Bogen, K T
2007-01-30
As reflected in the 2005 USEPA Guidelines for Cancer Risk Assessment, some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained using separate ''linear'' (genotoxic) vs. ''nonlinear'' (nongenotoxic) approaches to low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient to parameterize a biologically based model that reliably extrapolates risk to low levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach--similar to that used in reference dose procedures for classic toxicity endpoints--can address MOA uncertainty in a way that avoids explicit modeling of low-dose risk as a function of administered or internal dose. Even when a ''nonlinear'' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was illustrated for the rodent carcinogen naphthalene. Bioassay data, supplemental toxicokinetic data, and related physiologically based pharmacokinetic and 2-stage stochastic carcinogenesis modeling results all clearly indicate that naphthalene is a DMOA carcinogen. Plausibility bounds on rat-tumor-type specific DMOA-related uncertainty were obtained using a 2-stage model adapted to reflect the empirical link between genotoxic and cytotoxic effects of the most potent identified genotoxic naphthalene metabolites, 1,2- and 1,4-naphthoquinone. Resulting bounds each provided the basis for a corresponding
Bogen, K T
2007-05-11
A relatively simple, quantitative approach is proposed to address a specific, important gap in the appr approach recommended by the USEPA Guidelines for Cancer Risk Assessment to oach address uncertainty in carcinogenic mode of action of certain chemicals when risk is extrapolated from bioassay data. These Guidelines recognize that some chemical carcinogens may have a site-specific mode of action (MOA) that is dual, involving mutation in addition to cell-killing induced hyperplasia. Although genotoxicity may contribute to increased risk at all doses, the Guidelines imply that for dual MOA (DMOA) carcinogens, judgment be used to compare and assess results obtained using separate 'linear' (genotoxic) vs. 'nonlinear' (nongenotoxic) approaches to low low-level risk extrapolation. However, the Guidelines allow the latter approach to be used only when evidence is sufficient t to parameterize a biologically based model that reliably o extrapolates risk to low levels of concern. The Guidelines thus effectively prevent MOA uncertainty from being characterized and addressed when data are insufficient to parameterize such a model, but otherwise clearly support a DMOA. A bounding factor approach - similar to that used in reference dose procedures for classic toxicity endpoints - can address MOA uncertainty in a way that avoids explicit modeling of low low-dose risk as a function of administere administered or internal dose. Even when a 'nonlinear' toxicokinetic model cannot be fully validated, implications of DMOA uncertainty on low low-dose risk may be bounded with reasonable confidence when target tumor types happen to be extremely rare. This concept was i illustrated llustrated for a likely DMOA rodent carcinogen naphthalene, specifically to the issue of risk extrapolation from bioassay data on naphthalene naphthalene-induced nasal tumors in rats. Bioassay data, supplemental toxicokinetic data, and related physiologically based p pharmacokinetic and 2 harmacokinetic 2-stage
Stochastic dynamics of cancer initiation
NASA Astrophysics Data System (ADS)
Foo, Jasmine; Leder, Kevin; Michor, Franziska
2011-02-01
Most human cancer types result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Once the first change (or changes) have arisen, tumorigenesis is initiated and the subsequent emergence of additional alterations drives progression to more aggressive and ultimately invasive phenotypes. Elucidation of the dynamics of cancer initiation is of importance for an understanding of tumor evolution and cancer incidence data. In this paper, we develop a novel mathematical framework to study the processes of cancer initiation. Cells at risk of accumulating oncogenic mutations are organized into small compartments of cells and proliferate according to a stochastic process. During each cell division, an (epi)genetic alteration may arise which leads to a random fitness change, drawn from a probability distribution. Cancer is initiated when a cell gains a fitness sufficiently high to escape from the homeostatic mechanisms of the cell compartment. To investigate cancer initiation during a human lifetime, a 'race' between this fitness process and the aging process of the patient is considered; the latter is modeled as a second stochastic Markov process in an aging dimension. This model allows us to investigate the dynamics of cancer initiation and its dependence on the mutational fitness distribution. Our framework also provides a methodology to assess the effects of different life expectancy distributions on lifetime cancer incidence. We apply this methodology to colorectal tumorigenesis while considering life expectancy data of the US population to inform the dynamics of the aging process. We study how the probability of cancer initiation prior to death, the time until cancer initiation, and the mutational profile of the cancer-initiating cell depends on the shape of the mutational fitness distribution and life expectancy of the population.
Stochastic elimination of cancer cells.
Michor, Franziska; Nowak, Martin A; Frank, Steven A; Iwasa, Yoh
2003-01-01
Tissues of multicellular organisms consist of stem cells and differentiated cells. Stem cells divide to produce new stem cells or differentiated cells. Differentiated cells divide to produce new differentiated cells. We show that such a tissue design can reduce the rate of fixation of mutations that increase the net proliferation rate of cells. It has, however, no consequence for the rate of fixation of neutral mutations. We calculate the optimum relative abundance of stem cells that minimizes the rate of generating cancer cells. There is a critical fraction of stem cell divisions that is required for a stochastic elimination ('wash out') of cancer cells. PMID:14561289
A stochastic model for immunotherapy of cancer
Baar, Martina; Coquille, Loren; Mayer, Hannah; Hölzel, Michael; Rogava, Meri; Tüting, Thomas; Bovier, Anton
2016-01-01
We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are modelled as single particles (individuals) in the stochastic system. The main expansions of the model are distinguishing cancer cells by phenotype and genotype, including environment-dependent phenotypic plasticity that does not affect the genotype, taking into account the effects of therapy and introducing a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic models) may help to understand the resistance of tumours to therapeutic approaches and may have non-trivial consequences on tumour treatment protocols. This is supported through numerical simulations. PMID:27063839
Gompertzian stochastic model with delay effect to cervical cancer growth
NASA Astrophysics Data System (ADS)
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah
2015-02-01
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
Gompertzian stochastic model with delay effect to cervical cancer growth
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah
2015-02-03
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
Second Cancers After Fractionated Radiotherapy: Stochastic Population Dynamics Effects
NASA Technical Reports Server (NTRS)
Sachs, Rainer K.; Shuryak, Igor; Brenner, David; Fakir, Hatim; Hahnfeldt, Philip
2007-01-01
When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well characterized doses of a well studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze ifradiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this I I model. Combining Monte Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance >> mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; b) weekend
Kerns, Sarah L.; Stock, Richard; Stone, Nelson; Buckstein, Michael; Shao, Yongzhao; Campbell, Christopher; Rath, Lynda; De Ruysscher, Dirk; Lammering, Guido; Hixson, Rosetta; Cesaretti, Jamie; Terk, Mitchell; Ostrer, Harry; Rosenstein, Barry S.
2013-01-01
Purpose: To identify single nucleotide polymorphisms (SNPs) associated with development of erectile dysfunction (ED) among prostate cancer patients treated with radiation therapy. Methods and Materials: A 2-stage genome-wide association study was performed. Patients were split randomly into a stage I discovery cohort (132 cases, 103 controls) and a stage II replication cohort (128 cases, 102 controls). The discovery cohort was genotyped using Affymetrix 6.0 genome-wide arrays. The 940 top ranking SNPs selected from the discovery cohort were genotyped in the replication cohort using Illumina iSelect custom SNP arrays. Results: Twelve SNPs identified in the discovery cohort and validated in the replication cohort were associated with development of ED following radiation therapy (Fisher combined P values 2.1 Multiplication-Sign 10{sup -5} to 6.2 Multiplication-Sign 10{sup -4}). Notably, these 12 SNPs lie in or near genes involved in erectile function or other normal cellular functions (adhesion and signaling) rather than DNA damage repair. In a multivariable model including nongenetic risk factors, the odds ratios for these SNPs ranged from 1.6 to 5.6 in the pooled cohort. There was a striking relationship between the cumulative number of SNP risk alleles an individual possessed and ED status (Sommers' D P value = 1.7 Multiplication-Sign 10{sup -29}). A 1-allele increase in cumulative SNP score increased the odds for developing ED by a factor of 2.2 (P value = 2.1 Multiplication-Sign 10{sup -19}). The cumulative SNP score model had a sensitivity of 84% and specificity of 75% for prediction of developing ED at the radiation therapy planning stage. Conclusions: This genome-wide association study identified a set of SNPs that are associated with development of ED following radiation therapy. These candidate genetic predictors warrant more definitive validation in an independent cohort.
Towards Predictive Stochastic Dynamical Modeling of Cancer Genesis and Progression
Ao, P.; Galas, D.; Hood, L.; Yin, L.; Zhu, X.M.
2011-01-01
Based on an innovative endogenous network hypothesis on cancer genesis and progression we have been working towards a quantitative cancer theory along the systems biology perspective. Here we give a brief report on our progress and illustrate that combing ideas from evolutionary and molecular biology, mathematics, engineering, and physics, such quantitative approach is feasible. PMID:20640781
Stochastic Effects in Computational Biology of Space Radiation Cancer Risk
NASA Technical Reports Server (NTRS)
Cucinotta, Francis A.; Pluth, Janis; Harper, Jane; O'Neill, Peter
2007-01-01
Estimating risk from space radiation poses important questions on the radiobiology of protons and heavy ions. We are considering systems biology models to study radiation induced repair foci (RIRF) at low doses, in which less than one-track on average transverses the cell, and the subsequent DNA damage processing and signal transduction events. Computational approaches for describing protein regulatory networks coupled to DNA and oxidative damage sites include systems of differential equations, stochastic equations, and Monte-Carlo simulations. We review recent developments in the mathematical description of protein regulatory networks and possible approaches to radiation effects simulation. These include robustness, which states that regulatory networks maintain their functions against external and internal perturbations due to compensating properties of redundancy and molecular feedback controls, and modularity, which leads to general theorems for considering molecules that interact through a regulatory mechanism without exchange of matter leading to a block diagonal reduction of the connecting pathways. Identifying rate-limiting steps, robustness, and modularity in pathways perturbed by radiation damage are shown to be valid techniques for reducing large molecular systems to realistic computer simulations. Other techniques studied are the use of steady-state analysis, and the introduction of composite molecules or rate-constants to represent small collections of reactants. Applications of these techniques to describe spatial and temporal distributions of RIRF and cell populations following low dose irradiation are described.
Moderate stem-cell telomere shortening rate postpones cancer onset in a stochastic model
NASA Astrophysics Data System (ADS)
Holbek, Simon; Bendtsen, Kristian Moss; Juul, Jeppe
2013-10-01
Mammalian cells are restricted from proliferating indefinitely. Telomeres at the end of each chromosome are shortened at cell division and when they reach a critical length, the cell will enter permanent cell cycle arrest—a state known as senescence. This mechanism is thought to be tumor suppressing, as it helps prevent precancerous cells from dividing uncontrollably. Stem cells express the enzyme telomerase, which elongates the telomeres, thereby postponing senescence. However, unlike germ cells and most types of cancer cells, stem cells only express telomerase at levels insufficient to fully maintain the length of their telomeres, leading to a slow decline in proliferation potential. It is not yet fully understood how this decline influences the risk of cancer and the longevity of the organism. We here develop a stochastic model to explore the role of telomere dynamics in relation to both senescence and cancer. The model describes the accumulation of cancerous mutations in a multicellular organism and creates a coherent theoretical framework for interpreting the results of several recent experiments on telomerase regulation. We demonstrate that the longest average cancer-free lifespan before cancer onset is obtained when stem cells start with relatively long telomeres that are shortened at a steady rate at cell division. Furthermore, the risk of cancer early in life can be reduced by having a short initial telomere length. Finally, our model suggests that evolution will favor a shorter than optimal average cancer-free lifespan in order to postpone cancer onset until late in life.
NASA Astrophysics Data System (ADS)
Warren, Patrick B.
2009-09-01
A recently proposed model for skin cell proliferation [E. Clayton , Nature (London) 446, 185 (2007)] is extended to incorporate mitotic autoregulation, and hence homeostasis as a fixed point of the dynamics. Unlimited cell proliferation in such a model can be viewed as a model for carcinogenesis. One way in which this can arise is homeostatic metastability, in which the cell populations escape from the homeostatic basin of attraction by a large but rare stochastic fluctuation. Such an event can be viewed as the final step in a multistage model of carcinogenesis. Homeostatic metastability offers a possible explanation for the peculiar epidemiology of lung cancer in ex-smokers.
NASA Astrophysics Data System (ADS)
Zamani Dahaj, Seyed Alireza; Kumar, Niraj; Sundaram, Bala; Celli, Jonathan; Kulkarni, Rahul
The phenotypic heterogeneity of cancer cells is critical to their survival under stress. A significant contribution to heterogeneity of cancer calls derives from the epithelial-mesenchymal transition (EMT), a conserved cellular program that is crucial for embryonic development. Several studies have investigated the role of EMT in growth of early stage tumors into invasive malignancies. Also, EMT has been closely associated with the acquisition of chemoresistance properties in cancer cells. Motivated by these studies, we analyze multi-phenotype stochastic models of the evolution of cancers cell populations under stress. We derive analytical results for time-dependent probability distributions that provide insights into the competing rates underlying phenotypic switching (e.g. during EMT) and the corresponding survival of cancer cells. Experimentally, we evaluate these model-based predictions by imaging human pancreatic cancer cell lines grown with and without cytotoxic agents and measure growth kinetics, survival, morphological changes and (terminal evaluation of) biomarkers with associated epithelial and mesenchymal phenotypes. The results derived suggest approaches for distinguishing between adaptation and selection scenarios for survival in the presence of external stresses.
Figueredo, Grazziela P; Siebers, Peer-Olaf; Owen, Markus R; Reps, Jenna; Aickelin, Uwe
2014-01-01
There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, representation of individual behaviours rather than aggregates and individual memory. In this paper we investigate the potential contribution of agent-based modelling and simulation when contrasted with stochastic versions of ODE models using early-stage cancer examples. We seek answers to the following questions: (1) Does this new stochastic formulation produce similar results to the agent-based version? (2) Can these methods be used interchangeably? (3) Do agent-based models outcomes reveal any benefit when compared to the Gillespie results? To answer these research questions we investigate three well-established mathematical models describing interactions between tumour cells and immune elements. These case studies were re-conceptualised under an agent-based perspective and also converted to the Gillespie algorithm formulation. Our interest in this work, therefore, is to establish a methodological discussion regarding the usability of different simulation approaches, rather than provide further biological insights into the investigated case studies. Our results show that it is possible to obtain equivalent models that implement the same mechanisms; however, the incapacity of the Gillespie algorithm to retain individual memory of past events affects the similarity of some results. Furthermore, the emergent behaviour of ABMS produces extra patters of behaviour in the system, which was not obtained by the Gillespie algorithm. PMID:24752131
Zhu, Peican; Aliabadi, Hamidreza Montazeri; Uludağ, Hasan; Han, Jie
2016-01-01
The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates to influence the relative cell growth. The vulnerabilities of some targets such as Myc and S6K are found to vary significantly depending on the weights of the sub-pathways; this will be indicative of the chosen target to require customization for therapy. When these targets are utilized, the response of breast cancers from different patients will be highly variable because of the known heterogeneities in signaling pathways among the patients. The targets whose vulnerabilities are invariably high might be more universally acceptable targets. PMID:26988076
A stochastic Markov chain model to describe lung cancer growth and metastasis.
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. PMID:22558094
Hermann, Philipp; Mrkvička, Tomáš; Mattfeldt, Torsten; Minárová, Mária; Helisová, Kateřina; Nicolis, Orietta; Wartner, Fabian; Stehlík, Milan
2015-08-15
Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper. PMID:25847279
Mattfeldt, T; Gottfried, H; Schmidt, V; Kestler, H A
2000-05-01
Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. Whether first-order parameters or stochastic-geometric functions are more important for the classification of the texture of biological tissues is not known. In the present study, volume and surface area per unit reference volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. The information provided by the latter functions was summarized by the total absolute areas between the estimated curves and their horizontal reference lines. These areas are considered as indicators of deviation of the tissue texture from a completely uncorrelated volume process and from the Boolean model with convex grains, respectively. We used both areas and the first-order parameters for the classification of cases using artificial neural networks (ANNs). Learning vector quantization and multilayer feedforward networks with backpropagation were applied as neural paradigms. Applications included distinction between mastopathy and mammary cancer (40 cases), between benign prostatic hyperplasia and prostatic cancer (70 cases) and between chronic pancreatitis and pancreatic cancer (60 cases). The same data sets were also classified with linear discriminant analysis. The stereological estimates in combination with ANNs or discriminant analysis provided high accuracy in the classification of individual cases. The question of which category of estimator is the most informative cannot be answered globally, but must be explored empirically for each specific data set. Using learning vector quantization, better results could often be obtained than by multilayer feedforward networks with backpropagation. PMID:10810010
Analysis of retinoblastoma age incidence data using a fully stochastic cancer model
Little, Mark P.; Kleinerman, Ruth A.; Stiller, Charles A.; Li, Guangquan; Kroll, Mary E.; Murphy, Michael F.G.
2011-01-01
Retinoblastoma (RB) is an important ocular malignancy of childhood. It has been commonly accepted for some time that knockout of the two alleles of the RB1 gene is the principal molecular target associated with the occurrence of RB. In this paper, we examine the validity of the two-hit theory for retinoblastoma by comparing the fit of a stochastic model with two or more mutational stages. Unlike many such models, our model assumes a fully stochastic stem cell compartment, which is crucial to its behavior. Models are fitted to a population-based dataset comprising 1,553 cases of retinoblastoma for the period 1962–2000 in Great Britain (England, Scotland, Wales). The population incidence of retinoblastoma is best described by a fully stochastic model with two stages, although models with a deterministic stem cell compartment yield equivalent fit; models with three or more stages fit much less well. The results strongly suggest that knockout of the two alleles of the RB1 gene is necessary and may be largely sufficient for the development of retinoblastoma, in support of Knudson’s two-hit hypothesis. PMID:21387305
Stochastic Tunneling and Metastable States During the Somatic Evolution of Cancer
Ashcroft, Peter; Michor, Franziska; Galla, Tobias
2015-01-01
Tumors initiate when a population of proliferating cells accumulates a certain number and type of genetic and/or epigenetic alterations. The population dynamics of such sequential acquisition of (epi)genetic alterations has been the topic of much investigation. The phenomenon of stochastic tunneling, where an intermediate mutant in a sequence does not reach fixation in a population before generating a double mutant, has been studied using a variety of computational and mathematical methods. However, the field still lacks a comprehensive analytical description since theoretical predictions of fixation times are available only for cases in which the second mutant is advantageous. Here, we study stochastic tunneling in a Moran model. Analyzing the deterministic dynamics of large populations we systematically identify the parameter regimes captured by existing approaches. Our analysis also reveals fitness landscapes and mutation rates for which finite populations are found in long-lived metastable states. These are landscapes in which the final mutant is not the most advantageous in the sequence, and resulting metastable states are a consequence of a mutation–selection balance. The escape from these states is driven by intrinsic noise, and their location affects the probability of tunneling. Existing methods no longer apply. In these regimes it is the escape from the metastable states that is the key bottleneck; fixation is no longer limited by the emergence of a successful mutant lineage. We used the so-called Wentzel–Kramers–Brillouin method to compute fixation times in these parameter regimes, successfully validated by stochastic simulations. Our work fills a gap left by previous approaches and provides a more comprehensive description of the acquisition of multiple mutations in populations of somatic cells. PMID:25624316
A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer
Liu, Yifang; Lee, Chi-Guhn; Chan, Timothy C. Y.; Cho, Young-Bin; Islam, Mohammad K.
2014-02-15
Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxels on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.
Solan, Eilon; Vieille, Nicolas
2015-01-01
In 1953, Lloyd Shapley contributed his paper “Stochastic games” to PNAS. In this paper, he defined the model of stochastic games, which were the first general dynamic model of a game to be defined, and proved that it admits a stationary equilibrium. In this Perspective, we summarize the historical context and the impact of Shapley’s contribution. PMID:26556883
The National Center for Environmental Assessment (NCEA) has conducted and supported research addressing uncertainties in 2-stage clonal growth models for cancer as applied to formaldehyde. In this report, we summarized publications resulting from this research effort, discussed t...
Evaluation of 2-Stage Injection Technique in Children.
Sandeep, Valasingam; Kumar, Manikya; Jyostna, P; Duggi, Vijay
2016-01-01
Effective pain control during local anesthetic injection is the cornerstone of behavior guidance in pediatric dentistry. The aim of this study was to evaluate the practical efficacy of a 2-stage injection technique in reducing injection pain in children. This was a split-mouth, randomized controlled crossover trial. One hundred cooperative children aged 7 to 13 years in need of bilateral local anesthetic injections (inferior alveolar nerve block, posterior superior alveolar nerve block, or maxillary and mandibular buccal infiltrations) for restorative, endodontic, and extraction treatments were recruited for the study. Children were randomly allocated to receive either the 2-stage injection technique or conventional technique at the first appointment. The other technique was used at the successive visit after 1 week. Subjective and objective evaluation of pain was done using the Wong-Baker FACES Pain Rating Scale (FPS) and Sound Eye Motor (SEM) scale, respectively. The comparison of pain scores was done by Wilcoxon sign-rank test. Both FPS and SEM scores were significantly lower when the 2-stage injection technique of local anesthetic nerve block/infiltration was used compared with the conventional technique. The 2-stage injection technique is a simple and effective means of reducing injection pain in children. PMID:26866405
2–stage stochastic Runge–Kutta for stochastic delay differential equations
Rosli, Norhayati; Jusoh Awang, Rahimah; Bahar, Arifah; Yeak, S. H.
2015-05-15
This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. General formulation of stochastic Runge-Kutta for SDDEs is introduced and Stratonovich Taylor series expansion for numerical solution of SRK2 is presented. Local truncation error of SRK2 is measured by comparing the Stratonovich Taylor expansion of the exact solution with the computed solution. Numerical experiment is performed to assure the validity of the method in simulating the strong solution of SDDEs.
Kossenko, M M; Hoffman, D A; Thomas, T L
2000-07-01
The Mayak Industrial Association, located in the South Ural Mountains, began operation in 1948 and was the first Russian site for the production and separation of plutonium. During the early days of operation, technological failures resulted in the release of large amounts of radioactive waste into the Techa River. Residents who lived in villages on the banks of the Techa and Iset Rivers were exposed to varying levels of radioactivity. The objective of this study is to assess stochastic (carcinogenic) effects in populations exposed to offsite releases of radioactive materials from the Mayak nuclear facility in Russia. Subjects of the present study are those individuals who lived during the period January 1950 through December 1960 in any of the exposed villages along the Techa River in Chelyabinsk Oblast. Death certificates and cancer incidence data have been routinely collected in the past from a five-rayon catchment area of Chelyabinsk Oblast. The registry of exposed residents along the Techa River assembled and maintained by the Urals Research Center for Radiation Medicine for the past 40 y is the basis for identifying study subjects for this project. Specific study objectives are to evaluate the incidence of cancer among current and former residents of Chelyabinsk Oblast who are in the exposed Techa River cohort; integrate results from the dose-reconstruction study to estimate doses for risk assessment; and develop a structure for maintaining continued follow-up of the cohort for cancer incidence. In the earlier part of our collaborative effort, the focus has been to enhance the cancer morbidity registry by updating it with cancer cases diagnosed through 1997, to conduct a series of validation procedures to ensure completeness and accuracy of the registry, and to reduce the numbers of subjects lost to follow-up. A feasibility study to determine cancer morbidity in migrants from the catchment area has been proposed. Our preliminary analyses of cancer morbidity
Direct vs 2-stage approaches to structured motif finding
2012-01-01
Background The notion of DNA motif is a mathematical abstraction used to model regions of the DNA (known as Transcription Factor Binding Sites, or TFBSs) that are bound by a given Transcription Factor to regulate gene expression or repression. In turn, DNA structured motifs are a mathematical counterpart that models sets of TFBSs that work in concert in the gene regulations processes of higher eukaryotic organisms. Typically, a structured motif is composed of an ordered set of isolated (or simple) motifs, separated by a variable, but somewhat constrained number of “irrelevant” base-pairs. Discovering structured motifs in a set of DNA sequences is a computationally hard problem that has been addressed by a number of authors using either a direct approach, or via the preliminary identification and successive combination of simple motifs. Results We describe a computational tool, named SISMA, for the de-novo discovery of structured motifs in a set of DNA sequences. SISMA is an exact, enumerative algorithm, meaning that it finds all the motifs conforming to the specifications. It does so in two stages: first it discovers all the possible component simple motifs, then combines them in a way that respects the given constraints. We developed SISMA mainly with the aim of understanding the potential benefits of such a 2-stage approach w.r.t. direct methods. In fact, no 2-stage software was available for the general problem of structured motif discovery, but only a few tools that solved restricted versions of the problem. We evaluated SISMA against other published tools on a comprehensive benchmark made of both synthetic and real biological datasets. In a significant number of cases, SISMA outperformed the competitors, exhibiting a good performance also in most of the cases in which it was inferior. Conclusions A reflection on the results obtained lead us to conclude that a 2-stage approach can be implemented with many advantages over direct approaches. Some of these
NASA Astrophysics Data System (ADS)
Eichhorn, Ralf; Aurell, Erik
2014-04-01
'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response
Bisognano, J.; Leemann, C.
1982-03-01
Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron.
2012-01-01
Background Reaction-diffusion based models have been widely used in the literature for modeling the growth of solid tumors. Many of the current models treat both diffusion/consumption of nutrients and cell proliferation. The majority of these models use classical transport/mass conservation equations for describing the distribution of molecular species in tumor spheroids, and the Fick's law for describing the flux of uncharged molecules (i.e oxygen, glucose). Commonly, the equations for the cell movement and proliferation are first order differential equations describing the rate of change of the velocity of the cells with respect to the spatial coordinates as a function of the nutrient's gradient. Several modifications of these equations have been developed in the last decade to explicitly indicate that the tumor includes cells, interstitial fluids and extracellular matrix: these variants provided a model of tumor as a multiphase material with these as the different phases. Most of the current reaction-diffusion tumor models are deterministic and do not model the diffusion as a local state-dependent process in a non-homogeneous medium at the micro- and meso-scale of the intra- and inter-cellular processes, respectively. Furthermore, a stochastic reaction-diffusion model in which diffusive transport of the molecular species of nutrients and chemotherapy drugs as well as the interactions of the tumor cells with these species is a novel approach. The application of this approach to he scase of non-small cell lung cancer treated with gemcitabine is also novel. Methods We present a stochastic reaction-diffusion model of non-small cell lung cancer growth in the specification formalism of the tool Redi, we recently developed for simulating reaction-diffusion systems. We also describe how a spatial gradient of nutrients and oncological drugs affects the tumor progression. Our model is based on a generalization of the Fick's first diffusion law that allows to model
The 2-stage liver transplant: 3 clinical scenarios.
Gedik, Ender; Bıçakçıoğlu, Murat; Otan, Emrah; İlksen Toprak, Hüseyin; Işık, Burak; Aydın, Cemalettin; Kayaalp, Cüneyt; Yılmaz, Sezai
2015-04-01
The main goal of 2-stage liver transplant is to provide time to obtain a new liver source. We describe our experience of 3 patients with 3 different clinical conditions. A 57-year-old man was retransplanted successfully with this technique due to hepatic artery thrombosis. However, a 38-year-old woman with fulminant toxic hepatitis and a 5-year-old-boy with abdominal trauma had poor outcome. This technique could serve as a rescue therapy for liver transplant patients who have toxic liver syndrome or abdominal trauma. These patients required intensive support during long anhepatic states. The transplant team should decide early whether to use this technique before irreversible conditions develop. PMID:25894175
Rood, A S; McGavran, P D; Aanenson, J W; Till, J E
2001-08-01
Carbon tetrachloride is a degreasing agent that was used at the Rocky Flats Plant (RFP) in Colorado to clean product components and equipment. The chemical is considered a volatile organic compound and a probable human carcinogen. During the time the plant operated (1953-1989), most of the carbon tetrachloride was released to the atmosphere through building exhaust ducts. A smaller amount was released to the air via evaporation from open-air burn pits and ground-surface discharge points. Airborne releases from the plant were conservatively estimated to be equivalent to the amount of carbon tetrachloride consumed annually by the plant, which was estimated to be between 3.6 and 180 Mg per year. This assumption was supported by calculations that showed that most of the carbon tetrachloride discharged to the ground surface would subsequently be released to the atmosphere. Atmospheric transport of carbon tetrachloride from the plant to the surrounding community was estimated using a Gaussian Puff dispersion model (RATCHET). Time-integrated concentrations were estimated for nine hypothetical but realistic exposure scenarios that considered variation in lifestyle, location, age, and gender. Uncertainty distributions were developed for cancer slope factors and atmospheric dispersion factors. These uncertainties were propagated through to the final risk estimate using Monte Carlo techniques. The geometric mean risk estimates varied from 5.2 x 10(-6) for a hypothetical rancher or laborer working near the RFP to 3.4 x 10(-9) for an infant scenario. The distribution of incremental lifetime cancer incidence risk for the hypothetical rancher was between 1.3 x 10(-6) (5% value) and 2.1 x 10(-5) (95% value). These estimates are similar to or exceed estimated cancer risks posed by releases of radionuclides from the site. PMID:11726020
Blaskiewicz, M.
