agarwal et al. with the limitations of the large modifications along streamlines for matching production data only. The constructed model by stochastic streamline calibration forecasted transport of plume which was similar to that of a reference model. By this, we can expect the proposed approach to be applied to the construction of an aquifer model and forecasting of the aquifer performances of interest.

Fault model of the 2014 Cephalonia seismic sequence - Evidence of spatiotemporal fault segmentation along the NW edge of Aegean Arc

NASA Astrophysics Data System (ADS)

Saltogianni, Vasso; Moschas, Fanis; Stiros, Stathis

2017-04-01

Finite fault models (FFM) are presented for the two main shocks of the 2014 Cephalonia (Ionian Sea, Greece) seismic sequence (M 6.0) which produced extreme peak ground accelerations ( 0.7g) in the west edge of the Aegean Arc, an area in which the poor coverage by seismological and GPS/INSAR data makes FFM a real challenge. Modeling was based on co-seismic GPS data and on the recently introduced TOPological INVersion algorithm. The latter is a novel uniform grid search-based technique in n-dimensional spaces, is based on the concept of stochastic variables and which can identify multiple unconstrained ("free") solutions in a specified search space. Derived FFMs for the 2014 earthquakes correspond to an essentially strike slip fault and of part of a shallow thrust, the surface projection of both of which run roughly along the west coast of Cephalonia. Both faults correlate with pre-existing faults. The 2014 faults, in combination with the faults of the 2003 and 2015 Leucas earthquakes farther NE, form a string of oblique slip, partly overlapping fault segments with variable geometric and kinematic characteristics along the NW edge of the Aegean Arc. This composite fault, usually regarded as the Cephalonia Transform Fault, accommodates shear along this part of the Arc. Because of the highly fragmented crust, dominated by major thrusts in this area, fault activity is associated with 20km long segments and magnitude 6.0-6.5 earthquakes recurring in intervals of a few seconds to 10 years.

Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects

PubMed Central

Baumann, Hendrik; Sandmann, Werner

2016-01-01

Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity. PMID:27010993

Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects.

PubMed

Baumann, Hendrik; Sandmann, Werner

2016-01-01

Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity.

A stochastic hybrid systems based framework for modeling dependent failure processes

PubMed Central

Fan, Mengfei; Zeng, Zhiguo; Zio, Enrico; Kang, Rui; Chen, Ying

2017-01-01

In this paper, we develop a framework to model and analyze systems that are subject to dependent, competing degradation processes and random shocks. The degradation processes are described by stochastic differential equations, whereas transitions between the system discrete states are triggered by random shocks. The modeling is, then, based on Stochastic Hybrid Systems (SHS), whose state space is comprised of a continuous state determined by stochastic differential equations and a discrete state driven by stochastic transitions and reset maps. A set of differential equations are derived to characterize the conditional moments of the state variables. System reliability and its lower bounds are estimated from these conditional moments, using the First Order Second Moment (FOSM) method and Markov inequality, respectively. The developed framework is applied to model three dependent failure processes from literature and a comparison is made to Monte Carlo simulations. The results demonstrate that the developed framework is able to yield an accurate estimation of reliability with less computational costs compared to traditional Monte Carlo-based methods. PMID:28231313

A stochastic hybrid systems based framework for modeling dependent failure processes.

PubMed

Fan, Mengfei; Zeng, Zhiguo; Zio, Enrico; Kang, Rui; Chen, Ying

2017-01-01

In this paper, we develop a framework to model and analyze systems that are subject to dependent, competing degradation processes and random shocks. The degradation processes are described by stochastic differential equations, whereas transitions between the system discrete states are triggered by random shocks. The modeling is, then, based on Stochastic Hybrid Systems (SHS), whose state space is comprised of a continuous state determined by stochastic differential equations and a discrete state driven by stochastic transitions and reset maps. A set of differential equations are derived to characterize the conditional moments of the state variables. System reliability and its lower bounds are estimated from these conditional moments, using the First Order Second Moment (FOSM) method and Markov inequality, respectively. The developed framework is applied to model three dependent failure processes from literature and a comparison is made to Monte Carlo simulations. The results demonstrate that the developed framework is able to yield an accurate estimation of reliability with less computational costs compared to traditional Monte Carlo-based methods.

Distinct Sources of Deterministic and Stochastic Components of Action Timing Decisions in Rodent Frontal Cortex.

PubMed

Murakami, Masayoshi; Shteingart, Hanan; Loewenstein, Yonatan; Mainen, Zachary F

2017-05-17

The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of stochastic sodium channels on extracellular excitation of myelinated nerve fibers.

PubMed

Mino, Hiroyuki; Grill, Warren M

2002-06-01

The effects of the stochastic gating properties of sodium channels on the extracellular excitation properties of mammalian nerve fibers was determined by computer simulation. To reduce computation time, a hybrid multicompartment cable model including five central nodes of Ranvier containing stochastic sodium channels and 16 flanking nodes containing detenninistic membrane dynamics was developed. The excitation properties of the hybrid cable model were comparable with those of a full stochastic cable model including 21 nodes of Ranvier containing stochastic sodium channels, indicating the validity of the hybrid cable model. The hybrid cable model was used to investigate whether or not the excitation properties of extracellularly activated fibers were influenced by the stochastic gating of sodium channels, including spike latencies, strength-duration (SD), current-distance (IX), and recruitment properties. The stochastic properties of the sodium channels in the hybrid cable model had the greatest impact when considering the temporal dynamics of nerve fibers, i.e., a large variability in latencies, while they did not influence the SD, IX, or recruitment properties as compared with those of the conventional deterministic cable model. These findings suggest that inclusion of stochastic nodes is not important for model-based design of stimulus waveforms for activation of motor nerve fibers. However, in cases where temporal fine structure is important, for example in sensory neural prostheses in the auditory and visual systems, the stochastic properties of the sodium channels may play a key role in the design of stimulus waveforms.

A unified stochastic formulation of dissipative quantum dynamics. I. Generalized hierarchical equations

NASA Astrophysics Data System (ADS)

Hsieh, Chang-Yu; Cao, Jianshu

2018-01-01

We extend a standard stochastic theory to study open quantum systems coupled to a generic quantum environment. We exemplify the general framework by studying a two-level quantum system coupled bilinearly to the three fundamental classes of non-interacting particles: bosons, fermions, and spins. In this unified stochastic approach, the generalized stochastic Liouville equation (SLE) formally captures the exact quantum dissipations when noise variables with appropriate statistics for different bath models are applied. Anharmonic effects of a non-Gaussian bath are precisely encoded in the bath multi-time correlation functions that noise variables have to satisfy. Starting from the SLE, we devise a family of generalized hierarchical equations by averaging out the noise variables and expand bath multi-time correlation functions in a complete basis of orthonormal functions. The general hierarchical equations constitute systems of linear equations that provide numerically exact simulations of quantum dynamics. For bosonic bath models, our general hierarchical equation of motion reduces exactly to an extended version of hierarchical equation of motion which allows efficient simulation for arbitrary spectral densities and temperature regimes. Similar efficiency and flexibility can be achieved for the fermionic bath models within our formalism. The spin bath models can be simulated with two complementary approaches in the present formalism. (I) They can be viewed as an example of non-Gaussian bath models and be directly handled with the general hierarchical equation approach given their multi-time correlation functions. (II) Alternatively, each bath spin can be first mapped onto a pair of fermions and be treated as fermionic environments within the present formalism.

Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array

NASA Astrophysics Data System (ADS)

Wu, Xiaohua; Hu, Xiaosong; Moura, Scott; Yin, Xiaofeng; Pickert, Volker

2016-11-01

Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the random-variable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf.

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

PubMed Central

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, Stephen R.; Sandison, George A.; Kinahan, Paul E.

2015-01-01

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes. PMID:26251842

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

PubMed

Nyflot, Matthew J; Yang, Fei; Byrd, Darrin; Bowen, Stephen R; Sandison, George A; Kinahan, Paul E

2015-10-01

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Brownston, Lee

2012-01-01

Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

The critical domain size of stochastic population models.

PubMed

Reimer, Jody R; Bonsall, Michael B; Maini, Philip K

2017-02-01

Identifying the critical domain size necessary for a population to persist is an important question in ecology. Both demographic and environmental stochasticity impact a population's ability to persist. Here we explore ways of including this variability. We study populations with distinct dispersal and sedentary stages, which have traditionally been modelled using a deterministic integrodifference equation (IDE) framework. Individual-based models (IBMs) are the most intuitive stochastic analogues to IDEs but yield few analytic insights. We explore two alternate approaches; one is a scaling up to the population level using the Central Limit Theorem, and the other a variation on both Galton-Watson branching processes and branching processes in random environments. These branching process models closely approximate the IBM and yield insight into the factors determining the critical domain size for a given population subject to stochasticity.

Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

NASA Astrophysics Data System (ADS)

Jia, Chaoqing; Hu, Jun; Chen, Dongyan; Liu, Yurong; Alsaadi, Fuad E.

2018-07-01

In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

CSI 2264: Characterizing Young Stars in NGC 2264 with Stochastically Varying Light Curves

NASA Astrophysics Data System (ADS)

Stauffer, John; Cody, Ann Marie; Rebull, Luisa; Hillenbrand, Lynne A.; Turner, Neal J.; Carpenter, John; Carey, Sean; Terebey, Susan; Morales-Calderón, María; Alencar, Silvia H. P.; McGinnis, Pauline; Sousa, Alana; Bouvier, Jerome; Venuti, Laura; Hartmann, Lee; Calvet, Nuria; Micela, Giusi; Flaccomio, Ettore; Song, Inseok; Gutermuth, Rob; Barrado, David; Vrba, Frederick J.; Covey, Kevin; Herbst, William; Gillen, Edward; Medeiros Guimarães, Marcelo; Bouy, Herve; Favata, Fabio

2016-03-01

We provide CoRoT and Spitzer light curves and other supporting data for 17 classical T Tauri stars in NGC 2264 whose CoRoT light curves exemplify the “stochastic” light curve class as defined in 2014 by Cody et al. The most probable physical mechanism to explain the optical variability within this light curve class is time-dependent mass accretion onto the stellar photosphere, producing transient hot spots. Where we have appropriate spectral data, we show that the veiling variability in these stars is consistent in both amplitude and timescale with the optical light curve morphology. The veiling variability is also well-correlated with the strength of the He I 6678 Å emission line, predicted by models to arise in accretion shocks on or near the stellar photosphere. Stars with accretion burst light curve morphology also have variable mass accretion. The stochastic and accretion burst light curves can both be explained by a simple model of randomly occurring flux bursts, with the stochastic light curve class having a higher frequency of lower amplitude events. Members of the stochastic light curve class have only moderate mass accretion rates. Their Hα profiles usually have blueshifted absorption features, probably originating in a disk wind. The lack of periodic signatures in the light curves suggests that little of the variability is due to long-lived hot spots rotating into or out of our line of sight; instead, the primary driver of the observed photometric variability is likely to be instabilities in the inner disk that lead to variable mass accretion. Based on data from the Spitzer and CoRoT missions, as well as the Canada-France-Hawaii Telescope (CFHT) MegaCam CCD, and the European Southern Observatory Very Large Telescope, Paranal Chile, under program 088.C-0239. The CoRoT space mission was developed and is operated by the French space agency CNES, with particpiation of ESA’s RSSD and Science Programmes, Austria, Belgium, Brazil, Germany, and Spain. MegaCam is a joint project of CFHT and CEA/DAPNIA, which is operated by the National Research Council (NRC) of Canada, the Institute National des Sciences de l’Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii.

On the spatial decorrelation of stochastic solar resource variability at long timescales

DOE PAGES

Perez, Marc J. R.; Fthenakis, Vasilis M.

2015-05-16

Understanding the spatial and temporal characteristics of solar resource variability is important because it helps inform the discussion surrounding the merits of geographic dispersion and subsequent electrical interconnection of photovoltaics as part of a portfolio of future solutions for coping with this variability. The unpredictable resource variability arising from the stochastic nature of meteorological phenomena (from the passage of clouds to the movement of weather systems) is of most concern for achieving high PV penetration because unlike the passage of seasons or the shift from day to night, the uncertainty makes planning a challenge. A suitable proxy for unpredictable solarmore » resource variability at any given location is the series of variations in the clearness index from one time period to the next because the clearness index is largely independent of the predictable influence of solar geometry. At timescales shorter than one day, the correlation between these variations in clearness index at pairs of distinct geographic locations decreases with spatial extent and with timescale. As the aggregate variability across N decorrelated locations decreases as 1/√N, identifying the distance required to achieve this decorrelation is critical to quantifying the expected reduction in variability from geographic dispersion.« less

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

PubMed

Fatichi, S; Rimkus, S; Burlando, P; Bordoy, R

2014-09-15

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. Copyright © 2013 Elsevier B.V. All rights reserved.

Emergence of Lévy Walks from Second-Order Stochastic Optimization

NASA Astrophysics Data System (ADS)

Kuśmierz, Łukasz; Toyoizumi, Taro

2017-12-01

In natural foraging, many organisms seem to perform two different types of motile search: directed search (taxis) and random search. The former is observed when the environment provides cues to guide motion towards a target. The latter involves no apparent memory or information processing and can be mathematically modeled by random walks. We show that both types of search can be generated by a common mechanism in which Lévy flights or Lévy walks emerge from a second-order gradient-based search with noisy observations. No explicit switching mechanism is required—instead, continuous transitions between the directed and random motions emerge depending on the Hessian matrix of the cost function. For a wide range of scenarios, the Lévy tail index is α =1 , consistent with previous observations in foraging organisms. These results suggest that adopting a second-order optimization method can be a useful strategy to combine efficient features of directed and random search.

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2018-01-01

This study is concerned with the intrinsic temporal scales of the variability in the surface solar irradiance (SSI). The data consist of decennial time series of daily means of the SSI obtained from high-quality measurements of the broadband solar radiation impinging on a horizontal plane at ground level, issued from different Baseline Surface Radiation Network (BSRN) ground stations around the world. First, embedded oscillations sorted in terms of increasing timescales of the data are extracted by empirical mode decomposition (EMD). Next, Hilbert spectral analysis is applied to obtain an amplitude-modulation-frequency-modulation (AM-FM) representation of the data. The time-varying nature of the characteristic timescales of variability, along with the variations in the signal intensity, are thus revealed. A novel, adaptive null hypothesis based on the general statistical characteristics of noise is employed in order to discriminate between the different features of the data, those that have a deterministic origin and those being realizations of various stochastic processes. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency variability components, irrespective of the geographical location or of the local climate. Moreover, the amplitude of this latter feature is shown to be modulated by variations in the yearly cycle, which is indicative of nonlinear multiplicative cross-scale couplings. The study has possible implications on the modeling and the forecast of the surface solar radiation, by clearly discriminating the deterministic from the quasi-stochastic character of the data, at different local timescales.

Quantifying Intrinsic and Extrinsic Variability in Stochastic Gene Expression Models

PubMed Central

Singh, Abhyudai; Soltani, Mohammad

2013-01-01

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters. PMID:24391934

Quantifying intrinsic and extrinsic variability in stochastic gene expression models.

PubMed

Singh, Abhyudai; Soltani, Mohammad

2013-01-01

Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

STOCHASTIC DESCRIPTION OF SUBGRID POLLUTANT VARIABILITY IN CMAQ

EPA Science Inventory

This paper describes a tool for investigating and describing fine scale spatial variability in model concentration fields with the goal of improving the use of air quality models for driving exposure modeling to assess human risk to hazardous air pollutants or air toxics. Region...

Theory Learning as Stochastic Search in the Language of Thought

ERIC Educational Resources Information Center

Ullman, Tomer D.; Goodman, Noah D.; Tenenbaum, Joshua B.

2012-01-01

We present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While…

A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates

ERIC Educational Resources Information Center

Venables, Anne; Tan, Grace

2007-01-01

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…

Solving large scale traveling salesman problems by chaotic neurodynamics.

PubMed

Hasegawa, Mikio; Ikeguch, Tohru; Aihara, Kazuyuki

2002-03-01

We propose a novel approach for solving large scale traveling salesman problems (TSPs) by chaotic dynamics. First, we realize the tabu search on a neural network, by utilizing the refractory effects as the tabu effects. Then, we extend it to a chaotic neural network version. We propose two types of chaotic searching methods, which are based on two different tabu searches. While the first one requires neurons of the order of n2 for an n-city TSP, the second one requires only n neurons. Moreover, an automatic parameter tuning method of our chaotic neural network is presented for easy application to various problems. Last, we show that our method with n neurons is applicable to large TSPs such as an 85,900-city problem and exhibits better performance than the conventional stochastic searches and the tabu searches.

The internalist perspective on inevitable arbitrage in financial markets

NASA Astrophysics Data System (ADS)

Matsuno, Koichiro

2003-06-01

Arbitrage as an inevitable component of financial markets is due to the robust interplay between the continuous and the discontinuous stochastic variables appearing in the underlying dynamics. We present empirical evidence of such an arbitrage through the laboratory experiment on a portfolio management in the Japan-United States financial markets over the last several years, under the condition that the asset allocation was updated every day over the entire period. The portfolio management addressing the foreign exchange, the stock, and the bond markets was accomplished as referring to and processing only those empirical data that have been complied by and made available from the monetary authorities and the relevant financial markets so far. The averaged annual yield of the portfolio counted in the denomination of US currency was slightly greater than the averaged yield of the same physical assets counted in the denomination of Japanese currency, indicating the occurrence of arbitrage pricing in the financial markets. Daily update of asset allocation was conducted as referring to the predictive movement internal to the dynamics such that monetary flow variables, that are discontinuously stochastic upon the act of measurement internal to the markets, generate monetary stock variables that turn out to be both continuously stochastic and robust in the effect.

Trend assessment: applications for hydrology and climate research

NASA Astrophysics Data System (ADS)

Kallache, M.; Rust, H. W.; Kropp, J.

2005-02-01

The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.

Approximate reduction of linear population models governed by stochastic differential equations: application to multiregional models.

PubMed

Sanz, Luis; Alonso, Juan Antonio

2017-12-01

In this work we develop approximate aggregation techniques in the context of slow-fast linear population models governed by stochastic differential equations and apply the results to the treatment of populations with spatial heterogeneity. Approximate aggregation techniques allow one to transform a complex system involving many coupled variables and in which there are processes with different time scales, by a simpler reduced model with a fewer number of 'global' variables, in such a way that the dynamics of the former can be approximated by that of the latter. In our model we contemplate a linear fast deterministic process together with a linear slow process in which the parameters are affected by additive noise, and give conditions for the solutions corresponding to positive initial conditions to remain positive for all times. By letting the fast process reach equilibrium we build a reduced system with a lesser number of variables, and provide results relating the asymptotic behaviour of the first- and second-order moments of the population vector for the original and the reduced system. The general technique is illustrated by analysing a multiregional stochastic system in which dispersal is deterministic and the rate growth of the populations in each patch is affected by additive noise.

Improved Upper Limits on the Stochastic Gravitational-Wave Background from 2009-2010 LIGO and Virgo Data

NASA Astrophysics Data System (ADS)

Aasi, J.; Abbott, B. P.; Abbott, R.; Abbott, T.; Abernathy, M. R.; Accadia, T.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Affeldt, C.; Agathos, M.; Aggarwal, N.; Aguiar, O. D.; Ain, A.; Ajith, P.; Alemic, A.; Allen, B.; Allocca, A.; Amariutei, D.; Andersen, M.; Anderson, R.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C.; Areeda, J.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Austin, L.; Aylott, B. E.; Babak, S.; Baker, P. T.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barbet, M.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Bauchrowitz, J.; Bauer, Th. S.; Behnke, B.; Bejger, M.; Beker, M. G.; Belczynski, C.; Bell, A. S.; Bell, C.; Bergmann, G.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Beyersdorf, P. T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biscans, S.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bloemen, S.; Blom, M.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bond, C.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, Sukanta; Bosi, L.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brückner, F.; Buchman, S.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burman, R.; Buskulic, D.; Buy, C.; Cadonati, L.; Cagnoli, G.; Bustillo, J. Calderón; Calloni, E.; Camp, J. B.; Campsie, P.; Cannon, K. C.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Castiglia, A.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Celerier, C.; Cella, G.; Cepeda, C.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chu, Q.; Chua, S. S. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C.; Colombini, M.; Cominsky, L.; Constancio, M.; Conte, A.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corpuz, A.; Corsi, A.; Costa, C. A.; Coughlin, M. W.; Coughlin, S.; Coulon, J.-P.; Countryman, S.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dahl, K.; Canton, T. Dal; Damjanic, M.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daveloza, H.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; Dayanga, T.; Debreczeni, G.; Degallaix, J.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Dhurandhar, S.; Díaz, M.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Virgilio, A.; Donath, A.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dossa, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dwyer, S.; Eberle, T.; Edo, T.; Edwards, M.; Effler, A.; Eggenstein, H.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Endrőczi, G.; Essick, R.; Etzel, T.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fehrmann, H.; Fejer, M. M.; Feldbaum, D.; Feroz, F.; Ferrante, I.; Ferrini, F.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gair, J.; Gammaitoni, L.; Gaonkar, S.; Garufi, F.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, C.; Gleason, J.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gordon, N.; Gorodetsky, M. L.; Gossan, S.; Goßler, S.; Gouaty, R.; Gräf, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Groot, P.; Grote, H.; Grover, K.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gushwa, K.; Gustafson, E. K.; Gustafson, R.; Hammer, D.; Hammond, G.; Hanke, M.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hart, M.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Heidmann, A.; Heintze, M.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Heptonstall, A. W.; Heurs, M.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Hooper, S.; Hopkins, P.; Hosken, D. J.; Hough, J.; Howell, E. J.; Hu, Y.; Huerta, E.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh, M.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Iyer, B. R.; Izumi, K.; Jacobson, M.; James, E.; Jang, H.; Jaranowski, P.; Ji, Y.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Haris, K.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karlen, J.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Keiser, G. M.; Keitel, D.; Kelley, D. B.; Kells, W.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, C.; Kim, K.; Kim, N.; Kim, N. G.; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kline, J.; Koehlenbeck, S.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kremin, A.; Kringel, V.; Królak, A.; Kuehn, G.; Kumar, A.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Kwee, P.; Landry, M.; Lantz, B.; Larson, S.; Lasky, P. D.; Lawrie, C.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C.-H.; Lee, H. K.; Lee, H. M.; Lee, J.; Leonardi, M.; Leong, J. R.; Le Roux, A.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B.; Lewis, J.; Li, T. G. F.; Libbrecht, K.; Libson, A.; Lin, A. C.; Littenberg, T. B.; Litvine, V.; Lockerbie, N. A.; Lockett, V.; Lodhia, D.; Loew, K.; Logue, J.; Lombardi, A. L.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Lubinski, M. J.; Lück, H.; Luijten, E.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macarthur, J.; Macdonald, E. P.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magana-Sandoval, F.; Mageswaran, M.; Maglione, C.; Mailand, K.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Manca, G. M.; Mandel, I.; Mandic, V.; Mangano, V.; Mangini, N.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Martinelli, L.; Martynov, D.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; McLin, K.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Meinders, M.; Melatos, A.; Mendell, G.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyers, P.; Miao, H.; Michel, C.; Mikhailov, E. E.; Milano, L.; Milde, S.; Miller, J.; Minenkov, Y.; Mingarelli, C. M. F.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moe, B.; Moesta, P.; Mohan, M.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nagy, M. F.; Kumar, D. Nanda; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nelemans, G.; Neri, I.; Neri, M.; Newton, G.; Nguyen, T.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Ochsner, E.; O'Dell, J.; Oelker, E.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oppermann, P.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Padilla, C.; Pai, A.; Palashov, O.; Palomba, C.; Pan, H.; Pan, Y.; Pankow, C.; Paoletti, F.; Paoletti, R.; Paris, H.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Pedraza, M.; Penn, S.; Perreca, A.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pierro, V.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poeld, J.; Poggiani, R.; Poteomkin, A.; Powell, J.; Prasad, J.; Premachandra, S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Qin, J.; Quetschke, V.; Quintero, E.; Quiroga, G.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Raja, S.; Rajalakshmi, G.; Rakhmanov, M.; Ramet, C.; Ramirez, K.; Rapagnani, P.; Raymond, V.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Reid, S.; Reitze, D. H.; Rhoades, E.; Ricci, F.; Riles, K.; Robertson, N. A.; Robinet, F.; Rocchi, A.; Rodruck, M.; Rolland, L.; Rollins, J. G.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Salemi, F.; Sammut, L.; Sandberg, V.; Sanders, J. R.; Sannibale, V.; Santiago-Prieto, I.; Saracco, E.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Savage, R.; Scheuer, J.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sidery, T. L.; Siellez, K.; Siemens, X.; Sigg, D.; Simakov, D.; Singer, A.; Singer, L.; Singh, R.; Sintes, A. M.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Son, E. J.; Sorazu, B.; Souradeep, T.; Sperandio, L.; Staley, A.; Stebbins, J.; Steinlechner, J.; Steinlechner, S.; Stephens, B. C.; Steplewski, S.; Stevenson, S.; Stone, R.; Stops, D.; Strain, K. A.; Straniero, N.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, R.; ter Braack, A. P. M.; Thirugnanasambandam, M. P.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C. V.; Torrie, C. I.; Travasso, F.; Traylor, G.; Tse, M.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Urbanek, K.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van der Sluys, M. V.; van Heijningen, J.; van Veggel, A. A.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Verma, S. S.; Vetrano, F.; Viceré, A.; Vincent-Finley, R.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Vousden, W. D.; Vyachanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Walker, M.; Wallace, L.; Wang, M.; Wang, X.; Ward, R. L.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Wiesner, K.; Wilkinson, C.; Williams, K.; Williams, L.; Williams, R.; Williams, T.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yancey, C. C.; Yang, H.; Yang, Z.; Yoshida, S.; Yvert, M.; ZadroŻny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, Fan; Zhang, L.; Zhao, C.; Zhu, X. J.; Zucker, M. E.; Zuraw, S.; Zweizig, J.; LIGO; Virgo Collaboration

2014-12-01

Gravitational waves from a variety of sources are predicted to superpose to create a stochastic background. This background is expected to contain unique information from throughout the history of the Universe that is unavailable through standard electromagnetic observations, making its study of fundamental importance to understanding the evolution of the Universe. We carry out a search for the stochastic background with the latest data from the LIGO and Virgo detectors. Consistent with predictions from most stochastic gravitational-wave background models, the data display no evidence of a stochastic gravitational-wave signal. Assuming a gravitational-wave spectrum of ΩGW(f )=Ωα(f/fref ) α , we place 95% confidence level upper limits on the energy density of the background in each of four frequency bands spanning 41.5-1726 Hz. In the frequency band of 41.5-169.25 Hz for a spectral index of α =0 , we constrain the energy density of the stochastic background to be ΩGW(f )<5.6 ×1 0-6 . For the 600-1000 Hz band, ΩGW(f )<0.14 (f /900 Hz )3 , a factor of 2.5 lower than the best previously reported upper limits. We find ΩGW(f )<1.8 ×1 0-4 using a spectral index of zero for 170-600 Hz and ΩGW(f )<1.0 (f /1300 Hz )3 for 1000-1726 Hz, bands in which no previous direct limits have been placed. The limits in these four bands are the lowest direct measurements to date on the stochastic background. We discuss the implications of these results in light of the recent claim by the BICEP2 experiment of the possible evidence for inflationary gravitational waves.

Improved upper limits on the stochastic gravitational-wave background from 2009-2010 LIGO and Virgo data.

PubMed

Aasi, J; Abbott, B P; Abbott, R; Abbott, T; Abernathy, M R; Accadia, T; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Affeldt, C; Agathos, M; Aggarwal, N; Aguiar, O D; Ain, A; Ajith, P; Alemic, A; Allen, B; Allocca, A; Amariutei, D; Andersen, M; Anderson, R; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Arceneaux, C; Areeda, J; Aston, S M; Astone, P; Aufmuth, P; Aulbert, C; Austin, L; Aylott, B E; Babak, S; Baker, P T; Ballardin, G; Ballmer, S W; Barayoga, J C; Barbet, M; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bauchrowitz, J; Bauer, Th S; Behnke, B; Bejger, M; Beker, M G; Belczynski, C; Bell, A S; Bell, C; Bergmann, G; Bersanetti, D; Bertolini, A; Betzwieser, J; Beyersdorf, P T; Bilenko, I A; Billingsley, G; Birch, J; Biscans, S; Bitossi, M; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bloemen, S; Blom, M; Bock, O; Bodiya, T P; Boer, M; Bogaert, G; Bogan, C; Bond, C; Bondu, F; Bonelli, L; Bonnand, R; Bork, R; Born, M; Boschi, V; Bose, Sukanta; Bosi, L; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Bridges, D O; Brillet, A; Brinkmann, M; Brisson, V; Brooks, A F; Brown, D A; Brown, D D; Brückner, F; Buchman, S; Bulik, T; Bulten, H J; Buonanno, A; Burman, R; Buskulic, D; Buy, C; Cadonati, L; Cagnoli, G; Bustillo, J Calderón; Calloni, E; Camp, J B; Campsie, P; Cannon, K C; Canuel, B; Cao, J; Capano, C D; Carbognani, F; Carbone, L; Caride, S; Castiglia, A; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Celerier, C; Cella, G; Cepeda, C; Cesarini, E; Chakraborty, R; Chalermsongsak, T; Chamberlin, S J; Chao, S; Charlton, P; Chassande-Mottin, E; Chen, X; Chen, Y; Chincarini, A; Chiummo, A; Cho, H S; Chow, J; Christensen, N; Chu, Q; Chua, S S Y; Chung, S; Ciani, G; Clara, F; Clark, J A; Cleva, F; Coccia, E; Cohadon, P-F; Colla, A; Collette, C; Colombini, M; Cominsky, L; Constancio, M; Conte, A; Cook, D; Corbitt, T R; Cordier, M; Cornish, N; Corpuz, A; Corsi, A; Costa, C A; Coughlin, M W; Coughlin, S; Coulon, J-P; Countryman, S; Couvares, P; Coward, D M; Cowart, M; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Dahl, K; Canton, T Dal; Damjanic, M; Danilishin, S L; D'Antonio, S; Danzmann, K; Dattilo, V; Daveloza, H; Davier, M; Davies, G S; Daw, E J; Day, R; Dayanga, T; Debreczeni, G; Degallaix, J; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dereli, H; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Dhurandhar, S; Díaz, M; Di Fiore, L; Di Lieto, A; Di Palma, I; Di Virgilio, A; Donath, A; Donovan, F; Dooley, K L; Doravari, S; Dossa, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Dwyer, S; Eberle, T; Edo, T; Edwards, M; Effler, A; Eggenstein, H; Ehrens, P; Eichholz, J; Eikenberry, S S; Endrőczi, G; Essick, R; Etzel, T; Evans, M; Evans, T; Factourovich, M; Fafone, V; Fairhurst, S; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fehrmann, H; Fejer, M M; Feldbaum, D; Feroz, F; Ferrante, I; Ferrini, F; Fidecaro, F; Finn, L S; Fiori, I; Fisher, R P; Flaminio, R; Fournier, J-D; Franco, S; Frasca, S; Frasconi, F; Frede, M; Frei, Z; Freise, A; Frey, R; Fricke, T T; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gair, J; Gammaitoni, L; Gaonkar, S; Garufi, F; Gehrels, N; Gemme, G; Genin, E; Gennai, A; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, C; Gleason, J; Goetz, E; Goetz, R; Gondan, L; González, G; Gordon, N; Gorodetsky, M L; Gossan, S; Gossler, S; Gouaty, R; Gräf, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greenhalgh, R J S; Gretarsson, A M; Groot, P; Grote, H; Grover, K; Grunewald, S; Guidi, G M; Guido, C; Gushwa, K; Gustafson, E K; Gustafson, R; Hammer, D; Hammond, G; Hanke, M; Hanks, J; Hanna, C; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Hart, M; Hartman, M T; Haster, C-J; Haughian, K; Heidmann, A; Heintze, M; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Heptonstall, A W; Heurs, M; Hewitson, M; Hild, S; Hoak, D; Hodge, K A; Holt, K; Hooper, S; Hopkins, P; Hosken, D J; Hough, J; Howell, E J; Hu, Y; Huerta, E; Hughey, B; Husa, S; Huttner, S H; Huynh, M; Huynh-Dinh, T; Ingram, D R; Inta, R; Isogai, T; Ivanov, A; Iyer, B R; Izumi, K; Jacobson, M; James, E; Jang, H; Jaranowski, P; Ji, Y; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; K, Haris; Kalmus, P; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karlen, J; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, H; Kawabe, K; Kawazoe, F; Kéfélian, F; Keiser, G M; Keitel, D; Kelley, D B; Kells, W; Khalaidovski, A; Khalili, F Y; Khazanov, E A; Kim, C; Kim, K; Kim, N; Kim, N G; Kim, Y-M; King, E J; King, P J; Kinzel, D L; Kissel, J S; Klimenko, S; Kline, J; Koehlenbeck, S; Kokeyama, K; Kondrashov, V; Koranda, S; Korth, W Z; Kowalska, I; Kozak, D B; Kremin, A; Kringel, V; Królak, A; Kuehn, G; Kumar, A; Kumar, P; Kumar, R; Kuo, L; Kutynia, A; Kwee, P; Landry, M; Lantz, B; Larson, S; Lasky, P D; Lawrie, C; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lebigot, E O; Lee, C-H; Lee, H K; Lee, H M; Lee, J; Leonardi, M; Leong, J R; Le Roux, A; Leroy, N; Letendre, N; Levin, Y; Levine, B; Lewis, J; Li, T G F; Libbrecht, K; Libson, A; Lin, A C; Littenberg, T B; Litvine, V; Lockerbie, N A; Lockett, V; Lodhia, D; Loew, K; Logue, J; Lombardi, A L; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J; Lubinski, M J; Lück, H; Luijten, E; Lundgren, A P; Lynch, R; Ma, Y; Macarthur, J; Macdonald, E P; MacDonald, T; Machenschalk, B; MacInnis, M; Macleod, D M; Magana-Sandoval, F; Mageswaran, M; Maglione, C; Mailand, K; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Manca, G M; Mandel, I; Mandic, V; Mangano, V; Mangini, N; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A; Maros, E; Marque, J; Martelli, F; Martin, I W; Martin, R M; Martinelli, L; Martynov, D; Marx, J N; Mason, K; Masserot, A; Massinger, T J; Matichard, F; Matone, L; Matzner, R A; Mavalvala, N; Mazumder, N; Mazzolo, G; McCarthy, R; McClelland, D E; McGuire, S C; McIntyre, G; McIver, J; McLin, K; Meacher, D; Meadors, G D; Mehmet, M; Meidam, J; Meinders, M; Melatos, A; Mendell, G; Mercer, R A; Meshkov, S; Messenger, C; Meyers, P; Miao, H; Michel, C; Mikhailov, E E; Milano, L; Milde, S; Miller, J; Minenkov, Y; Mingarelli, C M F; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moe, B; Moesta, P; Mohan, M; Mohapatra, S R P; Moraru, D; Moreno, G; Morgado, N; Morriss, S R; Mossavi, K; Mours, B; Mow-Lowry, C M; Mueller, C L; Mueller, G; Mukherjee, S; Mullavey, A; Munch, J; Murphy, D; Murray, P G; Mytidis, A; Nagy, M F; Kumar, D Nanda; Nardecchia, I; Naticchioni, L; Nayak, R K; Necula, V; Nelemans, G; Neri, I; Neri, M; Newton, G; Nguyen, T; Nitz, A; Nocera, F; Nolting, D; Normandin, M E N; Nuttall, L K; Ochsner, E; O'Dell, J; Oelker, E; Oh, J J; Oh, S H; Ohme, F; Oppermann, P; O'Reilly, B; O'Shaughnessy, R; Osthelder, C; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Padilla, C; Pai, A; Palashov, O; Palomba, C; Pan, H; Pan, Y; Pankow, C; Paoletti, F; Paoletti, R; Paris, H; Pasqualetti, A; Passaquieti, R; Passuello, D; Pedraza, M; Penn, S; Perreca, A; Phelps, M; Pichot, M; Pickenpack, M; Piergiovanni, F; Pierro, V; Pinard, L; Pinto, I M; Pitkin, M; Poeld, J; Poggiani, R; Poteomkin, A; Powell, J; Prasad, J; Premachandra, S; Prestegard, T; Price, L R; Prijatelj, M; Privitera, S; Prodi, G A; Prokhorov, L; Puncken, O; Punturo, M; Puppo, P; Qin, J; Quetschke, V; Quintero, E; Quiroga, G; Quitzow-James, R; Raab, F J; Rabeling, D S; Rácz, I; Radkins, H; Raffai, P; Raja, S; Rajalakshmi, G; Rakhmanov, M; Ramet, C; Ramirez, K; Rapagnani, P; Raymond, V; Re, V; Read, J; Reed, C M; Regimbau, T; Reid, S; Reitze, D H; Rhoades, E; Ricci, F; Riles, K; Robertson, N A; Robinet, F; Rocchi, A; Rodruck, M; Rolland, L; Rollins, J G; Romano, J D; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Salemi, F; Sammut, L; Sandberg, V; Sanders, J R; Sannibale, V; Santiago-Prieto, I; Saracco, E; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Savage, R; Scheuer, J; Schilling, R; Schnabel, R; Schofield, R M S; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Shaddock, D; Shah, S; Shahriar, M S; Shaltev, M; Shapiro, B; Shawhan, P; Shoemaker, D H; Sidery, T L; Siellez, K; Siemens, X; Sigg, D; Simakov, D; Singer, A; Singer, L; Singh, R; Sintes, A M; Slagmolen, B J J; Slutsky, J; Smith, J R; Smith, M; Smith, R J E; Smith-Lefebvre, N D; Son, E J; Sorazu, B; Souradeep, T; Sperandio, L; Staley, A; Stebbins, J; Steinlechner, J; Steinlechner, S; Stephens, B C; Steplewski, S; Stevenson, S; Stone, R; Stops, D; Strain, K A; Straniero, N; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Susmithan, S; Sutton, P J; Swinkels, B; Tacca, M; Talukder, D; Tanner, D B; Tarabrin, S P; Taylor, R; Ter Braack, A P M; Thirugnanasambandam, M P; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, V; Tokmakov, K V; Tomlinson, C; Toncelli, A; Tonelli, M; Torre, O; Torres, C V; Torrie, C I; Travasso, F; Traylor, G; Tse, M; Ugolini, D; Unnikrishnan, C S; Urban, A L; Urbanek, K; Vahlbruch, H; Vajente, G; Valdes, G; Vallisneri, M; van den Brand, J F J; Van Den Broeck, C; van der Putten, S; van der Sluys, M V; van Heijningen, J; van Veggel, A A; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Verma, S S; Vetrano, F; Viceré, A; Vincent-Finley, R; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Vousden, W D; Vyachanin, S P; Wade, A; Wade, L; Wade, M; Walker, M; Wallace, L; Wang, M; Wang, X; Ward, R L; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Wessels, P; West, M; Westphal, T; Wette, K; Whelan, J T; White, D J; Whiting, B F; Wiesner, K; Wilkinson, C; Williams, K; Williams, L; Williams, R; Williams, T; Williamson, A R; Willis, J L; Willke, B; Wimmer, M; Winkler, W; Wipf, C C; Wiseman, A G; Wittel, H; Woan, G; Worden, J; Yablon, J; Yakushin, I; Yamamoto, H; Yancey, C C; Yang, H; Yang, Z; Yoshida, S; Yvert, M; Zadrożny, A; Zanolin, M; Zendri, J-P; Zhang, Fan; Zhang, L; Zhao, C; Zhu, X J; Zucker, M E; Zuraw, S; Zweizig, J

2014-12-05

Gravitational waves from a variety of sources are predicted to superpose to create a stochastic background. This background is expected to contain unique information from throughout the history of the Universe that is unavailable through standard electromagnetic observations, making its study of fundamental importance to understanding the evolution of the Universe. We carry out a search for the stochastic background with the latest data from the LIGO and Virgo detectors. Consistent with predictions from most stochastic gravitational-wave background models, the data display no evidence of a stochastic gravitational-wave signal. Assuming a gravitational-wave spectrum of Ω_{GW}(f)=Ω_{α}(f/f_{ref})^{α}, we place 95% confidence level upper limits on the energy density of the background in each of four frequency bands spanning 41.5-1726 Hz. In the frequency band of 41.5-169.25 Hz for a spectral index of α=0, we constrain the energy density of the stochastic background to be Ω_{GW}(f)<5.6×10^{-6}. For the 600-1000 Hz band, Ω_{GW}(f)<0.14(f/900  Hz)^{3}, a factor of 2.5 lower than the best previously reported upper limits. We find Ω_{GW}(f)<1.8×10^{-4} using a spectral index of zero for 170-600 Hz and Ω_{GW}(f)<1.0(f/1300  Hz)^{3} for 1000-1726 Hz, bands in which no previous direct limits have been placed. The limits in these four bands are the lowest direct measurements to date on the stochastic background. We discuss the implications of these results in light of the recent claim by the BICEP2 experiment of the possible evidence for inflationary gravitational waves.

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less

Stochastic mixed-mode oscillations in a three-species predator-prey model

NASA Astrophysics Data System (ADS)

Sadhu, Susmita; Kuehn, Christian

2018-03-01

The effect of demographic stochasticity, in the form of Gaussian white noise, in a predator-prey model with one fast and two slow variables is studied. We derive the stochastic differential equations (SDEs) from a discrete model. For suitable parameter values, the deterministic drift part of the model admits a folded node singularity and exhibits a singular Hopf bifurcation. We focus on the parameter regime near the Hopf bifurcation, where small amplitude oscillations exist as stable dynamics in the absence of noise. In this regime, the stochastic model admits noise-driven mixed-mode oscillations (MMOs), which capture the intermediate dynamics between two cycles of population outbreaks. We perform numerical simulations to calculate the distribution of the random number of small oscillations between successive spikes for varying noise intensities and distance to the Hopf bifurcation. We also study the effect of noise on a suitable Poincaré map. Finally, we prove that the stochastic model can be transformed into a normal form near the folded node, which can be linked to recent results on the interplay between deterministic and stochastic small amplitude oscillations. The normal form can also be used to study the parameter influence on the noise level near folded singularities.

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

A method for minimum risk portfolio optimization under hybrid uncertainty

NASA Astrophysics Data System (ADS)

Egorova, Yu E.; Yazenin, A. V.

2018-03-01

In this paper, we investigate a minimum risk portfolio model under hybrid uncertainty when the profitability of financial assets is described by fuzzy random variables. According to Feng, the variance of a portfolio is defined as a crisp value. To aggregate fuzzy information the weakest (drastic) t-norm is used. We construct an equivalent stochastic problem of the minimum risk portfolio model and specify the stochastic penalty method for solving it.

Improved result on stability analysis of discrete stochastic neural networks with time delay

NASA Astrophysics Data System (ADS)

Wu, Zhengguang; Su, Hongye; Chu, Jian; Zhou, Wuneng

2009-04-01

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

THE DISTRIBUTION OF ROUNDS FIRED IN STOCHASTIC DUELS

DTIC Science & Technology

This paper continues the development of the theory of Stochastic Duels to include the distribution of the number of rounds fired. Most generally...the duel between two contestants who fire at each other with constant kill probabilities per round is considered. The time between rounds fired may be...at the beginning of the duel may be limited and is a discrete random variable. Besides the distribution of rounds fired, its first two moments and

Stochastically generating tree diameter lists to populate forest stands based on the linkage variables forest type and stand age

Treesearch

Bernard R. Parresol; F. Thomas Lloyd

2003-01-01

Forest inventory data were used to develop a standage-driven, stochastic predictor of unit-area, frequency weighted lists of breast high tree diameters (DBH). The average of mean statistics from 40 simulation prediction sets of an independent 78-plot validation dataset differed from the observed validation means by 0.5 cm for DBH, and by 12 trees/h for density. The 40...

A straightforward method to compute average stochastic oscillations from data samples.

PubMed

Júlvez, Jorge

2015-10-19

Many biological systems exhibit sustained stochastic oscillations in their steady state. Assessing these oscillations is usually a challenging task due to the potential variability of the amplitude and frequency of the oscillations over time. As a result of this variability, when several stochastic replications are averaged, the oscillations are flattened and can be overlooked. This can easily lead to the erroneous conclusion that the system reaches a constant steady state. This paper proposes a straightforward method to detect and asses stochastic oscillations. The basis of the method is in the use of polar coordinates for systems with two species, and cylindrical coordinates for systems with more than two species. By slightly modifying these coordinate systems, it is possible to compute the total angular distance run by the system and the average Euclidean distance to a reference point. This allows us to compute confidence intervals, both for the average angular speed and for the distance to a reference point, from a set of replications. The use of polar (or cylindrical) coordinates provides a new perspective of the system dynamics. The mean trajectory that can be obtained by averaging the usual cartesian coordinates of the samples informs about the trajectory of the center of mass of the replications. In contrast to such a mean cartesian trajectory, the mean polar trajectory can be used to compute the average circular motion of those replications, and therefore, can yield evidence about sustained steady state oscillations. Both, the coordinate transformation and the computation of confidence intervals, can be carried out efficiently. This results in an efficient method to evaluate stochastic oscillations.

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

PubMed Central

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-01-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons. PMID:22096452

Impact of Stochastic Parameterization Schemes on Coupled and Uncoupled Climate Simulations with the Community Earth System Model

NASA Astrophysics Data System (ADS)

Christensen, H. M.; Berner, J.; Coleman, D.; Palmer, T.

2015-12-01

Stochastic parameterizations have been used for more than a decade in atmospheric models to represent the variability of unresolved sub-grid processes. They have a beneficial effect on the spread and mean state of medium- and extended-range forecasts (Buizza et al. 1999, Palmer et al. 2009). There is also increasing evidence that stochastic parameterization of unresolved processes could be beneficial for the climate of an atmospheric model through noise enhanced variability, noise-induced drift (Berner et al. 2008), and by enabling the climate simulator to explore other flow regimes (Christensen et al. 2015; Dawson and Palmer 2015). We present results showing the impact of including the Stochastically Perturbed Parameterization Tendencies scheme (SPPT) in coupled runs of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 4 (CAM4) with historical forcing. The SPPT scheme accounts for uncertainty in the CAM physical parameterization schemes, including the convection scheme, by perturbing the parametrised temperature, moisture and wind tendencies with a multiplicative noise term. SPPT results in a large improvement in the variability of the CAM4 modeled climate. In particular, SPPT results in a significant improvement to the representation of the El Nino-Southern Oscillation in CAM4, improving the power spectrum, as well as both the inter- and intra-annual variability of tropical pacific sea surface temperatures. References: Berner, J., Doblas-Reyes, F. J., Palmer, T. N., Shutts, G. J., & Weisheimer, A., 2008. Phil. Trans. R. Soc A, 366, 2559-2577 Buizza, R., Miller, M. and Palmer, T. N., 1999. Q.J.R. Meteorol. Soc., 125, 2887-2908. Christensen, H. M., I. M. Moroz & T. N. Palmer, 2015. Clim. Dynam., doi: 10.1007/s00382-014-2239-9 Dawson, A. and T. N. Palmer, 2015. Clim. Dynam., doi: 10.1007/s00382-014-2238-x Palmer, T.N., R. Buizza, F. Doblas-Reyes, et al., 2009, ECMWF technical memorandum 598.