2011-01-01
Stochastic Cooling was invented by Simon van der Meer and was demonstrated at the CERN ISR and ICE (Initial Cooling Experiment). Operational systems were developed at Fermilab and CERN. A complete theory of cooling of unbunched beams was developed, and was applied at CERN and Fermilab. Several new and existing rings employ coasting beam cooling. Bunched beam cooling was demonstrated in ICE and has been observed in several rings designed for coasting beam cooling. High energy bunched beams have proven more difficult. Signal suppression was achieved in the Tevatron, though operational cooling was not pursued at Fermilab. Longitudinal cooling was achieved in the RHIC collider. More recently a vertical cooling system in RHIC cooled both transverse dimensions via betatron coupling.
Brennan,J.M.; Blaskiewicz, M. M.; Severino, F.
2009-05-04
After the success of longitudinal stochastic cooling of bunched heavy ion beam in RHIC, transverse stochastic cooling in the vertical plane of Yellow ring was installed and is being commissioned with proton beam. This report presents the status of the effort and gives an estimate, based on simulation, of the RHIC luminosity with stochastic cooling in all planes.
... Leukemia Liver cancer Non-Hodgkin lymphoma Ovarian cancer Pancreatic cancer Testicular cancer Thyroid cancer Uterine cancer ... have any symptoms. In certain cancers, such as pancreatic cancer, symptoms often do not start until the disease ...
Fluctuations as stochastic deformation.
Kazinski, P O
2008-04-01
A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium. PMID:18517590
Fluctuations as stochastic deformation
NASA Astrophysics Data System (ADS)
Kazinski, P. O.
2008-04-01
A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium.
Stochastic Convection Parameterizations
NASA Technical Reports Server (NTRS)
Teixeira, Joao; Reynolds, Carolyn; Suselj, Kay; Matheou, Georgios
2012-01-01
computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts
A Stochastic Employment Problem
ERIC Educational Resources Information Center
Wu, Teng
2013-01-01
The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…
Stochastic Processes in Electrochemistry.
Singh, Pradyumna S; Lemay, Serge G
2016-05-17
Stochastic behavior becomes an increasingly dominant characteristic of electrochemical systems as we probe them on the smallest scales. Advances in the tools and techniques of nanoelectrochemistry dictate that stochastic phenomena will become more widely manifest in the future. In this Perspective, we outline the conceptual tools that are required to analyze and understand this behavior. We draw on examples from several specific electrochemical systems where important information is encoded in, and can be derived from, apparently random signals. This Perspective attempts to serve as an accessible introduction to understanding stochastic phenomena in electrochemical systems and outlines why they cannot be understood with conventional macroscopic descriptions. PMID:27120701
Spring, William Joseph
2009-04-13
We consider quantum analogues of n-parameter stochastic processes, associated integrals and martingale properties extending classical results obtained in [1, 2, 3], and quantum results in [4, 5, 6, 7, 8, 9, 10].
Dynamics of Double Stochastic Operators
NASA Astrophysics Data System (ADS)
Saburov, Mansoor
2016-03-01
A double stochastic operator is a generalization of a double stochastic matrix. In this paper, we study the dynamics of double stochastic operators. We give a criterion for a regularity of a double stochastic operator in terms of absences of its periodic points. We provide some examples to insure that, in general, a trajectory of a double stochastic operator may converge to any interior point of the simplex.
... body. Cancerous cells are also called malignant cells. Causes Cancer grows out of cells in the body. Normal ... of many cancers remains unknown. The most common cause of cancer-related death is lung cancer. In the U.S., ...
NASA Astrophysics Data System (ADS)
Venturi, Daniele
2005-11-01
Stochastic bifurcations and stability of natural convective flows in 2d and 3d enclosures are investigated by the multi-element generalized polynomial chaos (ME-gPC) method (Xiu and Karniadakis, SISC, vol. 24, 2002). The Boussinesq approximation for the variation of physical properties is assumed. The stability analysis is first carried out in a deterministic sense, to determine steady state solutions and primary and secondary bifurcations. Stochastic simulations are then conducted around discontinuities and transitional regimes. It is found that these highly non-linear phenomena can be efficiently captured by the ME-gPC method. Finally, the main findings of the stochastic analysis and their implications for heat transfer will be discussed.
Stochastic Feedforward Control Technique
NASA Technical Reports Server (NTRS)
Halyo, Nesim
1990-01-01
Class of commanded trajectories modeled as stochastic process. Advanced Transport Operating Systems (ATOPS) research and development program conducted by NASA Langley Research Center aimed at developing capabilities for increases in capacities of airports, safe and accurate flight in adverse weather conditions including shear, winds, avoidance of wake vortexes, and reduced consumption of fuel. Advances in techniques for design of modern controls and increased capabilities of digital flight computers coupled with accurate guidance information from Microwave Landing System (MLS). Stochastic feedforward control technique developed within context of ATOPS program.
NASA Astrophysics Data System (ADS)
Pierret, Frédéric
2016-02-01
We derived the equations of Celestial Mechanics governing the variation of the orbital elements under a stochastic perturbation, thereby generalizing the classical Gauss equations. Explicit formulas are given for the semimajor axis, the eccentricity, the inclination, the longitude of the ascending node, the pericenter angle, and the mean anomaly, which are expressed in term of the angular momentum vector H per unit of mass and the energy E per unit of mass. Together, these formulas are called the stochastic Gauss equations, and they are illustrated numerically on an example from satellite dynamics.
Stochastic modeling of rainfall
Guttorp, P.
1996-12-31
We review several approaches in the literature for stochastic modeling of rainfall, and discuss some of their advantages and disadvantages. While stochastic precipitation models have been around at least since the 1850`s, the last two decades have seen an increased development of models based (more or less) on the physical processes involved in precipitation. There are interesting questions of scale and measurement that pertain to these modeling efforts. Recent modeling efforts aim at including meteorological variables, and may be useful for regional down-scaling of general circulation models.
STOCHASTIC COOLING FOR BUNCHED BEAMS.
BLASKIEWICZ, M.
2005-05-16
Problems associated with bunched beam stochastic cooling are reviewed. A longitudinal stochastic cooling system for RHIC is under construction and has been partially commissioned. The state of the system and future plans are discussed.
Stochastic entrainment of a stochastic oscillator.
Wang, Guanyu; Peskin, Charles S
2015-11-01
In this work, we consider a stochastic oscillator described by a discrete-state continuous-time Markov chain, in which the states are arranged in a circle, and there is a constant probability per unit time of jumping from one state to the next in a specified direction around the circle. At each of a sequence of equally spaced times, the oscillator has a specified probability of being reset to a particular state. The focus of this work is the entrainment of the oscillator by this periodic but stochastic stimulus. We consider a distinguished limit, in which (i) the number of states of the oscillator approaches infinity, as does the probability per unit time of jumping from one state to the next, so that the natural mean period of the oscillator remains constant, (ii) the resetting probability approaches zero, and (iii) the period of the resetting signal approaches a multiple, by a ratio of small integers, of the natural mean period of the oscillator. In this distinguished limit, we use analytic and numerical methods to study the extent to which entrainment occurs. PMID:26651734
Stochastic Models of Human Growth.
ERIC Educational Resources Information Center
Goodrich, Robert L.
Stochastic difference equations of the Box-Jenkins form provide an adequate family of models on which to base the stochastic theory of human growth processes, but conventional time series identification methods do not apply to available data sets. A method to identify structure and parameters of stochastic difference equation models of human…
Tollestrup, A.V.; Dugan, G
1983-12-01
Major headings in this review include: proton sources; antiproton production; antiproton sources and Liouville, the role of the Debuncher; transverse stochastic cooling, time domain; the accumulator; frequency domain; pickups and kickers; Fokker-Planck equation; calculation of constants in the Fokker-Planck equation; and beam feedback. (GHT)
Focus on stochastic thermodynamics
NASA Astrophysics Data System (ADS)
Van den Broeck, Christian; Sasa, Shin-ichi; Seifert, Udo
2016-02-01
We introduce the thirty papers collected in this ‘focus on’ issue. The contributions explore conceptual issues within and around stochastic thermodynamics, use this framework for the theoretical modeling and experimental investigation of specific systems, and provide further perspectives on and for this active field.
2-stage revision of 120 deep infected hip and knee prostheses using gentamicin-PMMA beads.
Janssen, Daniël M C; Geurts, Jan A P; Jütten, Liesbeth M C; Walenkamp, Geert H I M
2016-08-01
Background and purpose - A 2-stage revision is the most common treatment for late deep prosthesis-related infections and in all cases of septic loosening. However, there is no consensus about the optimal interval between the 2 stages. Patients and methods - We retrospectively studied 120 deep infections of total hip (n = 95) and knee (n = 25) prostheses that had occurred over a period of 25 years. The mean follow-up time was 5 (2-20) years. All infections had been treated with extraction, 1 or more debridements with systemic antibiotics, and implantation of gentamicin-PMMA beads. There had been different time intervals between extraction and reimplantation: median 14 (11-47) days for short-term treatment with uninterrupted hospital stay, and 7 (3-22) months for long-term treatment with temporary discharge. We analyzed the outcome regarding resolution of the infection and clinical results. Results - 88% (105/120) of the infections healed, with no difference in healing rate between short- and long-term treatment. 82 prostheses were reimplanted. In the most recent decade, we treated patients more often with a long-term treatment but reduced the length of time between the extraction and the reimplantation. More reimplantations were performed in long-term treatments than in short-term treatments, despite more having difficult-to-treat infections with worse soft-tissue condition. Interpretation - Patient, wound, and infection considerations resulted in an individualized treatment with different intervals between stages. The 2-stage revision treatment in combination with local gentamicin-PMMA beads gave good results even with difficult prosthesis infections and gentamicin-resistant bacteria. PMID:26822990
Adaptive stochastic cellular automata: Applications
NASA Astrophysics Data System (ADS)
Qian, S.; Lee, Y. C.; Jones, R. D.; Barnes, C. W.; Flake, G. W.; O'Rourke, M. K.; Lee, K.; Chen, H. H.; Sun, G. Z.; Zhang, Y. Q.; Chen, D.; Giles, C. L.
1990-09-01
The stochastic learning cellular automata model has been applied to the problem of controlling unstable systems. Two example unstable systems studied are controlled by an adaptive stochastic cellular automata algorithm with an adaptive critic. The reinforcement learning algorithm and the architecture of the stochastic CA controller are presented. Learning to balance a single pole is discussed in detail. Balancing an inverted double pendulum highlights the power of the stochastic CA approach. The stochastic CA model is compared to conventional adaptive control and artificial neural network approaches.
Stochastic computing with biomolecular automata
NASA Astrophysics Data System (ADS)
Adar, Rivka; Benenson, Yaakov; Linshiz, Gregory; Rosner, Amit; Tishby, Naftali; Shapiro, Ehud
2004-07-01
Stochastic computing has a broad range of applications, yet electronic computers realize its basic step, stochastic choice between alternative computation paths, in a cumbersome way. Biomolecular computers use a different computational paradigm and hence afford novel designs. We constructed a stochastic molecular automaton in which stochastic choice is realized by means of competition between alternative biochemical pathways, and choice probabilities are programmed by the relative molar concentrations of the software molecules coding for the alternatives. Programmable and autonomous stochastic molecular automata have been shown to perform direct analysis of disease-related molecular indicators in vitro and may have the potential to provide in situ medical diagnosis and cure.
Ishibashi, Tomoko; Ishikawa, Seiji; Suzuki, Akiko; Miyawaki, Yutaka; Kawano, Tatsuyuki; Makita, Koshi
2016-02-15
Tracheogastric tube fistulas are rare but fatal complications after esophagectomy. Anesthetic management for a patient with this complication is challenging because air leakage and mechanical ventilation may cause aspiration. We present a case report of the anesthetic management of a patient having 2-stage surgical repair combined with endoscopic mucosal resection for a giant carinal tracheogastric tube fistula. The first stage was separation of the gastric tube above the fistula with spontaneous breathing under local anesthesia and sedation. The second stage was complete separation and reconstruction of the digestive tract under epidural and general anesthesia with spontaneous breathing and pressure support before insertion of a decompression tube. PMID:26862719
Stochastic response surface methodology: A study in the human health area
Oliveira, Teresa A. Oliveira, Amílcar; Leal, Conceição
2015-03-10
In this paper we review Stochastic Response Surface Methodology as a tool for modeling uncertainty in the context of Risk Analysis. An application in the survival analysis in the breast cancer context is implemented with R software.
Aerodynamic characteristics of the National Launch System (NLS) 1 1/2 stage launch vehicle
NASA Technical Reports Server (NTRS)
Springer, A. M.; Pokora, D. C.
1994-01-01
The National Aeronautics and Space Administration (NASA) is studying ways of assuring more reliable and cost effective means to space. One launch system studied was the NLS which included the l l/2 stage vehicle. This document encompasses the aerodynamic characteristics of the 1 l/2 stage vehicle. To support the detailed configuration definition two wind tunnel tests were conducted in the NASA Marshall Space Flight Center's 14x14-Inch Trisonic Wind Tunnel during 1992. The tests were a static stability and a pressure test, each utilizing 0.004 scale models. The static stability test resulted in the forces and moments acting on the vehicle. The aerodynamics for the reference configuration with and without feedlines and an evaluation of three proposed engine shroud configurations were also determined. The pressure test resulted in pressure distributions over the reference vehicle with and without feedlines including the reference engine shrouds. These pressure distributions were integrated and balanced to the static stability coefficients resulting in distributed aerodynamic loads on the vehicle. The wind tunnel tests covered a Mach range of 0.60 to 4.96. These ascent flight aerodynamic characteristics provide the basis for trajectory and performance analysis, loads determination, and guidance and control evaluation.
Stochastic ice stream dynamics
NASA Astrophysics Data System (ADS)
Mantelli, Elisa; Bertagni, Matteo Bernard; Ridolfi, Luca
2016-08-01
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.
VAWT stochastic wind simulator
Strickland, J.H.
1987-04-01
A stochastic wind simulation for VAWTs (VSTOC) has been developed which yields turbulent wind-velocity fluctuations for rotationally sampled points. This allows three-component wind-velocity fluctuations to be simulated at specified nodal points on the wind-turbine rotor. A first-order convection scheme is used which accounts for the decrease in streamwise velocity as the flow passes through the wind-turbine rotor. The VSTOC simulation is independent of the particular analytical technique used to predict the aerodynamic and performance characteristics of the turbine. The VSTOC subroutine may be used simply as a subroutine in a particular VAWT prediction code or it may be used as a subroutine in an independent processor. The independent processor is used to interact with a version of the VAWT prediction code which is segmented into deterministic and stochastic modules. Using VSTOC in this fashion is very efficient with regard to decreasing computer time for the overall calculation process.
BLASKIEWICZ,M.BRENNAN,J.M.CAMERON,P.WEI,J.
2003-05-12
Emittance growth due to Intra-Beam Scattering significantly reduces the heavy ion luminosity lifetime in RHIC. Stochastic cooling of the stored beam could improve things considerably by counteracting IBS and preventing particles from escaping the rf bucket [1]. High frequency bunched-beam stochastic cooling is especially challenging but observations of Schottky signals in the 4-8 GHz band indicate that conditions are favorable in RHIC [2]. We report here on measurements of the longitudinal beam transfer function carried out with a pickup kicker pair on loan from FNAL TEVATRON. Results imply that for ions a coasting beam description is applicable and we outline some general features of a viable momentum cooling system for RHIC.
Samuelson, P A
1971-02-01
Because a commodity like wheat can be carried forward from one period to the next, speculative arbitrage serves to link its prices at different points of time. Since, however, the size of the harvest depends on complicated probability processes impossible to forecast with certainty, the minimal model for understanding market behavior must involve stochastic processes. The present study, on the basis of the axiom that it is the expected rather than the known-for-certain prices which enter into all arbitrage relations and carryover decisions, determines the behavior of price as the solution to a stochastic-dynamic-programming problem. The resulting stationary time series possesses an ergodic state and normative properties like those often observed for real-world bourses. PMID:16591903
Stochastic ice stream dynamics.
Mantelli, Elisa; Bertagni, Matteo Bernard; Ridolfi, Luca
2016-08-01
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
Dorogovtsev, Andrei A
2010-06-29
For sets in a Hilbert space the concept of quadratic entropy is introduced. It is shown that this entropy is finite for the range of a stochastic flow of Brownian particles on R. This implies, in particular, the fact that the total time of the free travel in the Arratia flow of all particles that started from a bounded interval is finite. Bibliography: 10 titles.
Ultimate open pit stochastic optimization
NASA Astrophysics Data System (ADS)
Marcotte, Denis; Caron, Josiane
2013-02-01
Classical open pit optimization (maximum closure problem) is made on block estimates, without directly considering the block grades uncertainty. We propose an alternative approach of stochastic optimization. The stochastic optimization is taken as the optimal pit computed on the block expected profits, rather than expected grades, computed from a series of conditional simulations. The stochastic optimization generates, by construction, larger ore and waste tonnages than the classical optimization. Contrary to the classical approach, the stochastic optimization is conditionally unbiased for the realized profit given the predicted profit. A series of simulated deposits with different variograms are used to compare the stochastic approach, the classical approach and the simulated approach that maximizes expected profit among simulated designs. Profits obtained with the stochastic optimization are generally larger than the classical or simulated pit. The main factor controlling the relative gain of stochastic optimization compared to classical approach and simulated pit is shown to be the information level as measured by the boreholes spacing/range ratio. The relative gains of the stochastic approach over the classical approach increase with the treatment costs but decrease with mining costs. The relative gains of the stochastic approach over the simulated pit approach increase both with the treatment and mining costs. At early stages of an open pit project, when uncertainty is large, the stochastic optimization approach appears preferable to the classical approach or the simulated pit approach for fair comparison of the values of alternative projects and for the initial design and planning of the open pit.
Quantum Spontaneous Stochasticity
NASA Astrophysics Data System (ADS)
Drivas, Theodore; Eyink, Gregory
Classical Newtonian dynamics is expected to be deterministic, but recent fluid turbulence theory predicts that a particle advected at high Reynolds-numbers by ''nearly rough'' flows moves nondeterministically. Small stochastic perturbations to the flow velocity or to the initial data lead to persistent randomness, even in the limit where the perturbations vanish! Such ``spontaneous stochasticity'' has profound consequences for astrophysics, geophysics, and our daily lives. We show that a similar effect occurs with a quantum particle in a ''nearly rough'' force, for the semi-classical (large-mass) limit, where spreading of the wave-packet is usually expected to be negligible and dynamics to be deterministic Newtonian. Instead, there are non-zero probabilities to observe multiple, non-unique solutions of the classical equations. Although the quantum wave-function remains split, rapid phase oscillations prevent any coherent superposition of the branches. Classical spontaneous stochasticity has not yet been seen in controlled laboratory experiments of fluid turbulence, but the corresponding quantum effects may be observable by current techniques. We suggest possible experiments with neutral atomic-molecular systems in repulsive electric dipole potentials.
... your life Being exposed to chemicals that can cause cancer Being at risk for skin cancer Depending on ... than nonsmokers. Other forms of tobacco can also cause cancer, such as cigars, chewing tobacco and snuff. If ...
Qualitatively stability of nonstandard 2-stage explicit Runge-Kutta methods of order two
NASA Astrophysics Data System (ADS)
Khalsaraei, M. M.; Khodadosti, F.
2016-02-01
When one solves differential equations, modeling physical phenomena, it is of great importance to take physical constraints into account. More precisely, numerical schemes have to be designed such that discrete solutions satisfy the same constraints as exact solutions. Nonstandard finite differences (NSFDs) schemes can improve the accuracy and reduce computational costs of traditional finite difference schemes. In addition NSFDs produce numerical solutions which also exhibit essential properties of solution. In this paper, a class of nonstandard 2-stage Runge-Kutta methods of order two (we call it nonstandard RK2) is considered. The preservation of some qualitative properties by this class of methods are discussed. In order to illustrate our results, we provide some numerical examples.
Unsteady hot streak simulation through a 1-1/2 stage turbine engine
NASA Astrophysics Data System (ADS)
Takahashi, R. K.; Ni, R. H.
1991-06-01
The temperature redistribution process in a 1-1/2 stage turbine (consisting of a first stator, first rotor, and second stator) was analyzed using an unsteady 3D Euler flow solver. The study concentrated on tracking a hot streak from the inlet of the first stator to the exit of the second stator. The redistribution of the hot streak in the second stator passage was very different from that in the rotor passage, with no signs of temperature segregation in the second stator passage, and with rotor-generated vortices which persist through the second stator passage and partake in redistributing the remains of the hot streak. The unsteady code predicts different time-averaged temperatures and secondary flow in the second stator passage than in the steady multistage code, although the steady code may be sufficient for predicting time-averaged pressure loadings on both rotor and second stator airfoils, and time-averaged secondary flow vortices in the rotor passage.
The contemporary role of 1 vs. 2-stage repair for proximal hypospadias
Dason, Shawn; Wong, Nathan
2014-01-01
This review discusses the most commonly employed techniques in the repair of proximal hypospadias, highlighting the advantages and disadvantages of single versus staged surgical techniques. Hypospadias can have a spectrum of severity with a urethral meatus ranging from the perineum to the glans. Associated abnormalities are commonly found with proximal hypospadias and encompass a large spectrum, including ventral curvature (VC) up to 50 degrees or more, ventral skin deficiency, a flattened glans, penile torsion and penoscrotal transposition. Our contemporary understanding of hypospadiology is comprised of a foundation built by experts who have described a number of techniques and their outcomes, combined with survey data detailing practice patterns. The two largest components of hypospadias repair include repair of VC and urethroplasty. VC greater than 20 degrees is considered clinically relevant to warrant surgical correction. To repair VC, the penis is first degloved—a procedure that may reduce or remove curvature by itself in some cases. Residual curvature is then repaired with dorsal plication techniques, transection of the urethral plate, and/or ventral lengthening techniques. Urethroplasty takes the form of 1- or 2-stage repairs. One-stage options include the tubularized incised urethroplasty (TIP) or various graft or flap-based techniques. Two-stage options also include grafts or flaps, including oral mucosal and preputial skin grafting. One stage repairs are an attractive option in that they may reduce cost, hospital stay, anesthetic risks, and time to the final result. The downside is that these repairs require mastery of multiple techniques may be more complex, and—depending on technique—have higher complication rates. Two-stage repairs are preferred by the majority of surveyed hypospadiologists. The 2-stage repair is versatile and has satisfactory outcomes, but necessitates a second procedure. Given the lack of clear high-quality evidence
A retrodictive stochastic simulation algorithm
Vaughan, T.G. Drummond, P.D.; Drummond, A.J.
2010-05-20
In this paper we describe a simple method for inferring the initial states of systems evolving stochastically according to master equations, given knowledge of the final states. This is achieved through the use of a retrodictive stochastic simulation algorithm which complements the usual predictive stochastic simulation approach. We demonstrate the utility of this new algorithm by applying it to example problems, including the derivation of likely ancestral states of a gene sequence given a Markovian model of genetic mutation.
Stochastic calculus in physics
Fox, R.F.
1987-03-01
The relationship of Ito-Stratonovich stochastic calculus to studies of weakly colored noise is explained. A functional calculus approach is used to obtain an effective Fokker-Planck equation for the weakly colored noise regime. In a smooth limit, this representation produces the Stratonovich version of the Ito-Stratonovich calculus for white noise. It also provides an approach to steady state behavior for strongly colored noise. Numerical simulation algorithms are explored, and a novel suggestion is made for efficient and accurate simulation of white noise equations.
Stochastic ontogenetic growth model
NASA Astrophysics Data System (ADS)
West, B. J.; West, D.
2012-02-01
An ontogenetic growth model (OGM) for a thermodynamically closed system is generalized to satisfy both the first and second law of thermodynamics. The hypothesized stochastic ontogenetic growth model (SOGM) is shown to entail the interspecies allometry relation by explicitly averaging the basal metabolic rate and the total body mass over the steady-state probability density for the total body mass (TBM). This is the first derivation of the interspecies metabolic allometric relation from a dynamical model and the asymptotic steady-state distribution of the TBM is fit to data and shown to be inverse power law.
Stochastic thermodynamics of resetting
NASA Astrophysics Data System (ADS)
Fuchs, Jaco; Goldt, Sebastian; Seifert, Udo
2016-03-01
Stochastic dynamics with random resetting leads to a non-equilibrium steady state. Here, we consider the thermodynamics of resetting by deriving the first and second law for resetting processes far from equilibrium. We identify the contributions to the entropy production of the system which arise due to resetting and show that they correspond to the rate with which information is either erased or created. Using Landauer's principle, we derive a bound on the amount of work that is required to maintain a resetting process. We discuss different regimes of resetting, including a Maxwell demon scenario where heat is extracted from a bath at constant temperature.
Stochastic power flow modeling
Not Available
1980-06-01
The stochastic nature of customer demand and equipment failure on large interconnected electric power networks has produced a keen interest in the accurate modeling and analysis of the effects of probabilistic behavior on steady state power system operation. The principle avenue of approach has been to obtain a solution to the steady state network flow equations which adhere both to Kirchhoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques. Clearly the need of the present is to develop sound techniques for producing meaningful data to serve as input. This research has addressed this end and serves to bridge the gap between electric demand modeling, equipment failure analysis, etc., and the area of algorithm development. Therefore, the scope of this work lies squarely on developing an efficient means of producing sensible input information in the form of probability distributions for the many types of solution algorithms that have been developed. Two major areas of development are described in detail: a decomposition of stochastic processes which gives hope of stationarity, ergodicity, and perhaps even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.
Stochastic blind motion deblurring.
Xiao, Lei; Gregson, James; Heide, Felix; Heidrich, Wolfgang
2015-10-01
Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can, therefore, only be obtained with the help of prior information in the form of (often nonconvex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with Peak Signal-to-Noise Ratio (PSNR) values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms. PMID:25974941
Stochastic Quantum Gas Dynamics
NASA Astrophysics Data System (ADS)
Proukakis, Nick P.; Cockburn, Stuart P.
2010-03-01
We study the dynamics of weakly-interacting finite temperature Bose gases via the Stochastic Gross-Pitaevskii equation (SGPE). As a first step, we demonstrate [jointly with A. Negretti (Ulm, Germany) and C. Henkel (Potsdam, Germany)] that the SGPE provides a significantly better method for generating an equilibrium state than the number-conserving Bogoliubov method (except for low temperatures and small atom numbers). We then study [jointly with H. Nistazakis and D.J. Frantzeskakis (University of Athens, Greece), P.G.Kevrekidis (University of Massachusetts) and T.P. Horikis (University of Ioannina, Greece)] the dynamics of dark solitons in elongated finite temperature condensates. We demonstrate numerical shot-to-shot variations in soliton trajectories (S.P. Cockburn et al., arXiv:0909.1660.), finding individual long-lived trajectories as in experiments. In our simulations, these variations arise from fluctuations in the phase and density of the underlying medium. We provide a detailed statistical analysis, proposing regimes for the controlled experimental demonstration of this effect; we also discuss the extent to which simpler models can be used to mimic the features of ensemble-averaged stochastic trajectories.
NASA Technical Reports Server (NTRS)
Whitney, W. J.; Behning, F. P.; Moffitt, T. P.; Hotz, G. M.
1980-01-01
The stage group performance of a 4 1/2 stage turbine with an average stage loading factor of 4.66 and high specific work output was determined in cold air at design equivalent speed. The four stage turbine configuration produced design equivalent work output with an efficiency of 0.856; a barely discernible difference from the 0.855 obtained for the complete 4 1/2 stage turbine in a previous investigation. The turbine was designed and the procedure embodied the following design features: (1) controlled vortex flow, (2) tailored radial work distribution, and (3) control of the location of the boundary-layer transition point on the airfoil suction surface. The efficiency forecast for the 4 1/2 stage turbine was 0.886, and the value predicted using a reference method was 0.862. The stage group performance results were used to determine the individual stage efficiencies for the condition at which design 4 1/2 stage work output was obtained. The efficiencies of stages one and four were about 0.020 lower than the predicted value, that of stage two was 0.014 lower, and that of stage three was about equal to the predicted value. Thus all the stages operated reasonably close to their expected performance levels, and the overall (4 1/2 stage) performance was not degraded by any particularly inefficient component.
Variance decomposition in stochastic simulators
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Richard V. Field, Jr.; Emery, John M.; Grigoriu, Mircea Dan
2015-05-19
The stochastic collocation (SC) and stochastic Galerkin (SG) methods are two well-established and successful approaches for solving general stochastic problems. A recently developed method based on stochastic reduced order models (SROMs) can also be used. Herein we provide a comparison of the three methods for some numerical examples; our evaluation only holds for the examples considered in the paper. The purpose of the comparisons is not to criticize the SC or SG methods, which have proven very useful for a broad range of applications, nor is it to provide overall ratings of these methods as compared to the SROM method.more » Furthermore, our objectives are to present the SROM method as an alternative approach to solving stochastic problems and provide information on the computational effort required by the implementation of each method, while simultaneously assessing their performance for a collection of specific problems.« less
Richard V. Field, Jr.; Emery, John M.; Grigoriu, Mircea Dan
2015-05-19
The stochastic collocation (SC) and stochastic Galerkin (SG) methods are two well-established and successful approaches for solving general stochastic problems. A recently developed method based on stochastic reduced order models (SROMs) can also be used. Herein we provide a comparison of the three methods for some numerical examples; our evaluation only holds for the examples considered in the paper. The purpose of the comparisons is not to criticize the SC or SG methods, which have proven very useful for a broad range of applications, nor is it to provide overall ratings of these methods as compared to the SROM method. Furthermore, our objectives are to present the SROM method as an alternative approach to solving stochastic problems and provide information on the computational effort required by the implementation of each method, while simultaneously assessing their performance for a collection of specific problems.
Variance decomposition in stochastic simulators
NASA Astrophysics Data System (ADS)
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Unsteady Aero Computation of a 1 1/2 Stage Large Scale Rotating Turbine
NASA Technical Reports Server (NTRS)
To, Wai-Ming
2012-01-01
This report is the documentation of the work performed for the Subsonic Rotary Wing Project under the NASA s Fundamental Aeronautics Program. It was funded through Task Number NNC10E420T under GESS-2 Contract NNC06BA07B in the period of 10/1/2010 to 8/31/2011. The objective of the task is to provide support for the development of variable speed power turbine technology through application of computational fluid dynamics analyses. This includes work elements in mesh generation, multistage URANS simulations, and post-processing of the simulation results for comparison with the experimental data. The unsteady CFD calculations were performed with the TURBO code running in multistage single passage (phase lag) mode. Meshes for the blade rows were generated with the NASA developed TCGRID code. The CFD performance is assessed and improvements are recommended for future research in this area. For that, the United Technologies Research Center's 1 1/2 stage Large Scale Rotating Turbine was selected to be the candidate engine configuration for this computational effort because of the completeness and availability of the data.
A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game
NASA Astrophysics Data System (ADS)
Fang, Xiang-Sheng; Zhu, Ping; Liu, Run-Ran; Liu, En-Yu; Wei, Gui-Yi
2012-10-01
In this study, we propose a spatial prisoner's dilemma game model with a 2-stage strategy updating rule, and focus on the cooperation behavior of the system. In the first stage, i.e., the pre-learning stage, a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors. If the player makes up his mind to update, he enters into the second stage, i.e., the learning stage, and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule. The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor. Generally, the cooperation frequency decreases as the pre-learning factor increases; but a high cooperation level can be obtained in the intermediate region of -3 < β < -1. We then give some explanations via studying the co-action of pre-learning and learning. Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.
Stochastic models of gene expression and post-transcriptional regulation
NASA Astrophysics Data System (ADS)
Pendar, Hodjat; Kulkarni, Rahul; Jia, Tao
2011-10-01
The intrinsic stochasticity of gene expression can give rise to phenotypic heterogeneity in a population of genetically identical cells. Correspondingly, there is considerable interest in understanding how different molecular mechanisms impact the 'noise' in gene expression. Of particular interest are post-transcriptional regulatory mechanisms involving genes called small RNAs, which control important processes such as development and cancer. We propose and analyze general stochastic models of gene expression and derive exact analytical expressions quantifying the noise in protein distributions [1]. Focusing on specific regulatory mechanisms, we analyze a general model for post-transcriptional regulation of stochastic gene expression [2]. The results obtained provide new insights into the role of post-transcriptional regulation in controlling the noise in gene expression. [4pt] [1] T. Jia and R. V. Kulkarni, Phys. Rev. Lett.,106, 058102 (2011) [0pt] [2] T. Jia and R. V. Kulkarni, Phys. Rev. Lett., 105, 018101 (2010)
NASA Astrophysics Data System (ADS)
Umut Caglar, Mehmet; Pal, Ranadip
2012-10-01
Biological systems are inherently stochastic such that they require the use of probabilistic models to understand and simulate their behaviors. However, stochastic models are extremely complex and computationally expensive which restricts their application to smaller order systems. Probabilistic modeling of larger systems can help to recognize the underlying mechanisms of complex diseases, including cancer. The fine-scale stochastic behavior of genetic regulatory networks is often modeled using stochastic master equations. The inherently high computational complexity of the stochastic master equation simulation presents a challenge in its application to biological system modeling even when the model parameters can be properly estimated. In this article, we present a new approach to stochastic model simulation based on Kronecker product analysis and approximation of Zassenhaus formula for matrix exponentials. Simulation results illustrate the comparative performance of our modeling approach to stochastic master equations with significantly lower computational complexity. We also provide a stochastic upper bound on the deviation of the steady state distribution of our model from the steady state distribution of the stochastic master equation.
Cancer begins in your cells, which are the building blocks of your body. Normally, your body forms ... be benign or malignant. Benign tumors aren't cancer while malignant ones are. Cells from malignant tumors ...
Biochemical simulations: stochastic, approximate stochastic and hybrid approaches
2009-01-01
Computer simulations have become an invaluable tool to study the sometimes counterintuitive temporal dynamics of (bio-)chemical systems. In particular, stochastic simulation methods have attracted increasing interest recently. In contrast to the well-known deterministic approach based on ordinary differential equations, they can capture effects that occur due to the underlying discreteness of the systems and random fluctuations in molecular numbers. Numerous stochastic, approximate stochastic and hybrid simulation methods have been proposed in the literature. In this article, they are systematically reviewed in order to guide the researcher and help her find the appropriate method for a specific problem. PMID:19151097
Stochastic reconstruction of sandstones
Manwart; Torquato; Hilfer
2000-07-01
A simulated annealing algorithm is employed to generate a stochastic model for a Berea sandstone and a Fontainebleau sandstone, with each a prescribed two-point probability function, lineal-path function, and "pore size" distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples. PMID:11088546
RES: Regularized Stochastic BFGS Algorithm
NASA Astrophysics Data System (ADS)
Mokhtari, Aryan; Ribeiro, Alejandro
2014-12-01
RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.
A stochastic multi-symplectic scheme for stochastic Maxwell equations with additive noise
Hong, Jialin; Zhang, Liying
2014-07-01
In this paper we investigate a stochastic multi-symplectic method for stochastic Maxwell equations with additive noise. Based on the stochastic version of variational principle, we find a way to obtain the stochastic multi-symplectic structure of three-dimensional (3-D) stochastic Maxwell equations with additive noise. We propose a stochastic multi-symplectic scheme and show that it preserves the stochastic multi-symplectic conservation law and the local and global stochastic energy dissipative properties, which the equations themselves possess. Numerical experiments are performed to verify the numerical behaviors of the stochastic multi-symplectic scheme.
de la Peña-López, Roberto; Remolina-Bonilla, Yuly Andrea
2016-09-01
Cancer is a group of diseases which represents a significant public health problem in Mexico and worldwide. In Mexico neoplasms are the second leading cause of death. An increased morbidity and mortality are expected in the next decades. Several preventable risk factors for cancer development have been identified, the most relevant including tobacco use, which accounts for 30% of the cancer cases; and obesity, associated to another 30%. These factors, in turn, are related to sedentarism, alcohol abuse and imbalanced diets. Some agents are well knokn to cause cancer such as ionizing radiation, viruses such as the papilloma virus (HPV) and hepatitis virus (B and C), and more recently environmental pollution exposure and red meat consumption have been pointed out as carcinogens by the International Agency for Research in Cancer (IARC). The scientific evidence currently available is insufficient to consider milk either as a risk factor or protective factor against different types of cancer. PMID:27603890
Stochastic superparameterization in quasigeostrophic turbulence
Grooms, Ian; Majda, Andrew J.
2014-08-15
In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and vortices. Conventional superparameterization simulates large scale dynamics on a coarse grid in a physical domain, and couples these dynamics to high-resolution simulations on periodic domains embedded in the coarse grid. Stochastic superparameterization replaces the nonlinear, deterministic eddy equations on periodic embedded domains by quasilinear stochastic approximations on formally infinite embedded domains. The result is a seamless algorithm which never uses a small scale grid and is far cheaper than conventional SP, but with significant success in difficult test problems. Various design choices in the algorithm are investigated in detail here, including decoupling the timescale of evolution on the embedded domains from the length of the time step used on the coarse grid, and sensitivity to certain assumed properties of the eddies (e.g. the shape of the assumed eddy energy spectrum). We present four closures based on stochastic superparameterization which elucidate the properties of the underlying framework: a ‘null hypothesis’ stochastic closure that uncouples the eddies from the mean, a stochastic closure with nonlinearly coupled eddies and mean, a nonlinear deterministic closure, and a stochastic closure based on energy conservation. The different algorithms are compared and contrasted on a stringent test suite for quasigeostrophic turbulence involving two-layer dynamics on a β-plane forced by an imposed background shear. The success of the algorithms developed here suggests that they may be fruitfully applied to more realistic situations. They are expected to be particularly useful in providing accurate and
Stochastic roots of growth phenomena
NASA Astrophysics Data System (ADS)
De Lauro, E.; De Martino, S.; De Siena, S.; Giorno, V.
2014-05-01
We show that the Gompertz equation describes the evolution in time of the median of a geometric stochastic process. Therefore, we induce that the process itself generates the growth. This result allows us further to exploit a stochastic variational principle to take account of self-regulation of growth through feedback of relative density variations. The conceptually well defined framework so introduced shows its usefulness by suggesting a form of control of growth by exploiting external actions.
Brennan J. M.; Blaskiewicz, M.; Mernick, K.
2012-05-20
The full 6-dimensional [x,x'; y,y'; z,z'] stochastic cooling system for RHIC was completed and operational for the FY12 Uranium-Uranium collider run. Cooling enhances the integrated luminosity of the Uranium collisions by a factor of 5, primarily by reducing the transverse emittances but also by cooling in the longitudinal plane to preserve the bunch length. The components have been deployed incrementally over the past several runs, beginning with longitudinal cooling, then cooling in the vertical planes but multiplexed between the Yellow and Blue rings, next cooling both rings simultaneously in vertical (the horizontal plane was cooled by betatron coupling), and now simultaneous horizontal cooling has been commissioned. The system operated between 5 and 9 GHz and with 3 x 10{sup 8} Uranium ions per bunch and produces a cooling half-time of approximately 20 minutes. The ultimate emittance is determined by the balance between cooling and emittance growth from Intra-Beam Scattering. Specific details of the apparatus and mathematical techniques for calculating its performance have been published elsewhere. Here we report on: the method of operation, results with beam, and comparison of results to simulations.
A prospective randomized study of 1- and 2-stage sinus inlay bone grafts: 1-year follow-up.
Wannfors, K; Johansson, B; Hallman, M; Strandkvist, T
2000-01-01
The purpose of the present study was to compare the success of and surgical differences between 1- and 2-stage sinus inlay bone grafts and implants after 1 year in function. The individual risk for implant failure in grafted areas among 1-stage patients was about twice the risk in 2-stage patients (odds ratio 2.3, CI 0.6; 8.5). The risk for implant failure in non-grafted areas was significantly lower (P < .05) than in grafted areas, regardless of the technique used. Forty edentulous patients, selected according to strict inclusion criteria from consecutive referrals, were allocated to one or other of the 2 sinus-inlay procedures. Twenty patients received bone blocks fixed by implants to the residual alveolar crest in a 1-stage procedure (group 1). In another 20 patients, particulated bone was condensed against the antral floor and left to heal for 6 months before implants were placed (group 2). An almost equal number of implants was placed in the patients of each group, 76 in the 1-stage procedure and 74 in the 2-stage procedure. Additionally, 72 and 66 implants were placed in the anterior non-grafted regions of group 1 and group 2 patients, respectively. After 1 year in function, a total of 20 implants failed in 1-stage patients, versus 11 in 2-stage patients. Sixteen and 8 implants, respectively, of these were placed in grafted bone. All but one 1-stage patient received the planned fixed prosthetic restorations, but 1 restoration was redesigned after the first year in function because of a functionally unacceptable prosthetic design. At the 1-year follow-up, one 2-stage patient lost her prosthesis as the result of multiple implant failures. Bruxism and postoperative infections were the only parameters that could be related to implant failure, however, depending on the statistical method used. PMID:11055129
Stacking with stochastic cooling
NASA Astrophysics Data System (ADS)
Caspers, Fritz; Möhl, Dieter
2004-10-01
Accumulation of large stacks of antiprotons or ions with the aid of stochastic cooling is more delicate than cooling a constant intensity beam. Basically the difficulty stems from the fact that the optimized gain and the cooling rate are inversely proportional to the number of particles 'seen' by the cooling system. Therefore, to maintain fast stacking, the newly injected batch has to be strongly 'protected' from the Schottky noise of the stack. Vice versa the stack has to be efficiently 'shielded' against the high gain cooling system for the injected beam. In the antiproton accumulators with stacking ratios up to 105 the problem is solved by radial separation of the injection and the stack orbits in a region of large dispersion. An array of several tapered cooling systems with a matched gain profile provides a continuous particle flux towards the high-density stack core. Shielding of the different systems from each other is obtained both through the spatial separation and via the revolution frequencies (filters). In the 'old AA', where the antiproton collection and stacking was done in one single ring, the injected beam was further shielded during cooling by means of a movable shutter. The complexity of these systems is very high. For more modest stacking ratios, one might use azimuthal rather than radial separation of stack and injected beam. Schematically half of the circumference would be used to accept and cool new beam and the remainder to house the stack. Fast gating is then required between the high gain cooling of the injected beam and the low gain stack cooling. RF-gymnastics are used to merge the pre-cooled batch with the stack, to re-create free space for the next injection, and to capture the new batch. This scheme is less demanding for the storage ring lattice, but at the expense of some reduction in stacking rate. The talk reviews the 'radial' separation schemes and also gives some considerations to the 'azimuthal' schemes.
NASA Technical Reports Server (NTRS)
Whitney, W. J.
1977-01-01
The stage work distribution among the three stages was very close to the design value. The specific work output-mass flow characteristics of the three stages were closely matched. The efficiency of the 3 1/2 stage turbine at design specific work output and design speed was within 0.008 of the estimated value, and this agreement was felt to demonstrate the adequacy of the prediction method in the high stage loading factor regime.
A Stochastic Collocation Algorithm for Uncertainty Analysis
NASA Technical Reports Server (NTRS)
Mathelin, Lionel; Hussaini, M. Yousuff; Zang, Thomas A. (Technical Monitor)
2003-01-01
This report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters.
Enhanced algorithms for stochastic programming
Krishna, A.S.
1993-09-01
In this dissertation, we present some of the recent advances made in solving two-stage stochastic linear programming problems of large size and complexity. Decomposition and sampling are two fundamental components of techniques to solve stochastic optimization problems. We describe improvements to the current techniques in both these areas. We studied different ways of using importance sampling techniques in the context of Stochastic programming, by varying the choice of approximation functions used in this method. We have concluded that approximating the recourse function by a computationally inexpensive piecewise-linear function is highly efficient. This reduced the problem from finding the mean of a computationally expensive functions to finding that of a computationally inexpensive function. Then we implemented various variance reduction techniques to estimate the mean of a piecewise-linear function. This method achieved similar variance reductions in orders of magnitude less time than, when we directly applied variance-reduction techniques directly on the given problem. In solving a stochastic linear program, the expected value problem is usually solved before a stochastic solution and also to speed-up the algorithm by making use of the information obtained from the solution of the expected value problem. We have devised a new decomposition scheme to improve the convergence of this algorithm.
Stochastic simulation in systems biology
Székely, Tamás; Burrage, Kevin
2014-01-01
Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity). In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology. There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest. PMID:25505503
Stochastic models: theory and simulation.
Field, Richard V., Jr.
2008-03-01
Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.
Variance decomposition in stochastic simulators.
Le Maître, O P; Knio, O M; Moraes, A
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models. PMID:26133418
NASA Technical Reports Server (NTRS)
Lacksonen, Thomas A.
1994-01-01
Small space flight project design at NASA Langley Research Center goes through a multi-phase process from preliminary analysis to flight operations. The process insures that each system achieves its technical objectives with demonstrated quality and within planned budgets and schedules. A key technical component of early phases is decision analysis, which is a structure procedure for determining the best of a number of feasible concepts based upon project objectives. Feasible system concepts are generated by the designers and analyzed for schedule, cost, risk, and technical measures. Each performance measure value is normalized between the best and worst values and a weighted average score of all measures is calculated for each concept. The concept(s) with the highest scores are retained, while others are eliminated from further analysis. This project automated and enhanced the decision analysis process. Automation of the decision analysis process was done by creating a user-friendly, menu-driven, spreadsheet macro based decision analysis software program. The program contains data entry dialog boxes, automated data and output report generation, and automated output chart generation. The enhancements to the decision analysis process permit stochastic data entry and analysis. Rather than enter single measure values, the designers enter the range and most likely value for each measure and concept. The data can be entered at the system or subsystem level. System level data can be calculated as either sum, maximum, or product functions of the subsystem data. For each concept, the probability distributions are approximated for each measure and the total score for each concept as either constant, triangular, normal, or log-normal distributions. Based on these distributions, formulas are derived for the probability that the concept meets any given constraint, the probability that the concept meets all constraints, and the probability that the concept is within a given
Stochastic determination of matrix determinants.
Dorn, Sebastian; Ensslin, Torsten A
2015-07-01
Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved. Due to currently exploding data volumes, linear operations-matrices-acting on the data are often not accessible directly but are only represented indirectly in form of a computer routine. Such a routine implements the transformation a data vector undergoes under matrix multiplication. While efficient probing routines to estimate a matrix's diagonal or trace, based solely on such computationally affordable matrix-vector multiplications, are well known and frequently used in signal inference, there is no stochastic estimate for its determinant. We introduce a probing method for the logarithm of a determinant of a linear operator. Our method rests upon a reformulation of the log-determinant by an integral representation and the transformation of the involved terms into stochastic expressions. This stochastic determinant determination enables large-size applications in Bayesian inference, in particular evidence calculations, model comparison, and posterior determination. PMID:26274302
Mechanical autonomous stochastic heat engines
NASA Astrophysics Data System (ADS)
Serra-Garcia, Marc; Foehr, Andre; Moleron, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara; . Team
Stochastic heat engines extract work from the Brownian motion of a set of particles out of equilibrium. So far, experimental demonstrations of stochastic heat engines have required extreme operating conditions or nonautonomous external control systems. In this talk, we will present a simple, purely classical, autonomous stochastic heat engine that uses the well-known tension induced nonlinearity in a string. Our engine operates between two heat baths out of equilibrium, and transfers energy from the hot bath to a work reservoir. This energy transfer occurs even if the work reservoir is at a higher temperature than the hot reservoir. The talk will cover a theoretical investigation and experimental results on a macroscopic setup subject to external noise excitations. This system presents an opportunity for the study of non equilibrium thermodynamics and is an interesting candidate for innovative energy conversion devices.
Stochastic Control of Pharmacokinetic Systems
Schumitzky, Alan; Milman, Mark; Katz, Darryl; D'Argenio, David Z.; Jelliffe, Roger W.
1983-01-01
The application of stochastic control theory to the clinical problem of designing a dosage regimen for a pharmacokinetic system is considered. This involves defining a patient-dependent pharmacokinetic model and a clinically appropriate therapeutic goal. Most investigators have attacked the dosage regimen problem by first estimating the values of the patient's unknown model parameters and then controlling the system as if those parameter estimates were in fact the true values. We have developed an alternative approach utilizing stochastic control theory in which the estimation and control phases of the problem are not separated. Mathematical results are given which show that this approach yields significant potential improvement in attaining, for example, therapeutic serum level goals over methods in which estimation and control are separated. Finally, a computer simulation is given for the optimal stochastic control of an aminoglycoside regimen which shows that this approach is feasible for practical applications.
Correlation functions in stochastic inflation
NASA Astrophysics Data System (ADS)
Vennin, Vincent; Starobinsky, Alexei A.
2015-09-01
Combining the stochastic and formalisms, we derive non-perturbative analytical expressions for all correlation functions of scalar perturbations in single-field, slow-roll inflation. The standard, classical formulas are recovered as saddle-point limits of the full results. This yields a classicality criterion that shows that stochastic effects are small only if the potential is sub-Planckian and not too flat. The saddle-point approximation also provides an expansion scheme for calculating stochastic corrections to observable quantities perturbatively in this regime. In the opposite regime, we show that a strong suppression in the power spectrum is generically obtained, and we comment on the physical implications of this effect.
Stochastic determination of matrix determinants
NASA Astrophysics Data System (ADS)
Dorn, Sebastian; Enßlin, Torsten A.
2015-07-01
Matrix determinants play an important role in data analysis, in particular when Gaussian processes are involved. Due to currently exploding data volumes, linear operations—matrices—acting on the data are often not accessible directly but are only represented indirectly in form of a computer routine. Such a routine implements the transformation a data vector undergoes under matrix multiplication. While efficient probing routines to estimate a matrix's diagonal or trace, based solely on such computationally affordable matrix-vector multiplications, are well known and frequently used in signal inference, there is no stochastic estimate for its determinant. We introduce a probing method for the logarithm of a determinant of a linear operator. Our method rests upon a reformulation of the log-determinant by an integral representation and the transformation of the involved terms into stochastic expressions. This stochastic determinant determination enables large-size applications in Bayesian inference, in particular evidence calculations, model comparison, and posterior determination.
Nonlinear optimization for stochastic simulations.
Johnson, Michael M.; Yoshimura, Ann S.; Hough, Patricia Diane; Ammerlahn, Heidi R.
2003-12-01
This report describes research targeting development of stochastic optimization algorithms and their application to mission-critical optimization problems in which uncertainty arises. The first section of this report covers the enhancement of the Trust Region Parallel Direct Search (TRPDS) algorithm to address stochastic responses and the incorporation of the algorithm into the OPT++ optimization library. The second section describes the Weapons of Mass Destruction Decision Analysis Center (WMD-DAC) suite of systems analysis tools and motivates the use of stochastic optimization techniques in such non-deterministic simulations. The third section details a batch programming interface designed to facilitate criteria-based or algorithm-driven execution of system-of-system simulations. The fourth section outlines the use of the enhanced OPT++ library and batch execution mechanism to perform systems analysis and technology trade-off studies in the WMD detection and response problem domain.
QB1 - Stochastic Gene Regulation
Munsky, Brian
2012-07-23
Summaries of this presentation are: (1) Stochastic fluctuations or 'noise' is present in the cell - Random motion and competition between reactants, Low copy, quantization of reactants, Upstream processes; (2) Fluctuations may be very important - Cell-to-cell variability, Cell fate decisions (switches), Signal amplification or damping, stochastic resonances; and (3) Some tools are available to mode these - Kinetic Monte Carlo simulations (SSA and variants), Moment approximation methods, Finite State Projection. We will see how modeling these reactions can tell us more about the underlying processes of gene regulation.
Stochastic kinetic mean field model
NASA Astrophysics Data System (ADS)
Erdélyi, Zoltán; Pasichnyy, Mykola; Bezpalchuk, Volodymyr; Tomán, János J.; Gajdics, Bence; Gusak, Andriy M.
2016-07-01
This paper introduces a new model for calculating the change in time of three-dimensional atomic configurations. The model is based on the kinetic mean field (KMF) approach, however we have transformed that model into a stochastic approach by introducing dynamic Langevin noise. The result is a stochastic kinetic mean field model (SKMF) which produces results similar to the lattice kinetic Monte Carlo (KMC). SKMF is, however, far more cost-effective and easier to implement the algorithm (open source program code is provided on
Stochastic Cooling Developments at GSI
Nolden, F.; Beckert, K.; Beller, P.; Dolinskii, A.; Franzke, B.; Jandewerth, U.; Nesmiyan, I.; Peschke, C.; Petri, P.; Steck, M.; Caspers, F.; Moehl, D.; Thorndahl, L.
2006-03-20
Stochastic Cooling is presently used at the existing storage ring ESR as a first stage of cooling for secondary heavy ion beams. In the frame of the FAIR project at GSI, stochastic cooling is planned to play a major role for the preparation of high quality antiproton and rare isotope beams. The paper describes the existing ESR system, the first stage cooling system at the planned Collector Ring, and will also cover first steps toward the design of an antiproton collection system at the planned RESR ring.
Stochastic modeling of Lagrangian accelerations
NASA Astrophysics Data System (ADS)
Reynolds, Andy
2002-11-01
It is shown how Sawford's second-order Lagrangian stochastic model (Phys. Fluids A 3, 1577-1586, 1991) for fluid-particle accelerations can be combined with a model for the evolution of the dissipation rate (Pope and Chen, Phys. Fluids A 2, 1437-1449, 1990) to produce a Lagrangian stochastic model that is consistent with both the measured distribution of Lagrangian accelerations (La Porta et al., Nature 409, 1017-1019, 2001) and Kolmogorov's similarity theory. The later condition is found not to be satisfied when a constant dissipation rate is employed and consistency with prescribed acceleration statistics is enforced through fulfilment of a well-mixed condition.
Stochastic Optimization of Complex Systems
Birge, John R.
2014-03-20
This project focused on methodologies for the solution of stochastic optimization problems based on relaxation and penalty methods, Monte Carlo simulation, parallel processing, and inverse optimization. The main results of the project were the development of a convergent method for the solution of models that include expectation constraints as in equilibrium models, improvement of Monte Carlo convergence through the use of a new method of sample batch optimization, the development of new parallel processing methods for stochastic unit commitment models, and the development of improved methods in combination with parallel processing for incorporating automatic differentiation methods into optimization.
Some remarks on Nelson's stochastic field
NASA Astrophysics Data System (ADS)
Lim, S. C.
1980-09-01
An attempt to extend Nelson's stochastic quantization procedure to tensor fields indicates that the results of Guerra et al. on the connection between a euclidean Markov scalar field and a stochastic scalar field fails to hold for tensor fields.
Partial ASL extensions for stochastic programming.
2010-03-31
partially completed extensions for stochastic programming to the AMPL/solver interface library (ASL).modeling and experimenting with stochastic recourse problems. This software is not primarily for military applications
Theory, technology, and technique of stochastic cooling
Marriner, J.
1993-10-01
The theory and technological implementation of stochastic cooling is described. Theoretical and technological limitations are discussed. Data from existing stochastic cooling systems are shown to illustrate some useful techniques.
The Hamiltonian Mechanics of Stochastic Acceleration
Burby, J. W.
2013-07-17
We show how to nd the physical Langevin equation describing the trajectories of particles un- dergoing collisionless stochastic acceleration. These stochastic di erential equations retain not only one-, but two-particle statistics, and inherit the Hamiltonian nature of the underlying microscopic equations. This opens the door to using stochastic variational integrators to perform simulations of stochastic interactions such as Fermi acceleration. We illustrate the theory by applying it to two example problems.
Stochastically forced zonal flows
NASA Astrophysics Data System (ADS)
Srinivasan, Kaushik
an approximate equation for the vorticity correlation function that is then solved perturbatively. The Reynolds stress of the pertubative solution can then be expressed as a function of the mean-flow and its y-derivatives. In particular, it is shown that as long as the forcing breaks mirror-symmetry, the Reynolds stress has a wave-like term, as a result of which the mean-flow is governed by a dispersive wave equation. In a separate study, Reynolds stress induced by an anisotropically forced unbounded Couette flow with uniform shear gamma, on a beta-plane, is calculated in conjunction with the eddy diffusivity of a co-evolving passive tracer. The flow is damped by linear drag on a time scale mu--1. The stochastic forcing is controlled by a parameter alpha, that characterizes whether eddies are elongated along the zonal direction (alpha < 0), the meridional direction (alpha > 0) or are isotropic (alpha = 0). The Reynolds stress varies linearly with alpha and non-linearly and non-monotonically with gamma; but the Reynolds stress is independent of beta. For positive values of alpha, the Reynolds stress displays an "anti-frictional" effect (energy is transferred from the eddies to the mean flow) and a frictional effect for negative values of alpha. With gamma = beta =0, the meridional tracer eddy diffusivity is v'2/(2mu), where v' is the meridional eddy velocity. In general, beta and gamma suppress the diffusivity below v'2/(2mu).