Turbulent fluctuations and the excitation of Z Cam outbursts

NASA Astrophysics Data System (ADS)

Ross, Johnathan; Latter, Henrik N.

2017-09-01

Z Cam variables are a subclass of dwarf nova that lie near a global bifurcation between outbursting ('limit cycle') and non-outbursting ('standstill') states. It is believed that variations in the secondary star's mass-injection rate instigate transitions between the two regimes. In this paper, we explore an alternative trigger for these transitions: stochastic fluctuations in the disc's turbulent viscosity. We employ simple one-zone and global viscous models which, though inappropriate for detailed matching to observed light curves, clearly indicate that turbulent disc fluctuations induce outbursts when the system is sufficiently close to the global bifurcation point. While the models easily produce the observed 'outburst/dip' pairs exhibited by Z Cam and Nova-like variables, they struggle to generate long trains of outbursts. We conclude that mass transfer variability is the dominant physical process determining the overall Z Cam standstill/outburst pattern, but that viscous stochasticity provides an additional ingredient explaining some of the secondary features observed.

Effects of in-sewer processes: a stochastic model approach.

PubMed

Vollertsen, J; Nielsen, A H; Yang, W; Hvitved-Jacobsen, T

2005-01-01

Transformations of organic matter, nitrogen and sulfur in sewers can be simulated taking into account the relevant transformation and transport processes. One objective of such simulation is the assessment and management of hydrogen sulfide formation and corrosion. Sulfide is formed in the biofilms and sediments of the water phase, but corrosion occurs on the moist surfaces of the sewer gas phase. Consequently, both phases and the transport of volatile substances between these phases must be included. Furthermore, wastewater composition and transformations in sewers are complex and subject to high, natural variability. This paper presents the latest developments of the WATS model concept, allowing integrated aerobic, anoxic and anaerobic simulation of the water phase and of gas phase processes. The resulting model is complex and with high parameter variability. An example applying stochastic modeling shows how this complexity and variability can be taken into account.

Predicting ecosystem stability from community composition and biodiversity.

PubMed

de Mazancourt, Claire; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B; Haegeman, Bart; Wayne Polley, H; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J; Loreau, Michel

2013-05-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species' responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22-75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability. © 2013 Blackwell Publishing Ltd/CNRS.

Spatial vs. individual variability with inheritance in a stochastic Lotka-Volterra system

NASA Astrophysics Data System (ADS)

Dobramysl, Ulrich; Tauber, Uwe C.

2012-02-01

We investigate a stochastic spatial Lotka-Volterra predator-prey model with randomized interaction rates that are either affixed to the lattice sites and quenched, and / or specific to individuals in either population. In the latter situation, we include rate inheritance with mutations from the particles' progenitors. Thus we arrive at a simple model for competitive evolution with environmental variability and selection pressure. We employ Monte Carlo simulations in zero and two dimensions to study the time evolution of both species' densities and their interaction rate distributions. The predator and prey concentrations in the ensuing steady states depend crucially on the environmental variability, whereas the temporal evolution of the individualized rate distributions leads to largely neutral optimization. Contrary to, e.g., linear gene expression models, this system does not experience fixation at extreme values. An approximate description of the resulting data is achieved by means of an effective master equation approach for the interaction rate distribution.

Predicting ecosystem stability from community composition and biodiversity

USGS Publications Warehouse

Mazancourt, Claire de; Isbell, Forest; Larocque, Allen; Berendse, Frank; De Luca, Enrica; Grace, James B.; Haegeman, Bart; Polley, H. Wayne; Roscher, Christiane; Schmid, Bernhard; Tilman, David; van Ruijven, Jasper; Weigelt, Alexandra; Wilsey, Brian J.; Loreau, Michel

2013-01-01

As biodiversity is declining at an unprecedented rate, an important current scientific challenge is to understand and predict the consequences of biodiversity loss. Here, we develop a theory that predicts the temporal variability of community biomass from the properties of individual component species in monoculture. Our theory shows that biodiversity stabilises ecosystems through three main mechanisms: (1) asynchrony in species’ responses to environmental fluctuations, (2) reduced demographic stochasticity due to overyielding in species mixtures and (3) reduced observation error (including spatial and sampling variability). Parameterised with empirical data from four long-term grassland biodiversity experiments, our prediction explained 22–75% of the observed variability, and captured much of the effect of species richness. Richness stabilised communities mainly by increasing community biomass and reducing the strength of demographic stochasticity. Our approach calls for a re-evaluation of the mechanisms explaining the effects of biodiversity on ecosystem stability.

A Reserve-based Method for Mitigating the Impact of Renewable Energy

NASA Astrophysics Data System (ADS)

Krad, Ibrahim

The fundamental operating paradigm of today's power systems is undergoing a significant shift. This is partially motivated by the increased desire for incorporating variable renewable energy resources into generation portfolios. While these generating technologies offer clean energy at zero marginal cost, i.e. no fuel costs, they also offer unique operating challenges for system operators. Perhaps the biggest operating challenge these resources introduce is accommodating their intermittent fuel source availability. For this reason, these generators increase the system-wide variability and uncertainty. As a result, system operators are revisiting traditional operating strategies to more efficiently incorporate these generation resources to maximize the benefit they provide while minimizing the challenges they introduce. One way system operators have accounted for system variability and uncertainty is through the use of operating reserves. Operating reserves can be simplified as excess capacity kept online during real time operations to help accommodate unforeseen fluctuations in demand. With new generation resources, a new class of operating reserves has emerged that is generally known as flexibility, or ramping, reserves. This new reserve class is meant to better position systems to mitigate severe ramping in the net load profile. The best way to define this new requirement is still under investigation. Typical requirement definitions focus on the additional uncertainty introduced by variable generation and there is room for improvement regarding explicit consideration for the variability they introduce. An exogenous reserve modification method is introduced in this report that can improve system reliability with minimal impacts on total system wide production costs. Another potential solution to this problem is to formulate the problem as a stochastic programming problem. The unit commitment and economic dispatch problems are typically formulated as deterministic problems due to fast solution times and the solutions being sufficient for operations. Improvements in technical computing hardware have reignited interest in stochastic modeling. The variability of wind and solar naturally lends itself to stochastic modeling. The use of explicit reserve requirements in stochastic models is an area of interest for power system researchers. This report introduces a new reserve modification implementation based on previous results to be used in a stochastic modeling framework. With technological improvements in distributed generation technologies, microgrids are currently being researched and implemented. Microgrids are small power systems that have the ability to serve their demand with their own generation resources and may have a connection to a larger power system. As battery technologies improve, they are becoming a more viable option in these distributed power systems and research is necessary to determine the most efficient way to utilize them. This report will investigate several unique operating strategies for batteries in small power systems and analyze their benefits. These new operating strategies will help reduce operating costs and improve system reliability.

Stationary responses of a Rayleigh viscoelastic system with zero barrier impacts under external random excitation.

PubMed

Wang, Deli; Xu, Wei; Zhao, Xiangrong

2016-03-01

This paper aims to deal with the stationary responses of a Rayleigh viscoelastic system with zero barrier impacts under external random excitation. First, the original stochastic viscoelastic system is converted to an equivalent stochastic system without viscoelastic terms by approximately adding the equivalent stiffness and damping. Relying on the means of non-smooth transformation of state variables, the above system is replaced by a new system without an impact term. Then, the stationary probability density functions of the system are observed analytically through stochastic averaging method. By considering the effects of the biquadratic nonlinear damping coefficient and the noise intensity on the system responses, the effectiveness of the theoretical method is tested by comparing the analytical results with those generated from Monte Carlo simulations. Additionally, it does deserve attention that some system parameters can induce the occurrence of stochastic P-bifurcation.

A DG approach to the numerical solution of the Stein-Stein stochastic volatility option pricing model

NASA Astrophysics Data System (ADS)

Hozman, J.; Tichý, T.

2017-12-01

Stochastic volatility models enable to capture the real world features of the options better than the classical Black-Scholes treatment. Here we focus on pricing of European-style options under the Stein-Stein stochastic volatility model when the option value depends on the time, on the price of the underlying asset and on the volatility as a function of a mean reverting Orstein-Uhlenbeck process. A standard mathematical approach to this model leads to the non-stationary second-order degenerate partial differential equation of two spatial variables completed by the system of boundary and terminal conditions. In order to improve the numerical valuation process for a such pricing equation, we propose a numerical technique based on the discontinuous Galerkin method and the Crank-Nicolson scheme. Finally, reference numerical experiments on real market data illustrate comprehensive empirical findings on options with stochastic volatility.

The multinomial simulation algorithm for discrete stochastic simulation of reaction-diffusion systems.

PubMed

Lampoudi, Sotiria; Gillespie, Dan T; Petzold, Linda R

2009-03-07

The Inhomogeneous Stochastic Simulation Algorithm (ISSA) is a variant of the stochastic simulation algorithm in which the spatially inhomogeneous volume of the system is divided into homogeneous subvolumes, and the chemical reactions in those subvolumes are augmented by diffusive transfers of molecules between adjacent subvolumes. The ISSA can be prohibitively slow when the system is such that diffusive transfers occur much more frequently than chemical reactions. In this paper we present the Multinomial Simulation Algorithm (MSA), which is designed to, on the one hand, outperform the ISSA when diffusive transfer events outnumber reaction events, and on the other, to handle small reactant populations with greater accuracy than deterministic-stochastic hybrid algorithms. The MSA treats reactions in the usual ISSA fashion, but uses appropriately conditioned binomial random variables for representing the net numbers of molecules diffusing from any given subvolume to a neighbor within a prescribed distance. Simulation results illustrate the benefits of the algorithm.

Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Chunyuan; Stevens, Andrew J.; Chen, Changyou

2016-08-10

Learning the representation of shape cues in 2D & 3D objects for recognition is a fundamental task in computer vision. Deep neural networks (DNNs) have shown promising performance on this task. Due to the large variability of shapes, accurate recognition relies on good estimates of model uncertainty, ignored in traditional training of DNNs, typically learned via stochastic optimization. This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (SG-MCMC) to learn weight uncertainty in DNNs. It yields principled Bayesian interpretations for the commonly used Dropout/DropConnect techniques and incorporates them into the SG-MCMC framework. Extensive experiments on 2D &more » 3D shape datasets and various DNN models demonstrate the superiority of the proposed approach over stochastic optimization. Our approach yields higher recognition accuracy when used in conjunction with Dropout and Batch-Normalization.« less

Synthetic Sediments and Stochastic Groundwater Hydrology

NASA Astrophysics Data System (ADS)

Wilson, J. L.

2002-12-01

For over twenty years the groundwater community has pursued the somewhat elusive goal of describing the effects of aquifer heterogeneity on subsurface flow and chemical transport. While small perturbation stochastic moment methods have significantly advanced theoretical understanding, why is it that stochastic applications use instead simulations of flow and transport through multiple realizations of synthetic geology? Allan Gutjahr was a principle proponent of the Fast Fourier Transform method for the synthetic generation of aquifer properties and recently explored new, more geologically sound, synthetic methods based on multi-scale Markov random fields. Focusing on sedimentary aquifers, how has the state-of-the-art of synthetic generation changed and what new developments can be expected, for example, to deal with issues like conceptual model uncertainty, the differences between measurement and modeling scales, and subgrid scale variability? What will it take to get stochastic methods, whether based on moments, multiple realizations, or some other approach, into widespread application?

On Volterra quadratic stochastic operators with continual state space

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ganikhodjaev, Nasir; Hamzah, Nur Zatul Akmar

2015-05-15

Let (X,F) be a measurable space, and S(X,F) be the set of all probability measures on (X,F) where X is a state space and F is σ - algebraon X. We consider a nonlinear transformation (quadratic stochastic operator) defined by (Vλ)(A) = ∫{sub X}∫{sub X}P(x,y,A)dλ(x)dλ(y), where P(x, y, A) is regarded as a function of two variables x and y with fixed A ∈ F . A quadratic stochastic operator V is called a regular, if for any initial measure the strong limit lim{sub n→∞} V{sup n }(λ) is exists. In this paper, we construct a family of quadratic stochastic operators defined on themore » segment X = [0,1] with Borel σ - algebra F on X , prove their regularity and show that the limit measure is a Dirac measure.« less

A stochastic spatiotemporal model of a response-regulator network in the Caulobacter crescentus cell cycle

NASA Astrophysics Data System (ADS)

Li, Fei; Subramanian, Kartik; Chen, Minghan; Tyson, John J.; Cao, Yang

2016-06-01

The asymmetric cell division cycle in Caulobacter crescentus is controlled by an elaborate molecular mechanism governing the production, activation and spatial localization of a host of interacting proteins. In previous work, we proposed a deterministic mathematical model for the spatiotemporal dynamics of six major regulatory proteins. In this paper, we study a stochastic version of the model, which takes into account molecular fluctuations of these regulatory proteins in space and time during early stages of the cell cycle of wild-type Caulobacter cells. We test the stochastic model with regard to experimental observations of increased variability of cycle time in cells depleted of the divJ gene product. The deterministic model predicts that overexpression of the divK gene blocks cell cycle progression in the stalked stage; however, stochastic simulations suggest that a small fraction of the mutants cells do complete the cell cycle normally.

Anomalous scaling of stochastic processes and the Moses effect

NASA Astrophysics Data System (ADS)

Chen, Lijian; Bassler, Kevin E.; McCauley, Joseph L.; Gunaratne, Gemunu H.

2017-04-01

The state of a stochastic process evolving over a time t is typically assumed to lie on a normal distribution whose width scales like t1/2. However, processes in which the probability distribution is not normal and the scaling exponent differs from 1/2 are known. The search for possible origins of such "anomalous" scaling and approaches to quantify them are the motivations for the work reported here. In processes with stationary increments, where the stochastic process is time-independent, autocorrelations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the Joseph effect and the Noah effect, respectively. If the increments are nonstationary, then scaling of increments with t can also lead to anomalous scaling, a mechanism we refer to as the Moses effect. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes are used to illustrate each effect. Intraday financial time series data are analyzed, revealing that their anomalous scaling is due only to the Moses effect. In the context of financial market data, we reiterate that the Joseph exponent, not the Hurst exponent, is the appropriate measure to test the efficient market hypothesis.

Anomalous scaling of stochastic processes and the Moses effect.

PubMed

Chen, Lijian; Bassler, Kevin E; McCauley, Joseph L; Gunaratne, Gemunu H

2017-04-01

The state of a stochastic process evolving over a time t is typically assumed to lie on a normal distribution whose width scales like t^{1/2}. However, processes in which the probability distribution is not normal and the scaling exponent differs from 1/2 are known. The search for possible origins of such "anomalous" scaling and approaches to quantify them are the motivations for the work reported here. In processes with stationary increments, where the stochastic process is time-independent, autocorrelations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the Joseph effect and the Noah effect, respectively. If the increments are nonstationary, then scaling of increments with t can also lead to anomalous scaling, a mechanism we refer to as the Moses effect. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes are used to illustrate each effect. Intraday financial time series data are analyzed, revealing that their anomalous scaling is due only to the Moses effect. In the context of financial market data, we reiterate that the Joseph exponent, not the Hurst exponent, is the appropriate measure to test the efficient market hypothesis.

Simulation-based planning for theater air warfare

NASA Astrophysics Data System (ADS)

Popken, Douglas A.; Cox, Louis A., Jr.

2004-08-01

Planning for Theatre Air Warfare can be represented as a hierarchy of decisions. At the top level, surviving airframes must be assigned to roles (e.g., Air Defense, Counter Air, Close Air Support, and AAF Suppression) in each time period in response to changing enemy air defense capabilities, remaining targets, and roles of opposing aircraft. At the middle level, aircraft are allocated to specific targets to support their assigned roles. At the lowest level, routing and engagement decisions are made for individual missions. The decisions at each level form a set of time-sequenced Courses of Action taken by opposing forces. This paper introduces a set of simulation-based optimization heuristics operating within this planning hierarchy to optimize allocations of aircraft. The algorithms estimate distributions for stochastic outcomes of the pairs of Red/Blue decisions. Rather than using traditional stochastic dynamic programming to determine optimal strategies, we use an innovative combination of heuristics, simulation-optimization, and mathematical programming. Blue decisions are guided by a stochastic hill-climbing search algorithm while Red decisions are found by optimizing over a continuous representation of the decision space. Stochastic outcomes are then provided by fast, Lanchester-type attrition simulations. This paper summarizes preliminary results from top and middle level models.

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

A probabilistic fatigue analysis of multiple site damage

NASA Technical Reports Server (NTRS)

Rohrbaugh, S. M.; Ruff, D.; Hillberry, B. M.; Mccabe, G.; Grandt, A. F., Jr.

1994-01-01

The variability in initial crack size and fatigue crack growth is incorporated in a probabilistic model that is used to predict the fatigue lives for unstiffened aluminum alloy panels containing multiple site damage (MSD). The uncertainty of the damage in the MSD panel is represented by a distribution of fatigue crack lengths that are analytically derived from equivalent initial flaw sizes. The variability in fatigue crack growth rate is characterized by stochastic descriptions of crack growth parameters for a modified Paris crack growth law. A Monte-Carlo simulation explicitly describes the MSD panel by randomly selecting values from the stochastic variables and then grows the MSD cracks with a deterministic fatigue model until the panel fails. Different simulations investigate the influences of the fatigue variability on the distributions of remaining fatigue lives. Six cases that consider fixed and variable conditions of initial crack size and fatigue crack growth rate are examined. The crack size distribution exhibited a dominant effect on the remaining fatigue life distribution, and the variable crack growth rate exhibited a lesser effect on the distribution. In addition, the probabilistic model predicted that only a small percentage of the life remains after a lead crack develops in the MSD panel.

Discontinuity of the annuity curves. III. Two types of vital variability in Drosophila melanogaster.

PubMed

Bychkovskaia, I B; Mylnikov, S V; Mozhaev, G A

2016-01-01

We confirm five-phased construction of Drosophila annuity curves established earlier. Annuity curves were composed of stable five-phase component and variable one. Variable component was due to differences in phase durations. As stable, so variable components were apparent for 60 generations. Stochastic component was described as well. Viability variance which characterize «reaction norm» was apparent for all generation as well. Thus, both types of variability seem to be inherited.

Stochastic approach to data analysis in fluorescence correlation spectroscopy.

PubMed

Rao, Ramachandra; Langoju, Rajesh; Gösch, Michael; Rigler, Per; Serov, Alexandre; Lasser, Theo

2006-09-21

Fluorescence correlation spectroscopy (FCS) has emerged as a powerful technique for measuring low concentrations of fluorescent molecules and their diffusion constants. In FCS, the experimental data is conventionally fit using standard local search techniques, for example, the Marquardt-Levenberg (ML) algorithm. A prerequisite for these categories of algorithms is the sound knowledge of the behavior of fit parameters and in most cases good initial guesses for accurate fitting, otherwise leading to fitting artifacts. For known fit models and with user experience about the behavior of fit parameters, these local search algorithms work extremely well. However, for heterogeneous systems or where automated data analysis is a prerequisite, there is a need to apply a procedure, which treats FCS data fitting as a black box and generates reliable fit parameters with accuracy for the chosen model in hand. We present a computational approach to analyze FCS data by means of a stochastic algorithm for global search called PGSL, an acronym for Probabilistic Global Search Lausanne. This algorithm does not require any initial guesses and does the fitting in terms of searching for solutions by global sampling. It is flexible as well as computationally faster at the same time for multiparameter evaluations. We present the performance study of PGSL for two-component with triplet fits. The statistical study and the goodness of fit criterion for PGSL are also presented. The robustness of PGSL on noisy experimental data for parameter estimation is also verified. We further extend the scope of PGSL by a hybrid analysis wherein the output of PGSL is fed as initial guesses to ML. Reliability studies show that PGSL and the hybrid combination of both perform better than ML for various thresholds of the mean-squared error (MSE).

Population activity statistics dissect subthreshold and spiking variability in V1.

PubMed

Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

2017-07-01

Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity. Copyright © 2017 the American Physiological Society.

A scalable moment-closure approximation for large-scale biochemical reaction networks

PubMed Central

Kazeroonian, Atefeh; Theis, Fabian J.; Hasenauer, Jan

2017-01-01

Abstract Motivation: Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. The number of state variables of these approximating models, however, grows at least quadratically with the number of biochemical species. This limits their application to small- and medium-sized processes. Results: In this article, we present a scalable moment-closure approximation (sMA) for the simulation of statistical moments of large-scale stochastic processes. The sMA exploits the structure of the biochemical reaction network to reduce the covariance matrix. We prove that sMA yields approximating models whose number of state variables depends predominantly on local properties, i.e. the average node degree of the reaction network, instead of the overall network size. The resulting complexity reduction is assessed by studying a range of medium- and large-scale biochemical reaction networks. To evaluate the approximation accuracy and the improvement in computational efficiency, we study models for JAK2/STAT5 signalling and NFκB signalling. Our method is applicable to generic biochemical reaction networks and we provide an implementation, including an SBML interface, which renders the sMA easily accessible. Availability and implementation: The sMA is implemented in the open-source MATLAB toolbox CERENA and is available from https://github.com/CERENADevelopers/CERENA. Contact: jan.hasenauer@helmholtz-muenchen.de or atefeh.kazeroonian@tum.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881983

Integration of Coastal Ocean Dynamics Application Radar (CODAR) and Short-Term Predictive System (STPS): Surface Current Estimates into the Search and Rescue Optimal Planning System (SAROPS)

DTIC Science & Technology

2005-11-01

walk (Markovian in position) techniques to perform these simulations ( Breivik et al, 2004; Spaulding and Howlett, 1996; Spaulding and Jayko, 1991; ASA...studies. Model 1 is used in most search and rescue models to make trajectory predictions ( Breivik et al, 2004; Spaulding and Howlett, 1996; Spaulding...ocean gyres: Part II hierarchy of stochastic models, Journal of Physical Oceanography, Vol. 32, 797-830. March 2002. Breivik , O., A. Allen, C. Wettre

Identifying variably saturated water-flow patterns in a steep hillslope under intermittent heavy rainfall

USGS Publications Warehouse

El-Kadi, A. I.; Torikai, J.D.

2001-01-01

The objective of this paper is to identify water-flow patterns in part of an active landslide, through the use of numerical simulations and data obtained during a field study. The approaches adopted include measuring rainfall events and pore-pressure responses in both saturated and unsaturated soils at the site. To account for soil variability, the Richards equation is solved within deterministic and stochastic frameworks. The deterministic simulations considered average water-retention data, adjusted retention data to account for stones or cobbles, retention functions for a heterogeneous pore structure, and continuous retention functions for preferential flow. The stochastic simulations applied the Monte Carlo approach which considers statistical distribution and autocorrelation of the saturated conductivity and its cross correlation with the retention function. Although none of the models is capable of accurately predicting field measurements, appreciable improvement in accuracy was attained using stochastic, preferential flow, and heterogeneous pore-structure models. For the current study, continuum-flow models provide reasonable accuracy for practical purposes, although they are expected to be less accurate than multi-domain preferential flow models.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

NASA Astrophysics Data System (ADS)

Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

2016-12-01

Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

Developmental phenotypic plasticity helps bridge stochastic weather events associated with climate change.

PubMed

Burggren, Warren

2018-05-10

The slow, inexorable rise in annual average global temperatures and acidification of the oceans are often advanced as consequences of global change. However, many environmental changes, especially those involving weather (as opposed to climate), are often stochastic, variable and extreme, particularly in temperate terrestrial or freshwater habitats. Moreover, few studies of animal and plant phenotypic plasticity employ realistic (i.e. short-term, stochastic) environmental change in their protocols. Here, I posit that the frequently abrupt environmental changes (days, weeks, months) accompanying much longer-term general climate change (e.g. global warming over decades or centuries) require consideration of the true nature of environmental change (as opposed to statistical means) coupled with an expansion of focus to consider developmental phenotypic plasticity. Such plasticity can be in multiple forms - obligatory/facultative, beneficial/deleterious - depending upon the degree and rate of environmental variability at specific points in organismal development. Essentially, adult phenotypic plasticity, as important as it is, will be irrelevant if developing offspring lack sufficient plasticity to create modified phenotypes necessary for survival. © 2018. Published by The Company of Biologists Ltd.

An uncertainty model of acoustic metamaterials with random parameters

NASA Astrophysics Data System (ADS)

He, Z. C.; Hu, J. Y.; Li, Eric

2018-01-01

Acoustic metamaterials (AMs) are man-made composite materials. However, the random uncertainties are unavoidable in the application of AMs due to manufacturing and material errors which lead to the variance of the physical responses of AMs. In this paper, an uncertainty model based on the change of variable perturbation stochastic finite element method (CVPS-FEM) is formulated to predict the probability density functions of physical responses of AMs with random parameters. Three types of physical responses including the band structure, mode shapes and frequency response function of AMs are studied in the uncertainty model, which is of great interest in the design of AMs. In this computation, the physical responses of stochastic AMs are expressed as linear functions of the pre-defined random parameters by using the first-order Taylor series expansion and perturbation technique. Then, based on the linear function relationships of parameters and responses, the probability density functions of the responses can be calculated by the change-of-variable technique. Three numerical examples are employed to demonstrate the effectiveness of the CVPS-FEM for stochastic AMs, and the results are validated by Monte Carlo method successfully.

Stochastic investigation of wind process for climatic variability identification

NASA Astrophysics Data System (ADS)

Deligiannis, Ilias; Tyrogiannis, Vassilis; Daskalou, Olympia; Dimitriadis, Panayiotis; Markonis, Yannis; Iliopoulou, Theano; Koutsoyiannis, Demetris

2016-04-01

The wind process is considered one of the hydrometeorological processes that generates and drives the climate dynamics. We use a dataset comprising hourly wind records to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e., mean process variance vs. scale) for various time periods. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

Stochastic investigation of precipitation process for climatic variability identification

NASA Astrophysics Data System (ADS)

Sotiriadou, Alexia; Petsiou, Amalia; Feloni, Elisavet; Kastis, Paris; Iliopoulou, Theano; Markonis, Yannis; Tyralis, Hristos; Dimitriadis, Panayiotis; Koutsoyiannis, Demetris

2016-04-01

The precipitation process is important not only to hydrometeorology but also to renewable energy resources management. We use a dataset consisting of daily and hourly records around the globe to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e., mean process variance vs. scale). Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

Multivariate stochastic simulation with subjective multivariate normal distributions

Treesearch

P. J. Ince; J. Buongiorno

1991-01-01

In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

Linear theory for filtering nonlinear multiscale systems with model error

PubMed Central

Berry, Tyrus; Harlim, John

2014-01-01

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline. PMID:25002829

STOCHASTIC DUELS OF LIMITED TIME-DURATION,

DTIC Science & Technology

This paper continues the development of the theory of stochastic duels to include the case where there is a time limitation on the duration of the... duel . An example of this situation is fighter-bomber combat in which the fighter has a very limited fuel supply. In this duel , two contestants (with...variable or (2) constant. The duel proceeds until one or both of the contestants are killed or until the time limit is exceeded. The time limit is either

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

PubMed Central

Liu, Fei; Heiner, Monika; Yang, Ming

2016-01-01

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information. PMID:26910830

Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.

PubMed

Kilinc, Deniz; Demir, Alper

2017-08-01

The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.

NMR lineshape equations for hindered methyl group: a comparison of the semi-classical and quantum mechanical models

NASA Astrophysics Data System (ADS)

Bernatowicz, P.; Szymański, S.

2003-09-01

The semiclassical and quantum mechanical NMR lineshape equations for a hindered methyl group are compared. In both the approaches, the stochastic dynamics can be interpreted in terms of a progressive symmetrization of the spin density matrix. However, the respective ways of achieving the same limiting symmetry can be remarkably different. From numerical lineshape simulations it is inferred that in the regime of intermediate exchange, where the conventional theory predicts occurrence of a single Lorentzian, the actual spectrum can have nontrivial features. This observation may open new perspectives in the search for nonclassical effects in the stochastic behavior of methyl groups in liquid-phase NMR.

An inexact mixed risk-aversion two-stage stochastic programming model for water resources management under uncertainty.

PubMed

Li, W; Wang, B; Xie, Y L; Huang, G H; Liu, L

2015-02-01

Uncertainties exist in the water resources system, while traditional two-stage stochastic programming is risk-neutral and compares the random variables (e.g., total benefit) to identify the best decisions. To deal with the risk issues, a risk-aversion inexact two-stage stochastic programming model is developed for water resources management under uncertainty. The model was a hybrid methodology of interval-parameter programming, conditional value-at-risk measure, and a general two-stage stochastic programming framework. The method extends on the traditional two-stage stochastic programming method by enabling uncertainties presented as probability density functions and discrete intervals to be effectively incorporated within the optimization framework. It could not only provide information on the benefits of the allocation plan to the decision makers but also measure the extreme expected loss on the second-stage penalty cost. The developed model was applied to a hypothetical case of water resources management. Results showed that that could help managers generate feasible and balanced risk-aversion allocation plans, and analyze the trade-offs between system stability and economy.

A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

NASA Astrophysics Data System (ADS)

Pham, Minh Tu; Vernieuwe, Hilde; De Baets, Bernard; Verhoest, Niko E. C.

2018-02-01

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis.

Integrator Windup Protection-Techniques and a STOVL Aircraft Engine Controller Application

NASA Technical Reports Server (NTRS)

KrishnaKumar, K.; Narayanaswamy, S.

1997-01-01

Integrators are included in the feedback loop of a control system to eliminate the steady state errors in the commanded variables. The integrator windup problem arises if the control actuators encounter operational limits before the steady state errors are driven to zero by the integrator. The typical effects of windup are large system oscillations, high steady state error, and a delayed system response following the windup. In this study, methods to prevent the integrator windup are examined to provide Integrator Windup Protection (IW) for an engine controller of a Short Take-Off and Vertical Landing (STOVL) aircraft. An unified performance index is defined to optimize the performance of the Conventional Anti-Windup (CAW) and the Modified Anti-Windup (MAW) methods. A modified Genetic Algorithm search procedure with stochastic parameter encoding is implemented to obtain the optimal parameters of the CAW scheme. The advantages and drawbacks of the CAW and MAW techniques are discussed and recommendations are made for the choice of the IWP scheme, given some characteristics of the system.

Optimal design of earth-moving machine elements with cusp catastrophe theory application

NASA Astrophysics Data System (ADS)

Pitukhin, A. V.; Skobtsov, I. G.

2017-10-01

This paper deals with the optimal design problem solution for the operator of an earth-moving machine with a roll-over protective structure (ROPS) in terms of the catastrophe theory. A brief description of the catastrophe theory is presented, the cusp catastrophe is considered, control parameters are viewed as Gaussian stochastic quantities in the first part of the paper. The statement of optimal design problem is given in the second part of the paper. It includes the choice of the objective function and independent design variables, establishment of system limits. The objective function is determined as mean total cost that includes initial cost and cost of failure according to the cusp catastrophe probability. Algorithm of random search method with an interval reduction subject to side and functional constraints is given in the last part of the paper. The way of optimal design problem solution can be applied to choose rational ROPS parameters, which will increase safety and reduce production and exploitation expenses.

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Christensen, Hannah M.; Juricke, Stephan; Subramanian, Aneesh; Watson, Peter A. G.; Weisheimer, Antje; Palmer, Tim N.

2017-03-01

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), together with coupled transient runs (1850-2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate - specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

Estimating radiative feedbacks from stochastic fluctuations in surface temperature and energy imbalance

NASA Astrophysics Data System (ADS)

Proistosescu, C.; Donohoe, A.; Armour, K.; Roe, G.; Stuecker, M. F.; Bitz, C. M.

2017-12-01

Joint observations of global surface temperature and energy imbalance provide for a unique opportunity to empirically constrain radiative feedbacks. However, the satellite record of Earth's radiative imbalance is relatively short and dominated by stochastic fluctuations. Estimates of radiative feedbacks obtained by regressing energy imbalance against surface temperature depend strongly on sampling choices and on assumptions about whether the stochastic fluctuations are primarily forced by atmospheric or oceanic variability (e.g. Murphy and Forster 2010, Dessler 2011, Spencer and Braswell 2011, Forster 2016). We develop a framework around a stochastic energy balance model that allows us to parse the different contributions of atmospheric and oceanic forcing based on their differing impacts on the covariance structure - or lagged regression - of temperature and radiative imbalance. We validate the framework in a hierarchy of general circulation models: the impact of atmospheric forcing is examined in unforced control simulations of fixed sea-surface temperature and slab ocean model versions; the impact of oceanic forcing is examined in coupled simulations with prescribed ENSO variability. With the impact of atmospheric and oceanic forcing constrained, we are able to predict the relationship between temperature and radiative imbalance in a fully coupled control simulation, finding that both forcing sources are needed to explain the structure of the lagged-regression. We further model the dependence of feedback estimates on sampling interval by considering the effects of a finite equilibration time for the atmosphere, and issues of smoothing and aliasing. Finally, we develop a method to fit the stochastic model to the short timeseries of temperature and radiative imbalance by performing a Bayesian inference based on a modified version of the spectral Whittle likelihood. We are thus able to place realistic joint uncertainty estimates on both stochastic forcing and radiative feedbacks derived from observational records. We find that these records are, as of yet, too short to be useful in constraining radiative feedbacks, and we provide estimates of how the uncertainty narrows as a function of record length.

Assembly Mechanism of the Contractile Ring for Cytokinesis by Fission Yeast

NASA Astrophysics Data System (ADS)

Vavylonis, Dimitrios; Wu, Jian-Qiu; Huang, Xiaolei; O'Shaughnessy, Ben; Pollard, Thomas

2008-03-01

Animals and fungi assemble a contractile ring of actin filaments and the motor protein myosin to separate into individual daughter cells during cytokinesis. We studied the mechanism of contractile ring assembly in fission yeast with high time resolution confocal microscopy, computational image analysis methods, and numerical simulations. Approximately 63 nodes containing myosin, broadly distributed around the cell equator, assembled into a ring through stochastic motions, making many starts, stops, and changes of direction as they condense into a ring. Estimates of node friction coefficients from the mean square displacement of stationary nodes imply forces for node movement are greater than ˜ 4 pN, similarly to forces by a few molecular motors. Skeletonization and topology analysis of images of cells expressing fluorescent actin filament markers showed transient linear elements extending in all directions from myosin nodes and establishing connections among them. We propose a model with traction between nodes depending on transient connections established by stochastic search and capture (``search, capture, pull and release''). Numerical simulations of the model using parameter values obtained from experiment succesfully condense nodes into a continuous ring.

Stochastic optimization of broadband reflecting photonic structures.

PubMed

Estrada-Wiese, D; Del Río-Chanona, E A; Del Río, J A

2018-01-19

Photonic crystals (PCs) are built to control the propagation of light within their structure. These can be used for an assortment of applications where custom designed devices are of interest. Among them, one-dimensional PCs can be produced to achieve the reflection of specific and broad wavelength ranges. However, their design and fabrication are challenging due to the diversity of periodic arrangement and layer configuration that each different PC needs. In this study, we present a framework to design high reflecting PCs for any desired wavelength range. Our method combines three stochastic optimization algorithms (Random Search, Particle Swarm Optimization and Simulated Annealing) along with a reduced space-search methodology to obtain a custom and optimized PC configuration. The optimization procedure is evaluated through theoretical reflectance spectra calculated by using the Equispaced Thickness Method, which improves the simulations due to the consideration of incoherent light transmission. We prove the viability of our procedure by fabricating different reflecting PCs made of porous silicon and obtain good agreement between experiment and theory using a merit function. With this methodology, diverse reflecting PCs can be designed for any applications and fabricated with different materials.

Directed intermittent search for a hidden target on a dendritic tree

NASA Astrophysics Data System (ADS)

Newby, Jay M.; Bressloff, Paul C.

2009-08-01

Motivated by experimental observations of active (motor-driven) intracellular transport in neuronal dendrites, we analyze a stochastic model of directed intermittent search on a tree network. A particle injected from the cell body or soma into the primary branch of the dendritic tree randomly switches between a stationary search phase and a mobile nonsearch phase that is biased in the forward direction. A (synaptic) target is presented somewhere within the tree, which the particle can locate if it is within a certain range and in the searching phase. We approximate the moment generating function using Green’s function methods. The moment generating function is then used to compute the hitting probability and conditional mean first passage time to the target. We show that in contrast to a previously explored finite interval case, there is a range of parameters for which a bidirectional search strategy is more efficient than a unidirectional one in finding the target.

Modular and Stochastic Approaches to Molecular Pathway Models of ATM, TGF beta, and WNT Signaling

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; O'Neill, Peter; Ponomarev, Artem; Carra, Claudio; Whalen, Mary; Pluth, Janice M.

2009-01-01

Deterministic pathway models that describe the biochemical interactions of a group of related proteins, their complexes, activation through kinase, etc. are often the basis for many systems biology models. Low dose radiation effects present a unique set of challenges to these models including the importance of stochastic effects due to the nature of radiation tracks and small number of molecules activated, and the search for infrequent events that contribute to cancer risks. We have been studying models of the ATM, TGF -Smad and WNT signaling pathways with the goal of applying pathway models to the investigation of low dose radiation cancer risks. Modeling challenges include introduction of stochastic models of radiation tracks, their relationships to more than one substrate species that perturb pathways, and the identification of a representative set of enzymes that act on the dominant substrates. Because several pathways are activated concurrently by radiation the development of modular pathway approach is of interest.

A stochastic evolutionary model generating a mixture of exponential distributions

NASA Astrophysics Data System (ADS)

Fenner, Trevor; Levene, Mark; Loizou, George

2016-02-01

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in [T. Fenner, M. Levene, G. Loizou, J. Stat. Mech. 2015, P08015 (2015)] so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

Mathematical issues in eternal inflation

NASA Astrophysics Data System (ADS)

Singh Kohli, Ikjyot; Haslam, Michael C.

2015-04-01

In this paper, we consider the problem of the existence and uniqueness of solutions to the Einstein field equations for a spatially flat Friedmann-Lemaître-Robertson-Walker universe in the context of stochastic eternal inflation, where the stochastic mechanism is modelled by adding a stochastic forcing term representing Gaussian white noise to the Klein-Gordon equation. We show that under these considerations, the Klein-Gordon equation actually becomes a stochastic differential equation. Therefore, the existence and uniqueness of solutions to Einstein’s equations depend on whether the coefficients of this stochastic differential equation obey Lipschitz continuity conditions. We show that for any choice of V(φ ), the Einstein field equations are not globally well-posed, hence, any solution found to these equations is not guaranteed to be unique. Instead, the coefficients are at best locally Lipschitz continuous in the physical state space of the dynamical variables, which only exist up to a finite explosion time. We further perform Feller’s explosion test for an arbitrary power-law inflaton potential and prove that all solutions to the Einstein field equations explode in a finite time with probability one. This implies that the mechanism of stochastic inflation thus considered cannot be described to be eternal, since the very concept of eternal inflation implies that the process continues indefinitely. We therefore argue that stochastic inflation based on a stochastic forcing term would not produce an infinite number of universes in some multiverse ensemble. In general, since the Einstein field equations in both situations are not well-posed, we further conclude that the existence of a multiverse via the stochastic eternal inflation mechanism considered in this paper is still very much an open question that will require much deeper investigation.

A stochastic visco-hyperelastic model of human placenta tissue for finite element crash simulations.

PubMed

Hu, Jingwen; Klinich, Kathleen D; Miller, Carl S; Rupp, Jonathan D; Nazmi, Giseli; Pearlman, Mark D; Schneider, Lawrence W

2011-03-01

Placental abruption is the most common cause of fetal deaths in motor-vehicle crashes, but studies on the mechanical properties of human placenta are rare. This study presents a new method of developing a stochastic visco-hyperelastic material model of human placenta tissue using a combination of uniaxial tensile testing, specimen-specific finite element (FE) modeling, and stochastic optimization techniques. In our previous study, uniaxial tensile tests of 21 placenta specimens have been performed using a strain rate of 12/s. In this study, additional uniaxial tensile tests were performed using strain rates of 1/s and 0.1/s on 25 placenta specimens. Response corridors for the three loading rates were developed based on the normalized data achieved by test reconstructions of each specimen using specimen-specific FE models. Material parameters of a visco-hyperelastic model and their associated standard deviations were tuned to match both the means and standard deviations of all three response corridors using a stochastic optimization method. The results show a very good agreement between the tested and simulated response corridors, indicating that stochastic analysis can improve estimation of variability in material model parameters. The proposed method can be applied to develop stochastic material models of other biological soft tissues.

Stochastic simulation and analysis of biomolecular reaction networks

PubMed Central

Frazier, John M; Chushak, Yaroslav; Foy, Brent

2009-01-01

Background In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data. Results Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures. Conclusion The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior. PMID:19534796

A simplified model to evaluate the effect of fluid rheology on non-Newtonian flow in variable aperture fractures

NASA Astrophysics Data System (ADS)

Felisa, Giada; Ciriello, Valentina; Longo, Sandro; Di Federico, Vittorio

2017-04-01

Modeling of non-Newtonian flow in fractured media is essential in hydraulic fracturing operations, largely used for optimal exploitation of oil, gas and thermal reservoirs. Complex fluids interact with pre-existing rock fractures also during drilling operations, enhanced oil recovery, environmental remediation, and other natural phenomena such as magma and sand intrusions, and mud volcanoes. A first step in the modeling effort is a detailed understanding of flow in a single fracture, as the fracture aperture is typically spatially variable. A large bibliography exists on Newtonian flow in single, variable aperture fractures. Ultimately, stochastic modeling of aperture variability at the single fracture scale leads to determination of the flowrate under a given pressure gradient as a function of the parameters describing the variability of the aperture field and the fluid rheological behaviour. From the flowrate, a flow, or 'hydraulic', aperture can then be derived. The equivalent flow aperture for non-Newtonian fluids of power-law nature in single, variable aperture fractures has been obtained in the past both for deterministic and stochastic variations. Detailed numerical modeling of power-law fluid flow in a variable aperture fracture demonstrated that pronounced channelization effects are associated to a nonlinear fluid rheology. The availability of an equivalent flow aperture as a function of the parameters describing the fluid rheology and the aperture variability is enticing, as it allows taking their interaction into account when modeling flow in fracture networks at a larger scale. A relevant issue in non-Newtonian fracture flow is the rheological nature of the fluid. The constitutive model routinely used for hydro-fracturing modeling is the simple, two-parameter power-law. Yet this model does not characterize real fluids at low and high shear rates, as it implies, for shear-thinning fluids, an apparent viscosity which becomes unbounded for zero shear rate and tends to zero for infinite shear rate. On the contrary, the four-parameter Carreau constitutive equation includes asymptotic values of the apparent viscosity at those limits; in turn, the Carreau rheological equation is well approximated by the more tractable truncated power-law model. Results for flow of such fluids between parallel walls are already available. This study extends the adoption of the truncated power-law model to variable aperture fractures, with the aim of understanding the joint influence of rheology and aperture spatial variability. The aperture variation, modeled within a stochastic or deterministic framework, is taken to be one-dimensional and perpendicular to the flow direction; for stochastic modeling, the influence of different distribution functions is examined. Results are then compared with those obtained for pure power-law fluids for different combinations of model parameters. It is seen that the adoption of the pure power law model leads to significant overestimation of the flowrate with respect to the truncated model, more so for large external pressure gradient and/or aperture variability.

Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis

NASA Astrophysics Data System (ADS)

Jia, Ningning; Y Lam, Edmund

2010-04-01

Inverse lithography technology (ILT) synthesizes photomasks by solving an inverse imaging problem through optimization of an appropriate functional. Much effort on ILT is dedicated to deriving superior masks at a nominal process condition. However, the lower k1 factor causes the mask to be more sensitive to process variations. Robustness to major process variations, such as focus and dose variations, is desired. In this paper, we consider the focus variation as a stochastic variable, and treat the mask design as a machine learning problem. The stochastic gradient descent approach, which is a useful tool in machine learning, is adopted to train the mask design. Compared with previous work, simulation shows that the proposed algorithm is effective in producing robust masks.

A discontinuous Galerkin method for numerical pricing of European options under Heston stochastic volatility

NASA Astrophysics Data System (ADS)

Hozman, J.; Tichý, T.

2016-12-01

The paper is based on the results from our recent research on multidimensional option pricing problems. We focus on European option valuation when the price movement of the underlying asset is driven by a stochastic volatility following a square root process proposed by Heston. The stochastic approach incorporates a new additional spatial variable into this model and makes it very robust, i.e. it provides a framework to price a variety of options that is closer to reality. The main topic is to present the numerical scheme arising from the concept of discontinuous Galerkin methods and applicable to the Heston option pricing model. The numerical results are presented on artificial benchmarks as well as on reference market data.

A stochastic model of input effectiveness during irregular gamma rhythms.

PubMed

Dumont, Grégory; Northoff, Georg; Longtin, André

2016-02-01

Gamma-band synchronization has been linked to attention and communication between brain regions, yet the underlying dynamical mechanisms are still unclear. How does the timing and amplitude of inputs to cells that generate an endogenously noisy gamma rhythm affect the network activity and rhythm? How does such "communication through coherence" (CTC) survive in the face of rhythm and input variability? We present a stochastic modelling approach to this question that yields a very fast computation of the effectiveness of inputs to cells involved in gamma rhythms. Our work is partly motivated by recent optogenetic experiments (Cardin et al. Nature, 459(7247), 663-667 2009) that tested the gamma phase-dependence of network responses by first stabilizing the rhythm with periodic light pulses to the interneurons (I). Our computationally efficient model E-I network of stochastic two-state neurons exhibits finite-size fluctuations. Using the Hilbert transform and Kuramoto index, we study how the stochastic phase of its gamma rhythm is entrained by external pulses. We then compute how this rhythmic inhibition controls the effectiveness of external input onto pyramidal (E) cells, and how variability shapes the window of firing opportunity. For transferring the time variations of an external input to the E cells, we find a tradeoff between the phase selectivity and depth of rate modulation. We also show that the CTC is sensitive to the jitter in the arrival times of spikes to the E cells, and to the degree of I-cell entrainment. We further find that CTC can occur even if the underlying deterministic system does not oscillate; quasicycle-type rhythms induced by the finite-size noise retain the basic CTC properties. Finally a resonance analysis confirms the relative importance of the I cell pacing for rhythm generation. Analysis of whole network behaviour, including computations of synchrony, phase and shifts in excitatory-inhibitory balance, can be further sped up by orders of magnitude using two coupled stochastic differential equations, one for each population. Our work thus yields a fast tool to numerically and analytically investigate CTC in a noisy context. It shows that CTC can be quite vulnerable to rhythm and input variability, which both decrease phase preference.