Stochastic architecture for Hopfield neural nets
NASA Technical Reports Server (NTRS)
Pavel, Sandy
1992-01-01
An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.
Stability of stochastic switched SIRS models
NASA Astrophysics Data System (ADS)
Meng, Xiaoying; Liu, Xinzhi; Deng, Feiqi
2011-11-01
Stochastic stability problems of a stochastic switched SIRS model with or without distributed time delay are considered. By utilizing the Lyapunov methods, sufficient stability conditions of the disease-free equilibrium are established. Stability conditions about the subsystem of the stochastic switched SIRS systems are also obtained.
Stochastic resonance on a circle
Wiesenfeld, K. ); Pierson, D.; Pantazelou, E.; Dames, C.; Moss, F. )
1994-04-04
We describe a new realization of stochastic resonance, applicable to a broad class of systems, based on an underlying excitable dynamics with deterministic reinjection. A simple but general theory of such single-trigger'' systems is compared with analog simulations of the Fitzhugh-Nagumo model, as well as experimental data obtained from stimulated sensory neurons in the crayfish.
Stochastic-field cavitation model
Dumond, J.; Magagnato, F.; Class, A.
2013-07-15
Nonlinear phenomena can often be well described using probability density functions (pdf) and pdf transport models. Traditionally, the simulation of pdf transport requires Monte-Carlo codes based on Lagrangian “particles” or prescribed pdf assumptions including binning techniques. Recently, in the field of combustion, a novel formulation called the stochastic-field method solving pdf transport based on Eulerian fields has been proposed which eliminates the necessity to mix Eulerian and Lagrangian techniques or prescribed pdf assumptions. In the present work, for the first time the stochastic-field method is applied to multi-phase flow and, in particular, to cavitating flow. To validate the proposed stochastic-field cavitation model, two applications are considered. First, sheet cavitation is simulated in a Venturi-type nozzle. The second application is an innovative fluidic diode which exhibits coolant flashing. Agreement with experimental results is obtained for both applications with a fixed set of model constants. The stochastic-field cavitation model captures the wide range of pdf shapes present at different locations.
Universality in Stochastic Exponential Growth
NASA Astrophysics Data System (ADS)
Iyer-Biswas, Srividya; Crooks, Gavin E.; Scherer, Norbert F.; Dinner, Aaron R.
2014-07-01
Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth.
Stochastic cooling: recent theoretical directions
Bisognano, J.
1983-03-01
A kinetic-equation derivation of the stochastic-cooling Fokker-Planck equation of correlation is introduced to describe both the Schottky spectrum and signal suppression. Generalizations to nonlinear gain and coupling between degrees of freedom are presented. Analysis of bunch beam cooling is included.
Universality in stochastic exponential growth.
Iyer-Biswas, Srividya; Crooks, Gavin E; Scherer, Norbert F; Dinner, Aaron R
2014-07-11
Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth. PMID:25062238
Stochastic Resonance and Information Processing
NASA Astrophysics Data System (ADS)
Nicolis, C.
2014-12-01
A dynamical system giving rise to multiple steady states and subjected to noise and a periodic forcing is analyzed from the standpoint of information theory. It is shown that stochastic resonance has a clearcut signature on information entropy, information transfer and other related quantities characterizing information transduction within the system.
NASA Astrophysics Data System (ADS)
Zhang, Ming
2015-10-01
A theory of 2-stage acceleration of Galactic cosmic rays in supernova remnants is proposed. The first stage is accomplished by the supernova shock front, where a power-law spectrum is established up to a certain cutoff energy. It is followed by stochastic acceleration with compressible waves/turbulence in the downstream medium. With a broad \\propto {k}-2 spectrum for the compressible plasma fluctuations, the rate of stochastic acceleration is constant over a wide range of particle momentum. In this case, the stochastic acceleration process extends the power-law spectrum cutoff energy of Galactic cosmic rays to the knee without changing the spectral slope. This situation happens as long as the rate of stochastic acceleration is faster than 1/5 of the adiabatic cooling rate. A steeper spectrum of compressible plasma fluctuations that concentrate their power in long wavelengths will accelerate cosmic rays to the knee with a small bump before its cutoff in the comic-ray energy spectrum. This theory does not require a strong amplification of the magnetic field in the upstream interstellar medium in order to accelerate cosmic rays to the knee energy.
Algorithmic advances in stochastic programming
Morton, D.P.
1993-07-01
Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.
Stochastic resonance in visual sensitivity.
Kundu, Ajanta; Sarkar, Sandip
2015-04-01
It is well known from psychophysical studies that stochastic resonance, in its simplest threshold paradigm, can be used as a tool to measure the detection sensitivity to fine details in noise contaminated stimuli. In the present manuscript, we report simulation studies conducted in the similar threshold paradigm of stochastic resonance. We have estimated the contrast sensitivity in detecting noisy sine-wave stimuli, with varying area and spatial frequency, as a function of noise strength. In all the cases, the measured sensitivity attained a peak at intermediate noise strength, which indicate the occurrence of stochastic resonance. The peak sensitivity exhibited a strong dependence on area and spatial frequency of the stimulus. We show that the peak contrast sensitivity varies with spatial frequency in a nonmonotonic fashion and the qualitative nature of the sensitivity variation is in good agreement with human contrast sensitivity function. We also demonstrate that the peak sensitivity first increases and then saturates with increasing area, and this result is in line with the results of psychophysical experiments. Additionally, we also show that critical area, denoting the saturation of contrast sensitivity, decreases with spatial frequency and the associated maximum contrast sensitivity varies with spatial frequency in a manner that is consistent with the results of psychophysical experiments. In all the studies, the sensitivities were elevated via a nonlinear filtering operation called stochastic resonance. Because of this nonlinear effect, it was not guaranteed that the sensitivities, estimated at each frequency, would be in agreement with the corresponding results of psychophysical experiments; on the contrary, close agreements were observed between our results and the findings of psychophysical investigations. These observations indicate the utility of stochastic resonance in human vision and suggest that this paradigm can be useful in psychophysical studies
Stochastic scanning multiphoton multifocal microscopy.
Jureller, Justin E; Kim, Hee Y; Scherer, Norbert F
2006-04-17
Multiparticle tracking with scanning confocal and multiphoton fluorescence imaging is increasingly important for elucidating biological function, as in the transport of intracellular cargo-carrying vesicles. We demonstrate a simple rapid-sampling stochastic scanning multifocal multiphoton microscopy (SS-MMM) fluorescence imaging technique that enables multiparticle tracking without specialized hardware at rates 1,000 times greater than conventional single point raster scanning. Stochastic scanning of a diffractive optic generated 10x10 hexagonal array of foci with a white noise driven galvanometer yields a scan pattern that is random yet space-filling. SS-MMM creates a more uniformly sampled image with fewer spatio-temporal artifacts than obtained by conventional or multibeam raster scanning. SS-MMM is verified by simulation and experimentally demonstrated by tracking microsphere diffusion in solution. PMID:19516485
Stochastic Models of Quantum Decoherence
NASA Astrophysics Data System (ADS)
Kennerly, Sam
Suppose a single qubit is repeatedly prepared and evolved under imperfectly-controlled conditions. A drunk model represents uncontrolled interactions on each experimental trial as random or stochastic terms in the qubit's Hamiltonian operator. Time evolution of states is generated by a stochastic differential equation whose sample paths evolve according to the Schrodinger equation. For models with Gaussian white noise which is independent of the qubit's state, the expectation value of the solution obeys a master equation which is identical to the high-temperature limit of the Bloch equation. Drunk models predict that experimental data can appear consistent with decoherence even if qubit states evolve by unitary transformations. Examples are shown in which reversible evolution appears to cause irreversible information loss. This paradox is resolved by distinguishing between the true state of a system and the estimated state inferred from an experimental dataset.
Stochastic thermodynamics with information reservoirs.
Barato, Andre C; Seifert, Udo
2014-10-01
We generalize stochastic thermodynamics to include information reservoirs. Such information reservoirs, which can be modeled as a sequence of bits, modify the second law. For example, work extraction from a system in contact with a single heat bath becomes possible if the system also interacts with an information reservoir. We obtain an inequality, and the corresponding fluctuation theorem, generalizing the standard entropy production of stochastic thermodynamics. From this inequality we can derive an information processing entropy production, which gives the second law in the presence of information reservoirs. We also develop a systematic linear response theory for information processing machines. For a unicyclic machine powered by an information reservoir, the efficiency at maximum power can deviate from the standard value of 1/2. For the case where energy is consumed to erase the tape, the efficiency at maximum erasure rate is found to be 1/2. PMID:25375481
Stochastic background of atmospheric cascades
Wilk, G. ); Wlodarczyk, Z. )
1993-06-15
Fluctuations in the atmospheric cascades developing during the propagation of very high energy cosmic rays through the atmosphere are investigated using stochastic branching model of pure birth process with immigration. In particular, we show that the multiplicity distributions of secondaries emerging from gamma families are much narrower than those resulting from hadronic families. We argue that the strong intermittent like behaviour found recently in atmospheric families results from the fluctuations in the cascades themselves and are insensitive to the details of elementary interactions.
Discrete stability in stochastic programming
Lepp, R.
1994-12-31
In this lecture we study stability properties of stochastic programs with recourse where the probability measure is approximated by a sequence of weakly convergent discrete measures. Such discrete approximation approach gives us a possibility to analyze explicitly the behavior of the second stage correction function. The approach is based on modern functional analytical methods of an approximation of extremum problems in function spaces, especially on the notion of the discrete convergence of vectors to an essentially bounded measurable function.
Stochastic background of atmospheric cascades
NASA Astrophysics Data System (ADS)
Wilk, G.; WŁOdarczyk, Z.
1993-06-01
Fluctuations in the atmospheric cascades developing during the propagation of very high energy cosmic rays through the atmosphere are investigated using stochastic branching model of pure birth process with immigration. In particular, we show that the multiplicity distributions of secondaries emerging from gamma families are much narrower than those resulting from hadronic families. We argue that the strong intermittent like behaviour found recently in atmospheric families results from the fluctuations in the cascades themselves and are insensitive to the details of elementary interactions.
Stochastic cooling technology at Fermilab
NASA Astrophysics Data System (ADS)
Pasquinelli, Ralph J.
2004-10-01
The first antiproton cooling systems were installed and commissioned at Fermilab in 1984-1985. In the interim period, there have been several major upgrades, system improvements, and complete reincarnation of cooling systems. This paper will present some of the technology that was pioneered at Fermilab to implement stochastic cooling systems in both the Antiproton Source and Recycler accelerators. Current performance data will also be presented.
Symmetry and Stochastic Gene Regulation
NASA Astrophysics Data System (ADS)
Ramos, Alexandre F.; Hornos, José E. M.
2007-09-01
Lorentz-like noncompact Lie symmetry SO(2,1) is found in a spin-boson stochastic model for gene expression. The invariant of the algebra characterizes the switch decay to equilibrium. The azimuthal eigenvalue describes the affinity between the regulatory protein and the gene operator site. Raising and lowering operators are constructed and their actions increase or decrease the affinity parameter. The classification of the noise regime of the gene arises from the group theoretical numbers.
Stochastic neural nets and vision
NASA Astrophysics Data System (ADS)
Fall, Thomas C.
1991-03-01
A stochastic neural net shares with the normally defined neural nets the concept that information is processed by a system consisting of a set of nodes (neurons) connected by weighted links (axons). The normal neural net takes in inputs on an initial layer of neurons which fire appropriately; a neuron of the next layer fires depending on the sum of weights of the axons leading to it from fired neurons of the first layer. The stochastic neural net differs in that the neurons are more complex and that the vision activity is a dynamic process. The first layer (viewing layer) of neurons fires stochastically based on the average brightness of the area it sees and then has a refractory period. The viewing layer looks at the image for several clock cycles. The effect is like those photo sensitive sunglasses that darken in bright light. The neurons over the bright areas are most likely in a refractory period (and this can't fire) and the neurons over the dark areas are not. Now if we move the sensing layer with respect to the image so that a portion of the neurons formerly over the dark are now over the bright, they will likely all fire on that first cycle. Thus, on that cycle, one would see a flash from that portion significantly stronger than surrounding regions. Movement the other direction would produce a patch that is darker, but this effect is not as noticeable. These effects are collected in a collection layer. This paper will discuss the use of the stochastic neural net for edge detection and segmentation of some simple images.
Mechanical Autonomous Stochastic Heat Engine.
Serra-Garcia, Marc; Foehr, André; Molerón, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara
2016-07-01
Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using, e.g., thermal cycles implemented in optical traps. However, recent experimental demonstrations of classical stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle and consume more energy than they produce. We present a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir. PMID:27419553
Multiple fields in stochastic inflation
NASA Astrophysics Data System (ADS)
Assadullahi, Hooshyar; Firouzjahi, Hassan; Noorbala, Mahdiyar; Vennin, Vincent; Wands, David
2016-06-01
Stochastic effects in multi-field inflationary scenarios are investigated. A hierarchy of diffusion equations is derived, the solutions of which yield moments of the numbers of inflationary e-folds. Solving the resulting partial differential equations in multi-dimensional field space is more challenging than the single-field case. A few tractable examples are discussed, which show that the number of fields is, in general, a critical parameter. When more than two fields are present for instance, the probability to explore arbitrarily large-field regions of the potential, otherwise inaccessible to single-field dynamics, becomes non-zero. In some configurations, this gives rise to an infinite mean number of e-folds, regardless of the initial conditions. Another difference with respect to single-field scenarios is that multi-field stochastic effects can be large even at sub-Planckian energy. This opens interesting new possibilities for probing quantum effects in inflationary dynamics, since the moments of the numbers of e-folds can be used to calculate the distribution of primordial density perturbations in the stochastic-δ N formalism.
Mechanical Autonomous Stochastic Heat Engine
NASA Astrophysics Data System (ADS)
Serra-Garcia, Marc; Foehr, André; Molerón, Miguel; Lydon, Joseph; Chong, Christopher; Daraio, Chiara
2016-07-01
Stochastic heat engines are devices that generate work from random thermal motion using a small number of highly fluctuating degrees of freedom. Proposals for such devices have existed for more than a century and include the Maxwell demon and the Feynman ratchet. Only recently have they been demonstrated experimentally, using, e.g., thermal cycles implemented in optical traps. However, recent experimental demonstrations of classical stochastic heat engines are nonautonomous, since they require an external control system that prescribes a heating and cooling cycle and consume more energy than they produce. We present a heat engine consisting of three coupled mechanical resonators (two ribbons and a cantilever) subject to a stochastic drive. The engine uses geometric nonlinearities in the resonating ribbons to autonomously convert a random excitation into a low-entropy, nonpassive oscillation of the cantilever. The engine presents the anomalous heat transport property of negative thermal conductivity, consisting in the ability to passively transfer energy from a cold reservoir to a hot reservoir.
AESS: Accelerated Exact Stochastic Simulation
NASA Astrophysics Data System (ADS)
Jenkins, David D.; Peterson, Gregory D.
2011-12-01
The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summaryProgram title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution
2-Stage Classification Modeling
1994-11-01
CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using themore » most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.« less
Long time behaviour of a stochastic nanoparticle
NASA Astrophysics Data System (ADS)
Étoré, Pierre; Labbé, Stéphane; Lelong, Jérôme
2014-09-01
In this article, we are interested in the behaviour of a single ferromagnetic mono-domain particle submitted to an external field with a stochastic perturbation. This model is the first step toward the mathematical understanding of thermal effects on a ferromagnet. In a first part, we present the stochastic model and prove that the associated stochastic differential equation is well defined. The second part is dedicated to the study of the long time behaviour of the magnetic moment and in the third part we prove that the stochastic perturbation induces a non-reversibility phenomenon. Last, we illustrate these results through numerical simulations of our stochastic model. The main results presented in this article are on the one hand the rate of convergence of the magnetization toward the unique stable equilibrium of the deterministic model and on the other hand a sharp estimate of the hysteresis phenomenon induced by the stochastic perturbation (remember that with no perturbation, the magnetic moment remains constant).
Generalized spectral decomposition for stochastic nonlinear problems
Nouy, Anthony Le Maitre, Olivier P.
2009-01-10
We present an extension of the generalized spectral decomposition method for the resolution of nonlinear stochastic problems. The method consists in the construction of a reduced basis approximation of the Galerkin solution and is independent of the stochastic discretization selected (polynomial chaos, stochastic multi-element or multi-wavelets). Two algorithms are proposed for the sequential construction of the successive generalized spectral modes. They involve decoupled resolutions of a series of deterministic and low-dimensional stochastic problems. Compared to the classical Galerkin method, the algorithms allow for significant computational savings and require minor adaptations of the deterministic codes. The methodology is detailed and tested on two model problems, the one-dimensional steady viscous Burgers equation and a two-dimensional nonlinear diffusion problem. These examples demonstrate the effectiveness of the proposed algorithms which exhibit convergence rates with the number of modes essentially dependent on the spectrum of the stochastic solution but independent of the dimension of the stochastic approximation space.
Ant colony optimization and stochastic gradient descent.
Meuleau, Nicolas; Dorigo, Marco
2002-01-01
In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the family of ACO algorithms. We then use this insight to explore the mutual contributions of the two techniques. PMID:12171633
Stochastic Vorticity and Associated Filtering Theory
Amirdjanova, A.; Kallianpur, G.
2002-12-19
The focus of this work is on a two-dimensional stochastic vorticity equation for an incompressible homogeneous viscous fluid. We consider a signed measure-valued stochastic partial differential equation for a vorticity process based on the Skorohod-Ito evolution of a system of N randomly moving point vortices. A nonlinear filtering problem associated with the evolution of the vorticity is considered and a corresponding Fujisaki-Kallianpur-Kunita stochastic differential equation for the optimal filter is derived.
Stochastic Turing patterns on a network.
Asslani, Malbor; Di Patti, Francesca; Fanelli, Duccio
2012-10-01
The process of stochastic Turing instability on a scale-free network is discussed for a specific case study: the stochastic Brusselator model. The system is shown to spontaneously differentiate into activator-rich and activator-poor nodes outside the region of parameters classically deputed to the deterministic Turing instability. This phenomenon, as revealed by direct stochastic simulations, is explained analytically and eventually traced back to the finite-size corrections stemming from the inherent graininess of the scrutinized medium. PMID:23214650
Stochastic Turing patterns on a network
NASA Astrophysics Data System (ADS)
Asslani, Malbor; Di Patti, Francesca; Fanelli, Duccio
2012-10-01
The process of stochastic Turing instability on a scale-free network is discussed for a specific case study: the stochastic Brusselator model. The system is shown to spontaneously differentiate into activator-rich and activator-poor nodes outside the region of parameters classically deputed to the deterministic Turing instability. This phenomenon, as revealed by direct stochastic simulations, is explained analytically and eventually traced back to the finite-size corrections stemming from the inherent graininess of the scrutinized medium.
Stochastics In Circumplanetary Dust Dynamics
NASA Astrophysics Data System (ADS)
Spahn, F.; Krivov, A. V.; Sremcevic, M.; Schwarz, U.; Kurths, J.
Charged dust grains in circumplanetary environments experience, beyond various de- terministic forces, also stochastic perturbations: E.g., fluctuations of the magnetic field, the charge of the grains etc. Here, we investigate the dynamics of a dust population in a circular orbit around the planet which is perturbed by a stochastic magnetic field B , modeled by an isotropi- cally Gaussian white noise. The resulting perturbation equations give rise to a modi- 2 fied diffusion of the inclinations and eccentricities x D [t +/- sin[2nt]/(2n)] (x - alias for eccentricity e and the inclination i, t - time). The diffusion coefficient is found to be D = [G]2/n, where the gyrofrequency and the orbital frequency are denoted by G, and n, respectively. This behavior has been checked by numerical experiments. We have chosen dust grains (1µm in radius) initially moving in circular orbits around a planet (Jupiter) and integrated numerically their trajectories over their typical lifetimes (100 years). The particles were exposed to a Gaussian fluctuating magnetic field B obeying the same statistical properties as in the analytical treatment. In this case, the theoretical 2 findings have been confirmed according to x D t with a diffusion coefficient of D G/n. 2 The theoretical studies showed the statistical properties of B being of decisive im- portance. To this aim, we analyzed the magnetic field data measured by the Galileo magnetometer at Jupiter and found almost Gaussian fluctuations of about 5 % of the mean field and exponentially decaying correlations. This results in a diffusion in the space of orbital elements of at least 1...5 % (variations of inclinations and eccentric- ity) over the lifetime of the dust grains. For smaller dusty motes stochastics might well dominate the dynamics.
Hamilton's principle in stochastic mechanics
NASA Astrophysics Data System (ADS)
Pavon, Michele
1995-12-01
In this paper we establish three variational principles that provide new foundations for Nelson's stochastic mechanics in the case of nonrelativistic particles without spin. The resulting variational picture is much richer and of a different nature with respect to the one previously considered in the literature. We first develop two stochastic variational principles whose Hamilton-Jacobi-like equations are precisely the two coupled partial differential equations that are obtained from the Schrödinger equation (Madelung equations). The two problems are zero-sum, noncooperative, stochastic differential games that are familiar in the control theory literature. They are solved here by means of a new, absolutely elementary method based on Lagrange functionals. For both games the saddle-point equilibrium solution is given by the Nelson's process and the optimal controls for the two competing players are precisely Nelson's current velocity v and osmotic velocity u, respectively. The first variational principle includes as special cases both the Guerra-Morato variational principle [Phys. Rev. D 27, 1774 (1983)] and Schrödinger original variational derivation of the time-independent equation. It also reduces to the classical least action principle when the intensity of the underlying noise tends to zero. It appears as a saddle-point action principle. In the second variational principle the action is simply the difference between the initial and final configurational entropy. It is therefore a saddle-point entropy production principle. From the variational principles it follows, in particular, that both v(x,t) and u(x,t) are gradients of appropriate principal functions. In the variational principles, the role of the background noise has the intuitive meaning of attempting to contrast the more classical mechanical features of the system by trying to maximize the action in the first principle and by trying to increase the entropy in the second. Combining the two variational
Stochastic Models of Human Errors
NASA Technical Reports Server (NTRS)
Elshamy, Maged; Elliott, Dawn M. (Technical Monitor)
2002-01-01
Humans play an important role in the overall reliability of engineering systems. More often accidents and systems failure are traced to human errors. Therefore, in order to have meaningful system risk analysis, the reliability of the human element must be taken into consideration. Describing the human error process by mathematical models is a key to analyzing contributing factors. Therefore, the objective of this research effort is to establish stochastic models substantiated by sound theoretic foundation to address the occurrence of human errors in the processing of the space shuttle.
Stochastic solution to quantum dynamics
NASA Technical Reports Server (NTRS)
John, Sarah; Wilson, John W.
1994-01-01
The quantum Liouville equation in the Wigner representation is solved numerically by using Monte Carlo methods. For incremental time steps, the propagation is implemented as a classical evolution in phase space modified by a quantum correction. The correction, which is a momentum jump function, is simulated in the quasi-classical approximation via a stochastic process. The technique, which is developed and validated in two- and three- dimensional momentum space, extends an earlier one-dimensional work. Also, by developing a new algorithm, the application to bound state motion in an anharmonic quartic potential shows better agreement with exact solutions in two-dimensional phase space.
Stochastic thermodynamics of information processing
NASA Astrophysics Data System (ADS)
Cardoso Barato, Andre
2015-03-01
We consider two recent advancements on theoretical aspects of thermodynamics of information processing. First we show that the theory of stochastic thermodynamics can be generalized to include information reservoirs. These reservoirs can be seen as a sequence of bits which has its Shannon entropy changed due to the interaction with the system. Second we discuss bipartite systems, which provide a convenient description of Maxwell's demon. Analyzing a special class of bipartite systems we show that they can be used to study cellular information processing, allowing for the definition of an entropic rate that quantifies how much a cell learns about a fluctuating external environment and that is bounded by the thermodynamic entropy production.
Constrained Stochastic Extended Redundancy Analysis.
DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco
2015-06-01
We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA). PMID:24327066
Image-based histologic grade estimation using stochastic geometry analysis
NASA Astrophysics Data System (ADS)
Petushi, Sokol; Zhang, Jasper; Milutinovic, Aladin; Breen, David E.; Garcia, Fernando U.
2011-03-01
Background: Low reproducibility of histologic grading of breast carcinoma due to its subjectivity has traditionally diminished the prognostic value of histologic breast cancer grading. The objective of this study is to assess the effectiveness and reproducibility of grading breast carcinomas with automated computer-based image processing that utilizes stochastic geometry shape analysis. Methods: We used histology images stained with Hematoxylin & Eosin (H&E) from invasive mammary carcinoma, no special type cases as a source domain and study environment. We developed a customized hybrid semi-automated segmentation algorithm to cluster the raw image data and reduce the image domain complexity to a binary representation with the foreground representing regions of high density of malignant cells. A second algorithm was developed to apply stochastic geometry and texture analysis measurements to the segmented images and to produce shape distributions, transforming the original color images into a histogram representation that captures their distinguishing properties between various histological grades. Results: Computational results were compared against known histological grades assigned by the pathologist. The Earth Mover's Distance (EMD) similarity metric and the K-Nearest Neighbors (KNN) classification algorithm provided correlations between the high-dimensional set of shape distributions and a priori known histological grades. Conclusion: Computational pattern analysis of histology shows promise as an effective software tool in breast cancer histological grading.
RHIC stochastic cooling motion control
Gassner, D.; DeSanto, L.; Olsen, R.H.; Fu, W.; Brennan, J.M.; Liaw, CJ; Bellavia, S.; Brodowski, J.
2011-03-28
Relativistic Heavy Ion Collider (RHIC) beams are subject to Intra-Beam Scattering (IBS) that causes an emittance growth in all three-phase space planes. The only way to increase integrated luminosity is to counteract IBS with cooling during RHIC stores. A stochastic cooling system for this purpose has been developed, it includes moveable pick-ups and kickers in the collider that require precise motion control mechanics, drives and controllers. Since these moving parts can limit the beam path aperture, accuracy and reliability is important. Servo, stepper, and DC motors are used to provide actuation solutions for position control. The choice of motion stage, drive motor type, and controls are based on needs defined by the variety of mechanical specifications, the unique performance requirements, and the special needs required for remote operations in an accelerator environment. In this report we will describe the remote motion control related beam line hardware, position transducers, rack electronics, and software developed for the RHIC stochastic cooling pick-ups and kickers.
Stochastic models of viral infection
NASA Astrophysics Data System (ADS)
Chou, Tom
2009-03-01
We develop biophysical models of viral infections from a stochastic process perspective. The entry of enveloped viruses is treated as a stochastic multiple receptor and coreceptor engagement process that can lead to membrane fusion or endocytosis. The probabilities of entry via fusion and endocytosis are computed as functions of the receptor/coreceptor engagement rates. Since membrane fusion and endocytosis entry pathways can lead to very different infection outcomes, we delineate the parameter regimes conducive to each entry pathway. After entry, viral material is biochemically processed and degraded as it is transported towards the nucleus. Productive infections occur only when the material reaches the nucleus in the proper biochemical state. Thus, entry into the nucleus in an infectious state requires the proper timing of the cytoplasmic transport process. We compute the productive infection probability and show its nonmonotonic dependence on both transport speeds and biochemical transformation rates. Our results carry subtle consequences on the dosage and efficacy of antivirals such as reverse transcription inhibitors.
Stochastic Methods for Aircraft Design
NASA Technical Reports Server (NTRS)
Pelz, Richard B.; Ogot, Madara
1998-01-01
The global stochastic optimization method, simulated annealing (SA), was adapted and applied to various problems in aircraft design. The research was aimed at overcoming the problem of finding an optimal design in a space with multiple minima and roughness ubiquitous to numerically generated nonlinear objective functions. SA was modified to reduce the number of objective function evaluations for an optimal design, historically the main criticism of stochastic methods. SA was applied to many CFD/MDO problems including: low sonic-boom bodies, minimum drag on supersonic fore-bodies, minimum drag on supersonic aeroelastic fore-bodies, minimum drag on HSCT aeroelastic wings, FLOPS preliminary design code, another preliminary aircraft design study with vortex lattice aerodynamics, HSR complete aircraft aerodynamics. In every case, SA provided a simple, robust and reliable optimization method which found optimal designs in order 100 objective function evaluations. Perhaps most importantly, from this academic/industrial project, technology has been successfully transferred; this method is the method of choice for optimization problems at Northrop Grumman.