The effects of noise on binocular rivalry waves: a stochastic neural field model

NASA Astrophysics Data System (ADS)

Webber, Matthew A.; Bressloff, Paul C.

2013-03-01

We analyze the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how extrinsic noise in the activity variables leads to a diffusive-like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. We use our analysis to calculate the first-passage-time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation lead to quenched disorder in the neural fields during propagation of a wave.

A stochastic Iwan-type model for joint behavior variability modeling

NASA Astrophysics Data System (ADS)

Mignolet, Marc P.; Song, Pengchao; Wang, X. Q.

2015-08-01

This paper focuses overall on the development and validation of a stochastic model to describe the dissipation and stiffness properties of a bolted joint for which experimental data is available and exhibits a large scatter. An extension of the deterministic parallel-series Iwan model for the characterization of the force-displacement behavior of joints is first carried out. This new model involves dynamic and static coefficients of friction differing from each other and a broadly defined distribution of Jenkins elements. Its applicability is next investigated using the experimental data, i.e. stiffness and dissipation measurements obtained in harmonic testing of 9 nominally identical bolted joints. The model is found to provide a very good fit of the experimental data for each bolted joint notwithstanding the significant variability of their behavior. This finding suggests that this variability can be simulated through the randomization of only the parameters of the proposed Iwan-type model. The distribution of these parameters is next selected based on maximum entropy concepts and their corresponding parameters, i.e. the hyperparameters of the model, are identified using a maximum likelihood strategy. Proceeding with a Monte Carlo simulation of this stochastic Iwan model demonstrates that the experimental data fits well within the uncertainty band corresponding to the 5th and 95th percentiles of the model predictions which well supports the adequacy of the modeling effort.

Stochastic Parametrization for the Impact of Neglected Variability Patterns

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Hien, Steffen; Achatz, Ulrich; Horenko, Illia

2017-04-01

An efficient description of the gravity wave variability and the related spontaneous emission processes requires an empirical stochastic closure for the impact of neglected variability patterns (subgridscales or SGS). In particular, we focus on the analysis of the IGW emission within a tangent linear model which requires a stochastic SGS parameterization for taking the self interaction of the ageostrophic flow components into account. For this purpose, we identify the best SGS model in terms of exactness and simplicity by deploying a wide range of different data-driven model classes, including standard stationary regression models, autoregression and artificial neuronal networks models - as well as the family of nonstationary models like FEM-BV-VARX model class (Finite Element based vector autoregressive time series analysis with bounded variation of the model parameters). The models are used to investigate the main characteristics of the underlying dynamics and to explore the significant spatial and temporal neighbourhood dependencies. The best SGS model in terms of exactness and simplicity is obtained for the nonstationary FEM-BV-VARX setting, determining only direct spatial and temporal neighbourhood as significant - and allowing to drastically reduce the number of informations that are required for the optimal SGS. Additionally, the models are characterized by sets of vector- and matrix-valued parameters that must be inferred from big data sets provided by simulations - making it a task that can not be solved without deploying high-performance computing facilities (HPC).

Structured population dynamics: continuous size and discontinuous stage structures.

PubMed

Buffoni, Giuseppe; Pasquali, Sara

2007-04-01

A nonlinear stochastic model for the dynamics of a population with either a continuous size structure or a discontinuous stage structure is formulated in the Eulerian formalism. It takes into account dispersion effects due to stochastic variability of the development process of the individuals. The discrete equations of the numerical approximation are derived, and an analysis of the existence and stability of the equilibrium states is performed. An application to a copepod population is illustrated; numerical results of Eulerian and Lagrangian models are compared.

Compiling probabilistic, bio-inspired circuits on a field programmable analog array

PubMed Central

Marr, Bo; Hasler, Jennifer

2014-01-01

A field programmable analog array (FPAA) is presented as an energy and computational efficiency engine: a mixed mode processor for which functions can be compiled at significantly less energy costs using probabilistic computing circuits. More specifically, it will be shown that the core computation of any dynamical system can be computed on the FPAA at significantly less energy per operation than a digital implementation. A stochastic system that is dynamically controllable via voltage controlled amplifier and comparator thresholds is implemented, which computes Bernoulli random variables. From Bernoulli variables it is shown exponentially distributed random variables, and random variables of an arbitrary distribution can be computed. The Gillespie algorithm is simulated to show the utility of this system by calculating the trajectory of a biological system computed stochastically with this probabilistic hardware where over a 127X performance improvement over current software approaches is shown. The relevance of this approach is extended to any dynamical system. The initial circuits and ideas for this work were generated at the 2008 Telluride Neuromorphic Workshop. PMID:24847199

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation

PubMed Central

Müller, Eike H.; Scheichl, Rob; Shardlow, Tony

2015-01-01

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy. PMID:27547075

Stochastic investigation of temperature process for climatic variability identification

NASA Astrophysics Data System (ADS)

Lerias, Eleutherios; Kalamioti, Anna; Dimitriadis, Panayiotis; Markonis, Yannis; Iliopoulou, Theano; Koutsoyiannis, Demetris

2016-04-01

The temperature process is considered as the most characteristic hydrometeorological process and has been thoroughly examined in the climate-change framework. We use a dataset comprising hourly temperature and dew point records to identify statistical variability with emphasis on the last period. Specifically, we investigate the occurrence of mean, maximum and minimum values and we estimate statistical properties such as marginal probability distribution function and the type of decay of the climacogram (i.e., mean process variance vs. scale) for various time periods. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

Improving multilevel Monte Carlo for stochastic differential equations with application to the Langevin equation.

PubMed

Müller, Eike H; Scheichl, Rob; Shardlow, Tony

2015-04-08

This paper applies several well-known tricks from the numerical treatment of deterministic differential equations to improve the efficiency of the multilevel Monte Carlo (MLMC) method for stochastic differential equations (SDEs) and especially the Langevin equation. We use modified equations analysis as an alternative to strong-approximation theory for the integrator, and we apply this to introduce MLMC for Langevin-type equations with integrators based on operator splitting. We combine this with extrapolation and investigate the use of discrete random variables in place of the Gaussian increments, which is a well-known technique for the weak approximation of SDEs. We show that, for small-noise problems, discrete random variables can lead to an increase in efficiency of almost two orders of magnitude for practical levels of accuracy.

Variable Density Effects in Stochastic Lagrangian Models for Turbulent Combustion

DTIC Science & Technology

2016-07-20

PDF methods in dealing with chemical reaction and convection are preserved irrespective of density variation. Since the density variation in a typical...combustion process may be as large as factor of seven, including variable- density effects in PDF methods is of significance. Conventionally, the...strategy of modelling variable density flows in PDF methods is similar to that used for second-moment closure models (SMCM): models are developed based on

Stochastic empirical loading and dilution model (SELDM) version 1.0.0

USGS Publications Warehouse

Granato, Gregory E.

2013-01-01

The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.

Localization Transition Induced by Learning in Random Searches

NASA Astrophysics Data System (ADS)

Falcón-Cortés, Andrea; Boyer, Denis; Giuggioli, Luca; Majumdar, Satya N.

2017-10-01

We solve an adaptive search model where a random walker or Lévy flight stochastically resets to previously visited sites on a d -dimensional lattice containing one trapping site. Because of reinforcement, a phase transition occurs when the resetting rate crosses a threshold above which nondiffusive stationary states emerge, localized around the inhomogeneity. The threshold depends on the trapping strength and on the walker's return probability in the memoryless case. The transition belongs to the same class as the self-consistent theory of Anderson localization. These results show that similarly to many living organisms and unlike the well-studied Markovian walks, non-Markov movement processes can allow agents to learn about their environment and promise to bring adaptive solutions in search tasks.

Strategic planning for disaster recovery with stochastic last mile distribution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bent, Russell Whitford; Van Hentenryck, Pascal; Coffrin, Carleton

2010-01-01

This paper considers the single commodity allocation problem (SCAP) for disaster recovery, a fundamental problem faced by all populated areas. SCAPs are complex stochastic optimization problems that combine resource allocation, warehouse routing, and parallel fleet routing. Moreover, these problems must be solved under tight runtime constraints to be practical in real-world disaster situations. This paper formalizes the specification of SCAPs and introduces a novel multi-stage hybrid-optimization algorithm that utilizes the strengths of mixed integer programming, constraint programming, and large neighborhood search. The algorithm was validated on hurricane disaster scenarios generated by Los Alamos National Laboratory using state-of-the-art disaster simulation toolsmore » and is deployed to aid federal organizations in the US.« less

A deterministic and stochastic model for the system dynamics of tumor-immune responses to chemotherapy

NASA Astrophysics Data System (ADS)

Liu, Xiangdong; Li, Qingze; Pan, Jianxin

2018-06-01

Modern medical studies show that chemotherapy can help most cancer patients, especially for those diagnosed early, to stabilize their disease conditions from months to years, which means the population of tumor cells remained nearly unchanged in quite a long time after fighting against immune system and drugs. In order to better understand the dynamics of tumor-immune responses under chemotherapy, deterministic and stochastic differential equation models are constructed to characterize the dynamical change of tumor cells and immune cells in this paper. The basic dynamical properties, such as boundedness, existence and stability of equilibrium points, are investigated in the deterministic model. Extended stochastic models include stochastic differential equations (SDEs) model and continuous-time Markov chain (CTMC) model, which accounts for the variability in cellular reproduction, growth and death, interspecific competitions, and immune response to chemotherapy. The CTMC model is harnessed to estimate the extinction probability of tumor cells. Numerical simulations are performed, which confirms the obtained theoretical results.

Representing and computing regular languages on massively parallel networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, M.I.; O'Sullivan, J.A.; Boysam, B.

1991-01-01

This paper proposes a general method for incorporating rule-based constraints corresponding to regular languages into stochastic inference problems, thereby allowing for a unified representation of stochastic and syntactic pattern constraints. The authors' approach first established the formal connection of rules to Chomsky grammars, and generalizes the original work of Shannon on the encoding of rule-based channel sequences to Markov chains of maximum entropy. This maximum entropy probabilistic view leads to Gibb's representations with potentials which have their number of minima growing at precisely the exponential rate that the language of deterministically constrained sequences grow. These representations are coupled to stochasticmore » diffusion algorithms, which sample the language-constrained sequences by visiting the energy minima according to the underlying Gibbs' probability law. The coupling to stochastic search methods yields the all-important practical result that fully parallel stochastic cellular automata may be derived to generate samples from the rule-based constraint sets. The production rules and neighborhood state structure of the language of sequences directly determines the necessary connection structures of the required parallel computing surface. Representations of this type have been mapped to the DAP-510 massively-parallel processor consisting of 1024 mesh-connected bit-serial processing elements for performing automated segmentation of electron-micrograph images.« less

Modelling Evolutionary Algorithms with Stochastic Differential Equations.

PubMed

Heredia, Jorge Pérez

2017-11-20

There has been renewed interest in modelling the behaviour of evolutionary algorithms (EAs) by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogues of the additive and multiplicative drift theorems from runtime analysis. In addition, we derive a new more general multiplicative drift theorem that also covers non-elitist EAs. This theorem simultaneously allows for positive and negative results, providing information on the algorithm's progress even when the problem cannot be optimised efficiently. Finally, we provide results for some well-known heuristics namely Random Walk (RW), Random Local Search (RLS), the (1+1) EA, the Metropolis Algorithm (MA), and the Strong Selection Weak Mutation (SSWM) algorithm.

A Stochastic Dynamic Programming Model With Fuzzy Storage States Applied to Reservoir Operation Optimization

NASA Astrophysics Data System (ADS)

Mousavi, Seyed Jamshid; Mahdizadeh, Kourosh; Afshar, Abbas

2004-08-01

Application of stochastic dynamic programming (SDP) models to reservoir optimization calls for state variables discretization. As an important variable discretization of reservoir storage volume has a pronounced effect on the computational efforts. The error caused by storage volume discretization is examined by considering it as a fuzzy state variable. In this approach, the point-to-point transitions between storage volumes at the beginning and end of each period are replaced by transitions between storage intervals. This is achieved by using fuzzy arithmetic operations with fuzzy numbers. In this approach, instead of aggregating single-valued crisp numbers, the membership functions of fuzzy numbers are combined. Running a simulated model with optimal release policies derived from fuzzy and non-fuzzy SDP models shows that a fuzzy SDP with a coarse discretization scheme performs as well as a classical SDP having much finer discretized space. It is believed that this advantage in the fuzzy SDP model is due to the smooth transitions between storage intervals which benefit from soft boundaries.

A stochastic atmospheric model for remote sensing applications

NASA Technical Reports Server (NTRS)

Turner, R. E.

1983-01-01

There are many factors which reduce the accuracy of classification of objects in the satellite remote sensing of Earth's surface. One important factor is the variability in the scattering and absorptive properties of the atmospheric components such as particulates and the variable gases. For multispectral remote sensing of the Earth's surface in the visible and infrared parts of the spectrum the atmospheric particulates are a major source of variability in the received signal. It is difficult to design a sensor which will determine the unknown atmospheric components by remote sensing methods, at least to the accuracy needed for multispectral classification. The problem of spatial and temporal variations in the atmospheric quantities which can affect the measured radiances are examined. A method based upon the stochastic nature of the atmospheric components was developed, and, using actual data the statistical parameters needed for inclusion into a radiometric model was generated. Methods are then described for an improved correction of radiances. These algorithms will then result in a more accurate and consistent classification procedure.

Searching for Galactic hidden gas through interstellar scintillation: results from a test with the NTT-SOFI detector

NASA Astrophysics Data System (ADS)

Habibi, F.; Moniez, M.; Ansari, R.; Rahvar, S.

2011-01-01

Aims: Stars twinkle because their light propagates through the atmosphere. The same phenomenon is expected at a longer time scale when the light of remote stars crosses an interstellar molecular cloud, but it has never been observed at optical wavelength. In a favorable case, the light of a background star can be subject to stochastic fluctuations on the order of a few percent at a characteristic time scale of a few minutes. Our ultimate aim is to discover or exclude these scintillation effects to estimate the contribution of molecular hydrogen to the Galactic baryonic hidden mass. This feasibility study is a pathfinder toward an observational strategy to search for scintillation, probing the sensitivity of future surveys and estimating the background level. Methods: We searched for scintillation induced by molecular gas in visible dark nebulae as well as by hypothetical halo clumpuscules of cool molecular hydrogen (H2-He) during two nights. We took long series of 10 s infrared exposures with the ESO-NTT telescope toward stellar populations located behind visible nebulae and toward the Small Magellanic Cloud (SMC). We therefore searched for stars exhibiting stochastic flux variations similar to what is expected from the scintillation effect. According to our simulations of the scintillation process, this search should allow one to detect (stochastic) transverse gradients of column density in cool Galactic molecular clouds of order of ~ 3 × 10-5 g/cm2/10 000 km. Results: We found one light-curve that is compatible with a strong scintillation effect through a turbulent structure characterized by a diffusion radius Rdiff < 100 km in the B68 nebula. Complementary observations are needed to clarify the status of this candidate, and no firm conclusion can be established from this single observation. We can also infer limits on the existence of turbulent dense cores (of number density n > 109 cm-3) within the dark nebulae. Because no candidate is found toward the SMC, we are also able to establish upper limits on the contribution of gas clumpuscules to the Galactic halo mass. Conclusions: The limits set by this test do not seriously constrain the known models, but we show that the short time-scale monitoring for a few 106 star × hour in the visible band with a >4 m telescope and a fast readout camera should allow one to quantify the contribution of turbulent molecular gas to the Galactic halo. The LSST (Large Synoptic Survey Telescope) is perfectly suited for this search. This work is based on observations made at the European Southern Observatory, La Silla, Chile.

Stochastic Forcing for High-Resolution Regional and Global Ocean and Atmosphere-Ocean Coupled Ensemble Forecast System

NASA Astrophysics Data System (ADS)

Rowley, C. D.; Hogan, P. J.; Martin, P.; Thoppil, P.; Wei, M.

2017-12-01

An extended range ensemble forecast system is being developed in the US Navy Earth System Prediction Capability (ESPC), and a global ocean ensemble generation capability to represent uncertainty in the ocean initial conditions has been developed. At extended forecast times, the uncertainty due to the model error overtakes the initial condition as the primary source of forecast uncertainty. Recently, stochastic parameterization or stochastic forcing techniques have been applied to represent the model error in research and operational atmospheric, ocean, and coupled ensemble forecasts. A simple stochastic forcing technique has been developed for application to US Navy high resolution regional and global ocean models, for use in ocean-only and coupled atmosphere-ocean-ice-wave ensemble forecast systems. Perturbation forcing is added to the tendency equations for state variables, with the forcing defined by random 3- or 4-dimensional fields with horizontal, vertical, and temporal correlations specified to characterize different possible kinds of error. Here, we demonstrate the stochastic forcing in regional and global ensemble forecasts with varying perturbation amplitudes and length and time scales, and assess the change in ensemble skill measured by a range of deterministic and probabilistic metrics.

Adiabatic reduction of a model of stochastic gene expression with jump Markov process.

PubMed

Yvinec, Romain; Zhuge, Changjing; Lei, Jinzhi; Mackey, Michael C

2014-04-01

This paper considers adiabatic reduction in a model of stochastic gene expression with bursting transcription considered as a jump Markov process. In this model, the process of gene expression with auto-regulation is described by fast/slow dynamics. The production of mRNA is assumed to follow a compound Poisson process occurring at a rate depending on protein levels (the phenomena called bursting in molecular biology) and the production of protein is a linear function of mRNA numbers. When the dynamics of mRNA is assumed to be a fast process (due to faster mRNA degradation than that of protein) we prove that, with appropriate scalings in the burst rate, jump size or translational rate, the bursting phenomena can be transmitted to the slow variable. We show that, depending on the scaling, the reduced equation is either a stochastic differential equation with a jump Poisson process or a deterministic ordinary differential equation. These results are significant because adiabatic reduction techniques seem to have not been rigorously justified for a stochastic differential system containing a jump Markov process. We expect that the results can be generalized to adiabatic methods in more general stochastic hybrid systems.

Topological supersymmetry breaking: The definition and stochastic generalization of chaos and the limit of applicability of statistics

NASA Astrophysics Data System (ADS)

Ovchinnikov, Igor V.; Schwartz, Robert N.; Wang, Kang L.

2016-03-01

The concept of deterministic dynamical chaos has a long history and is well established by now. Nevertheless, its field theoretic essence and its stochastic generalization have been revealed only very recently. Within the newly found supersymmetric theory of stochastics (STS), all stochastic differential equations (SDEs) possess topological or de Rahm supersymmetry and stochastic chaos is the phenomenon of its spontaneous breakdown. Even though the STS is free of approximations and thus is technically solid, it is still missing a firm interpretational basis in order to be physically sound. Here, we make a few important steps toward the construction of the interpretational foundation for the STS. In particular, we discuss that one way to understand why the ground states of chaotic SDEs are conditional (not total) probability distributions, is that some of the variables have infinite memory of initial conditions and thus are not “thermalized”, i.e., cannot be described by the initial-conditions-independent probability distributions. As a result, the definitive assumption of physical statistics that the ground state is a steady-state total probability distribution is not valid for chaotic SDEs.

OPEN PROBLEM: Orbits' statistics in chaotic dynamical systems

NASA Astrophysics Data System (ADS)

Arnold, V.

2008-07-01

This paper shows how the measurement of the stochasticity degree of a finite sequence of real numbers, published by Kolmogorov in Italian in a journal of insurances' statistics, can be usefully applied to measure the objective stochasticity degree of sequences, originating from dynamical systems theory and from number theory. Namely, whenever the value of Kolmogorov's stochasticity parameter of a given sequence of numbers is too small (or too big), one may conclude that the conjecture describing this sequence as a sample of independent values of a random variables is highly improbable. Kolmogorov used this strategy fighting (in a paper in 'Doklady', 1940) against Lysenko, who had tried to disprove the classical genetics' law of Mendel experimentally. Calculating his stochasticity parameter value for the numbers from Lysenko's experiment reports, Kolmogorov deduced, that, while these numbers were different from the exact fulfilment of Mendel's 3 : 1 law, any smaller deviation would be a manifestation of the report's number falsification. The calculation of the values of the stochasticity parameter would be useful for many other generators of pseudorandom numbers and for many other chaotically looking statistics, including even the prime numbers distribution (discussed in this paper as an example).

Ensemble modeling of stochastic unsteady open-channel flow in terms of its time-space evolutionary probability distribution - Part 1: theoretical development

NASA Astrophysics Data System (ADS)

Dib, Alain; Kavvas, M. Levent

2018-03-01

The Saint-Venant equations are commonly used as the governing equations to solve for modeling the spatially varied unsteady flow in open channels. The presence of uncertainties in the channel or flow parameters renders these equations stochastic, thus requiring their solution in a stochastic framework in order to quantify the ensemble behavior and the variability of the process. While the Monte Carlo approach can be used for such a solution, its computational expense and its large number of simulations act to its disadvantage. This study proposes, explains, and derives a new methodology for solving the stochastic Saint-Venant equations in only one shot, without the need for a large number of simulations. The proposed methodology is derived by developing the nonlocal Lagrangian-Eulerian Fokker-Planck equation of the characteristic form of the stochastic Saint-Venant equations for an open-channel flow process, with an uncertain roughness coefficient. A numerical method for its solution is subsequently devised. The application and validation of this methodology are provided in a companion paper, in which the statistical results computed by the proposed methodology are compared against the results obtained by the Monte Carlo approach.

Evaluating Kuala Lumpur stock exchange oriented bank performance with stochastic frontiers

NASA Astrophysics Data System (ADS)

Baten, M. A.; Maznah, M. K.; Razamin, R.; Jastini, M. J.

2014-12-01

Banks play an essential role in the economic development and banks need to be efficient; otherwise, they may create blockage in the process of development in any country. The efficiency of banks in Malaysia is important and should receive greater attention. This study formulated an appropriate stochastic frontier model to investigate the efficiency of banks which are traded on Kuala Lumpur Stock Exchange (KLSE) market during the period 2005-2009. All data were analyzed to obtain the maximum likelihood method to estimate the parameters of stochastic production. Unlike the earlier studies which use balance sheet and income statements data, this study used market data as the input and output variables. It was observed that banks listed in KLSE exhibited a commendable overall efficiency level of 96.2% during 2005-2009 hence suggesting minimal input waste of 3.8%. Among the banks, the COMS (Cimb Group Holdings) bank is found to be highly efficient with a score of 0.9715 and BIMB (Bimb Holdings) bank is noted to have the lowest efficiency with a score of 0.9582. The results also show that Cobb-Douglas stochastic frontier model with truncated normal distributional assumption is preferable than Translog stochastic frontier model.

The stochastic dance of early HIV infection

NASA Astrophysics Data System (ADS)

Merrill, Stephen J.