Numerical tests of stochastic tomography
NASA Astrophysics Data System (ADS)
Ru-Shan, Wu; Xiao-Bi, Xie
1991-05-01
The method of stochastic tomography proposed by Wu is tested numerically. This method reconstructs the heterospectra (power spectra of heterogeneities) at all depths of a non-uniform random medium using measured joint transverse-angular coherence functions (JTACF) of transmission fluctuations on an array. The inversion method is based on a constrained least-squares inversion implemented via the singular value decomposition. The inversion is also applicable to reconstructions using transverse coherence functions (TCF) or angular coherence functions (ACF); these are merely special cases of JTACF. Through the analysis of sampling functions and singular values, and through numerical examples of reconstruction using theoretically generated coherence functions, we compare the resolution and robustness of reconstructions using TCF, ACF and JTACF. The JTACF can `focus' the coherence analysis at different depths and therefore has a better depth resolution than TCF and ACF. In addition, the JTACF contains much more information than the sum of TCF and ACF, and has much better noise resistance properties than TCF and ACF. Inversion of JTACF can give a reliable reconstruction of heterospectra at different depths even for data with 20% noise contamination. This demonstrates the feasibility of stochastic tomography using JTACF.
Stochastic models for cell division
NASA Astrophysics Data System (ADS)
Stukalin, Evgeny; Sun, Sean
2013-03-01
The probability of cell division per unit time strongly depends of age of cells, i.e., time elapsed since their birth. The theory of cell populations in the age-time representation is systematically applied for modeling cell division for different spreads in generation times. We use stochastic simulations to address the same issue at the level of individual cells. Our approach unlike deterministic theory enables to analyze the size fluctuations of cell colonies at different growth conditions (in the absence and in the presence of cell death, for initially synchronized and asynchronous cell populations, for conditions of restricted growth). We find the simple quantitative relation between the asymptotic values of relative size fluctuations around mean values for initially synchronized cell populations under growth and the coefficients of variation of generation times. Effect of initial age distribution for asynchronous growth of cell cultures is also studied by simulations. The influence of constant cell death on fluctuations of sizes of cell populations is found to be essential even for small cell death rates, i.e., for realistic growth conditions. The stochastic model is generalized for biologically relevant case that involves both cell reproduction and cell differentiation.
Stochastic Modeling of Laminar-Turbulent Transition
NASA Technical Reports Server (NTRS)
Rubinstein, Robert; Choudhari, Meelan
2002-01-01
Stochastic versions of stability equations are developed in order to develop integrated models of transition and turbulence and to understand the effects of uncertain initial conditions on disturbance growth. Stochastic forms of the resonant triad equations, a high Reynolds number asymptotic theory, and the parabolized stability equations are developed.
Bunched Beam Stochastic Cooling and Coherent Lines
Blaskiewicz, M.; Brennan, J. M.
2006-03-20
Strong coherent signals complicate bunched beam stochastic cooling, and development of the longitudinal stochastic cooling system for RHIC required dealing with coherence in heavy ion beams. Studies with proton beams revealed additional forms of coherence. This paper presents data and analysis for both sorts of beams.
Variational principles for stochastic fluid dynamics
Holm, Darryl D.
2015-01-01
This paper derives stochastic partial differential equations (SPDEs) for fluid dynamics from a stochastic variational principle (SVP). The paper proceeds by taking variations in the SVP to derive stochastic Stratonovich fluid equations; writing their Itô representation; and then investigating the properties of these stochastic fluid models in comparison with each other, and with the corresponding deterministic fluid models. The circulation properties of the stochastic Stratonovich fluid equations are found to closely mimic those of the deterministic ideal fluid models. As with deterministic ideal flows, motion along the stochastic Stratonovich paths also preserves the helicity of the vortex field lines in incompressible stochastic flows. However, these Stratonovich properties are not apparent in the equivalent Itô representation, because they are disguised by the quadratic covariation drift term arising in the Stratonovich to Itô transformation. This term is a geometric generalization of the quadratic covariation drift term already found for scalar densities in Stratonovich's famous 1966 paper. The paper also derives motion equations for two examples of stochastic geophysical fluid dynamics; namely, the Euler–Boussinesq and quasi-geostropic approximations.
From Complex to Simple: Interdisciplinary Stochastic Models
ERIC Educational Resources Information Center
Mazilu, D. A.; Zamora, G.; Mazilu, I.
2012-01-01
We present two simple, one-dimensional, stochastic models that lead to a qualitative understanding of very complex systems from biology, nanoscience and social sciences. The first model explains the complicated dynamics of microtubules, stochastic cellular highways. Using the theory of random walks in one dimension, we find analytical expressions…
Attainability analysis in stochastic controlled systems
Ryashko, Lev
2015-03-10
A control problem for stochastically forced nonlinear continuous-time systems is considered. We propose a method for construction of the regulator that provides a preassigned probabilistic distribution of random states in stochastic equilibrium. Geometric criteria of the controllability are obtained. Constructive technique for the specification of attainability sets is suggested.
Stochastic ion acceleration by beating electrostatic waves.
Jorns, B; Choueiri, E Y
2013-01-01
A study is presented of the stochasticity in the orbit of a single, magnetized ion produced by the particle's interaction with two beating electrostatic waves whose frequencies differ by the ion cyclotron frequency. A second-order Lie transform perturbation theory is employed in conjunction with a numerical analysis of the maximum Lyapunov exponent to determine the velocity conditions under which stochasticity occurs in this dynamical system. Upper and lower bounds in ion velocity are found for stochastic orbits with the lower bound approximately equal to the phase velocity of the slower wave. A threshold condition for the onset of stochasticity that is linear with respect to the wave amplitudes is also derived. It is shown that the onset of stochasticity occurs for beating electrostatic waves at lower total wave energy densities than for the case of a single electrostatic wave or two nonbeating electrostatic waves. PMID:23410446
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
NASA Astrophysics Data System (ADS)
Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles
2014-03-01
A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.
Wagstaff, Marcus James Dermot; Rooke, Michael; Caplash, Yugesh
2016-01-01
Objectives: To share our experience of an extensive calvarial reconstruction in a severely burn-injured, elderly patient in a 2-stage procedure utilizing a novel biodegradable temporizing matrix (NovoSorb BTM), followed by autograft. Materials and Methods: A 66-year-old patient with 75% full-thickness burns, including 7% total body surface area head and neck, with calvarial exposure of approximately 350 cm2, complicated by acute renal failure and smoke inhalation injury. Exposed calvarium was burred down to diploe and biodegradable temporizing matrix was applied. Over the next 29 days, the biodegradable temporizing matrix integrated by vascular and tissue ingrowth from the diploe. Delamination and grafting occurred, however, at 43 days postimplantation of biodegradable temporizing matrix due to skin graft donor-site constraints. Results: Graft take was complete, yielding a robust and aesthetically pleasing early result (26 days post–graft application). Conclusions: Biodegradable temporizing matrix offers an additional resource for reconstructive surgeons faced with fragile patients and complex wounds. PMID:27222681
Stochastic inflation and nonlinear gravity
NASA Astrophysics Data System (ADS)
Salopek, D. S.; Bond, J. R.
1991-02-01
We show how nonlinear effects of the metric and scalar fields may be included in stochastic inflation. Our formalism can be applied to non-Gaussian fluctuation models for galaxy formation. Fluctuations with wavelengths larger than the horizon length are governed by a network of Langevin equations for the physical fields. Stochastic noise terms arise from quantum fluctuations that are assumed to become classical at horizon crossing and that then contribute to the background. Using Hamilton-Jacobi methods, we solve the Arnowitt-Deser-Misner constraint equations which allows us to separate the growing modes from the decaying ones in the drift phase following each stochastic impulse. We argue that the most reasonable choice of time hypersurfaces for the Langevin system during inflation is T=ln(Ha), where H and a are the local values of the Hubble parameter and the scale factor, since T is the natural time for evolving the short-wavelength scalar field fluctuations in an inhomogeneous background. We derive a Fokker-Planck equation which describes how the probability distribution of scalar field values at a given spatial point evolves in T. Analytic Green's-function solutions obtained for a single scalar field self-interacting through an exponential potential are used to demonstrate (1) if the initial condition of the Hubble parameter is chosen to be consistent with microwave-background limits, H(φ0)/mρ<~10-4, then the fluctuations obey Gaussian statistics to a high precision, independent of the time hypersurface choice and operator-ordering ambiguities in the Fokker-Planck equation, and (2) for scales much larger than our present observable patch of the Universe, the distribution is non-Gaussian, with a tail extending to large energy densities; although there are no observable manifestations, it does show eternal inflation. Lattice simulations of our Langevin network for the exponential potential demonstrate how spatial correlations are incorporated. An initially
Stochastic models of neuronal dynamics
Harrison, L.M; David, O; Friston, K.J
2005-01-01
Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over state-space, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a neuron. Measurements can be modelled by taking expectations over this density, e.g. mean membrane potential, firing rate or energy consumption per neuron. The key motivation behind our approach is that ERPs represent an average response over many neurons. This means it is sufficient to model the probability density over neurons, because this implicitly models their average state. Although the dynamics of each neuron can be highly stochastic, the dynamics of the density is not. This means we can use Bayesian inference and estimation tools that have
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NASA Astrophysics Data System (ADS)
Mel'nikov, A. V.
1996-10-01
Contents Introduction Chapter I. Basic notions and results from contemporary martingale theory §1.1. General notions of the martingale theory §1.2. Convergence (a.s.) of semimartingales. The strong law of large numbers and the law of the iterated logarithm Chapter II. Stochastic differential equations driven by semimartingales §2.1. Basic notions and results of the theory of stochastic differential equations driven by semimartingales §2.2. The method of monotone approximations. Existence of strong solutions of stochastic equations with non-smooth coefficients §2.3. Linear stochastic equations. Properties of stochastic exponentials §2.4. Linear stochastic equations. Applications to models of the financial market Chapter III. Procedures of stochastic approximation as solutions of stochastic differential equations driven by semimartingales §3.1. Formulation of the problem. A general model and its relation to the classical one §3.2. A general description of the approach to the procedures of stochastic approximation. Convergence (a.s.) and asymptotic normality §3.3. The Gaussian model of stochastic approximation. Averaged procedures and their effectiveness Chapter IV. Statistical estimation in regression models with martingale noises §4.1. The formulation of the problem and classical regression models §4.2. Asymptotic properties of MLS-estimators. Strong consistency, asymptotic normality, the law of the iterated logarithm §4.3. Regression models with deterministic regressors §4.4. Sequential MLS-estimators with guaranteed accuracy and sequential statistical inferences Bibliography
Stochastic dynamics of dengue epidemics
NASA Astrophysics Data System (ADS)
de Souza, David R.; Tomé, Tânia; Pinho, Suani T. R.; Barreto, Florisneide R.; de Oliveira, Mário J.
2013-01-01
We use a stochastic Markovian dynamics approach to describe the spreading of vector-transmitted diseases, such as dengue, and the threshold of the disease. The coexistence space is composed of two structures representing the human and mosquito populations. The human population follows a susceptible-infected-recovered (SIR) type dynamics and the mosquito population follows a susceptible-infected-susceptible (SIS) type dynamics. The human infection is caused by infected mosquitoes and vice versa, so that the SIS and SIR dynamics are interconnected. We develop a truncation scheme to solve the evolution equations from which we get the threshold of the disease and the reproductive ratio. The threshold of the disease is also obtained by performing numerical simulations. We found that for certain values of the infection rates the spreading of the disease is impossible, for any death rate of infected mosquitoes.
Thermodynamics of stochastic Turing machines
NASA Astrophysics Data System (ADS)
Strasberg, Philipp; Cerrillo, Javier; Schaller, Gernot; Brandes, Tobias
2015-10-01
In analogy to Brownian computers we explicitly show how to construct stochastic models which mimic the behavior of a general-purpose computer (a Turing machine). Our models are discrete state systems obeying a Markovian master equation, which are logically reversible and have a well-defined and consistent thermodynamic interpretation. The resulting master equation, which describes a simple one-step process on an enormously large state space, allows us to thoroughly investigate the thermodynamics of computation for this situation. Especially in the stationary regime we can well approximate the master equation by a simple Fokker-Planck equation in one dimension. We then show that the entropy production rate at steady state can be made arbitrarily small, but the total (integrated) entropy production is finite and grows logarithmically with the number of computational steps.
Stochastic thermodynamics for active matter
NASA Astrophysics Data System (ADS)
Speck, Thomas
2016-05-01
The theoretical understanding of active matter, which is driven out of equilibrium by directed motion, is still fragmental and model oriented. Stochastic thermodynamics, on the other hand, is a comprehensive theoretical framework for driven systems that allows to define fluctuating work and heat. We apply these definitions to active matter, assuming that dissipation can be modelled by effective non-conservative forces. We show that, through the work, conjugate extensive and intensive observables can be defined even in non-equilibrium steady states lacking a free energy. As an illustration, we derive the expressions for the pressure and interfacial tension of active Brownian particles. The latter becomes negative despite the observed stable phase separation. We discuss this apparent contradiction, highlighting the role of fluctuations, and we offer a tentative explanation.
Stochastic sensing through covalent interactions
Bayley, Hagan; Shin, Seong-Ho; Luchian, Tudor; Cheley, Stephen
2013-03-26
A system and method for stochastic sensing in which the analyte covalently bonds to the sensor element or an adaptor element. If such bonding is irreversible, the bond may be broken by a chemical reagent. The sensor element may be a protein, such as the engineered P.sub.SH type or .alpha.HL protein pore. The analyte may be any reactive analyte, including chemical weapons, environmental toxins and pharmaceuticals. The analyte covalently bonds to the sensor element to produce a detectable signal. Possible signals include change in electrical current, change in force, and change in fluorescence. Detection of the signal allows identification of the analyte and determination of its concentration in a sample solution. Multiple analytes present in the same solution may be detected.
Thermodynamics of stochastic Turing machines.
Strasberg, Philipp; Cerrillo, Javier; Schaller, Gernot; Brandes, Tobias
2015-10-01
In analogy to Brownian computers we explicitly show how to construct stochastic models which mimic the behavior of a general-purpose computer (a Turing machine). Our models are discrete state systems obeying a Markovian master equation, which are logically reversible and have a well-defined and consistent thermodynamic interpretation. The resulting master equation, which describes a simple one-step process on an enormously large state space, allows us to thoroughly investigate the thermodynamics of computation for this situation. Especially in the stationary regime we can well approximate the master equation by a simple Fokker-Planck equation in one dimension. We then show that the entropy production rate at steady state can be made arbitrarily small, but the total (integrated) entropy production is finite and grows logarithmically with the number of computational steps. PMID:26565165
Multiscale Stochastic Simulation and Modeling
James Glimm; Xiaolin Li
2006-01-10
Acceleration driven instabilities of fluid mixing layers include the classical cases of Rayleigh-Taylor instability, driven by a steady acceleration and Richtmyer-Meshkov instability, driven by an impulsive acceleration. Our program starts with high resolution methods of numerical simulation of two (or more) distinct fluids, continues with analytic analysis of these solutions, and the derivation of averaged equations. A striking achievement has been the systematic agreement we obtained between simulation and experiment by using a high resolution numerical method and improved physical modeling, with surface tension. Our study is accompanies by analysis using stochastic modeling and averaged equations for the multiphase problem. We have quantified the error and uncertainty using statistical modeling methods.
Heuristic-biased stochastic sampling
Bresina, J.L.
1996-12-31
This paper presents a search technique for scheduling problems, called Heuristic-Biased Stochastic Sampling (HBSS). The underlying assumption behind the HBSS approach is that strictly adhering to a search heuristic often does not yield the best solution and, therefore, exploration off the heuristic path can prove fruitful. Within the HBSS approach, the balance between heuristic adherence and exploration can be controlled according to the confidence one has in the heuristic. By varying this balance, encoded as a bias function, the HBSS approach encompasses a family of search algorithms of which greedy search and completely random search are extreme members. We present empirical results from an application of HBSS to the realworld problem of observation scheduling. These results show that with the proper bias function, it can be easy to outperform greedy search.
Extinction of metastable stochastic populations.
Assaf, Michael; Meerson, Baruch
2010-02-01
We investigate the phenomenon of extinction of a long-lived self-regulating stochastic population, caused by intrinsic (demographic) noise. Extinction typically occurs via one of two scenarios depending on whether the absorbing state n=0 is a repelling (scenario A) or attracting (scenario B) point of the deterministic rate equation. In scenario A the metastable stochastic population resides in the vicinity of an attracting fixed point next to the repelling point n=0 . In scenario B there is an intermediate repelling point n=n1 between the attracting point n=0 and another attracting point n=n2 in the vicinity of which the metastable population resides. The crux of the theory is a dissipative variant of WKB (Wentzel-Kramers-Brillouin) approximation which assumes that the typical population size in the metastable state is large. Starting from the master equation, we calculate the quasistationary probability distribution of the population sizes and the (exponentially long) mean time to extinction for each of the two scenarios. When necessary, the WKB approximation is complemented (i) by a recursive solution of the quasistationary master equation at small n and (ii) by the van Kampen system-size expansion, valid near the fixed points of the deterministic rate equation. The theory yields both entropic barriers to extinction and pre-exponential factors, and holds for a general set of multistep processes when detailed balance is broken. The results simplify considerably for single-step processes and near the characteristic bifurcations of scenarios A and B. PMID:20365539
Stochastic inversion by ray continuation
Haas, A.; Viallix
1989-05-01
The conventional tomographic inversion consists in minimizing residuals between measured and modelled traveltimes. The process tends to be unstable and some additional constraints are required to stabilize it. The stochastic formulation generalizes the technique and sets it on firmer theoretical bases. The Stochastic Inversion by Ray Continuation (SIRC) is a probabilistic approach, which takes a priori geological information into account and uses probability distributions to characterize data correlations and errors. It makes it possible to tie uncertainties to the results. The estimated parameters are interval velocities and B-spline coefficients used to represent smoothed interfaces. Ray tracing is done by a continuation technique between source and receives. The ray coordinates are computed from one path to the next by solving a linear system derived from Fermat's principle. The main advantages are fast computations, accurate traveltimes and derivatives. The seismic traces are gathered in CMPs. For a particular CMP, several reflecting elements are characterized by their time gradient measured on the stacked section, and related to a mean emergence direction. The program capabilities are tested on a synthetic example as well as on a field example. The strategy consists in inverting the parameters for one layer, then for the next one down. An inversion step is divided in two parts. First the parameters for the layer concerned are inverted, while the parameters for the upper layers remain fixed. Then all the parameters are reinverted. The velocity-depth section computed by the program together with the corresponding errors can be used directly for the interpretation, as an initial model for depth migration or for the complete inversion program under development.
Multiple Stochastic Point Processes in Gene Expression
NASA Astrophysics Data System (ADS)
Murugan, Rajamanickam
2008-04-01
We generalize the idea of multiple-stochasticity in chemical reaction systems to gene expression. Using Chemical Langevin Equation approach we investigate how this multiple-stochasticity can influence the overall molecular number fluctuations. We show that the main sources of this multiple-stochasticity in gene expression could be the randomness in transcription and translation initiation times which in turn originates from the underlying bio-macromolecular recognition processes such as the site-specific DNA-protein interactions and therefore can be internally regulated by the supra-molecular structural factors such as the condensation/super-coiling of DNA. Our theory predicts that (1) in case of gene expression system, the variances ( φ) introduced by the randomness in transcription and translation initiation-times approximately scales with the degree of condensation ( s) of DNA or mRNA as φ ∝ s -6. From the theoretical analysis of the Fano factor as well as coefficient of variation associated with the protein number fluctuations we predict that (2) unlike the singly-stochastic case where the Fano factor has been shown to be a monotonous function of translation rate, in case of multiple-stochastic gene expression the Fano factor is a turn over function with a definite minimum. This in turn suggests that the multiple-stochastic processes can also be well tuned to behave like a singly-stochastic point processes by adjusting the rate parameters.
Solving stochastic epidemiological models using computer algebra
NASA Astrophysics Data System (ADS)
Hincapie, Doracelly; Ospina, Juan
2011-06-01
Mathematical modeling in Epidemiology is an important tool to understand the ways under which the diseases are transmitted and controlled. The mathematical modeling can be implemented via deterministic or stochastic models. Deterministic models are based on short systems of non-linear ordinary differential equations and the stochastic models are based on very large systems of linear differential equations. Deterministic models admit complete, rigorous and automatic analysis of stability both local and global from which is possible to derive the algebraic expressions for the basic reproductive number and the corresponding epidemic thresholds using computer algebra software. Stochastic models are more difficult to treat and the analysis of their properties requires complicated considerations in statistical mathematics. In this work we propose to use computer algebra software with the aim to solve epidemic stochastic models such as the SIR model and the carrier-borne model. Specifically we use Maple to solve these stochastic models in the case of small groups and we obtain results that do not appear in standard textbooks or in the books updated on stochastic models in epidemiology. From our results we derive expressions which coincide with those obtained in the classical texts using advanced procedures in mathematical statistics. Our algorithms can be extended for other stochastic models in epidemiology and this shows the power of computer algebra software not only for analysis of deterministic models but also for the analysis of stochastic models. We also perform numerical simulations with our algebraic results and we made estimations for the basic parameters as the basic reproductive rate and the stochastic threshold theorem. We claim that our algorithms and results are important tools to control the diseases in a globalized world.
Lobikin, Maria; Lobo, Daniel; Blackiston, Douglas J; Martyniuk, Christopher J; Tkachenko, Elizabeth; Levin, Michael
2015-10-01
Experimentally induced depolarization of resting membrane potential in "instructor cells" in Xenopus laevis embryos causes hyperpigmentation in an all-or-none fashion in some tadpoles due to excess proliferation and migration of melanocytes. We showed that this stochastic process involved serotonin signaling, adenosine 3',5'-monophosphate (cAMP), and the transcription factors cAMP response element-binding protein (CREB), Sox10, and Slug. Transcriptional microarray analysis of embryos taken at stage 15 (early neurula) and stage 45 (free-swimming tadpole) revealed changes in the abundance of 45 and 517 transcripts, respectively, between control embryos and embryos exposed to the instructor cell-depolarizing agent ivermectin. Bioinformatic analysis revealed that the human homologs of some of the differentially regulated genes were associated with cancer, consistent with the induced arborization and invasive behavior of converted melanocytes. We identified a physiological circuit that uses serotonergic signaling between instructor cells, melanotrope cells of the pituitary, and melanocytes to control the proliferation, cell shape, and migration properties of the pigment cell pool. To understand the stochasticity and properties of this multiscale signaling system, we applied a computational machine-learning method that iteratively explored network models to reverse-engineer a stochastic dynamic model that recapitulated the frequency of the all-or-none hyperpigmentation phenotype produced in response to various pharmacological and molecular genetic manipulations. This computational approach may provide insight into stochastic cellular decision-making that occurs during normal development and pathological conditions, such as cancer. PMID:26443706
Immigration-extinction dynamics of stochastic populations
NASA Astrophysics Data System (ADS)
Meerson, Baruch; Ovaskainen, Otso
2013-07-01
How high should be the rate of immigration into a stochastic population in order to significantly reduce the probability of observing the population become extinct? Is there any relation between the population size distributions with and without immigration? Under what conditions can one justify the simple patch occupancy models, which ignore the population distribution and its dynamics in a patch, and treat a patch simply as either occupied or empty? We answer these questions by exactly solving a simple stochastic model obtained by adding a steady immigration to a variant of the Verhulst model: a prototypical model of an isolated stochastic population.
A multilevel stochastic collocation method for SPDEs
Gunzburger, Max; Jantsch, Peter; Teckentrup, Aretha; Webster, Clayton
2015-03-10
We present a multilevel stochastic collocation method that, as do multilevel Monte Carlo methods, uses a hierarchy of spatial approximations to reduce the overall computational complexity when solving partial differential equations with random inputs. For approximation in parameter space, a hierarchy of multi-dimensional interpolants of increasing fidelity are used. Rigorous convergence and computational cost estimates for the new multilevel stochastic collocation method are derived and used to demonstrate its advantages compared to standard single-level stochastic collocation approximations as well as multilevel Monte Carlo methods.
Stochastic system identification in structural dynamics
Safak, Erdal
1988-01-01
Recently, new identification methods have been developed by using the concept of optimal-recursive filtering and stochastic approximation. These methods, known as stochastic identification, are based on the statistical properties of the signal and noise, and do not require the assumptions of current methods. The criterion for stochastic system identification is that the difference between the recorded output and the output from the identified system (i.e., the residual of the identification) should be equal to white noise. In this paper, first a brief review of the theory is given. Then, an application of the method is presented by using ambient vibration data from a nine-story building.
Topological charge conservation in stochastic optical fields
NASA Astrophysics Data System (ADS)
Roux, Filippus S.
2016-05-01
The fact that phase singularities in scalar stochastic optical fields are topologically conserved implies the existence of an associated conserved current, which can be expressed in terms of local correlation functions of the optical field and its transverse derivatives. Here, we derive the topological charge current for scalar stochastic optical fields and show that it obeys a conservation equation. We use the expression for the topological charge current to investigate the topological charge flow in inhomogeneous stochastic optical fields with a one-dimensional topological charge density.
Stochastic deformation of a thermodynamic symplectic structure
NASA Astrophysics Data System (ADS)
Kazinski, P. O.
2009-01-01
A stochastic deformation of a thermodynamic symplectic structure is studied. The stochastic deformation is analogous to the deformation of an algebra of observables such as deformation quantization, but for an imaginary deformation parameter (the Planck constant). Gauge symmetries of thermodynamics and corresponding stochastic mechanics, which describes fluctuations of a thermodynamic system, are revealed and gauge fields are introduced. A physical interpretation to the gauge transformations and gauge fields is given. An application of the formalism to a description of systems with distributed parameters in a local thermodynamic equilibrium is considered.
Stochastic deformation of a thermodynamic symplectic structure.
Kazinski, P O
2009-01-01
A stochastic deformation of a thermodynamic symplectic structure is studied. The stochastic deformation is analogous to the deformation of an algebra of observables such as deformation quantization, but for an imaginary deformation parameter (the Planck constant). Gauge symmetries of thermodynamics and corresponding stochastic mechanics, which describes fluctuations of a thermodynamic system, are revealed and gauge fields are introduced. A physical interpretation to the gauge transformations and gauge fields is given. An application of the formalism to a description of systems with distributed parameters in a local thermodynamic equilibrium is considered. PMID:19256999
Stochastic string models with continuous semimartingales
NASA Astrophysics Data System (ADS)
Bueno-Guerrero, Alberto; Moreno, Manuel; Navas, Javier F.
2015-09-01
This paper reformulates the stochastic string model of Santa-Clara and Sornette using stochastic calculus with continuous semimartingales. We present some new results, such as: (a) the dynamics of the short-term interest rate, (b) the PDE that must be satisfied by the bond price, and (c) an analytic expression for the price of a European bond call option. Additionally, we clarify some important features of the stochastic string model and show its relevance to price derivatives and the equivalence with an infinite dimensional HJM model to price European options.
Stochastic Satbility and Performance Robustness of Linear Multivariable Systems
NASA Technical Reports Server (NTRS)
Ryan, Laurie E.; Stengel, Robert F.
1990-01-01
Stochastic robustness, a simple technique used to estimate the robustness of linear, time invariant systems, is applied to a single-link robot arm control system. Concepts behind stochastic stability robustness are extended to systems with estimators and to stochastic performance robustness. Stochastic performance robustness measures based on classical design specifications are introduced, and the relationship between stochastic robustness measures and control system design parameters are discussed. The application of stochastic performance robustness, and the relationship between performance objectives and design parameters are demonstrated by means of example. The results prove stochastic robustness to be a good overall robustness analysis method that can relate robustness characteristics to control system design parameters.
Stochastic pump effect and geometric phases in dissipative and stochastic systems
Sinitsyn, Nikolai
2008-01-01
The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).
Stochastic resonance during a polymer translocation process
NASA Astrophysics Data System (ADS)
Mondal, Debasish; Muthukumar, Murugappan
We study the translocation of a flexible polymer in a confined geometry subjected to a time-periodic external drive to explore stochastic resonance. We describe the equilibrium translocation process in terms of a Fokker-Planck description and use a discrete two-state model to describe the effect of the external driving force on the translocation dynamics. We observe that no stochastic resonance is possible if the associated free-energy barrier is purely entropic in nature. The polymer chain experiences a stochastic resonance effect only in presence of an energy threshold in terms of polymer-pore interaction. Once stochastic resonance is feasible, the chain entropy controls the optimal synchronization conditions significantly.
Stochastic differential equation model to Prendiville processes
Granita; Bahar, Arifah
2015-10-22
The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution.
Quadratic Stochastic Operators with Countable State Space
NASA Astrophysics Data System (ADS)
Ganikhodjaev, Nasir
2016-03-01
In this paper, we provide the classes of Poisson and Geometric quadratic stochastic operators with countable state space, study the dynamics of these operators and discuss their application to economics.
Stochasticity in plant cellular growth and patterning
Meyer, Heather M.; Roeder, Adrienne H. K.
2014-01-01
Plants, along with other multicellular organisms, have evolved specialized regulatory mechanisms to achieve proper tissue growth and morphogenesis. During development, growing tissues generate specialized cell types and complex patterns necessary for establishing the function of the organ. Tissue growth is a tightly regulated process that yields highly reproducible outcomes. Nevertheless, the underlying cellular and molecular behaviors are often stochastic. Thus, how does stochasticity, together with strict genetic regulation, give rise to reproducible tissue development? This review draws examples from plants as well as other systems to explore stochasticity in plant cell division, growth, and patterning. We conclude that stochasticity is often needed to create small differences between identical cells, which are amplified and stabilized by genetic and mechanical feedback loops to begin cell differentiation. These first few differentiating cells initiate traditional patterning mechanisms to ensure regular development. PMID:25250034
Extending Stochastic Network Calculus to Loss Analysis
Yu, Li; Zheng, Jun
2013-01-01
Loss is an important parameter of Quality of Service (QoS). Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor. PMID:24228019
Synchronization of noisy systems by stochastic signals
Neiman, A.; Schimansky-Geier, L.; Moss, F.; Schimansky-Geier, L.; Shulgin, B.; Collins, J.J.
1999-07-01
We study, in terms of synchronization, the {ital nonlinear response} of noisy bistable systems to a stochastic external signal, represented by Markovian dichotomic noise. We propose a general kinetic model which allows us to conduct a full analytical study of the nonlinear response, including the calculation of cross-correlation measures, the mean switching frequency, and synchronization regions. Theoretical results are compared with numerical simulations of a noisy overdamped bistable oscillator. We show that dichotomic noise can instantaneously synchronize the switching process of the system. We also show that synchronization is most pronounced at an optimal noise level{emdash}this effect connects this phenomenon with aperiodic stochastic resonance. Similar synchronization effects are observed for a stochastic neuron model stimulated by a stochastic spike train. {copyright} {ital 1999} {ital The American Physical Society}
Stochastic structure formation in random media
NASA Astrophysics Data System (ADS)
Klyatskin, V. I.
2016-01-01
Stochastic structure formation in random media is considered using examples of elementary dynamical systems related to the two-dimensional geophysical fluid dynamics (Gaussian random fields) and to stochastically excited dynamical systems described by partial differential equations (lognormal random fields). In the latter case, spatial structures (clusters) may form with a probability of one in almost every system realization due to rare events happening with vanishing probability. Problems involving stochastic parametric excitation occur in fluid dynamics, magnetohydrodynamics, plasma physics, astrophysics, and radiophysics. A more complicated stochastic problem dealing with anomalous structures on the sea surface (rogue waves) is also considered, where the random Gaussian generation of sea surface roughness is accompanied by parametric excitation.
Bootstrap performance profiles in stochastic algorithms assessment
Costa, Lino; Espírito Santo, Isabel A.C.P.; Oliveira, Pedro
2015-03-10
Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.
Communication: Embedded fragment stochastic density functional theory
Neuhauser, Daniel; Baer, Roi; Rabani, Eran
2014-07-28
We develop a method in which the electronic densities of small fragments determined by Kohn-Sham density functional theory (DFT) are embedded using stochastic DFT to form the exact density of the full system. The new method preserves the scaling and the simplicity of the stochastic DFT but cures the slow convergence that occurs when weakly coupled subsystems are treated. It overcomes the spurious charge fluctuations that impair the applications of the original stochastic DFT approach. We demonstrate the new approach on a fullerene dimer and on clusters of water molecules and show that the density of states and the total energy can be accurately described with a relatively small number of stochastic orbitals.
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.
Stochastic description of quantum Brownian dynamics
NASA Astrophysics Data System (ADS)
Yan, Yun-An; Shao, Jiushu
2016-08-01
Classical Brownian motion has well been investigated since the pioneering work of Einstein, which inspired mathematicians to lay the theoretical foundation of stochastic processes. A stochastic formulation for quantum dynamics of dissipative systems described by the system-plus-bath model has been developed and found many applications in chemical dynamics, spectroscopy, quantum transport, and other fields. This article provides a tutorial review of the stochastic formulation for quantum dissipative dynamics. The key idea is to decouple the interaction between the system and the bath by virtue of the Hubbard-Stratonovich transformation or Itô calculus so that the system and the bath are not directly entangled during evolution, rather they are correlated due to the complex white noises introduced. The influence of the bath on the system is thereby defined by an induced stochastic field, which leads to the stochastic Liouville equation for the system. The exact reduced density matrix can be calculated as the stochastic average in the presence of bath-induced fields. In general, the plain implementation of the stochastic formulation is only useful for short-time dynamics, but not efficient for long-time dynamics as the statistical errors go very fast. For linear and other specific systems, the stochastic Liouville equation is a good starting point to derive the master equation. For general systems with decomposable bath-induced processes, the hierarchical approach in the form of a set of deterministic equations of motion is derived based on the stochastic formulation and provides an effective means for simulating the dissipative dynamics. A combination of the stochastic simulation and the hierarchical approach is suggested to solve the zero-temperature dynamics of the spin-boson model. This scheme correctly describes the coherent-incoherent transition (Toulouse limit) at moderate dissipation and predicts a rate dynamics in the overdamped regime. Challenging problems
Structural model uncertainty in stochastic simulation
McKay, M.D.; Morrison, J.D.
1997-09-01
Prediction uncertainty in stochastic simulation models can be described by a hierarchy of components: stochastic variability at the lowest level, input and parameter uncertainty at a higher level, and structural model uncertainty at the top. It is argued that a usual paradigm for analysis of input uncertainty is not suitable for application to structural model uncertainty. An approach more likely to produce an acceptable methodology for analyzing structural model uncertainty is one that uses characteristics specific to the particular family of models.
Complexity and synchronization in stochastic chaotic systems
NASA Astrophysics Data System (ADS)
Son Dang, Thai; Palit, Sanjay Kumar; Mukherjee, Sayan; Hoang, Thang Manh; Banerjee, Santo
2016-02-01
We investigate the complexity of a hyperchaotic dynamical system perturbed by noise and various nonlinear speech and music signals. The complexity is measured by the weighted recurrence entropy of the hyperchaotic and stochastic systems. The synchronization phenomenon between two stochastic systems with complex coupling is also investigated. These criteria are tested on chaotic and perturbed systems by mean conditional recurrence and normalized synchronization error. Numerical results including surface plots, normalized synchronization errors, complexity variations etc show the effectiveness of the proposed analysis.
Desynchronization of stochastically synchronized chemical oscillators
Snari, Razan; Tinsley, Mark R. E-mail: kshowalt@wvu.edu; Faramarzi, Sadegh; Showalter, Kenneth E-mail: kshowalt@wvu.edu; Wilson, Dan; Moehlis, Jeff; Netoff, Theoden Ivan
2015-12-15
Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed.
Sequential decision analysis for nonstationary stochastic processes
NASA Technical Reports Server (NTRS)
Schaefer, B.
1974-01-01
A formulation of the problem of making decisions concerning the state of nonstationary stochastic processes is given. An optimal decision rule, for the case in which the stochastic process is independent of the decisions made, is derived. It is shown that this rule is a generalization of the Bayesian likelihood ratio test; and an analog to Wald's sequential likelihood ratio test is given, in which the optimal thresholds may vary with time.
Stability of Stochastic Neutral Cellular Neural Networks
NASA Astrophysics Data System (ADS)
Chen, Ling; Zhao, Hongyong
In this paper, we study a class of stochastic neutral cellular neural networks. By constructing a suitable Lyapunov functional and employing the nonnegative semi-martingale convergence theorem we give some sufficient conditions ensuring the almost sure exponential stability of the networks. The results obtained are helpful to design stability of networks when stochastic noise is taken into consideration. Finally, two examples are provided to show the correctness of our analysis.
Desynchronization of stochastically synchronized chemical oscillators.
Snari, Razan; Tinsley, Mark R; Wilson, Dan; Faramarzi, Sadegh; Netoff, Theoden Ivan; Moehlis, Jeff; Showalter, Kenneth
2015-12-01
Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed. PMID:26723155
Desynchronization of stochastically synchronized chemical oscillators
NASA Astrophysics Data System (ADS)
Snari, Razan; Tinsley, Mark R.; Wilson, Dan; Faramarzi, Sadegh; Netoff, Theoden Ivan; Moehlis, Jeff; Showalter, Kenneth
2015-12-01
Experimental and theoretical studies are presented on the design of perturbations that enhance desynchronization in populations of oscillators that are synchronized by periodic entrainment. A phase reduction approach is used to determine optimal perturbation timing based upon experimentally measured phase response curves. The effectiveness of the perturbation waveforms is tested experimentally in populations of periodically and stochastically synchronized chemical oscillators. The relevance of the approach to therapeutic methods for disrupting phase coherence in groups of stochastically synchronized neuronal oscillators is discussed.
Stochastic resonance during a polymer translocation process.
Mondal, Debasish; Muthukumar, M
2016-04-14
We have studied the occurrence of stochastic resonance when a flexible polymer chain undergoes a single-file translocation through a nano-pore separating two spherical cavities, under a time-periodic external driving force. The translocation of the chain is controlled by a free energy barrier determined by chain length, pore length, pore-polymer interaction, and confinement inside the donor and receiver cavities. The external driving force is characterized by a frequency and amplitude. By combining the Fokker-Planck formalism for polymer translocation and a two-state model for stochastic resonance, we have derived analytical formulas for criteria for emergence of stochastic resonance during polymer translocation. We show that no stochastic resonance is possible if the free energy barrier for polymer translocation is purely entropic in nature. The polymer chain exhibits stochastic resonance only in the presence of an energy threshold in terms of polymer-pore interactions. Once stochastic resonance is feasible, the chain entropy controls the optimal synchronization conditions significantly. PMID:27083746
Stochastic resonance during a polymer translocation process
NASA Astrophysics Data System (ADS)
Mondal, Debasish; Muthukumar, M.
2016-04-01
We have studied the occurrence of stochastic resonance when a flexible polymer chain undergoes a single-file translocation through a nano-pore separating two spherical cavities, under a time-periodic external driving force. The translocation of the chain is controlled by a free energy barrier determined by chain length, pore length, pore-polymer interaction, and confinement inside the donor and receiver cavities. The external driving force is characterized by a frequency and amplitude. By combining the Fokker-Planck formalism for polymer translocation and a two-state model for stochastic resonance, we have derived analytical formulas for criteria for emergence of stochastic resonance during polymer translocation. We show that no stochastic resonance is possible if the free energy barrier for polymer translocation is purely entropic in nature. The polymer chain exhibits stochastic resonance only in the presence of an energy threshold in terms of polymer-pore interactions. Once stochastic resonance is feasible, the chain entropy controls the optimal synchronization conditions significantly.
Automated Flight Routing Using Stochastic Dynamic Programming
NASA Technical Reports Server (NTRS)
Ng, Hok K.; Morando, Alex; Grabbe, Shon
2010-01-01
Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.
Multidimensional stochastic approximation Monte Carlo.
Zablotskiy, Sergey V; Ivanov, Victor A; Paul, Wolfgang
2016-06-01
Stochastic Approximation Monte Carlo (SAMC) has been established as a mathematically founded powerful flat-histogram Monte Carlo method, used to determine the density of states, g(E), of a model system. We show here how it can be generalized for the determination of multidimensional probability distributions (or equivalently densities of states) of macroscopic or mesoscopic variables defined on the space of microstates of a statistical mechanical system. This establishes this method as a systematic way for coarse graining a model system, or, in other words, for performing a renormalization group step on a model. We discuss the formulation of the Kadanoff block spin transformation and the coarse-graining procedure for polymer models in this language. We also apply it to a standard case in the literature of two-dimensional densities of states, where two competing energetic effects are present g(E_{1},E_{2}). We show when and why care has to be exercised when obtaining the microcanonical density of states g(E_{1}+E_{2}) from g(E_{1},E_{2}). PMID:27415383
Stochastic models of intracellular transport
NASA Astrophysics Data System (ADS)
Bressloff, Paul C.; Newby, Jay M.
2013-01-01
The interior of a living cell is a crowded, heterogenuous, fluctuating environment. Hence, a major challenge in modeling intracellular transport is to analyze stochastic processes within complex environments. Broadly speaking, there are two basic mechanisms for intracellular transport: passive diffusion and motor-driven active transport. Diffusive transport can be formulated in terms of the motion of an overdamped Brownian particle. On the other hand, active transport requires chemical energy, usually in the form of adenosine triphosphate hydrolysis, and can be direction specific, allowing biomolecules to be transported long distances; this is particularly important in neurons due to their complex geometry. In this review a wide range of analytical methods and models of intracellular transport is presented. In the case of diffusive transport, narrow escape problems, diffusion to a small target, confined and single-file diffusion, homogenization theory, and fractional diffusion are considered. In the case of active transport, Brownian ratchets, random walk models, exclusion processes, random intermittent search processes, quasi-steady-state reduction methods, and mean-field approximations are considered. Applications include receptor trafficking, axonal transport, membrane diffusion, nuclear transport, protein-DNA interactions, virus trafficking, and the self-organization of subcellular structures.
Stochastic slowdown in evolutionary processes.
Altrock, Philipp M; Gokhale, Chaitanya S; Traulsen, Arne
2010-07-01
We examine birth-death processes with state dependent transition probabilities and at least one absorbing boundary. In evolution, this describes selection acting on two different types in a finite population where reproductive events occur successively. If the two types have equal fitness the system performs a random walk. If one type has a fitness advantage it is favored by selection, which introduces a bias (asymmetry) in the transition probabilities. How long does it take until advantageous mutants have invaded and taken over? Surprisingly, we find that the average time of such a process can increase, even if the mutant type always has a fitness advantage. We discuss this finding for the Moran process and develop a simplified model which allows a more intuitive understanding. We show that this effect can occur for weak but nonvanishing bias (selection) in the state dependent transition rates and infer the scaling with system size. We also address the Wright-Fisher model commonly used in population genetics, which shows that this stochastic slowdown is not restricted to birth-death processes. PMID:20866666
A novel stochastic optimization algorithm.
Li, B; Jiang, W
2000-01-01
This paper presents a new stochastic approach SAGACIA based on proper integration of simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) for solving complex optimization problems. SAGACIA combines the advantages of SAA, GA, and CA together. It has the following features: (1) it is not the simple mix of SAA, GA, and CA; (2) it works from a population; (3) it can be easily used to solve optimization problems either with continuous variables or with discrete variables, and it does not need coding and decoding,; and (4) it can easily escape from local minima and converge quickly. Good solutions can be obtained in a very short time. The search process of SAGACIA can be explained with Markov chains. In this paper, it is proved that SAGACIA has the property of global asymptotical convergence. SAGACIA has been applied to solve such problems as scheduling, the training of artificial neural networks, and the optimizing of complex functions. In all the test cases, the performance of SAGACIA is better than that of SAA, GA, and CA. PMID:18244742
Multidimensional stochastic approximation Monte Carlo
NASA Astrophysics Data System (ADS)
Zablotskiy, Sergey V.; Ivanov, Victor A.; Paul, Wolfgang
2016-06-01
Stochastic Approximation Monte Carlo (SAMC) has been established as a mathematically founded powerful flat-histogram Monte Carlo method, used to determine the density of states, g (E ) , of a model system. We show here how it can be generalized for the determination of multidimensional probability distributions (or equivalently densities of states) of macroscopic or mesoscopic variables defined on the space of microstates of a statistical mechanical system. This establishes this method as a systematic way for coarse graining a model system, or, in other words, for performing a renormalization group step on a model. We discuss the formulation of the Kadanoff block spin transformation and the coarse-graining procedure for polymer models in this language. We also apply it to a standard case in the literature of two-dimensional densities of states, where two competing energetic effects are present g (E1,E2) . We show when and why care has to be exercised when obtaining the microcanonical density of states g (E1+E2) from g (E1,E2) .
Lower hybrid wavepacket stochasticity revisited
Fuchs, V.; Krlín, L.; Pánek, R.; Preinhaelter, J.; Seidl, J.; Urban, J.
2014-02-12
Analysis is presented in support of the explanation in Ref. [1] for the observation of relativistic electrons during Lower Hybrid (LH) operation in EC pre-heated plasma at the WEGA stellarator [1,2]. LH power from the WEGA TE11 circular waveguide, 9 cm diameter, un-phased, 2.45 GHz antenna, is radiated into a B≅0.5 T, Ðœ„n{sub e}≅5×10{sup 17} 1/m{sup 3} plasma at T{sub e}≅10 eV bulk temperature with an EC generated 50 keV component [1]. The fast electrons cycle around flux or drift surfaces with few collisions, sufficient for randomizing phases but insufficient for slowing fast electrons down, and thus repeatedly interact with the rf field close to the antenna mouth, gaining energy in the process. Our antenna calculations reveal a standing electric field pattern at the antenna mouth, with which we formulate the electron dynamics via a relativistic Hamiltonian. A simple approximation of the equations of motion leads to a relativistic generalization of the area-preserving Fermi-Ulam (F-U) map [3], allowing phase-space global stochasticity analysis. At typical WEGA plasma and antenna conditions, the F-U map predicts an LH driven current of about 230 A, at about 225 W of dissipated power, in good agreement with the measurements and analysis reported in [1].
Stochastic phase-change neurons
NASA Astrophysics Data System (ADS)
Tuma, Tomas; Pantazi, Angeliki; Le Gallo, Manuel; Sebastian, Abu; Eleftheriou, Evangelos
2016-08-01
Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.
Stochastic phase-change neurons.
Tuma, Tomas; Pantazi, Angeliki; Le Gallo, Manuel; Sebastian, Abu; Eleftheriou, Evangelos
2016-08-01
Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals. PMID:27183057
Stochastic Resonance In Visual Perception
NASA Astrophysics Data System (ADS)
Simonotto, Enrico
1996-03-01
Stochastic resonance (SR) is a well established physical phenomenon wherein some measure of the coherence of a weak signal can be optimized by random fluctuations, or "noise" (K. Wiesenfeld and F. Moss, Nature), 373, 33 (1995). In all experiments to date the coherence has been measured using numerical analysis of the data, for example, signal-to-noise ratios obtained from power spectra. But, can this analysis be replaced by a perceptive task? Previously we had demonstrated this possibility with a numerical model of perceptual bistability applied to the interpretation of ambiguous figures(M. Riani and E. Simonotto, Phys. Rev. Lett.), 72, 3120 (1994). Here I describe an experiment wherein SR is detected in visual perception. A recognizible grayscale photograph was digitized and presented. The picture was then placed beneath a threshold. Every pixel for which the grayscale exceeded the threshold was painted white, and all others black. For large enough threshold, the picture is unrecognizable, but the addition of a random number to every pixel renders it interpretable(C. Seife and M. Roberts, The Economist), 336, 59, July 29 (1995). However the addition of dynamical noise to the pixels much enhances an observer's ability to interpret the picture. Here I report the results of psychophysics experiments wherein the effects of both the intensity of the noise and its correlation time were studied.
X. Frank Xu
2010-03-30
Multiscale modeling of stochastic systems, or uncertainty quantization of multiscale modeling is becoming an emerging research frontier, with rapidly growing engineering applications in nanotechnology, biotechnology, advanced materials, and geo-systems, etc. While tremendous efforts have been devoted to either stochastic methods or multiscale methods, little combined work had been done on integration of multiscale and stochastic methods, and there was no method formally available to tackle multiscale problems involving uncertainties. By developing an innovative Multiscale Stochastic Finite Element Method (MSFEM), this research has made a ground-breaking contribution to the emerging field of Multiscale Stochastic Modeling (MSM) (Fig 1). The theory of MSFEM basically decomposes a boundary value problem of random microstructure into a slow scale deterministic problem and a fast scale stochastic one. The slow scale problem corresponds to common engineering modeling practices where fine-scale microstructure is approximated by certain effective constitutive constants, which can be solved by using standard numerical solvers. The fast scale problem evaluates fluctuations of local quantities due to random microstructure, which is important for scale-coupling systems and particularly those involving failure mechanisms. The Green-function-based fast-scale solver developed in this research overcomes the curse-of-dimensionality commonly met in conventional approaches, by proposing a random field-based orthogonal expansion approach. The MSFEM formulated in this project paves the way to deliver the first computational tool/software on uncertainty quantification of multiscale systems. The applications of MSFEM on engineering problems will directly enhance our modeling capability on materials science (composite materials, nanostructures), geophysics (porous media, earthquake), biological systems (biological tissues, bones, protein folding). Continuous development of MSFEM will
Replication timing and its emergence from stochastic processes
Bechhoefer, John; Rhind, Nicholas
2012-01-01
The temporal organization of DNA replication has puzzled cell biologists since before the mechanism of replication was understood. The realization that replication timing correlates with important features, such as transcription, chromatin structure and genome evolution, and is misregulated in cancer and aging has only deepened the fascination. Many ideas about replication timing have been proposed, but most have been short on mechanistic detail. However, recent work has begun to elucidate basic principles of replication timing. In particular, mathematical modeling of replication kinetics in several systems has shown that the reproducible replication timing patterns seen in population studies can be explained by stochastic origin firing at the single-cell level. This work suggests that replication timing need not be controlled by a hierarchical mechanism that imposes replication timing from a central regulator, but instead results from simple rules that affect individual origins. PMID:22520729
Vaginal cancer; Cancer - vagina; Tumor - vaginal ... Most vaginal cancers occur when another cancer, such as cervical or endometrial cancer , spreads. This is called secondary vaginal cancer. Cancer ...
Random musings on stochastics (Lorenz Lecture)
NASA Astrophysics Data System (ADS)
Koutsoyiannis, D.
2014-12-01
In 1960 Lorenz identified the chaotic nature of atmospheric dynamics, thus highlighting the importance of the discovery of chaos by Poincare, 70 years earlier, in the motion of three bodies. Chaos in the macroscopic world offered a natural way to explain unpredictability, that is, randomness. Concurrently with Poincare's discovery, Boltzmann introduced statistical physics, while soon after Borel and Lebesgue laid the foundation of measure theory, later (in 1930s) used by Kolmogorov as the formal foundation of probability theory. Subsequently, Kolmogorov and Khinchin introduced the concepts of stochastic processes and stationarity, and advanced the concept of ergodicity. All these areas are now collectively described by the term "stochastics", which includes probability theory, stochastic processes and statistics. As paradoxical as it may seem, stochastics offers the tools to deal with chaos, even if it results from deterministic dynamics. As chaos entails uncertainty, it is more informative and effective to replace the study of exact system trajectories with that of probability densities. Also, as the exact laws of complex systems can hardly be deduced by synthesis of the detailed interactions of system components, these laws should inevitably be inferred by induction, based on observational data and using statistics. The arithmetic of stochastics is quite different from that of regular numbers. Accordingly, it needs the development of intuition and interpretations which differ from those built upon deterministic considerations. Using stochastic tools in a deterministic context may result in mistaken conclusions. In an attempt to contribute to a more correct interpretation and use of stochastic concepts in typical tasks of nonlinear systems, several examples are studied, which aim (a) to clarify the difference in the meaning of linearity in deterministic and stochastic context; (b) to contribute to a more attentive use of stochastic concepts (entropy, statistical
Stochastic modeling of the auroral electrojet index
NASA Astrophysics Data System (ADS)
Anh, V. V.; Yong, J. M.; Yu, Z. G.
2008-10-01
Substorms are often identified by bursts of activities in the magnetosphere-ionosphere system characterized by the auroral electrojet (AE) index. The highly complex nature of substorm-related bursts suggests that a stochastic approach would be needed. Stochastic models including fractional Brownian motion, linear fractional stable motion, Fokker-Planck equation and Itô-type stochastic differential equation have been suggested to model the AE index. This paper provides a stochastic model for the AE in the form of fractional stochastic differential equation. The long memory of the AE time series is represented by a fractional derivative, while its bursty behavior is modeled by a Lévy noise with inverse Gaussian marginal distribution. The equation has the form of the classical Stokes-Boussinesq-Basset equation of motion for a spherical particle in a fluid with retarded viscosity. Parameter estimation and approximation schemes are detailed for the simulation of the equation. The fractional order of the equation conforms with the previous finding that the fluctuations of the magnetosphere-ionosphere system as seen in the AE reflect the fluctuations in the solar wind: they both possess the same extent of long-range dependence. The introduction of a fractional derivative term into the equation to capture the extent of long-range dependence together with an inverse Gaussian noise input describe the right amount of intermittency inherent in the AE data.
Non-Markovian stochastic evolution equations
NASA Astrophysics Data System (ADS)
Costanza, G.
2014-05-01
Non-Markovian continuum stochastic and deterministic equations are derived from a set of discrete stochastic and deterministic evolution equations. Examples are given of discrete evolution equations whose updating rules depend on two or more previous time steps. Among them, the continuum stochastic evolution equation of the Newton second law, the stochastic evolution equation of a wave equation, the stochastic evolution equation for the scalar meson field, etc. are obtained as special cases. Extension to systems of evolution equations and other extensions are considered and examples are given. The concept of isomorphism and almost isomorphism are introduced in order to compare the coefficients of the continuum evolution equations of two different smoothing procedures that arise from two different approaches. Usually these discrepancies arising from two sources: On the one hand, the use of different representations of the generalized functions appearing in the models and, on the other hand, the different approaches used to describe the models. These new concept allows to overcome controversies that were appearing during decades in the literature.
Stochastic volatility models and Kelvin waves
NASA Astrophysics Data System (ADS)
Lipton, Alex; Sepp, Artur
2008-08-01
We use stochastic volatility models to describe the evolution of an asset price, its instantaneous volatility and its realized volatility. In particular, we concentrate on the Stein and Stein model (SSM) (1991) for the stochastic asset volatility and the Heston model (HM) (1993) for the stochastic asset variance. By construction, the volatility is not sign definite in SSM and is non-negative in HM. It is well known that both models produce closed-form expressions for the prices of vanilla option via the Lewis-Lipton formula. However, the numerical pricing of exotic options by means of the finite difference and Monte Carlo methods is much more complex for HM than for SSM. Until now, this complexity was considered to be an acceptable price to pay for ensuring that the asset volatility is non-negative. We argue that having negative stochastic volatility is a psychological rather than financial or mathematical problem, and advocate using SSM rather than HM in most applications. We extend SSM by adding volatility jumps and obtain a closed-form expression for the density of the asset price and its realized volatility. We also show that the current method of choice for solving pricing problems with stochastic volatility (via the affine ansatz for the Fourier-transformed density function) can be traced back to the Kelvin method designed in the 19th century for studying wave motion problems arising in fluid dynamics.
Stochastic resonance in models of neuronal ensembles
NASA Astrophysics Data System (ADS)
Chialvo, Dante R.; Longtin, André; Müautller-Gerking, Johannes
1997-02-01
Two recently suggested mechanisms for the neuronal encoding of sensory information involving the effect of stochastic resonance with aperiodic time-varying inputs are considered. It is shown, using theoretical arguments and numerical simulations, that the nonmonotonic behavior with increasing noise of the correlation measures used for the so-called aperiodic stochastic resonance (ASR) scenario does not rely on the cooperative effect typical of stochastic resonance in bistable and excitable systems. Rather, ASR with slowly varying signals is more properly interpreted as linearization by noise. Consequently, the broadening of the ``resonance curve'' in the multineuron stochastic resonance without tuning scenario can also be explained by this linearization. Computation of the input-output correlation as a function of both signal frequency and noise for the model system further reveals conditions where noise-induced firing with aperiodic inputs will benefit from stochastic resonance rather than linearization by noise. Thus, our study clarifies the tuning requirements for the optimal transduction of subthreshold aperiodic signals. It also shows that a single deterministic neuron can perform as well as a network when biased into a suprathreshold regime. Finally, we show that the inclusion of a refractory period in the spike-detection scheme produces a better correlation between instantaneous firing rate and input signal.
Discrete analysis of stochastic NMR.II
NASA Astrophysics Data System (ADS)
Wong, S. T. S.; Rods, M. S.; Newmark, R. D.; Budinger, T. F.
Stochastic NMR is an efficient technique for high-field in vivo imaging and spectroscopic studies where the peak RF power required may be prohibitively high for conventional pulsed NMR techniques. A stochastic NMR experiment excites the spin system with a sequence of RF pulses where the flip angles or the phases of the pulses are samples of a discrete stochastic process. In a previous paper the stochastic experiment was analyzed and analytic expressions for the input-output cross-correlations, average signal power, and signal spectral density were obtained for a general stochastic RF excitation. In this paper specific cases of excitation with random phase, fixed flip angle, and excitation with two random components in quadrature are analyzed. The input-output cross-correlation for these two types of excitations is shown to be Lorentzian. Line broadening is the only spectral distortion as the RF excitation power is increased. The systematic noise power is inversely proportional to the number of data points N used in the spectral reconstruction. The use of a complete maximum length sequence (MLS) may improve the signal-to-systematic-noise ratio by 20 dB relative to random binary excitation, but peculiar features in the higher-order autocorrelations of MLS cause noise-like distortion in the reconstructed spectra when the excitation power is high. The amount of noise-like distortion depends on the choice of the MLS generator.