2005-12-01

The stochastic nature of early HIV infection is described in a series of models, each of which captures aspects of the dance of HIV during the early stages of infection. It is to this highly variable target that the immune response must respond. The adaptability of the various components of the immune response is an important aspect of the system's operation, as the nature of the pathogens that the response will be required to respond to and the order in which those responses must be made cannot be known beforehand. As HIV infection has direct influence over cells responsible for the immune response, the dance predicts that the immune response will be also in a variable state of readiness and capability for this task of adaptation. The description of the stochastic dance of HIV here will use the tools of stochastic models, and for the most part, simulation. The justification for this approach is that the early stages and the development of HIV diversity require that the model to be able to describe both individual sample path and patient-to-patient variability. In addition, as early viral dynamics are best described using branching processes, the explosive growth of these models both predicts high variability and rapid response of HIV to changes in system parameters.In this paper, a basic viral growth model based on a time dependent continuous-time branching process is used to describe the growth of HIV infected cells in the macrophage and lymphocyte populations. Immigration from the reservoir population is added to the basic model to describe the incubation time distribution. This distribution is deduced directly from the modeling assumptions and the model of viral growth. A system of two branching processes, one in the infected macrophage population and one in the infected lymphocyte population is used to provide a description of the relationship between the development of HIV diversity as it relates to tropism (host cell preference). The role of the immune response to HIV and HIV infected cells is used to describe the movement of the infection from a few infected macrophages to a disease of infected CD4+ T lymphocytes.

Stochastic processes on multiple scales: averaging, decimation and beyond

NASA Astrophysics Data System (ADS)

Bo, Stefano; Celani, Antonio

The recent advances in handling microscopic systems are increasingly motivating stochastic modeling in a large number of physical, chemical and biological phenomena. Relevant processes often take place on widely separated time scales. In order to simplify the description, one usually focuses on the slower degrees of freedom and only the average effect of the fast ones is retained. It is then fundamental to eliminate such fast variables in a controlled fashion, carefully accounting for their net effect on the slower dynamics. We shall present how this can be done by either decimating or coarse-graining the fast processes and discuss applications to physical, biological and chemical examples. With the same tools we will address the fate of functionals of the stochastic trajectories (such as residence times, counting statistics, fluxes, entropy production, etc.) upon elimination of the fast variables. In general, for functionals, such elimination can present additional difficulties. In some cases, it is not possible to express them in terms of the effective trajectories on the slow degrees of freedom but additional details of the fast processes must be retained. We will focus on such cases and show how naive procedures can lead to inconsistent results.

Variable-free exploration of stochastic models: a gene regulatory network example.

PubMed

Erban, Radek; Frewen, Thomas A; Wang, Xiao; Elston, Timothy C; Coifman, Ronald; Nadler, Boaz; Kevrekidis, Ioannis G

2007-04-21

Finding coarse-grained, low-dimensional descriptions is an important task in the analysis of complex, stochastic models of gene regulatory networks. This task involves (a) identifying observables that best describe the state of these complex systems and (b) characterizing the dynamics of the observables. In a previous paper [R. Erban et al., J. Chem. Phys. 124, 084106 (2006)] the authors assumed that good observables were known a priori, and presented an equation-free approach to approximate coarse-grained quantities (i.e., effective drift and diffusion coefficients) that characterize the long-time behavior of the observables. Here we use diffusion maps [R. Coifman et al., Proc. Natl. Acad. Sci. U.S.A. 102, 7426 (2005)] to extract appropriate observables ("reduction coordinates") in an automated fashion; these involve the leading eigenvectors of a weighted Laplacian on a graph constructed from network simulation data. We present lifting and restriction procedures for translating between physical variables and these data-based observables. These procedures allow us to perform equation-free, coarse-grained computations characterizing the long-term dynamics through the design and processing of short bursts of stochastic simulation initialized at appropriate values of the data-based observables.

Escalated convergent artificial bee colony

NASA Astrophysics Data System (ADS)

Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

2016-03-01

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

Analysis of isothermal and cooling-rate-dependent immersion freezing by a unifying stochastic ice nucleation model

NASA Astrophysics Data System (ADS)

Alpert, Peter A.; Knopf, Daniel A.

2016-02-01

Immersion freezing is an important ice nucleation pathway involved in the formation of cirrus and mixed-phase clouds. Laboratory immersion freezing experiments are necessary to determine the range in temperature, T, and relative humidity, RH, at which ice nucleation occurs and to quantify the associated nucleation kinetics. Typically, isothermal (applying a constant temperature) and cooling-rate-dependent immersion freezing experiments are conducted. In these experiments it is usually assumed that the droplets containing ice nucleating particles (INPs) all have the same INP surface area (ISA); however, the validity of this assumption or the impact it may have on analysis and interpretation of the experimental data is rarely questioned. Descriptions of ice active sites and variability of contact angles have been successfully formulated to describe ice nucleation experimental data in previous research; however, we consider the ability of a stochastic freezing model founded on classical nucleation theory to reproduce previous results and to explain experimental uncertainties and data scatter. A stochastic immersion freezing model based on first principles of statistics is presented, which accounts for variable ISA per droplet and uses parameters including the total number of droplets, Ntot, and the heterogeneous ice nucleation rate coefficient, Jhet(T). This model is applied to address if (i) a time and ISA-dependent stochastic immersion freezing process can explain laboratory immersion freezing data for different experimental methods and (ii) the assumption that all droplets contain identical ISA is a valid conjecture with subsequent consequences for analysis and interpretation of immersion freezing. The simple stochastic model can reproduce the observed time and surface area dependence in immersion freezing experiments for a variety of methods such as: droplets on a cold-stage exposed to air or surrounded by an oil matrix, wind and acoustically levitated droplets, droplets in a continuous-flow diffusion chamber (CFDC), the Leipzig aerosol cloud interaction simulator (LACIS), and the aerosol interaction and dynamics in the atmosphere (AIDA) cloud chamber. Observed time-dependent isothermal frozen fractions exhibiting non-exponential behavior can be readily explained by this model considering varying ISA. An apparent cooling-rate dependence of Jhet is explained by assuming identical ISA in each droplet. When accounting for ISA variability, the cooling-rate dependence of ice nucleation kinetics vanishes as expected from classical nucleation theory. The model simulations allow for a quantitative experimental uncertainty analysis for parameters Ntot, T, RH, and the ISA variability. The implications of our results for experimental analysis and interpretation of the immersion freezing process are discussed.

The Stochastic predictability limits of GCM internal variability and the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2017-04-01

Over the past ten years, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an intermediate "macroweather" regime, spanning the range of scales from ≈10 days to ≈30 years. Macroweather statistics are characterized by two fundamental symmetries: scaling and the factorization of the joint space-time statistics. In the time domain, the scaling has low intermittency with the additional property that successive fluctuations tend to cancel. In space, on the contrary the scaling has high (multifractal) intermittency corresponding to the existence of different climate zones. These properties have fundamental implications for macroweather forecasting: a) the temporal scaling implies that the system has a long range memory that can be exploited for forecasting; b) the low temporal intermittency implies that mathematically well-established (Gaussian) forecasting techniques can be used; and c), the statistical factorization property implies that although spatial correlations (including teleconnections) may be large, if long enough time series are available, they are not necessarily useful in improving forecasts. Theoretically, these conditions imply the existence of stochastic predictability limits in our talk, we show that these limits apply to GCM's. Based on these statistical implications, we developed the Stochastic Seasonal and Interannual Prediction System (StocSIPS) for the prediction of temperature from regional to global scales and from one month to many years horizons. One of the main components of StocSIPS is the separation and prediction of both the internal and externally forced variabilities. In order to test the theoretical assumptions and consequences for predictability and predictions, we use 41 different CMIP5 model outputs from preindustrial control runs that have fixed external forcings: whose variability is purely internally generated. We first show that these statistical assumptions hold with relatively good accuracy and then we performed hindcasts at global and regional scales from monthly to annual time resolutions using StocSIPS. We obtained excellent agreement between the hindcast Mean Square Skill Score (MSSS) and the theoretical stochastic limits. We also show the application of StocSIPS to the prediction of average global temperature and compare our results with those obtained using multi-model ensemble approaches. StocSIPS has numerous advantages including a) higher MSSS for large time horizons, b) the from convergence to the real - not model - climate, c) much higher computational speed, d) no need for data assimilation, e) no ad hoc post processing and f) no need for downscaling.

Stochastic stability of parametrically excited random systems

NASA Astrophysics Data System (ADS)

Labou, M.

2004-01-01

Multidegree-of-freedom dynamic systems subjected to parametric excitation are analyzed for stochastic stability. The variation of excitation intensity with time is described by the sum of a harmonic function and a stationary random process. The stability boundaries are determined by the stochastic averaging method. The effect of random parametric excitation on the stability of trivial solutions of systems of differential equations for the moments of phase variables is studied. It is assumed that the frequency of harmonic component falls within the region of combination resonances. Stability conditions for the first and second moments are obtained. It turns out that additional parametric excitation may have a stabilizing or destabilizing effect, depending on the values of certain parameters of random excitation. As an example, the stability of a beam in plane bending is analyzed.

Establishing a beachhead: A stochastic population model with an Allee effect applied to species invasion

USGS Publications Warehouse

Ackleh, A.S.; Allen, L.J.S.; Carter, J.

2007-01-01

We formulated a spatially explicit stochastic population model with an Allee effect in order to explore how invasive species may become established. In our model, we varied the degree of migration between local populations and used an Allee effect with variable birth and death rates. Because of the stochastic component, population sizes below the Allee effect threshold may still have a positive probability for successful invasion. The larger the network of populations, the greater the probability of an invasion occurring when initial population sizes are close to or above the Allee threshold. Furthermore, if migration rates are low, one or more than one patch may be successfully invaded, while if migration rates are high all patches are invaded. ?? 2007 Elsevier Inc. All rights reserved.

A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.

2012-01-01

A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…

A fire management simulation model using stochastic arrival times

Treesearch

Eric L. Smith

1987-01-01

Fire management simulation models are used to predict the impact of changes in the fire management program on fire outcomes. As with all models, the goal is to abstract reality without seriously distorting relationships between variables of interest. One important variable of fire organization performance is the length of time it takes to get suppression units to the...

Historical range of variability in landscape structure: a simulation study in Oregon, USA.

Treesearch

Etsuko Nonaka; Thomas A. Spies

2005-01-01

We estimated the historical range of variability (HRV) of forest landscape structure under natural disturbance regimes at the scale of a physiographic province (Oregon Coast Range, 2 million ha) and evaluated the similarity to HRV of current and future landscapes under alternative management scenarios. We used a stochastic fire simulation model to simulate...

Regression and Geostatistical Techniques: Considerations and Observations from Experiences in NE-FIA

Treesearch

Rachel Riemann; Andrew Lister

2005-01-01

Maps of forest variables improve our understanding of the forest resource by allowing us to view and analyze it spatially. The USDA Forest Service's Northeastern Forest Inventory and Analysis unit (NE-FIA) has used geostatistical techniques, particularly stochastic simulation, to produce maps and spatial data sets of FIA variables. That work underscores the...

A well-posed and stable stochastic Galerkin formulation of the incompressible Navier–Stokes equations with random data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pettersson, Per, E-mail: per.pettersson@uib.no; Nordström, Jan, E-mail: jan.nordstrom@liu.se; Doostan, Alireza, E-mail: alireza.doostan@colorado.edu

2016-02-01

We present a well-posed stochastic Galerkin formulation of the incompressible Navier–Stokes equations with uncertainty in model parameters or the initial and boundary conditions. The stochastic Galerkin method involves representation of the solution through generalized polynomial chaos expansion and projection of the governing equations onto stochastic basis functions, resulting in an extended system of equations. A relatively low-order generalized polynomial chaos expansion is sufficient to capture the stochastic solution for the problem considered. We derive boundary conditions for the continuous form of the stochastic Galerkin formulation of the velocity and pressure equations. The resulting problem formulation leads to an energy estimatemore » for the divergence. With suitable boundary data on the pressure and velocity, the energy estimate implies zero divergence of the velocity field. Based on the analysis of the continuous equations, we present a semi-discretized system where the spatial derivatives are approximated using finite difference operators with a summation-by-parts property. With a suitable choice of dissipative boundary conditions imposed weakly through penalty terms, the semi-discrete scheme is shown to be stable. Numerical experiments in the laminar flow regime corroborate the theoretical results and we obtain high-order accurate results for the solution variables and the velocity divergence converges to zero as the mesh is refined.« less

Stochastic models of the Social Security trust funds.

PubMed

Burdick, Clark; Manchester, Joyce

Each year in March, the Board of Trustees of the Social Security trust funds reports on the current and projected financial condition of the Social Security programs. Those programs, which pay monthly benefits to retired workers and their families, to the survivors of deceased workers, and to disabled workers and their families, are financed through the Old-Age, Survivors, and Disability Insurance (OASDI) Trust Funds. In their 2003 report, the Trustees present, for the first time, results from a stochastic model of the combined OASDI trust funds. Stochastic modeling is an important new tool for Social Security policy analysis and offers the promise of valuable new insights into the financial status of the OASDI trust funds and the effects of policy changes. The results presented in this article demonstrate that several stochastic models deliver broadly consistent results even though they use very different approaches and assumptions. However, they also show that the variation in trust fund outcomes differs as the approach and assumptions are varied. Which approach and assumptions are best suited for Social Security policy analysis remains an open question. Further research is needed before the promise of stochastic modeling is fully realized. For example, neither parameter uncertainty nor variability in ultimate assumption values is recognized explicitly in the analyses. Despite this caveat, stochastic modeling results are already shedding new light on the range and distribution of trust fund outcomes that might occur in the future.

CERENA: ChEmical REaction Network Analyzer--A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics.

PubMed

Kazeroonian, Atefeh; Fröhlich, Fabian; Raue, Andreas; Theis, Fabian J; Hasenauer, Jan

2016-01-01

Gene expression, signal transduction and many other cellular processes are subject to stochastic fluctuations. The analysis of these stochastic chemical kinetics is important for understanding cell-to-cell variability and its functional implications, but it is also challenging. A multitude of exact and approximate descriptions of stochastic chemical kinetics have been developed, however, tools to automatically generate the descriptions and compare their accuracy and computational efficiency are missing. In this manuscript we introduced CERENA, a toolbox for the analysis of stochastic chemical kinetics using Approximations of the Chemical Master Equation solution statistics. CERENA implements stochastic simulation algorithms and the finite state projection for microscopic descriptions of processes, the system size expansion and moment equations for meso- and macroscopic descriptions, as well as the novel conditional moment equations for a hybrid description. This unique collection of descriptions in a single toolbox facilitates the selection of appropriate modeling approaches. Unlike other software packages, the implementation of CERENA is completely general and allows, e.g., for time-dependent propensities and non-mass action kinetics. By providing SBML import, symbolic model generation and simulation using MEX-files, CERENA is user-friendly and computationally efficient. The availability of forward and adjoint sensitivity analyses allows for further studies such as parameter estimation and uncertainty analysis. The MATLAB code implementing CERENA is freely available from http://cerenadevelopers.github.io/CERENA/.

Stochastic Ocean Eddy Perturbations in a Coupled General Circulation Model.

NASA Astrophysics Data System (ADS)

Howe, N.; Williams, P. D.; Gregory, J. M.; Smith, R. S.

2014-12-01

High-resolution ocean models, which are eddy permitting and resolving, require large computing resources to produce centuries worth of data. Also, some previous studies have suggested that increasing resolution does not necessarily solve the problem of unresolved scales, because it simply introduces a new set of unresolved scales. Applying stochastic parameterisations to ocean models is one solution that is expected to improve the representation of small-scale (eddy) effects without increasing run-time. Stochastic parameterisation has been shown to have an impact in atmosphere-only models and idealised ocean models, but has not previously been studied in ocean general circulation models. Here we apply simple stochastic perturbations to the ocean temperature and salinity tendencies in the low-resolution coupled climate model, FAMOUS. The stochastic perturbations are implemented according to T(t) = T(t-1) + (ΔT(t) + ξ(t)), where T is temperature or salinity, ΔT is the corresponding deterministic increment in one time step, and ξ(t) is Gaussian noise. We use high-resolution HiGEM data coarse-grained to the FAMOUS grid to provide information about the magnitude and spatio-temporal correlation structure of the noise to be added to the lower resolution model. Here we present results of adding white and red noise, showing the impacts of an additive stochastic perturbation on mean climate state and variability in an AOGCM.

Stochastic inflation in phase space: is slow roll a stochastic attractor?

NASA Astrophysics Data System (ADS)

Grain, Julien; Vennin, Vincent

2017-05-01

An appealing feature of inflationary cosmology is the presence of a phase-space attractor, ``slow roll'', which washes out the dependence on initial field velocities. We investigate the robustness of this property under backreaction from quantum fluctuations using the stochastic inflation formalism in the phase-space approach. A Hamiltonian formulation of stochastic inflation is presented, where it is shown that the coarse-graining procedure—where wavelengths smaller than the Hubble radius are integrated out—preserves the canonical structure of free fields. This means that different sets of canonical variables give rise to the same probability distribution which clarifies the literature with respect to this issue. The role played by the quantum-to-classical transition is also analysed and is shown to constrain the coarse-graining scale. In the case of free fields, we find that quantum diffusion is aligned in phase space with the slow-roll direction. This implies that the classical slow-roll attractor is immune to stochastic effects and thus generalises to a stochastic attractor regardless of initial conditions, with a relaxation time at least as short as in the classical system. For non-test fields or for test fields with non-linear self interactions however, quantum diffusion and the classical slow-roll flow are misaligned. We derive a condition on the coarse-graining scale so that observational corrections from this misalignment are negligible at leading order in slow roll.

CERENA: ChEmical REaction Network Analyzer—A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics

PubMed Central

Kazeroonian, Atefeh; Fröhlich, Fabian; Raue, Andreas; Theis, Fabian J.; Hasenauer, Jan

2016-01-01

Gene expression, signal transduction and many other cellular processes are subject to stochastic fluctuations. The analysis of these stochastic chemical kinetics is important for understanding cell-to-cell variability and its functional implications, but it is also challenging. A multitude of exact and approximate descriptions of stochastic chemical kinetics have been developed, however, tools to automatically generate the descriptions and compare their accuracy and computational efficiency are missing. In this manuscript we introduced CERENA, a toolbox for the analysis of stochastic chemical kinetics using Approximations of the Chemical Master Equation solution statistics. CERENA implements stochastic simulation algorithms and the finite state projection for microscopic descriptions of processes, the system size expansion and moment equations for meso- and macroscopic descriptions, as well as the novel conditional moment equations for a hybrid description. This unique collection of descriptions in a single toolbox facilitates the selection of appropriate modeling approaches. Unlike other software packages, the implementation of CERENA is completely general and allows, e.g., for time-dependent propensities and non-mass action kinetics. By providing SBML import, symbolic model generation and simulation using MEX-files, CERENA is user-friendly and computationally efficient. The availability of forward and adjoint sensitivity analyses allows for further studies such as parameter estimation and uncertainty analysis. The MATLAB code implementing CERENA is freely available from http://cerenadevelopers.github.io/CERENA/. PMID:26807911

Algebraic Functions of H-Functions with Specific Dependency Structure.

DTIC Science & Technology

1984-05-01

a study of its characteristic function. Such analysis is reproduced in books by Springer (17), Anderson (23), Feller (34,35), Mood and Graybill (52...following linearity property for expectations of jointly distributed random variables is derived. r 1 Theorem 1.1: If X and Y are real random variables...appear in American Journal of Mathematical and Management Science. 13. Mathai, A.M., and R.K. Saxena, "On linear combinations of stochastic variables

Impact of stochasticity in immigration and reintroduction on colonizing and extirpating populations.

PubMed

Rajakaruna, Harshana; Potapov, Alexei; Lewis, Mark

2013-05-01

A thorough quantitative understanding of populations at the edge of extinction is needed to manage both invasive and extirpating populations. Immigration can govern the population dynamics when the population levels are low. It increases the probability of a population establishing (or reestablishing) before going extinct (EBE). However, the rate of immigration can be highly fluctuating. Here, we investigate how the stochasticity in immigration impacts the EBE probability for small populations in variable environments. We use a population model with an Allee effect described by a stochastic differential equation (SDE) and employ the Fokker-Planck diffusion approximation to quantify the EBE probability. We find that, the effect of the stochasticity in immigration on the EBE probability depends on both the intrinsic growth rate (r) and the mean rate of immigration (p). In general, if r is large and positive (e.g. invasive species introduced to favorable habitats), or if p is greater than the rate of population decline due to the demographic Allee effect (e.g., effective stocking of declining populations), then the stochasticity in immigration decreases the EBE probability. If r is large and negative (e.g. endangered populations in unfavorable habitats), or if the rate of decline due to the demographic Allee effect is much greater than p (e.g., weak stocking of declining populations), then the stochasticity in immigration increases the EBE probability. However, the mean time for EBE decreases with the increasing stochasticity in immigration with both positive and negative large r. Thus, results suggest that ecological management of populations involves a tradeoff as to whether to increase or decrease the stochasticity in immigration in order to optimize the desired outcome. Moreover, the control of invasive species spread through stochastic means, for example, by stochastic monitoring and treatment of vectors such as ship-ballast water, may be suitable strategies given the environmental and demographic uncertainties at introductions. Similarly, the recovery of declining and extirpated populations through stochastic stocking, translocation, and reintroduction, may also be suitable strategies. Copyright © 2013 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lei, Huan; Baker, Nathan A.; Li, Xiantao

We present a data-driven approach to determine the memory kernel and random noise of the generalized Langevin equation. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. Further, we show that such an approximation can be constructed to arbitrarily high order. Within these approximations, the generalized Langevin dynamics can be embedded in an extended stochastic model without memory. We demonstrate how to introduce the stochastic noise so that the fluctuation-dissipation theorem is exactly satisfied.

A binomial stochastic kinetic approach to the Michaelis-Menten mechanism

NASA Astrophysics Data System (ADS)

Lente, Gábor

2013-05-01

This Letter presents a new method that gives an analytical approximation of the exact solution of the stochastic Michaelis-Menten mechanism without computationally demanding matrix operations. The method is based on solving the deterministic rate equations and then using the results as guiding variables of calculating probability values using binomial distributions. This principle can be generalized to a number of different kinetic schemes and is expected to be very useful in the evaluation of measurements focusing on the catalytic activity of one or a few individual enzyme molecules.

Stochastic locality and master-field simulations of very large lattices

NASA Astrophysics Data System (ADS)

Lüscher, Martin

2018-03-01

In lattice QCD and other field theories with a mass gap, the field variables in distant regions of a physically large lattice are only weakly correlated. Accurate stochastic estimates of the expectation values of local observables may therefore be obtained from a single representative field. Such master-field simulations potentially allow very large lattices to be simulated, but require various conceptual and technical issues to be addressed. In this talk, an introduction to the subject is provided and some encouraging results of master-field simulations of the SU(3) gauge theory are reported.

Modeling the net flows of U.S. mutual funds with stochastic catastrophe theory

NASA Astrophysics Data System (ADS)

Clark, A.

2006-04-01

Using the recent work of Hartelman, van der Maas, and Wagenmakers, we demonstrate the use of invariant stochastic catastrophe models in finance for modeling net flows (the difference between purchases and redemptions of fund shares) of U.S. mutual funds. We validate Goetzmann et al. and others' work concerning the importance of sentiment variables on stock fund flows. We also answer some of the questions Goetzmann et al. and Brown et al. pose at the end of their respective papers. We end with possible experiments for experimental economists and sociophysicists.

Developing deterioration models for Wyoming bridges.

DOT National Transportation Integrated Search

2016-05-01

Deterioration models for the Wyoming Bridge Inventory were developed using both stochastic and deterministic models. : The selection of explanatory variables is investigated and a new method using LASSO regression to eliminate human bias : in explana...