EDITORIAL: Stochasticity in fusion plasmas
NASA Astrophysics Data System (ADS)
Finken, K. H.
2006-04-01
In recent years the importance of externally imposed resonant magnetic fields on plasma has become more and more recognized. These fields will cause ergodization at well defined plasma layers and can induce large size islands at rational q-surfaces. A hope for future large scale tokamak devices is the development of a reliable method for mitigating the large ELMs of type 1 ELMy-H-modes by modifying the edge transport. Other topics of interest for fusion reactors are the option of distributing the heat to a large area and optimizing methods for heat and particle exhaust, or the understanding of the transport around tearing mode instabilities. The cluster of papers in this issue of Nuclear Fusion is a successor to the 2004 special issue (Nuclear Fusion 44 S1-122 ) intended to raise interest in the subject. The contents of this present issue are based on presentations at the Second Workshop on Stochasticity in Fusion Plasmas (SFP) held in Juelich, Germany, 15-17 March 2005. The SFP workshops have been stimulated by the installation of the Dynamic Ergodic Divertor (DED) in the TEXTOR tokamak. It has attracted colleagues working on various plasma configurations such as tokamaks, stellarators or reversed field pinches. The workshop was originally devoted to phenomena on the plasma edge but it has been broadened to transport questions over the whole plasma cross-section. It is a meeting place for experimental and theoretical working groups. The next workshop is planned for February/March 2007 in Juelich, Germany. For details see http://www.fz-juelich.de/sfp/. The content of the workshop is summarized in the following conference summary (K.H. Finken 2006 Nuclear Fusion 46 S107-112). At the workshop experimental results on the plasma transport resulting from ergodization in various devices were presented. Highlights were the results from DIII-D on the mitigation of ELMs (see also T.E. Evans et al 2005 Nuclear Fusion 45 595 ). Theoretical work was focused around the topics
Stochastic approach to equilibrium and nonequilibrium thermodynamics
NASA Astrophysics Data System (ADS)
Tomé, Tânia; de Oliveira, Mário J.
2015-04-01
We develop the stochastic approach to thermodynamics based on stochastic dynamics, which can be discrete (master equation) and continuous (Fokker-Planck equation), and on two assumptions concerning entropy. The first is the definition of entropy itself and the second the definition of entropy production rate, which is non-negative and vanishes in thermodynamic equilibrium. Based on these assumptions, we study interacting systems with many degrees of freedom in equilibrium or out of thermodynamic equilibrium and how the macroscopic laws are derived from the stochastic dynamics. These studies include the quasiequilibrium processes; the convexity of the equilibrium surface; the monotonic time behavior of thermodynamic potentials, including entropy; the bilinear form of the entropy production rate; the Onsager coefficients and reciprocal relations; and the nonequilibrium steady states of chemical reactions.
Stochastic Differential Equation of Earthquakes Series
NASA Astrophysics Data System (ADS)
Mariani, Maria C.; Tweneboah, Osei K.; Gonzalez-Huizar, Hector; Serpa, Laura
2016-07-01
This work is devoted to modeling earthquake time series. We propose a stochastic differential equation based on the superposition of independent Ornstein-Uhlenbeck processes driven by a Γ (α, β ) process. Superposition of independent Γ (α, β ) Ornstein-Uhlenbeck processes offer analytic flexibility and provides a class of continuous time processes capable of exhibiting long memory behavior. The stochastic differential equation is applied to the study of earthquakes by fitting the superposed Γ (α, β ) Ornstein-Uhlenbeck model to earthquake sequences in South America containing very large events (Mw ≥ 8). We obtained very good fit of the observed magnitudes of the earthquakes with the stochastic differential equations, which supports the use of this methodology for the study of earthquakes sequence.
Modeling stochasticity in biochemical reaction networks
NASA Astrophysics Data System (ADS)
Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.
2016-03-01
Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.
Stochastic resonance in geomagnetic polarity reversals.
Consolini, Giuseppe; De Michelis, Paola
2003-02-01
Among noise-induced cooperative phenomena a peculiar relevance is played by stochastic resonance. In this paper we offer evidence that geomagnetic polarity reversals may be due to a stochastic resonance process. In detail, analyzing the distribution function P(tau) of polarity residence times (chrons), we found the evidence of a stochastic synchronization process, i.e., a series of peaks in the P(tau) at T(n) approximately (2n+1)T(Omega)/2 with n=0,1,...,j and T(omega) approximately 0.1 Myr. This result is discussed in connection with both the typical time scale of Earth's orbit eccentricity variation and the recent results on the typical time scale of climatic long-term variation. PMID:12633403
Stochastic Differential Equation of Earthquakes Series
NASA Astrophysics Data System (ADS)
Mariani, Maria C.; Tweneboah, Osei K.; Gonzalez-Huizar, Hector; Serpa, Laura
2016-05-01
This work is devoted to modeling earthquake time series. We propose a stochastic differential equation based on the superposition of independent Ornstein-Uhlenbeck processes driven by a Γ (α, β ) process. Superposition of independent Γ (α, β ) Ornstein-Uhlenbeck processes offer analytic flexibility and provides a class of continuous time processes capable of exhibiting long memory behavior. The stochastic differential equation is applied to the study of earthquakes by fitting the superposed Γ (α, β ) Ornstein-Uhlenbeck model to earthquake sequences in South America containing very large events (Mw ≥ 8). We obtained very good fit of the observed magnitudes of the earthquakes with the stochastic differential equations, which supports the use of this methodology for the study of earthquakes sequence.
Stochastic Averaging of Duhem Hysteretic Systems
NASA Astrophysics Data System (ADS)
YING, Z. G.; ZHU, W. Q.; NI, Y. Q.; KO, J. M.
2002-06-01
The response of Duhem hysteretic system to externally and/or parametrically non-white random excitations is investigated by using the stochastic averaging method. A class of integrable Duhem hysteresis models covering many existing hysteresis models is identified and the potential energy and dissipated energy of Duhem hysteretic component are determined. The Duhem hysteretic system under random excitations is replaced equivalently by a non-hysteretic non-linear random system. The averaged Ito's stochastic differential equation for the total energy is derived and the Fokker-Planck-Kolmogorov equation associated with the averaged Ito's equation is solved to yield stationary probability density of total energy, from which the statistics of system response can be evaluated. It is observed that the numerical results by using the stochastic averaging method is in good agreement with that from digital simulation.
Derivatives of the Stochastic Growth Rate
Steinsaltz, David; Tuljapurkar, Shripad; Horvitz, Carol
2011-01-01
We consider stochastic matrix models for population driven by random environments which form a Markov chain. The top Lyapunov exponent a, which describes the long-term growth rate, depends smoothly on the demographic parameters (represented as matrix entries) and on the parameters that define the stochastic matrix of the driving Markov chain. The derivatives of a — the “stochastic elasticities” — with respect to changes in the demographic parameters were derived by Tuljapurkar (1990). These results are here extended to a formula for the derivatives with respect to changes in the Markov chain driving the environments. We supplement these formulas with rigorous bounds on computational estimation errors, and with rigorous derivations of both the new and the old formulas. PMID:21463645
Regeneration of stochastic processes: an inverse method
NASA Astrophysics Data System (ADS)
Ghasemi, F.; Peinke, J.; Sahimi, M.; Rahimi Tabar, M. R.
2005-10-01
We propose a novel inverse method that utilizes a set of data to construct a simple equation that governs the stochastic process for which the data have been measured, hence enabling us to reconstruct the stochastic process. As an example, we analyze the stochasticity in the beat-to-beat fluctuations in the heart rates of healthy subjects as well as those with congestive heart failure. The inverse method provides a novel technique for distinguishing the two classes of subjects in terms of a drift and a diffusion coefficients which behave completely differently for the two classes of subjects, hence potentially providing a novel diagnostic tool for distinguishing healthy subjects from those with congestive heart failure, even at the early stages of the disease development.
Stochastic approach to equilibrium and nonequilibrium thermodynamics.
Tomé, Tânia; de Oliveira, Mário J
2015-04-01
We develop the stochastic approach to thermodynamics based on stochastic dynamics, which can be discrete (master equation) and continuous (Fokker-Planck equation), and on two assumptions concerning entropy. The first is the definition of entropy itself and the second the definition of entropy production rate, which is non-negative and vanishes in thermodynamic equilibrium. Based on these assumptions, we study interacting systems with many degrees of freedom in equilibrium or out of thermodynamic equilibrium and how the macroscopic laws are derived from the stochastic dynamics. These studies include the quasiequilibrium processes; the convexity of the equilibrium surface; the monotonic time behavior of thermodynamic potentials, including entropy; the bilinear form of the entropy production rate; the Onsager coefficients and reciprocal relations; and the nonequilibrium steady states of chemical reactions. PMID:25974471
Computational stochastic model of ions implantation
Zmievskaya, Galina I. Bondareva, Anna L.; Levchenko, Tatiana V.; Maino, Giuseppe
2015-03-10
Implantation flux ions into crystal leads to phase transition /PT/ 1-st kind. Damaging lattice is associated with processes clustering vacancies and gaseous bubbles as well their brownian motion. System of stochastic differential equations /SDEs/ Ito for evolution stochastic dynamical variables corresponds to the superposition Wiener processes. The kinetic equations in partial derivatives /KE/, Kolmogorov-Feller and Einstein-Smolukhovskii, were formulated for nucleation into lattice of weakly soluble gases. According theory, coefficients of stochastic and kinetic equations uniquely related. Radiation stimulated phase transition are characterized by kinetic distribution functions /DFs/ of implanted clusters versus their sizes and depth of gas penetration into lattice. Macroscopic parameters of kinetics such as the porosity and stress calculated in thin layers metal/dielectric due to Xe{sup ++} irradiation are attracted as example. Predictions of porosity, important for validation accumulation stresses in surfaces, can be applied at restoring of objects the cultural heritage.
Structural factoring approach for analyzing stochastic networks
NASA Technical Reports Server (NTRS)
Hayhurst, Kelly J.; Shier, Douglas R.
1991-01-01
The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.
NASA Technical Reports Server (NTRS)
Sadunas, J. A.; French, E. P.; Sexton, H.
1973-01-01
A 1/25 scale model S-2 stage base region thermal environment test is presented. Analytical results are included which reflect the effect of engine operating conditions, model scale, turbo-pump exhaust gas injection on base region thermal environment. Comparisons are made between full scale flight data, model test data, and analytical results. The report is prepared in two volumes. The description of analytical predictions and comparisons with flight data are presented. Tabulation of the test data is provided.
Existence Theory for Stochastic Power Law Fluids
NASA Astrophysics Data System (ADS)
Breit, Dominic
2015-06-01
We consider the equations of motion for an incompressible non-Newtonian fluid in a bounded Lipschitz domain during the time interval (0, T) together with a stochastic perturbation driven by a Brownian motion W. The balance of momentum reads as where v is the velocity, the pressure and f an external volume force. We assume the common power law model and show the existence of martingale weak solution provided . Our approach is based on the -truncation and a harmonic pressure decomposition which are adapted to the stochastic setting.
Stochastic processes in muon ionization cooling
NASA Astrophysics Data System (ADS)
Errede, D.; Makino, K.; Berz, M.; Johnstone, C. J.; Van Ginneken, A.
2004-02-01
A muon ionization cooling channel consists of three major components: the magnet optics, an acceleration cavity, and an energy absorber. The absorber of liquid hydrogen contained by thin aluminum windows is the only component which introduces stochastic processes into the otherwise deterministic acceleration system. The scattering dynamics of the transverse coordinates is described by Gaussian distributions. The asymmetric energy loss function is represented by the Vavilov distribution characterized by the minimum number of collisions necessary for a particle undergoing loss of the energy distribution average resulting from the Bethe-Bloch formula. Examples of the interplay between stochastic processes and deterministic beam dynamics are given.
Fuzzy stochastic elements method. Spectral approach
NASA Astrophysics Data System (ADS)
Sniady, Pawel; Mazur-Sniady, Krystyna; Sieniawska, Roza; Zukowski, Stanislaw
2013-05-01
We study a complex dynamic problem, which concerns a structure with uncertain parameters subjected to a stochastic excitation. Formulation of such a problem introduces fuzzy random variables for parameters of the structure and fuzzy stochastic processes for the load process. The uncertainty has two sources, namely the randomness of structural parameters such as geometry characteristics, material and damping properties, load process and imprecision of the theoretical model and incomplete information or uncertain data. All of these have a great influence on the response of the structure. By analyzing such problems we describe the random variability using the probability theory and the imprecision by use of fuzzy sets. Due to the fact that it is difficult to find an analytic expression for the inversion of the stochastic operator in the stochastic differential equation, a number of approximate methods have been proposed in the literature which can be connected to the finite element method. To evaluate the effects of excitation in the frequency domain we use the spectral density function. The spectral analysis is widely used in stochastic dynamics field of linear systems for stationary random excitation. The concept of the evolutionary spectral density is used in the case of non-stationary random excitation. We solve the considered problem using fuzzy stochastic finite element method. The solution is based on the idea of a fuzzy random frequency response vector for stationary input excitation and a transient fuzzy random frequency response vector for the fuzzy non-stationary one. We use the fuzzy random frequency response vector and the transient fuzzy random frequency response vector in the context of spectral analysis in order to determine the influence of structural uncertainty on the fuzzy random response of the structure. We study a linear system with random parameters subjected to two particular cases of stochastic excitation in a frequency domain. The first one
Digital switching noise as a stochastic process
NASA Astrophysics Data System (ADS)
Boselli, Giorgio; Trucco, Gabriella; Liberali, Valentino
2007-06-01
Switching activity of logic gates in a digital system is a deterministic process, depending on both circuit parameters and input signals. However, the huge number of logic blocks in a digital system makes digital switching a cognitively stochastic process. Switching activity is the source of the so-called "digital noise", which can be analyzed using a stochastic approach. For an asynchronous digital network, we can model digital switching currents as a shot noise process, deriving both its amplitude distribution and its power spectral density. From spectral distribution of digital currents, we can also calculate the spectral distribution and the power of disturbances injected into the on-chip power supply lines.
Stochastic time-optimal control problems
NASA Technical Reports Server (NTRS)
Zhang, W.; Elliot, D.
1988-01-01
Two types of stochastic time-optimal controls in a one-dimensional setting are considered. Multidimensional problems, in the case of complete state information available and the system modeled by stochastic differential equations, are studied under the formulation of minimizing the expected transient-response time. The necessary condition of optimality is the satisfaction for the value function of a parabolic partial differential equation with boundary conditions. The sufficient condition of optimality is also provided, based on Dynkin's formula. Finally, three examples are given.
Monostable array-enhanced stochastic resonance.
Lindner, J F; Breen, B J; Wills, M E; Bulsara, A R; Ditto, W L
2001-05-01
We present a simple nonlinear system that exhibits multiple distinct stochastic resonances. By adjusting the noise and coupling of an array of underdamped, monostable oscillators, we modify the array's natural frequencies so that the spectral response of a typical oscillator in an array of N oscillators exhibits N-1 different stochastic resonances. Such families of resonances may elucidate and facilitate a variety of noise-mediated cooperative phenomena, such as noise-enhanced propagation, in a broad class of similar nonlinear systems. PMID:11414887
Stochastic stability and instability of model ecosystems
NASA Technical Reports Server (NTRS)
Ladde, G. S.; Siljak, D. D.
1975-01-01
In this work, we initiate a stability study of multispecies communities in stochastic environment by using Ito's differential equations as community models. By applying the direct method of Liapunov, we obtain sufficient conditions for stability and instability in the mean of the equilibrium populations. The conditions are expressed in terms of the dominant diagonal property of community matrices, which is a suitable mechanism for resolving the central problem of 'complexity vs stability' in model ecosystems. As a by-product of this analysis we exhibit important structural properties of the stochastic density-dependent models, and establish tolerance of community stability to a broad class of nonlinear time-varying perturbations.
Stochastic resonance in an intracellular genetic perceptron.
Bates, Russell; Blyuss, Oleg; Zaikin, Alexey
2014-03-01
Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity. PMID:24730883
Stochastic cellular automata model of neural networks.
Goltsev, A V; de Abreu, F V; Dorogovtsev, S N; Mendes, J F F
2010-06-01
We propose a stochastic dynamical model of noisy neural networks with complex architectures and discuss activation of neural networks by a stimulus, pacemakers, and spontaneous activity. This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behaviors. We show that if spontaneous activity (noise) reaches a threshold level then global neural oscillations emerge. Stochastic resonance is a precursor of this dynamical phase transition. These oscillations are an intrinsic property of even small groups of 50 neurons. PMID:20866454
Pricing foreign equity option with stochastic volatility
NASA Astrophysics Data System (ADS)
Sun, Qi; Xu, Weidong
2015-11-01
In this paper we propose a general foreign equity option pricing framework that unifies the vast foreign equity option pricing literature and incorporates the stochastic volatility into foreign equity option pricing. Under our framework, the time-changed Lévy processes are used to model the underlying assets price of foreign equity option and the closed form pricing formula is obtained through the use of characteristic function methodology. Numerical tests indicate that stochastic volatility has a dramatic effect on the foreign equity option prices.
Fermilab recycler stochastic cooling commissioning and performance
D. Broemmelsiek; Ralph Pasquinelli
2003-06-04
The Fermilab Recycler is a fixed 8 GeV kinetic energy storage ring located in the Fermilab Main Injector tunnel near the ceiling. The Recycler has two roles in Run II. First, to store antiprotons from the Fermilab Antiproton Accumulator so that the antiproton production rate is no longer compromised by large numbers of antiprotons stored in the Accumulator. Second, to receive antiprotons from the Fermilab Tevatron at the end of luminosity periods. To perform each of these roles, stochastic cooling in the Recycler is needed to preserve and cool antiprotons in preparation for transfer to the Tevatron. The commissioning and performance of the Recycler stochastic cooling systems will be reviewed.
On orthogonality preserving quadratic stochastic operators
Mukhamedov, Farrukh; Taha, Muhammad Hafizuddin Mohd
2015-05-15
A quadratic stochastic operator (in short QSO) is usually used to present the time evolution of differing species in biology. Some quadratic stochastic operators have been studied by Lotka and Volterra. In the present paper, we first give a simple characterization of Volterra QSO in terms of absolutely continuity of discrete measures. Further, we introduce a notion of orthogonal preserving QSO, and describe such kind of operators defined on two dimensional simplex. It turns out that orthogonal preserving QSOs are permutations of Volterra QSO. The associativity of genetic algebras generated by orthogonal preserving QSO is studied too.
Binomial moment equations for stochastic reaction systems.
Barzel, Baruch; Biham, Ofer
2011-04-15
A highly efficient formulation of moment equations for stochastic reaction networks is introduced. It is based on a set of binomial moments that capture the combinatorics of the reaction processes. The resulting set of equations can be easily truncated to include moments up to any desired order. The number of equations is dramatically reduced compared to the master equation. This formulation enables the simulation of complex reaction networks, involving a large number of reactive species much beyond the feasibility limit of any existing method. It provides an equation-based paradigm to the analysis of stochastic networks, complementing the commonly used Monte Carlo simulations. PMID:21568538
Minimum uncertainty and squeezing in diffusion processes and stochastic quantization
NASA Technical Reports Server (NTRS)
Demartino, S.; Desiena, S.; Illuminati, Fabrizo; Vitiello, Giuseppe
1994-01-01
We show that uncertainty relations, as well as minimum uncertainty coherent and squeezed states, are structural properties for diffusion processes. Through Nelson stochastic quantization we derive the stochastic image of the quantum mechanical coherent and squeezed states.
Absolute Value Boundedness, Operator Decomposition, and Stochastic Media and Equations
NASA Technical Reports Server (NTRS)
Adomian, G.; Miao, C. C.
1973-01-01
The research accomplished during this period is reported. Published abstracts and technical reports are listed. Articles presented include: boundedness of absolute values of generalized Fourier coefficients, propagation in stochastic media, and stationary conditions for stochastic differential equations.
Propagation of ultra-short solitons in stochastic Maxwell's equations
Kurt, Levent; Schäfer, Tobias
2014-01-15
We study the propagation of ultra-short short solitons in a cubic nonlinear medium modeled by nonlinear Maxwell's equations with stochastic variations of media. We consider three cases: variations of (a) the dispersion, (b) the phase velocity, (c) the nonlinear coefficient. Using a modified multi-scale expansion for stochastic systems, we derive new stochastic generalizations of the short pulse equation that approximate the solutions of stochastic nonlinear Maxwell's equations. Numerical simulations show that soliton solutions of the short pulse equation propagate stably in stochastic nonlinear Maxwell's equations and that the generalized stochastic short pulse equations approximate the solutions to the stochastic Maxwell's equations over the distances under consideration. This holds for both a pathwise comparison of the stochastic equations as well as for a comparison of the resulting probability densities.
A Note on the Stochastic Nature of Feynman Quantum Paths
NASA Astrophysics Data System (ADS)
L. Botelho, Luiz C.
2016-06-01
We propose a Fresnel stochastic white noise framework to analyze the stochastic nature of the Feynman paths entering on the Feynman Path Integral expression for the Feynman Propagator of a particle quantum mechanically moving under a time-independent potential.
STOCHASTICITY IN STREAM FISH COMMUNITIES: AN ALTERNATIVE INTERPRETATION
Grossman argued that communities can be classified as either deterministic or stochastic. Deterministic communities are characterized by persistence (or succession toward a climax) while communities that lack these properties are stochastic. These descriptors were intended to rep...
Stochastic Human Exposure and Dose Simulation Model for Pesticides
SHEDS-Pesticides (Stochastic Human Exposure and Dose Simulation Model for Pesticides) is a physically-based stochastic model developed to quantify exposure and dose of humans to multimedia, multipathway pollutants. Probabilistic inputs are combined in physical/mechanistic algorit...
On strongly GA-convex functions and stochastic processes
Bekar, Nurgül Okur; Akdemir, Hande Günay; İşcan, İmdat
2014-08-20
In this study, we introduce strongly GA-convex functions and stochastic processes. We provide related well-known Kuhn type results and Hermite-Hadamard type inequality for strongly GA-convex functions and stochastic processes.
On strongly GA-convex functions and stochastic processes
NASA Astrophysics Data System (ADS)
Bekar, Nurgül Okur; Akdemir, Hande Günay; Işcan, Imdat
2014-08-01
In this study, we introduce strongly GA-convex functions and stochastic processes. We provide related well-known Kuhn type results and Hermite-Hadamard type inequality for strongly GA-convex functions and stochastic processes.
Cao Yang . E-mail: ycao@cs.ucsb.edu; Gillespie, Dan . E-mail: GillespieDT@mailaps.org; Petzold, Linda . E-mail: petzold@engineering.ucsb.edu
2005-07-01
In this paper, we introduce a multiscale stochastic simulation algorithm (MSSA) which makes use of Gillespie's stochastic simulation algorithm (SSA) together with a new stochastic formulation of the partial equilibrium assumption (PEA). This method is much more efficient than SSA alone. It works even with a very small population of fast species. Implementation details are discussed, and an application to the modeling of the heat shock response of E. Coli is presented which demonstrates the excellent efficiency and accuracy obtained with the new method.
Towards sub-optimal stochastic control of partially observable stochastic systems
NASA Technical Reports Server (NTRS)
Ruzicka, G. J.
1980-01-01
A class of multidimensional stochastic control problems with noisy data and bounded controls encountered in aerospace design is examined. The emphasis is on suboptimal design, the optimality being taken in quadratic mean sense. To that effect the problem is viewed as a stochastic version of the Lurie problem known from nonlinear control theory. The main result is a separation theorem (involving a nonlinear Kalman-like filter) suitable for Lurie-type approximations. The theorem allows for discontinuous characteristics. As a byproduct the existence of strong solutions to a class of non-Lipschitzian stochastic differential equations in dimensions is proven.
Stochastic model of the residual acceleration environment in microgravity
NASA Technical Reports Server (NTRS)
Vinals, Jorge
1994-01-01
We describe a theoretical investigation of the effects that stochastic residual accelerations (g-jitter) onboard spacecraft can have on experiments conducted in a microgravity environment. We first introduce a stochastic model of the residual acceleration field, and develop a numerical algorithm to solve the equations governing fluid flow that allow for a stochastic body force. We next summarize our studies of two generic situations: stochastic parametric resonance and the onset of convective flow induced by a fluctuating acceleration field.
Fingering in Stochastic Growth Models
Aristotelous, Andreas C.; Durrett, Richard
2015-01-01
Motivated by the widespread use of hybrid-discrete cellular automata in modeling cancer, two simple growth models are studied on the two dimensional lattice that incorporate a nutrient, assumed to be oxygen. In the first model the oxygen concentration u(x, t) is computed based on the geometry of the growing blob, while in the second one u(x, t) satisfies a reaction-diffusion equation. A threshold θ value exists such that cells give birth at rate β(u(x, t) − θ)+ and die at rate δ(θ − u(x, t)+. In the first model, a phase transition was found between growth as a solid blob and “fingering” at a threshold θc = 0.5, while in the second case fingering always occurs, i.e., θc = 0. PMID:26430353
Dosimetry robustness with stochastic optimization
NASA Astrophysics Data System (ADS)
Nohadani, Omid; Seco, Joao; Martin, Benjamin C.; Bortfeld, Thomas
2009-06-01
All radiation therapy treatment planning relies on accurate dose calculation. Uncertainties in dosimetric prediction can significantly degrade an otherwise optimal plan. In this work, we introduce a robust optimization method which handles dosimetric errors and warrants for high-quality IMRT plans. Unlike other dose error estimations, we do not rely on the detailed knowledge about the sources of the uncertainty and use a generic error model based on random perturbation. This generality is sought in order to cope with a large variety of error sources. We demonstrate the method on a clinical case of lung cancer and show that our method provides plans that are more robust against dosimetric errors and are clinically acceptable. In fact, the robust plan exhibits a two-fold improved equivalent uniform dose compared to the non-robust but optimized plan. The achieved speedup will allow computationally extensive multi-criteria or beam-angle optimization approaches to warrant for dosimetrically relevant plans.
Teaching Tip: When a Matrix and Its Inverse Are Stochastic
ERIC Educational Resources Information Center
Ding, J.; Rhee, N. H.
2013-01-01
A stochastic matrix is a square matrix with nonnegative entries and row sums 1. The simplest example is a permutation matrix, whose rows permute the rows of an identity matrix. A permutation matrix and its inverse are both stochastic. We prove the converse, that is, if a matrix and its inverse are both stochastic, then it is a permutation matrix.
A stochastic approach to robust broadband structural control
NASA Technical Reports Server (NTRS)
Macmartin, Douglas G.; Hall, Steven R.
1992-01-01
Viewgraphs on a stochastic approach to robust broadband structural control are presented. Topics covered include: travelling wave model; dereverberated mobility model; computation of dereverberated mobility; power flow; impedance matching; stochastic systems; control problem; control of stochastic systems; using cost functional; Bernoulli-Euler beam example; compensator design; 'power' dual variables; dereverberation of complex structure; and dereverberated transfer function.
Cardaliaguet, P.; Rainer, C.
2013-08-01
We introduce a new notion of pathwise strategies for stochastic differential games. This allows us to give a correct meaning to some statement asserted in Cardaliaguet and Rainer (Appl. Math. Optim. 59: 1-36, 2009)
Stochastic mechanics and the Kepler problem
NASA Astrophysics Data System (ADS)
Garbaczewski, Piotr
1986-05-01
The stochastic mechanics of Nelson and Guerra is formulated for the hydrogen atom. We demonstrate that this simple quantum system can be described in terms of three independent Gaussian Markov processes which are driven (controlled) by the classical Kepler problem. It reveals a manifest connection between the classical and quantized versions of the Kepler problem.
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.
Stochastic Perturbations in Type I Planetary Migraiton
NASA Astrophysics Data System (ADS)
Adams, Fred C.; Bloch, A. M.