A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

NASA Astrophysics Data System (ADS)

Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

2017-04-01

The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of land-use/land-cover changes and river regulation on network-scale connectivity.

Non-Gaussian Multi-resolution Modeling of Magnetosphere-Ionosphere Coupling Processes

NASA Astrophysics Data System (ADS)

Fan, M.; Paul, D.; Lee, T. C. M.; Matsuo, T.

2016-12-01

The most dynamic coupling between the magnetosphere and ionosphere occurs in the Earth's polar atmosphere. Our objective is to model scale-dependent stochastic characteristics of high-latitude ionospheric electric fields that originate from solar wind magnetosphere-ionosphere interactions. The Earth's high-latitude ionospheric electric field exhibits considerable variability, with increasing non-Gaussian characteristics at decreasing spatio-temporal scales. Accurately representing the underlying stochastic physical process through random field modeling is crucial not only for scientific understanding of the energy, momentum and mass exchanges between the Earth's magnetosphere and ionosphere, but also for modern technological systems including telecommunication, navigation, positioning and satellite tracking. While a lot of efforts have been made to characterize the large-scale variability of the electric field in the context of Gaussian processes, no attempt has been made so far to model the small-scale non-Gaussian stochastic process observed in the high-latitude ionosphere. We construct a novel random field model using spherical needlets as building blocks. The double localization of spherical needlets in both spatial and frequency domains enables the model to capture the non-Gaussian and multi-resolutional characteristics of the small-scale variability. The estimation procedure is computationally feasible due to the utilization of an adaptive Gibbs sampler. We apply the proposed methodology to the computational simulation output from the Lyon-Fedder-Mobarry (LFM) global magnetohydrodynamics (MHD) magnetosphere model. Our non-Gaussian multi-resolution model results in characterizing significantly more energy associated with the small-scale ionospheric electric field variability in comparison to Gaussian models. By accurately representing unaccounted-for additional energy and momentum sources to the Earth's upper atmosphere, our novel random field modeling approach will provide a viable remedy to the current numerical models' systematic biases resulting from the underestimation of high-latitude energy and momentum sources.

Fault Tolerant Optimal Control.

DTIC Science & Technology

1982-08-01

subsystem is modelled by deterministic or stochastic finite-dimensional vector differential or difference equations. The parameters of these equations...is no partial differential equation that must be solved. Thus we can sidestep the inability to solve the Bellman equation for control problems with x...transition models and cost functionals can be reduced to the search for solutions of nonlinear partial differential equations using ’verification

Efficient and Robust Signal Approximations

DTIC Science & Technology

2009-05-01

otherwise. Remark. Permutation matrices are both orthogonal and doubly- stochastic [62]. We will now show how to further simplify the Robust Coding...reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching...Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: signal processing, image compression, independent component analysis , sparse

Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

PubMed

Chen, I-Jen; Foloppe, Nicolas

2013-12-15

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nonequilibrium steady state in open quantum systems: Influence action, stochastic equation and power balance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hsiang, J.-T., E-mail: cosmology@gmail.com; Department of Physics, National Dong Hwa University, Hualien 97401, Taiwan; Hu, B.L.

2015-11-15

The existence and uniqueness of a steady state for nonequilibrium systems (NESS) is a fundamental subject and a main theme of research in statistical mechanics for decades. For Gaussian systems, such as a chain of classical harmonic oscillators connected at each end to a heat bath, and for classical anharmonic oscillators under specified conditions, definitive answers exist in the form of proven theorems. Answering this question for quantum many-body systems poses a challenge for the present. In this work we address this issue by deriving the stochastic equations for the reduced system with self-consistent backaction from the two baths, calculatingmore » the energy flow from one bath to the chain to the other bath, and exhibiting a power balance relation in the total (chain + baths) system which testifies to the existence of a NESS in this system at late times. Its insensitivity to the initial conditions of the chain corroborates to its uniqueness. The functional method we adopt here entails the use of the influence functional, the coarse-grained and stochastic effective actions, from which one can derive the stochastic equations and calculate the average values of physical variables in open quantum systems. This involves both taking the expectation values of quantum operators of the system and the distributional averages of stochastic variables stemming from the coarse-grained environment. This method though formal in appearance is compact and complete. It can also easily accommodate perturbative techniques and diagrammatic methods from field theory. Taken all together it provides a solid platform for carrying out systematic investigations into the nonequilibrium dynamics of open quantum systems and quantum thermodynamics. -- Highlights: •Nonequilibrium steady state (NESS) for interacting quantum many-body systems. •Derivation of stochastic equations for quantum oscillator chain with two heat baths. •Explicit calculation of the energy flow from one bath to the chain to the other bath. •Power balance relation shows the existence of NESS insensitive to initial conditions. •Functional method as a viable platform for issues in quantum thermodynamics.« less

The welfare effects of integrating renewable energy into electricity markets

NASA Astrophysics Data System (ADS)

Lamadrid, Alberto J.

The challenges of deploying more renewable energy sources on an electric grid are caused largely by their inherent variability. In this context, energy storage can help make the electric delivery system more reliable by mitigating this variability. This thesis analyzes a series of models for procuring electricity and ancillary services for both individuals and social planners with high penetrations of stochastic wind energy. The results obtained for an individual decision maker using stochastic optimization are ambiguous, with closed form solutions dependent on technological parameters, and no consideration of the system reliability. The social planner models correctly reflect the effect of system reliability, and in the case of a Stochastic, Security Constrained Optimal Power Flow (S-SC-OPF or SuperOPF), determine reserve capacity endogenously so that system reliability is maintained. A single-period SuperOPF shows that including ramping costs in the objective function leads to more wind spilling and increased capacity requirements for reliability. However, this model does not reflect the inter temporal tradeoffs of using Energy Storage Systems (ESS) to improve reliability and mitigate wind variability. The results with the multiperiod SuperOPF determine the optimum use of storage for a typical day, and compare the effects of collocating ESS at wind sites with the same amount of storage (deferrable demand) located at demand centers. The collocated ESS has slightly lower operating costs and spills less wind generation compared to deferrable demand, but the total amount of conventional generating capacity needed for system adequacy is higher. In terms of the total system costs, that include the capital cost of conventional generating capacity, the costs with deferrable demand is substantially lower because the daily demand profile is flattened and less conventional generation capacity is then needed for reliability purposes. The analysis also demonstrates that the optimum daily pattern of dispatch and reserves is seriously distorted if the stochastic characteristics of wind generation are ignored.

Feature selection with harmony search.

PubMed

Diao, Ren; Shen, Qiang

2012-12-01

Many search strategies have been exploited for the task of feature selection (FS), in an effort to identify more compact and better quality subsets. Such work typically involves the use of greedy hill climbing (HC), or nature-inspired heuristics, in order to discover the optimal solution without going through exhaustive search. In this paper, a novel FS approach based on harmony search (HS) is presented. It is a general approach that can be used in conjunction with many subset evaluation techniques. The simplicity of HS is exploited to reduce the overall complexity of the search process. The proposed approach is able to escape from local solutions and identify multiple solutions owing to the stochastic nature of HS. Additional parameter control schemes are introduced to reduce the effort and impact of parameter configuration. These can be further combined with the iterative refinement strategy, tailored to enforce the discovery of quality subsets. The resulting approach is compared with those that rely on HC, genetic algorithms, and particle swarm optimization, accompanied by in-depth studies of the suggested improvements.

Deterministic alternatives to the full configuration interaction quantum Monte Carlo method for strongly correlated systems

NASA Astrophysics Data System (ADS)

Tubman, Norm; Whaley, Birgitta

The development of exponential scaling methods has seen great progress in tackling larger systems than previously thought possible. One such technique, full configuration interaction quantum Monte Carlo, allows exact diagonalization through stochastically sampling of determinants. The method derives its utility from the information in the matrix elements of the Hamiltonian, together with a stochastic projected wave function, which are used to explore the important parts of Hilbert space. However, a stochastic representation of the wave function is not required to search Hilbert space efficiently and new deterministic approaches have recently been shown to efficiently find the important parts of determinant space. We shall discuss the technique of Adaptive Sampling Configuration Interaction (ASCI) and the related heat-bath Configuration Interaction approach for ground state and excited state simulations. We will present several applications for strongly correlated Hamiltonians. This work was supported through the Scientific Discovery through Advanced Computing (SciDAC) program funded by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences.

Influence of vectors' risk-spreading strategies and environmental stochasticity on the epidemiology and evolution of vector-borne diseases: the example of Chagas' disease.

PubMed

Pelosse, Perrine; Kribs-Zaleta, Christopher M; Ginoux, Marine; Rabinovich, Jorge E; Gourbière, Sébastien; Menu, Frédéric

2013-01-01

Insects are known to display strategies that spread the risk of encountering unfavorable conditions, thereby decreasing the extinction probability of genetic lineages in unpredictable environments. To what extent these strategies influence the epidemiology and evolution of vector-borne diseases in stochastic environments is largely unknown. In triatomines, the vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, juvenile development time varies between individuals and such variation most likely decreases the extinction risk of vector populations in stochastic environments. We developed a simplified multi-stage vector-borne SI epidemiological model to investigate how vector risk-spreading strategies and environmental stochasticity influence the prevalence and evolution of a parasite. This model is based on available knowledge on triatomine biodemography, but its conceptual outcomes apply, to a certain extent, to other vector-borne diseases. Model comparisons between deterministic and stochastic settings led to the conclusion that environmental stochasticity, vector risk-spreading strategies (in particular an increase in the length and variability of development time) and their interaction have drastic consequences on vector population dynamics, disease prevalence, and the relative short-term evolution of parasite virulence. Our work shows that stochastic environments and associated risk-spreading strategies can increase the prevalence of vector-borne diseases and favor the invasion of more virulent parasite strains on relatively short evolutionary timescales. This study raises new questions and challenges in a context of increasingly unpredictable environmental variations as a result of global climate change and human interventions such as habitat destruction or vector control.

Influence of Vectors’ Risk-Spreading Strategies and Environmental Stochasticity on the Epidemiology and Evolution of Vector-Borne Diseases: The Example of Chagas’ Disease

PubMed Central

Pelosse, Perrine; Kribs-Zaleta, Christopher M.; Ginoux, Marine; Rabinovich, Jorge E.; Gourbière, Sébastien; Menu, Frédéric

2013-01-01

Insects are known to display strategies that spread the risk of encountering unfavorable conditions, thereby decreasing the extinction probability of genetic lineages in unpredictable environments. To what extent these strategies influence the epidemiology and evolution of vector-borne diseases in stochastic environments is largely unknown. In triatomines, the vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas’ disease, juvenile development time varies between individuals and such variation most likely decreases the extinction risk of vector populations in stochastic environments. We developed a simplified multi-stage vector-borne SI epidemiological model to investigate how vector risk-spreading strategies and environmental stochasticity influence the prevalence and evolution of a parasite. This model is based on available knowledge on triatomine biodemography, but its conceptual outcomes apply, to a certain extent, to other vector-borne diseases. Model comparisons between deterministic and stochastic settings led to the conclusion that environmental stochasticity, vector risk-spreading strategies (in particular an increase in the length and variability of development time) and their interaction have drastic consequences on vector population dynamics, disease prevalence, and the relative short-term evolution of parasite virulence. Our work shows that stochastic environments and associated risk-spreading strategies can increase the prevalence of vector-borne diseases and favor the invasion of more virulent parasite strains on relatively short evolutionary timescales. This study raises new questions and challenges in a context of increasingly unpredictable environmental variations as a result of global climate change and human interventions such as habitat destruction or vector control. PMID:23951018

Water Cycle Dynamics in a Changing Environment: Advancing Hydrologic Science through Synthesis

NASA Astrophysics Data System (ADS)

Sivapalan, M.; Kumar, P.; Rhoads, B. L.; Wuebbles, D.

2007-12-01

As one ponders a changing environment -- climate, hydrology, land use, biogeochemical cycles, human dynamics -- there is an increasing need to understand the long term evolution of the linked component systems (e.g., climatic, hydrologic and ecological) through conceptual and quantitative models. The most challenging problem toward this goal is to understand and incorporate the rich dynamics of multiple linked systems with weak and strong coupling, and with many internal variables that exhibit multi-scale interactions. The richness of these interactions leads to fluctuations in one variable that in turn drive the dynamics of other related variables. The key question then becomes: Do these complexities lend an inherently stochastic character to the system, rendering deterministic prediction and modeling of limited value, or do they translate into constrained self- organization through which emerges order, and a limited group of "active" processes (that may change from time to time) that determine the general evolution of the system through a series of structured states with a distinct signature? This is a grand challenge for predictability and therefore requires community effort. The interconnectivity and hence synthesis of knowledge across the fields should be natural for hydrologists since the global water cycle and its regional manifestations directly correspond to the information flows for mass and energy transformations across the media, and across the disciplines. Further, the rich history of numerical, conceptual and stochastic modeling in hydrology provides the training and breadth for addressing the multi- scale, complex system dynamics challenges posed by the evolution question. Theory and observational analyses that necessitate stepping back from the existing knowledge paradigms and looking at the integrated system are needed. In this talk we will present the outlines of a new NSF-funded community effort that attempts to forge inter- disciplinary synthesis through research efforts aimed at "improving predictability of water cycle dynamics in a changing environment." The synthesis activities have brought together inter-disciplinary scientific teams to address specific open problems such as: (i) human-nature interactions and adaptations; (ii) role of the biosphere in water cycle dynamics; (iii) human induced changes to water cycle dynamics; and (iv) structure of landscapes and their evolution through time. All synthesis activities will be underpinned by common unifying themes: (a) hydrology as the science of interacting processes; (b) variability as the driver of interactions and ecosystem functioning; (c) search for emergent behavior and organizing principles; and (d) complexity theory and non- equilibrium thermodynamics.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

DTIC Science & Technology

2012-09-30

characterization of extratropical storms and extremes and link these to LFV modes. Mingfang Ting, Yochanan Kushnir, Andrew W. Robertson...simulating and predicting a wide range of climate phenomena including ENSO, tropical Atlantic sea surface temperatures (SSTs), storm track variability...into empirical prediction models. Use observations to improve low-order dynamical MJO models. Adam Sobel, Daehyun Kim. Extratropical variability

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

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

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

Optimal chemotactic responses in stochastic environments

PubMed Central

Godány, Martin

2017-01-01

Although the “adaptive” strategy used by Escherichia coli has dominated our understanding of bacterial chemotaxis, the environmental conditions under which this strategy emerged is still poorly understood. In this work, we study the performance of various chemotactic strategies under a range of stochastic time- and space-varying attractant distributions in silico. We describe a novel “speculator” response in which the bacterium compare the current attractant concentration to the long-term average; if it is higher then they tumble persistently, while if it is lower than the average, bacteria swim away in search of more favorable conditions. We demonstrate how this response explains the experimental behavior of aerobically-grown Rhodobacter sphaeroides and that under spatially complex but slowly-changing nutrient conditions the speculator response is as effective as the adaptive strategy of E. coli. PMID:28644830

Data-driven Climate Modeling and Prediction

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.

2016-12-01

Global climate models aim to simulate a broad range of spatio-temporal scales of climate variability with state vector having many millions of degrees of freedom. On the other hand, while detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that a major fraction of large-scale climate variability can be predicted in a much lower-dimensional phase space. Low-dimensional models can simulate and predict this fraction of climate variability, provided they are able to account for linear and nonlinear interactions between the modes representing large scales of climate dynamics, as well as their interactions with a much larger number of modes representing fast and small scales. This presentation will highlight several new applications by Multilayered Stochastic Modeling (MSM) [Kondrashov, Chekroun and Ghil, 2015] framework that has abundantly proven its efficiency in the modeling and real-time forecasting of various climate phenomena. MSM is a data-driven inverse modeling technique that aims to obtain a low-order nonlinear system of prognostic equations driven by stochastic forcing, and estimates both the dynamical operator and the properties of the driving noise from multivariate time series of observations or a high-end model's simulation. MSM leads to a system of stochastic differential equations (SDEs) involving hidden (auxiliary) variables of fast-small scales ranked by layers, which interact with the macroscopic (observed) variables of large-slow scales to model the dynamics of the latter, and thus convey memory effects. New MSM climate applications focus on development of computationally efficient low-order models by using data-adaptive decomposition methods that convey memory effects by time-embedding techniques, such as Multichannel Singular Spectrum Analysis (M-SSA) [Ghil et al. 2002] and recently developed Data-Adaptive Harmonic (DAH) decomposition method [Chekroun and Kondrashov, 2016]. In particular, new results by DAH-MSM modeling and prediction of Arctic Sea Ice, as well as decadal predictions of near-surface Earth temperatures will be presented.

Application of tabu search to deterministic and stochastic optimization problems

NASA Astrophysics Data System (ADS)

Gurtuna, Ozgur

During the past two decades, advances in computer science and operations research have resulted in many new optimization methods for tackling complex decision-making problems. One such method, tabu search, forms the basis of this thesis. Tabu search is a very versatile optimization heuristic that can be used for solving many different types of optimization problems. Another research area, real options, has also gained considerable momentum during the last two decades. Real options analysis is emerging as a robust and powerful method for tackling decision-making problems under uncertainty. Although the theoretical foundations of real options are well-established and significant progress has been made in the theory side, applications are lagging behind. A strong emphasis on practical applications and a multidisciplinary approach form the basic rationale of this thesis. The fundamental concepts and ideas behind tabu search and real options are investigated in order to provide a concise overview of the theory supporting both of these two fields. This theoretical overview feeds into the design and development of algorithms that are used to solve three different problems. The first problem examined is a deterministic one: finding the optimal servicing tours that minimize energy and/or duration of missions for servicing satellites around Earth's orbit. Due to the nature of the space environment, this problem is modeled as a time-dependent, moving-target optimization problem. Two solution methods are developed: an exhaustive method for smaller problem instances, and a method based on tabu search for larger ones. The second and third problems are related to decision-making under uncertainty. In the second problem, tabu search and real options are investigated together within the context of a stochastic optimization problem: option valuation. By merging tabu search and Monte Carlo simulation, a new method for studying options, Tabu Search Monte Carlo (TSMC) method, is developed. The theoretical underpinnings of the TSMC method and the flow of the algorithm are explained. Its performance is compared to other existing methods for financial option valuation. In the third, and final, problem, TSMC method is used to determine the conditions of feasibility for hybrid electric vehicles and fuel cell vehicles. There are many uncertainties related to the technologies and markets associated with new generation passenger vehicles. These uncertainties are analyzed in order to determine the conditions in which new generation vehicles can compete with established technologies.

Sparse learning of stochastic dynamical equations

NASA Astrophysics Data System (ADS)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

Stochastic ice stream dynamics

PubMed Central

Bertagni, Matteo Bernard; Ridolfi, Luca

2016-01-01

Ice streams are narrow corridors of fast-flowing ice that constitute the arterial drainage network of ice sheets. Therefore, changes in ice stream flow are key to understanding paleoclimate, sea level changes, and rapid disintegration of ice sheets during deglaciation. The dynamics of ice flow are tightly coupled to the climate system through atmospheric temperature and snow recharge, which are known exhibit stochastic variability. Here we focus on the interplay between stochastic climate forcing and ice stream temporal dynamics. Our work demonstrates that realistic climate fluctuations are able to (i) induce the coexistence of dynamic behaviors that would be incompatible in a purely deterministic system and (ii) drive ice stream flow away from the regime expected in a steady climate. We conclude that environmental noise appears to be crucial to interpreting the past behavior of ice sheets, as well as to predicting their future evolution. PMID:27457960

A common stochastic accumulator with effector-dependent noise can explain eye-hand coordination

PubMed Central

Gopal, Atul; Viswanathan, Pooja

2015-01-01

The computational architecture that enables the flexible coupling between otherwise independent eye and hand effector systems is not understood. By using a drift diffusion framework, in which variability of the reaction time (RT) distribution scales with mean RT, we tested the ability of a common stochastic accumulator to explain eye-hand coordination. Using a combination of behavior, computational modeling and electromyography, we show how a single stochastic accumulator to threshold, followed by noisy effector-dependent delays, explains eye-hand RT distributions and their correlation, while an alternate independent, interactive eye and hand accumulator model does not. Interestingly, the common accumulator model did not explain the RT distributions of the same subjects when they made eye and hand movements in isolation. Taken together, these data suggest that a dedicated circuit underlies coordinated eye-hand planning. PMID:25568161

Hybrid stochastic simplifications for multiscale gene networks.

PubMed

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-09-07

Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall

NASA Astrophysics Data System (ADS)

Lombardo, F.; Volpi, E.; Koutsoyiannis, D.; Serinaldi, F.

2017-06-01

Generating fine-scale time series of intermittent rainfall that are fully consistent with any given coarse-scale totals is a key and open issue in many hydrological problems. We propose a stationary disaggregation method that simulates rainfall time series with given dependence structure, wet/dry probability, and marginal distribution at a target finer (lower-level) time scale, preserving full consistency with variables at a parent coarser (higher-level) time scale. We account for the intermittent character of rainfall at fine time scales by merging a discrete stochastic representation of intermittency and a continuous one of rainfall depths. This approach yields a unique and parsimonious mathematical framework providing general analytical formulations of mean, variance, and autocorrelation function (ACF) for a mixed-type stochastic process in terms of mean, variance, and ACFs of both continuous and discrete components, respectively. To achieve the full consistency between variables at finer and coarser time scales in terms of marginal distribution and coarse-scale totals, the generated lower-level series are adjusted according to a procedure that does not affect the stochastic structure implied by the original model. To assess model performance, we study rainfall process as intermittent with both independent and dependent occurrences, where dependence is quantified by the probability that two consecutive time intervals are dry. In either case, we provide analytical formulations of main statistics of our mixed-type disaggregation model and show their clear accordance with Monte Carlo simulations. An application to rainfall time series from real world is shown as a proof of concept.

Characterization of flood and precipitation events in Southwestern Germany and stochastic simulation of extreme precipitation (Project FLORIS-SV)

NASA Astrophysics Data System (ADS)

Florian, Ehmele; Michael, Kunz

2016-04-01

Several major flood events occurred in Germany in the past 15-20 years especially in the eastern parts along the rivers Elbe and Danube. Examples include the major floods of 2002 and 2013 with an estimated loss of about 2 billion Euros each. The last major flood events in the State of Baden-Württemberg in southwest Germany occurred in the years 1978 and 1993/1994 along the rivers Rhine and Neckar with an estimated total loss of about 150 million Euros (converted) each. Flood hazard originates from a combination of different meteorological, hydrological and hydraulic processes. Currently there is no defined methodology available for evaluating and quantifying the flood hazard and related risk for larger areas or whole river catchments instead of single gauges. In order to estimate the probable maximum loss for higher return periods (e.g. 200 years, PML200), a stochastic model approach is designed since observational data are limited in time and space. In our approach, precipitation is linearly composed of three elements: background precipitation, orographically-induces precipitation, and a convectively-driven part. We use linear theory of orographic precipitation formation for the stochastic precipitation model (SPM), which is based on fundamental statistics of relevant atmospheric variables. For an adequate number of historic flood events, the corresponding atmospheric conditions and parameters are determined in order to calculate a probability density function (pdf) for each variable. This method involves all theoretically possible scenarios which may not have happened, yet. This work is part of the FLORIS-SV (FLOod RISk Sparkassen Versicherung) project and establishes the first step of a complete modelling chain of the flood risk. On the basis of the generated stochastic precipitation event set, hydrological and hydraulic simulations will be performed to estimate discharge and water level. The resulting stochastic flood event set will be used to quantify the flood risk and to estimate probable maximum loss (e.g. PML200) for a given property (buildings, industry) portfolio.

A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA.

PubMed

Sozou, P D; Kirkwood, T B

2001-12-21

Human diploid fibroblast cells can divide for only a limited number of times in vitro, a phenomenon known as replicative senescence or the Hayflick limit. Variability in doubling potential is observed within a clone of cells, and between two sister cells arising from a single mitotic division. This strongly suggests that the process by which cells become senescent is intrinsically stochastic. Among the various biochemical mechanisms that have been proposed to explain replicative senescence, particular interest has been focussed on the role of telomere reduction. In the absence of telomerase--an enzyme switched off in normal diploid fibro-blasts-cells lose telomeric DNA at each cell division. According to the telomere hypothesis of cell senescence, cells eventually reach a critically short telomere length and cell cycle arrest follows. In support of this concept, forced expression of telomerase in normal fibroblasts appears to prevent cell senescence. Nevertheless, the telomere hypothesis in its basic form has some difficulty in explaining the marked stochastic variations seen in the replicative lifespans of individual cells within a culture, and there is strong empirical and theoretical support for the concept that other kinds of damage may contribute to cellular ageing. We describe a stochastic network model of cell senescence in which a primary role is played by telomere reduction but in which other mechanisms (oxidative stress linked particularly to mitochondrial damage, and nuclear somatic mutations) also contribute. The model gives simulation results that are in good agreement with published data on intra-clonal variability in cell doubling potential and permits an analysis of how the various elements of the stochastic network interact. Such integrative models may aid in developing new experimental approaches aimed at unravelling the intrinsic complexity of the mechanisms contributing to human cell ageing. Copyright 2001 Academic Press.

A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.

PubMed

Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S

2016-06-01

Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs. Copyright © 2016 Elsevier Inc. All rights reserved.

Stochastic population dynamics in spatially extended predator-prey systems

NASA Astrophysics Data System (ADS)

Dobramysl, Ulrich; Mobilia, Mauro; Pleimling, Michel; Täuber, Uwe C.

2018-02-01

Spatially extended population dynamics models that incorporate demographic noise serve as case studies for the crucial role of fluctuations and correlations in biological systems. Numerical and analytic tools from non-equilibrium statistical physics capture the stochastic kinetics of these complex interacting many-particle systems beyond rate equation approximations. Including spatial structure and stochastic noise in models for predator-prey competition invalidates the neutral Lotka-Volterra population cycles. Stochastic models yield long-lived erratic oscillations stemming from a resonant amplification mechanism. Spatially extended predator-prey systems display noise-stabilized activity fronts that generate persistent correlations. Fluctuation-induced renormalizations of the oscillation parameters can be analyzed perturbatively via a Doi-Peliti field theory mapping of the master equation; related tools allow detailed characterization of extinction pathways. The critical steady-state and non-equilibrium relaxation dynamics at the predator extinction threshold are governed by the directed percolation universality class. Spatial predation rate variability results in more localized clusters, enhancing both competing species’ population densities. Affixing variable interaction rates to individual particles and allowing for trait inheritance subject to mutations induces fast evolutionary dynamics for the rate distributions. Stochastic spatial variants of three-species competition with ‘rock-paper-scissors’ interactions metaphorically describe cyclic dominance. These models illustrate intimate connections between population dynamics and evolutionary game theory, underscore the role of fluctuations to drive populations toward extinction, and demonstrate how space can support species diversity. Two-dimensional cyclic three-species May-Leonard models are characterized by the emergence of spiraling patterns whose properties are elucidated by a mapping onto a complex Ginzburg-Landau equation. Multiple-species extensions to general ‘food networks’ can be classified on the mean-field level, providing both fundamental understanding of ensuing cooperativity and profound insight into the rich spatio-temporal features and coarsening kinetics in the corresponding spatially extended systems. Novel space-time patterns emerge as a result of the formation of competing alliances; e.g. coarsening domains that each incorporate rock-paper-scissors competition games.

On the minimum of independent geometrically distributed random variables

NASA Technical Reports Server (NTRS)

Ciardo, Gianfranco; Leemis, Lawrence M.; Nicol, David

1994-01-01

The expectations E(X(sub 1)), E(Z(sub 1)), and E(Y(sub 1)) of the minimum of n independent geometric, modifies geometric, or exponential random variables with matching expectations differ. We show how this is accounted for by stochastic variability and how E(X(sub 1))/E(Y(sub 1)) equals the expected number of ties at the minimum for the geometric random variables. We then introduce the 'shifted geometric distribution' and show that there is a unique value of the shift for which the individual shifted geometric and exponential random variables match expectations both individually and in the minimums.

An energy-based perturbation and a taboo strategy for improving the searching ability of stochastic structural optimization methods

NASA Astrophysics Data System (ADS)

Cheng, Longjiu; Cai, Wensheng; Shao, Xueguang

2005-03-01

An energy-based perturbation and a new idea of taboo strategy are proposed for structural optimization and applied in a benchmark problem, i.e., the optimization of Lennard-Jones (LJ) clusters. It is proved that the energy-based perturbation is much better than the traditional random perturbation both in convergence speed and searching ability when it is combined with a simple greedy method. By tabooing the most wide-spread funnel instead of the visited solutions, the hit rate of other funnels can be significantly improved. Global minima of (LJ) clusters up to 200 atoms are found with high efficiency.

Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints

NASA Technical Reports Server (NTRS)

Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren

2015-01-01

Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.

Evaluating Kuala Lumpur stock exchange oriented bank performance with stochastic frontiers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baten, M. A.; Maznah, M. K.; Razamin, R.

Banks play an essential role in the economic development and banks need to be efficient; otherwise, they may create blockage in the process of development in any country. The efficiency of banks in Malaysia is important and should receive greater attention. This study formulated an appropriate stochastic frontier model to investigate the efficiency of banks which are traded on Kuala Lumpur Stock Exchange (KLSE) market during the period 2005–2009. All data were analyzed to obtain the maximum likelihood method to estimate the parameters of stochastic production. Unlike the earlier studies which use balance sheet and income statements data, this studymore » used market data as the input and output variables. It was observed that banks listed in KLSE exhibited a commendable overall efficiency level of 96.2% during 2005–2009 hence suggesting minimal input waste of 3.8%. Among the banks, the COMS (Cimb Group Holdings) bank is found to be highly efficient with a score of 0.9715 and BIMB (Bimb Holdings) bank is noted to have the lowest efficiency with a score of 0.9582. The results also show that Cobb-Douglas stochastic frontier model with truncated normal distributional assumption is preferable than Translog stochastic frontier model.« less

Phenomenological analysis of medical time series with regular and stochastic components

NASA Astrophysics Data System (ADS)

Timashev, Serge F.; Polyakov, Yuriy S.

2007-06-01

Flicker-Noise Spectroscopy (FNS), a general approach to the extraction and parameterization of resonant and stochastic components contained in medical time series, is presented. The basic idea of FNS is to treat the correlation links present in sequences of different irregularities, such as spikes, "jumps", and discontinuities in derivatives of different orders, on all levels of the spatiotemporal hierarchy of the system under study as main information carriers. The tools to extract and analyze the information are power spectra and difference moments (structural functions), which complement the information of each other. The structural function stochastic component is formed exclusively by "jumps" of the dynamic variable while the power spectrum stochastic component is formed by both spikes and "jumps" on every level of the hierarchy. The information "passport" characteristics that are determined by fitting the derived expressions to the experimental variations for the stochastic components of power spectra and structural functions are interpreted as the correlation times and parameters that describe the rate of "memory loss" on these correlation time intervals for different irregularities. The number of the extracted parameters is determined by the requirements of the problem under study. Application of this approach to the analysis of tremor velocity signals for a Parkinsonian patient is discussed.

Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance

PubMed Central

Zhang, Yunshun; Zheng, Rencheng; Shimono, Keisuke; Kaizuka, Tsutomu; Nakano, Kimihiko

2016-01-01

The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application. PMID:27763522

Effectiveness Testing of a Piezoelectric Energy Harvester for an Automobile Wheel Using Stochastic Resonance.

PubMed

Zhang, Yunshun; Zheng, Rencheng; Shimono, Keisuke; Kaizuka, Tsutomu; Nakano, Kimihiko

2016-10-17

The collection of clean power from ambient vibrations is considered a promising method for energy harvesting. For the case of wheel rotation, the present study investigates the effectiveness of a piezoelectric energy harvester, with the application of stochastic resonance to optimize the efficiency of energy harvesting. It is hypothesized that when the wheel rotates at variable speeds, the energy harvester is subjected to on-road noise as ambient excitations and a tangentially acting gravity force as a periodic modulation force, which can stimulate stochastic resonance. The energy harvester was miniaturized with a bistable cantilever structure, and the on-road noise was measured for the implementation of a vibrator in an experimental setting. A validation experiment revealed that the harvesting system was optimized to capture power that was approximately 12 times that captured under only on-road noise excitation and 50 times that captured under only the periodic gravity force. Moreover, the investigation of up-sweep excitations with increasing rotational frequency confirmed that stochastic resonance is effective in optimizing the performance of the energy harvester, with a certain bandwidth of vehicle speeds. An actual-vehicle experiment validates that the prototype harvester using stochastic resonance is capable of improving power generation performance for practical tire application.

Deterministic and stochastic models for middle east respiratory syndrome (MERS)

NASA Astrophysics Data System (ADS)

Suryani, Dessy Rizki; Zevika, Mona; Nuraini, Nuning

2018-03-01

World Health Organization (WHO) data stated that since September 2012, there were 1,733 cases of Middle East Respiratory Syndrome (MERS) with 628 death cases that occurred in 27 countries. MERS was first identified in Saudi Arabia in 2012 and the largest cases of MERS outside Saudi Arabia occurred in South Korea in 2015. MERS is a disease that attacks the respiratory system caused by infection of MERS-CoV. MERS-CoV transmission occurs directly through direct contact between infected individual with non-infected individual or indirectly through contaminated object by the free virus. Suspected, MERS can spread quickly because of the free virus in environment. Mathematical modeling is used to illustrate the transmission of MERS disease using deterministic model and stochastic model. Deterministic model is used to investigate the temporal dynamic from the system to analyze the steady state condition. Stochastic model approach using Continuous Time Markov Chain (CTMC) is used to predict the future states by using random variables. From the models that were built, the threshold value for deterministic models and stochastic models obtained in the same form and the probability of disease extinction can be computed by stochastic model. Simulations for both models using several of different parameters are shown, and the probability of disease extinction will be compared with several initial conditions.

Modelling uncertainty in incompressible flow simulation using Galerkin based generalized ANOVA

NASA Astrophysics Data System (ADS)

Chakraborty, Souvik; Chowdhury, Rajib

2016-11-01

This paper presents a new algorithm, referred to here as Galerkin based generalized analysis of variance decomposition (GG-ANOVA) for modelling input uncertainties and its propagation in incompressible fluid flow. The proposed approach utilizes ANOVA to represent the unknown stochastic response. Further, the unknown component functions of ANOVA are represented using the generalized polynomial chaos expansion (PCE). The resulting functional form obtained by coupling the ANOVA and PCE is substituted into the stochastic Navier-Stokes equation (NSE) and Galerkin projection is employed to decompose it into a set of coupled deterministic 'Navier-Stokes alike' equations. Temporal discretization of the set of coupled deterministic equations is performed by employing Adams-Bashforth scheme for convective term and Crank-Nicolson scheme for diffusion term. Spatial discretization is performed by employing finite difference scheme. Implementation of the proposed approach has been illustrated by two examples. In the first example, a stochastic ordinary differential equation has been considered. This example illustrates the performance of proposed approach with change in nature of random variable. Furthermore, convergence characteristics of GG-ANOVA has also been demonstrated. The second example investigates flow through a micro channel. Two case studies, namely the stochastic Kelvin-Helmholtz instability and stochastic vortex dipole, have been investigated. For all the problems results obtained using GG-ANOVA are in excellent agreement with benchmark solutions.

Hydrological parameter estimations from a conservative tracer test with variable-density effects at the Boise Hydrogeophysical Research Site

NASA Astrophysics Data System (ADS)

Dafflon, B.; Barrash, W.; Cardiff, M.; Johnson, T. C.

2011-12-01

Reliable predictions of groundwater flow and solute transport require an estimation of the detailed distribution of the parameters (e.g., hydraulic conductivity, effective porosity) controlling these processes. However, such parameters are difficult to estimate because of the inaccessibility and complexity of the subsurface. In this regard, developments in parameter estimation techniques and investigations of field experiments are still challenging and necessary to improve our understanding and the prediction of hydrological processes. Here we analyze a conservative tracer test conducted at the Boise Hydrogeophysical Research Site in 2001 in a heterogeneous unconfined fluvial aquifer. Some relevant characteristics of this test include: variable-density (sinking) effects because of the injection concentration of the bromide tracer, the relatively small size of the experiment, and the availability of various sources of geophysical and hydrological information. The information contained in this experiment is evaluated through several parameter estimation approaches, including a grid-search-based strategy, stochastic simulation of hydrological property distributions, and deterministic inversion using regularization and pilot-point techniques. Doing this allows us to investigate hydraulic conductivity and effective porosity distributions and to compare the effects of assumptions from several methods and parameterizations. Our results provide new insights into the understanding of variable-density transport processes and the hydrological relevance of incorporating various sources of information in parameter estimation approaches. Among others, the variable-density effect and the effective porosity distribution, as well as their coupling with the hydraulic conductivity structure, are seen to be significant in the transport process. The results also show that assumed prior information can strongly influence the estimated distributions of hydrological properties.

Analyzing long-term correlated stochastic processes by means of recurrence networks: Potentials and pitfalls

NASA Astrophysics Data System (ADS)

Zou, Yong; Donner, Reik V.; Kurths, Jürgen

2015-02-01

Long-range correlated processes are ubiquitous, ranging from climate variables to financial time series. One paradigmatic example for such processes is fractional Brownian motion (fBm). In this work, we highlight the potentials and conceptual as well as practical limitations when applying the recently proposed recurrence network (RN) approach to fBm and related stochastic processes. In particular, we demonstrate that the results of a previous application of RN analysis to fBm [Liu et al. Phys. Rev. E 89, 032814 (2014), 10.1103/PhysRevE.89.032814] are mainly due to an inappropriate treatment disregarding the intrinsic nonstationarity of such processes. Complementarily, we analyze some RN properties of the closely related stationary fractional Gaussian noise (fGn) processes and find that the resulting network properties are well-defined and behave as one would expect from basic conceptual considerations. Our results demonstrate that RN analysis can indeed provide meaningful results for stationary stochastic processes, given a proper selection of its intrinsic methodological parameters, whereas it is prone to fail to uniquely retrieve RN properties for nonstationary stochastic processes like fBm.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Calculating the Malliavin derivative of some stochastic mechanics problems

PubMed Central

Hauseux, Paul; Hale, Jack S.

2017-01-01

The Malliavin calculus is an extension of the classical calculus of variations from deterministic functions to stochastic processes. In this paper we aim to show in a practical and didactic way how to calculate the Malliavin derivative, the derivative of the expectation of a quantity of interest of a model with respect to its underlying stochastic parameters, for four problems found in mechanics. The non-intrusive approach uses the Malliavin Weight Sampling (MWS) method in conjunction with a standard Monte Carlo method. The models are expressed as ODEs or PDEs and discretised using the finite difference or finite element methods. Specifically, we consider stochastic extensions of; a 1D Kelvin-Voigt viscoelastic model discretised with finite differences, a 1D linear elastic bar, a hyperelastic bar undergoing buckling, and incompressible Navier-Stokes flow around a cylinder, all discretised with finite elements. A further contribution of this paper is an extension of the MWS method to the more difficult case of non-Gaussian random variables and the calculation of second-order derivatives. We provide open-source code for the numerical examples in this paper. PMID:29261776

Evaluation of hybrid inverse planning and optimization (HIPO) algorithm for optimization in real-time, high-dose-rate (HDR) brachytherapy for prostate.

PubMed

Pokharel, Shyam; Rana, Suresh; Blikenstaff, Joseph; Sadeghi, Amir; Prestidge, Bradley

2013-07-08

The purpose of this study is to investigate the effectiveness of the HIPO planning and optimization algorithm for real-time prostate HDR brachytherapy. This study consists of 20 patients who underwent ultrasound-based real-time HDR brachytherapy of the prostate using the treatment planning system called Oncentra Prostate (SWIFT version 3.0). The treatment plans for all patients were optimized using inverse dose-volume histogram-based optimization followed by graphical optimization (GRO) in real time. The GRO is manual manipulation of isodose lines slice by slice. The quality of the plan heavily depends on planner expertise and experience. The data for all patients were retrieved later, and treatment plans were created and optimized using HIPO algorithm with the same set of dose constraints, number of catheters, and set of contours as in the real-time optimization algorithm. The HIPO algorithm is a hybrid because it combines both stochastic and deterministic algorithms. The stochastic algorithm, called simulated annealing, searches the optimal catheter distributions for a given set of dose objectives. The deterministic algorithm, called dose-volume histogram-based optimization (DVHO), optimizes three-dimensional dose distribution quickly by moving straight downhill once it is in the advantageous region of the search space given by the stochastic algorithm. The PTV receiving 100% of the prescription dose (V100) was 97.56% and 95.38% with GRO and HIPO, respectively. The mean dose (D(mean)) and minimum dose to 10% volume (D10) for the urethra, rectum, and bladder were all statistically lower with HIPO compared to GRO using the student pair t-test at 5% significance level. HIPO can provide treatment plans with comparable target coverage to that of GRO with a reduction in dose to the critical structures.

Theory on the Coupled Stochastic Dynamics of Transcription and Splice-Site Recognition

PubMed Central

Murugan, Rajamanickam; Kreiman, Gabriel

2012-01-01

Eukaryotic genes are typically split into exons that need to be spliced together to form the mature mRNA. The splicing process depends on the dynamics and interactions among transcription by the RNA polymerase II complex (RNAPII) and the spliceosomal complex consisting of multiple small nuclear ribonucleo proteins (snRNPs). Here we propose a biophysically plausible initial theory of splicing that aims to explain the effects of the stochastic dynamics of snRNPs on the splicing patterns of eukaryotic genes. We consider two different ways to model the dynamics of snRNPs: pure three-dimensional diffusion and a combination of three- and one-dimensional diffusion along the emerging pre-mRNA. Our theoretical analysis shows that there exists an optimum position of the splice sites on the growing pre-mRNA at which the time required for snRNPs to find the 5′ donor site is minimized. The minimization of the overall search time is achieved mainly via the increase in non-specific interactions between the snRNPs and the growing pre-mRNA. The theory further predicts that there exists an optimum transcript length that maximizes the probabilities for exons to interact with the snRNPs. We evaluate these theoretical predictions by considering human and mouse exon microarray data as well as RNAseq data from multiple different tissues. We observe that there is a broad optimum position of splice sites on the growing pre-mRNA and an optimum transcript length, which are roughly consistent with the theoretical predictions. The theoretical and experimental analyses suggest that there is a strong interaction between the dynamics of RNAPII and the stochastic nature of snRNP search for 5′ donor splicing sites. PMID:23133354

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

PubMed

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

In Search of Determinism-Sensitive Region to Avoid Artefacts in Recurrence Plots

NASA Astrophysics Data System (ADS)

Wendi, Dadiyorto; Marwan, Norbert; Merz, Bruno

As an effort to reduce parameter uncertainties in constructing recurrence plots, and in particular to avoid potential artefacts, this paper presents a technique to derive artefact-safe region of parameter sets. This technique exploits both deterministic (incl. chaos) and stochastic signal characteristics of recurrence quantification (i.e. diagonal structures). It is useful when the evaluated signal is known to be deterministic. This study focuses on the recurrence plot generated from the reconstructed phase space in order to represent many real application scenarios when not all variables to describe a system are available (data scarcity). The technique involves random shuffling of the original signal to destroy its original deterministic characteristics. Its purpose is to evaluate whether the determinism values of the original and the shuffled signal remain closely together, and therefore suggesting that the recurrence plot might comprise artefacts. The use of such determinism-sensitive region shall be accompanied by standard embedding optimization approaches, e.g. using indices like false nearest neighbor and mutual information, to result in a more reliable recurrence plot parameterization.

Stochastic simulations on a model of circadian rhythm generation.

PubMed

Miura, Shigehiro; Shimokawa, Tetsuya; Nomura, Taishin

2008-01-01

Biological phenomena are often modeled by differential equations, where states of a model system are described by continuous real values. When we consider concentrations of molecules as dynamical variables for a set of biochemical reactions, we implicitly assume that numbers of the molecules are large enough so that their changes can be regarded as continuous and they are described deterministically. However, for a system with small numbers of molecules, changes in their numbers are apparently discrete and molecular noises become significant. In such cases, models with deterministic differential equations may be inappropriate, and the reactions must be described by stochastic equations. In this study, we focus a clock gene expression for a circadian rhythm generation, which is known as a system involving small numbers of molecules. Thus it is appropriate for the system to be modeled by stochastic equations and analyzed by methodologies of stochastic simulations. The interlocked feedback model proposed by Ueda et al. as a set of deterministic ordinary differential equations provides a basis of our analyses. We apply two stochastic simulation methods, namely Gillespie's direct method and the stochastic differential equation method also by Gillespie, to the interlocked feedback model. To this end, we first reformulated the original differential equations back to elementary chemical reactions. With those reactions, we simulate and analyze the dynamics of the model using two methods in order to compare them with the dynamics obtained from the original deterministic model and to characterize dynamics how they depend on the simulation methodologies.

Stochastic inflation in phase space: is slow roll a stochastic attractor?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grain, Julien; Vennin, Vincent, E-mail: julien.grain@ias.u-psud.fr, E-mail: vincent.vennin@port.ac.uk

An appealing feature of inflationary cosmology is the presence of a phase-space attractor, ''slow roll'', which washes out the dependence on initial field velocities. We investigate the robustness of this property under backreaction from quantum fluctuations using the stochastic inflation formalism in the phase-space approach. A Hamiltonian formulation of stochastic inflation is presented, where it is shown that the coarse-graining procedure—where wavelengths smaller than the Hubble radius are integrated out—preserves the canonical structure of free fields. This means that different sets of canonical variables give rise to the same probability distribution which clarifies the literature with respect to this issue.more » The role played by the quantum-to-classical transition is also analysed and is shown to constrain the coarse-graining scale. In the case of free fields, we find that quantum diffusion is aligned in phase space with the slow-roll direction. This implies that the classical slow-roll attractor is immune to stochastic effects and thus generalises to a stochastic attractor regardless of initial conditions, with a relaxation time at least as short as in the classical system. For non-test fields or for test fields with non-linear self interactions however, quantum diffusion and the classical slow-roll flow are misaligned. We derive a condition on the coarse-graining scale so that observational corrections from this misalignment are negligible at leading order in slow roll.« less

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Du, Xiaosong; Leifsson, Leifur; Grandin, Robert; Meeker, William; Roberts, Ronald; Song, Jiming

2018-04-01

Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination with model-assisted POD (MAPOD) methods. With the development of advanced physics-based methods, such as ultrasonic NDT testing, the empirical information, needed for POD methods, can be reduced. However, performing accurate numerical simulations can be prohibitively time-consuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through the physics-based simulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using non-intrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary least-squares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw in an aluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation model for this case, which is the UTSim2 model.

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

PubMed Central

McDonnell, Mark D.; Abbott, Derek

2009-01-01

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology. PMID:19562010