2009-05-01
This talk presents a generalized treatment of Type I planetary migration in the presence of stochastic perturbations. In many planet-forming disks, the Type I migration mechanism, driven by asymmetric torques, can compromise planet formation. If the disk also supports MHD instabilities, however, the corresponding turbulent fluctuations produce additional stochastic torques that modify the steady inward migration scenario. This work studies the migration of planetary cores in the presence of stochastic fluctuations using complementary methods, including iterative maps and a Fokker-Planck approach. Stochastic torques have two main effects: [1] Through outward diffusion, a small fraction of the planetary cores can survive in the face of Type I inward migration. [2] For a given starting condition, the result of any particular realization of migration is uncertain, so that results must be described in terms of the distributions of outcomes. In addition to exploring different regimes of parameter space, this paper considers the effects of the outer disk boundary condition and time-dependence of the torque parameters. For disks with finite radii, the fraction of surviving planets decreases exponentially with time. We find the survival fractions and decay rates for a range of disk models, and find the expected distribution of locations for surviving planets. The survival fraction is expected to lie in the range 0.01 < p_S < 0.1.
Perspective: Stochastic algorithms for chemical kinetics
Gillespie, Daniel T.; Hellander, Andreas; Petzold, Linda R.
2013-01-01
We outline our perspective on stochastic chemical kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional chemical kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with chemical reactions inside the subvolumes. PMID:23656106
Maximal stochastic transport in the Lorenz equations
NASA Astrophysics Data System (ADS)
Agarwal, Sahil; Wettlaufer, J. S.
2016-01-01
We calculate the stochastic upper bounds for the Lorenz equations using an extension of the background method. In analogy with Rayleigh-Bénard convection the upper bounds are for heat transport versus Rayleigh number. As might be expected, the stochastic upper bounds are larger than the deterministic counterpart of Souza and Doering [1], but their variation with noise amplitude exhibits interesting behavior. Below the transition to chaotic dynamics the upper bounds increase monotonically with noise amplitude. However, in the chaotic regime this monotonicity depends on the number of realizations in the ensemble; at a particular Rayleigh number the bound may increase or decrease with noise amplitude. The origin of this behavior is the coupling between the noise and unstable periodic orbits, the degree of which depends on the degree to which the ensemble represents the ergodic set. This is confirmed by examining the close returns plots of the full solutions to the stochastic equations and the numerical convergence of the noise correlations. The numerical convergence of both the ensemble and time averages of the noise correlations is sufficiently slow that it is the limiting aspect of the realization of these bounds. Finally, we note that the full solutions of the stochastic equations demonstrate that the effect of noise is equivalent to the effect of chaos.
Stochastic Resonance in Protein Folding Dynamics.
Davtyan, Aram; Platkov, Max; Gruebele, Martin; Papoian, Garegin A
2016-05-01
Although protein folding reactions are usually studied under static external conditions, it is likely that proteins fold in a locally fluctuating cellular environment in vivo. To mimic such behavior in in vitro experiments, the local temperature of the solvent can be modulated either harmonically or using correlated noise. In this study, coarse-grained molecular simulations are used to investigate these possibilities, and it is found that both periodic and correlated random fluctuations of the environment can indeed accelerate folding kinetics if the characteristic frequencies of the applied fluctuations are commensurate with the internal timescale of the folding reaction; this is consistent with the phenomenon of stochastic resonance observed in many other condensed-matter processes. To test this theoretical prediction, the folding dynamics of phosphoglycerate kinase under harmonic temperature fluctuations are experimentally probed using Förster resonance energy transfer fluorescence measurements. To analyze these experiments, a combination of theoretical approaches is developed, including stochastic simulations of folding kinetics and an analytical mean-field kinetic theory. The experimental observations are consistent with the theoretical predictions of stochastic resonance in phosphoglycerate kinase folding. When combined with an alternative experiment on the protein VlsE using a power spectrum analysis, elaborated in Dave et al., ChemPhysChem 2016, 10.1002/cphc.201501041, the overall data overwhelmingly point to the experimental confirmation of stochastic resonance in protein folding dynamics. PMID:26992148
Analysis of time series from stochastic processes
Gradisek; Siegert; Friedrich; Grabec
2000-09-01
Analysis of time series from stochastic processes governed by a Langevin equation is discussed. Several applications for the analysis are proposed based on estimates of drift and diffusion coefficients of the Fokker-Planck equation. The coefficients are estimated directly from a time series. The applications are illustrated by examples employing various synthetic time series and experimental time series from metal cutting. PMID:11088809
Stochastic models for turbulent reacting flows
Kerstein, A.
1993-12-01
The goal of this program is to develop and apply stochastic models of various processes occurring within turbulent reacting flows in order to identify the fundamental mechanisms governing these flows, to support experimental studies of these flows, and to further the development of comprehensive turbulent reacting flow models.
Uncertainty Representation in Stochastic Reservoir Optimization
NASA Astrophysics Data System (ADS)
Lamontagne, J. R.; Stedinger, J. R.; Shoemaker, C. A.; Tan, S. N.
2014-12-01
Water resources managers attempt to operate reservoir and hydropower systems to maximize system objectives, subject to a host of physical and policy constraints, and in light of uncertainty about future conditions. Optimization models are widely used to advise the decision making process. An important aspect of such models is how uncertainties related to future hydrologic and economic conditions are represented, and the extent to which different uncertainty representations affect the quality of recommended decisions. This study explores the consequences of different uncertainty representations in stochastic optimization models of hydropower systems by comparing simulated system performance using different stochastic optimization models. An important question is whether the added computational burden from greater uncertainty resolution (which can be prohibitive for operational models in many cases) actually improves model recommendations. This is particularly relevant as more complex, ensemble forecasts are incorporated into short- and mid-term planning models. Another important consideration is how watershed hydrology (both seasonal and episodic characteristics), system size, economic context, and the temporal resolution of the model influence how uncertainty should be represented. These topics are explored through several US examples including a sampling stochastic dynamic programming (SSDP) model of a small single-reservoir system on the Kennebec River in Maine, and a stochastic programming model of the large multi-reservoir Federal Columbia River system in the Pacific Northwest. These studies highlight the importance of flexible model frameworks which allow exploration of different representations of a system and of uncertainties before locking operational decision support system development into a specific representation.
Environmental variation, stochastic extinction, and competitive coexistence.
Adler, Peter B; Drake, John M
2008-11-01
Understanding how environmental fluctuations affect population persistence is essential for predicting the ecological impacts of expected future increases in climate variability. However, two bodies of theory make opposite predictions about the effect of environmental variation on persistence. Single-species theory, common in conservation biology and population viability analyses, suggests that environmental variation increases the risk of stochastic extinction. By contrast, coexistence theory has shown that environmental variation can buffer inferior competitors against competitive exclusion through a storage effect. We reconcile these two perspectives by showing that in the presence of demographic stochasticity, environmental variation can increase the chance of extinction while simultaneously stabilizing coexistence. Our stochastic simulations of a two-species storage effect model reveal a unimodal relationship between environmental variation and coexistence time, implying maximum coexistence at intermediate levels of environmental variation. The unimodal pattern reflects the fact that the stabilizing influence of the storage effect accumulates rapidly at low levels of environmental variation, whereas the risk of extinction due to the combined effects of environmental variation and demographic stochasticity increases most rapidly at higher levels of variation. Future increases in environmental variation could either increase or decrease an inferior competitor's expected persistence time, depending on the distance between the present level of environmental variation and the optimal level anticipated by this theory. PMID:18817458
Investigation of the stochastic properties of wind
NASA Astrophysics Data System (ADS)
Dimitriadis, Panayiotis; Koutsoyiannis, Demetris; Papanicolaou, Panos
2016-04-01
Understanding atmospheric motion in the form of wind is essential to many fields in hydroclimatics. The wind is considered one of the most important processes in hydrometeorology since, along with temperature, it generates and drives climate dynamics. Currently, the interest has increased due to its involvement to renewable energy resources through wind power production and forecasting. However, there seems to be a puzzle about which stochastic model best describes the wind process. In this analysis, we attempt to explain the reason around this confusion regarding the stochastic properties of the wind process using statistical as well as hydrometeorological reasoning, starting from the microscale of turbulence and extending the analysis to the macroscale of climatic processes. Particularly, some models seem to exhibit good agreement with data mostly due to instrumental errors. Moreover, we show that extending the theory of turbulence to the atmospheric motion can reveal stochastic properties that are not only accompanied with physical interference but also exhibit excellent agreement with wind measurements. Finally, we apply the theoretical analysis to multiple stations around the globe and we derive conclusions on the variation of stochastic parameters of wind regarding dominant climatic conditions.
Stochastic game dynamics under demographic fluctuations
Huang, Weini; Hauert, Christoph; Traulsen, Arne
2015-01-01
Frequency-dependent selection and demographic fluctuations play important roles in evolutionary and ecological processes. Under frequency-dependent selection, the average fitness of the population may increase or decrease based on interactions between individuals within the population. This should be reflected in fluctuations of the population size even in constant environments. Here, we propose a stochastic model that naturally combines these two evolutionary ingredients by assuming frequency-dependent competition between different types in an individual-based model. In contrast to previous game theoretic models, the carrying capacity of the population, and thus the population size, is determined by pairwise competition of individuals mediated by evolutionary games and demographic stochasticity. In the limit of infinite population size, the averaged stochastic dynamics is captured by deterministic competitive Lotka–Volterra equations. In small populations, demographic stochasticity may instead lead to the extinction of the entire population. Because the population size is driven by fitness in evolutionary games, a population of cooperators is less prone to go extinct than a population of defectors, whereas in the usual systems of fixed size the population would thrive regardless of its average payoff. PMID:26150518
STOCHASTIC INTEGRATION FOR TEMPERED FRACTIONAL BROWNIAN MOTION
MEERSCHAERT, MARK M.; SABZIKAR, FARZAD
2014-01-01
Tempered fractional Brownian motion is obtained when the power law kernel in the moving average representation of a fractional Brownian motion is multiplied by an exponential tempering factor. This paper develops the theory of stochastic integrals for tempered fractional Brownian motion. Along the way, we develop some basic results on tempered fractional calculus. PMID:24872598
Stochastic game dynamics under demographic fluctuations.
Huang, Weini; Hauert, Christoph; Traulsen, Arne
2015-07-21
Frequency-dependent selection and demographic fluctuations play important roles in evolutionary and ecological processes. Under frequency-dependent selection, the average fitness of the population may increase or decrease based on interactions between individuals within the population. This should be reflected in fluctuations of the population size even in constant environments. Here, we propose a stochastic model that naturally combines these two evolutionary ingredients by assuming frequency-dependent competition between different types in an individual-based model. In contrast to previous game theoretic models, the carrying capacity of the population, and thus the population size, is determined by pairwise competition of individuals mediated by evolutionary games and demographic stochasticity. In the limit of infinite population size, the averaged stochastic dynamics is captured by deterministic competitive Lotka-Volterra equations. In small populations, demographic stochasticity may instead lead to the extinction of the entire population. Because the population size is driven by fitness in evolutionary games, a population of cooperators is less prone to go extinct than a population of defectors, whereas in the usual systems of fixed size the population would thrive regardless of its average payoff. PMID:26150518
Stochastic motif extraction using hidden Markov model
Fujiwara, Yukiko; Asogawa, Minoru; Konagaya, Akihiko
1994-12-31
In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. A stochastic protein motif represents the small segments of protein sequences that have a certain function or structure. The stochastic motif, represented by an HMM, has conditional probabilities to deal with the stochastic nature of the motif. This HMM directive reflects the characteristics of the motif, such as a protein periodical structure or grouping. In order to obtain the optimal HMM, we developed the {open_quotes}iterative duplication method{close_quotes} for HMM topology learning. It starts from a small fully-connected network and iterates the network generation and parameter optimization until it achieves sufficient discrimination accuracy. Using this method, we obtained an HMM for a leucine zipper motif. Compared to the accuracy of a symbolic pattern representation with accuracy of 14.8 percent, an HMM achieved 79.3 percent in prediction. Additionally, the method can obtain an HMM for various types of zinc finger motifs, and it might separate the mixed data. We demonstrated that this approach is applicable to the validation of the protein databases; a constructed HMM b as indicated that one protein sequence annotated as {open_quotes}lencine-zipper like sequence{close_quotes} in the database is quite different from other leucine-zipper sequences in terms of likelihood, and we found this discrimination is plausible.
Fermilab Recycler Stochastic Cooling for Luminosity Production
Broemmelsiek, D.; Gattuso, C.
2006-03-20
The Fermilab Recycler began regularly delivering antiprotons for Tevatron luminosity operations in 2005. Methods for tuning the Recycler stochastic cooling system are presented. The unique conditions and resulting procedures for minimizing the longitudinal phase space density of the Recycler antiproton beam are outlined.
Stochastic Cooling with Schottky Band Overlap
Lebedev, Valeri
2006-03-20
Optimal use of stochastic cooling is essential to maximize the antiproton stacking rate for Tevatron Run II. Good understanding and characterization of the cooling is important for the optimization. The paper is devoted to derivation of the Fokker-Plank equations justified in the case of near or full Schottky base overlap for both longitudinal and transverse coolings.
Stochastic resonance: A residence time approach
Gammaitoni, L. |; Marchesoni, F. |; Menichella Saetta, E.; Santucci, S.
1996-06-01
The Stochastic Resonance phenomenon is described as a synchronization process between periodic signals and the random response in bistable systems. The residence time approach as a useful tool in characterizing hidden periodicities is discussed. {copyright} {ital 1996 American Institute of Physics.}
Stochastic processes, estimation theory and image enhancement
NASA Technical Reports Server (NTRS)
Assefi, T.
1978-01-01
An introductory account of stochastic processes, estimation theory, and image enhancement is presented. The book is primarily intended for first-year graduate students and practicing engineers and scientists whose work requires an acquaintance with the theory. Fundamental concepts of probability were reviewed that are required to support the main topics. The appendices discuss the remaining mathematical background.
STATISTICAL ANALYSIS OF A DETERMINISTIC STOCHASTIC ORBIT
Kaufman, Allan N.; Abarbanel, Henry D.I.; Grebogi, Celso
1980-05-01
If the solution of a deterministic equation is stochastic (in the sense of orbital instability), it can be subjected to a statistical analysis. This is illustrated for a coded orbit of the Chirikov mapping. Statistical dependence and the Markov assumption are tested. The Kolmogorov-Sinai entropy is related to the probability distribution for the orbit.
Stochastic Differential Games with Asymmetric Information
Cardaliaguet, Pierre Rainer, Catherine
2009-02-15
We investigate a two-player zero-sum stochastic differential game in which the players have an asymmetric information on the random payoff. We prove that the game has a value and characterize this value in terms of dual viscosity solutions of some second order Hamilton-Jacobi equation.
Vector Lyapunov Functions for Stochastic Interconnected Systems
NASA Technical Reports Server (NTRS)
Boussalis, D.
1985-01-01
Theoretical paper presents set of sufficient conditions for asymptotic and exponential stability with probability 1 for class of stochastic interconnected systems. Theory applicable to complicated, large-scale mechanical or electrical systems, and, for several design problems, it reduces computational difficulty by relating stability criteria to fundamental structural features of system.
A Note on Boolean Stochastic Processes
NASA Astrophysics Data System (ADS)
Fidaleo, Francesco
2015-03-01
For the quantum stochastic processes generated by the Boolean commutation relations, we prove the following version of De Finetti Theorem: each of such Boolean processes is exchangeable if and only if it is independent and identically distributed with respect to the tail algebra.
Stochastic models of intracellular calcium signals
NASA Astrophysics Data System (ADS)
Rüdiger, Sten
2014-01-01
Cellular signaling operates in a noisy environment shaped by low molecular concentrations and cellular heterogeneity. For calcium release through intracellular channels-one of the most important cellular signaling mechanisms-feedback by liberated calcium endows fluctuations with critical functions in signal generation and formation. In this review it is first described, under which general conditions the environment makes stochasticity relevant, and which conditions allow approximating or deterministic equations. This analysis provides a framework, in which one can deduce an efficient hybrid description combining stochastic and deterministic evolution laws. Within the hybrid approach, Markov chains model gating of channels, while the concentrations of calcium and calcium binding molecules (buffers) are described by reaction-diffusion equations. The article further focuses on the spatial representation of subcellular calcium domains related to intracellular calcium channels. It presents analysis for single channels and clusters of channels and reviews the effects of buffers on the calcium release. For clustered channels, we discuss the application and validity of coarse-graining as well as approaches based on continuous gating variables (Fokker-Planck and chemical Langevin equations). Comparison with recent experiments substantiates the stochastic and spatial approach, identifies minimal requirements for a realistic modeling, and facilitates an understanding of collective channel behavior. At the end of the review, implications of stochastic and local modeling for the generation and properties of cell-wide release and the integration of calcium dynamics into cellular signaling models are discussed.
Stochastic genetic networks with solvable structures
Lipan, Ovidiu
2014-12-10
We describe a set of basic stochastic biocircuits for which the Master Equation is completely solvable. Beside linear circuits, which are known to be solvable, we show that tree-like circuits with polynomial transition functions are also completely solvable. We associate a simple but unambiguous graphical representation to such circuits. The graphical representation shows the signal propagation through these simple circuits.
Stochastic noise in atomic force microscopy.
Labuda, Aleksander; Lysy, Martin; Paul, William; Miyahara, Yoichi; Grütter, Peter; Bennewitz, Roland; Sutton, Mark
2012-09-01
Having reached the quantum and thermodynamic limits of detection, atomic force microscopy (AFM) experiments are routinely being performed at the fundamental limit of signal to noise. A critical understanding of the statistical properties of noise leads to more accurate interpretation of data, optimization of experimental protocols, advancements in instrumentation, and new measurement techniques. Furthermore, accurate simulation of cantilever dynamics requires knowledge of stochastic behavior of the system, as stochastic noise may exceed the deterministic signals of interest, and even dominate the outcome of an experiment. In this article, the power spectral density (PSD), used to quantify stationary stochastic processes, is introduced in the context of a thorough noise analysis of the light source used to detect cantilever deflections. The statistical properties of PSDs are then outlined for various stationary, nonstationary, and deterministic noise sources in the context of AFM experiments. Following these developments, a method for integrating PSDs to provide an accurate standard deviation of linear measurements is described. Lastly, a method for simulating stochastic Gaussian noise from any arbitrary power spectral density is presented. The result demonstrates that mechanical vibrations of the AFM can cause a logarithmic velocity dependence of friction and induce multiple slip events in the atomic stick-slip process, as well as predicts an artifactual temperature dependence of friction measured by AFM. PMID:23030863
Stochastic thermodynamics for linear kinetic equations
NASA Astrophysics Data System (ADS)
Van den Broeck, C.; Toral, R.
2015-07-01
Stochastic thermodynamics is formulated for variables that are odd under time reversal. The invariance under spatial rotation of the collision rates due to the isotropy of the heat bath is shown to be a crucial ingredient. An alternative detailed fluctuation theorem is derived, expressed solely in terms of forward statistics. It is illustrated for a linear kinetic equation with kangaroo rates.
Test data sets for calibration of stochastic and fractional stochastic volatility models.
Pospíšil, Jan; Sobotka, Tomáš
2016-09-01
Data for calibration and out-of-sample error testing of option pricing models are provided alongside data obtained from optimization procedures in "On calibration of stochastic and fractional stochastic volatility models" [1]. Firstly we describe testing data sets, further calibration data obtained from combined optimizers is visually depicted - interactive 3d bar plots are provided. The data is suitable for a further comparison of other optimization routines and also to benchmark different pricing models. PMID:27419200
A Stochastic Maximum Principle for a Stochastic Differential Game of a Mean-Field Type
Hosking, John Joseph Absalom
2012-12-15
We construct a stochastic maximum principle (SMP) which provides necessary conditions for the existence of Nash equilibria in a certain form of N-agent stochastic differential game (SDG) of a mean-field type. The information structure considered for the SDG is of a possible asymmetric and partial type. To prove our SMP we take an approach based on spike-variations and adjoint representation techniques, analogous to that of S. Peng (SIAM J. Control Optim. 28(4):966-979, 1990) in the optimal stochastic control context. In our proof we apply adjoint representation procedures at three points. The first-order adjoint processes are defined as solutions to certain mean-field backward stochastic differential equations, and second-order adjoint processes of a first type are defined as solutions to certain backward stochastic differential equations. Second-order adjoint processes of a second type are defined as solutions of certain backward stochastic equations of a type that we introduce in this paper, and which we term conditional mean-field backward stochastic differential equations. From the resulting representations, we show that the terms relating to these second-order adjoint processes of the second type are of an order such that they do not appear in our final SMP equations. A comparable situation exists in an article by R. Buckdahn, B. Djehiche, and J. Li (Appl. Math. Optim. 64(2):197-216, 2011) that constructs a SMP for a mean-field type optimal stochastic control problem; however, the approach we take of using these second-order adjoint processes of a second type to deal with the type of terms that we refer to as the second form of quadratic-type terms represents an alternative to a development, to our setting, of the approach used in their article for their analogous type of term.
NASA Astrophysics Data System (ADS)
Nemenman, Ilya
2008-03-01
A variety of stochastic systems, from enzyme kinetics to epidemiology, exhibit pump-like behaviors, where adiabatic changes of parameters result in a nonzero directed current through the system. Using the stochastic path integral technique from mesoscopic physics, we have been able to relate these and similar phenomena to geometric effects in mesoscopic stochastic kinetics and construct their unifying theory. In the talk, this methodology will be demonstrated on three examples: (1) an adiabatic pump effect in the evolution of a Michaelis-Menten enzyme, treated as a classical two-state stochastic system; (2) a reversible ratchet; and (3) a related novel phenomenon in a previously unexplored domain, namely the SIS epidemiological model. In all of these examples, pump-like currents follow from very similar geometric phase contributions to the effective action in the stochastic path integral representation of the moment generating functional, and our construction provides the universal technique for identification, prediction, and calculation of these currents in an arbitrary mesoscopic stochastic framework.
Stochastic flux analysis of chemical reaction networks
2013-01-01
Background Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. Results We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. Conclusions We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. PMID:24314153
Stochastic hybrid modeling of intracellular calcium dynamics
NASA Astrophysics Data System (ADS)
Choi, TaiJung; Maurya, Mano Ram; Tartakovsky, Daniel M.; Subramaniam, Shankar
2010-10-01
Deterministic models of biochemical processes at the subcellular level might become inadequate when a cascade of chemical reactions is induced by a few molecules. Inherent randomness of such phenomena calls for the use of stochastic simulations. However, being computationally intensive, such simulations become infeasible for large and complex reaction networks. To improve their computational efficiency in handling these networks, we present a hybrid approach, in which slow reactions and fluxes are handled through exact stochastic simulation and their fast counterparts are treated partially deterministically through chemical Langevin equation. The classification of reactions as fast or slow is accompanied by the assumption that in the time-scale of fast reactions, slow reactions do not occur and hence do not affect the probability of the state. Our new approach also handles reactions with complex rate expressions such as Michaelis-Menten kinetics. Fluxes which cannot be modeled explicitly through reactions, such as flux of Ca2+ from endoplasmic reticulum to the cytosol through inositol 1,4,5-trisphosphate receptor channels, are handled deterministically. The proposed hybrid algorithm is used to model the regulation of the dynamics of cytosolic calcium ions in mouse macrophage RAW 264.7 cells. At relatively large number of molecules, the response characteristics obtained with the stochastic and deterministic simulations coincide, which validates our approach in the limit of large numbers. At low doses, the response characteristics of some key chemical species, such as levels of cytosolic calcium, predicted with stochastic simulations, differ quantitatively from their deterministic counterparts. These observations are ubiquitous throughout dose response, sensitivity, and gene-knockdown response analyses. While the relative differences between the peak-heights of the cytosolic [Ca2+] time-courses obtained from stochastic (mean of 16 realizations) and deterministic
Stochastic partial differential equations in turbulence related problems
NASA Technical Reports Server (NTRS)
Chow, P.-L.
1978-01-01
The theory of stochastic partial differential equations (PDEs) and problems relating to turbulence are discussed by employing the theories of Brownian motion and diffusion in infinite dimensions, functional differential equations, and functional integration. Relevant results in probablistic analysis, especially Gaussian measures in function spaces and the theory of stochastic PDEs of Ito type, are taken into account. Linear stochastic PDEs are analyzed through linearized Navier-Stokes equations with a random forcing. Stochastic equations for waves in random media as well as model equations in turbulent transport theory are considered. Markovian models in fully developed turbulence are discussed from a stochastic equation viewpoint.
Extinction risk depends strongly on factors contributing to stochasticity.
Melbourne, Brett A; Hastings, Alan
2008-07-01
Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models. Stochasticity can be divided into four categories, which include the probabilistic nature of birth and death at the level of individuals (demographic stochasticity), variation in population-level birth and death rates among times or locations (environmental stochasticity), the sex of individuals and variation in vital rates among individuals within a population (demographic heterogeneity). Mechanistic stochastic models that include all of these factors have not previously been developed to examine their combined effects on extinction risk. Here we derive a family of stochastic Ricker models using different combinations of all these stochastic factors, and show that extinction risk depends strongly on the combination of factors that contribute to stochasticity. Furthermore, we show that only with the full stochastic model can the relative importance of environmental and demographic variability, and therefore extinction risk, be correctly determined. Using the full model, we find that demographic sources of stochasticity are the prominent cause of variability in a laboratory population of Tribolium castaneum (red flour beetle), whereas using only the standard simpler models would lead to the erroneous conclusion that environmental variability dominates. Our results demonstrate that current estimates of extinction risk for natural populations could be greatly underestimated because variability has been mistakenly attributed to the environment rather than the demographic factors described here that entail much higher extinction risk for the same variability level. PMID:18596809
An applied mathematics perspective on stochastic modelling for climate.
Majda, Andrew J; Franzke, Christian; Khouider, Boualem
2008-07-28
Systematic strategies from applied mathematics for stochastic modelling in climate are reviewed here. One of the topics discussed is the stochastic modelling of mid-latitude low-frequency variability through a few teleconnection patterns, including the central role and physical mechanisms responsible for multiplicative noise. A new low-dimensional stochastic model is developed here, which mimics key features of atmospheric general circulation models, to test the fidelity of stochastic mode reduction procedures. The second topic discussed here is the systematic design of stochastic lattice models to capture irregular and highly intermittent features that are not resolved by a deterministic parametrization. A recent applied mathematics design principle for stochastic column modelling with intermittency is illustrated in an idealized setting for deep tropical convection; the practical effect of this stochastic model in both slowing down convectively coupled waves and increasing their fluctuations is presented here. PMID:18445572
Quantum stochastic calculus associated with quadratic quantum noises
NASA Astrophysics Data System (ADS)
Ji, Un Cig; Sinha, Kalyan B.
2016-02-01
We first study a class of fundamental quantum stochastic processes induced by the generators of a six dimensional non-solvable Lie †-algebra consisting of all linear combinations of the generalized Gross Laplacian and its adjoint, annihilation operator, creation operator, conservation, and time, and then we study the quantum stochastic integrals associated with the class of fundamental quantum stochastic processes, and the quantum Itô formula is revisited. The existence and uniqueness of solution of a quantum stochastic differential equation is proved. The unitarity conditions of solutions of quantum stochastic differential equations associated with the fundamental processes are examined. The quantum stochastic calculus extends the Hudson-Parthasarathy quantum stochastic calculus.
A stochastic model of eye lens growth.
Šikić, Hrvoje; Shi, Yanrong; Lubura, Snježana; Bassnett, Steven
2015-07-01
The size and shape of the ocular lens must be controlled with precision if light is to be focused sharply on the retina. The lifelong growth of the lens depends on the production of cells in the anterior epithelium. At the lens equator, epithelial cells differentiate into fiber cells, which are added to the surface of the existing fiber cell mass, increasing its volume and area. We developed a stochastic model relating the rates of cell proliferation and death in various regions of the lens epithelium to deposition of fiber cells and radial lens growth. Epithelial population dynamics were modeled as a branching process with emigration and immigration between proliferative zones. Numerical simulations were in agreement with empirical measurements and demonstrated that, operating within the strict confines of lens geometry, a stochastic growth engine can produce the smooth and precise growth necessary for lens function. PMID:25816743
Stochastic robustness of linear control systems
NASA Technical Reports Server (NTRS)
Stengel, Robert F.; Ryan, Laura E.
1990-01-01
A simple numerical procedure for estimating the stochastic robustness of a linear, time-invariant system is described. Monte Carlo evaluation of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This definition of robustness is an alternative to existing deterministic definitions that address both structured and unstructured parameter variations directly. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variations. Trivial extensions of the procedure admit alternate discriminants to be considered. Thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions also can be estimated. Results are particularly amenable to graphical presentation.
Conditional reversibility in nonequilibrium stochastic systems.
Bonança, Marcus V S; Jarzynski, Christopher
2016-02-01
For discrete-state stochastic systems obeying Markovian dynamics, we establish the counterpart of the conditional reversibility theorem obtained by Gallavotti for deterministic systems [Ann. de l'Institut Henri Poincaré (A) 70, 429 (1999)]. Our result states that stochastic trajectories conditioned on opposite values of entropy production are related by time reversal, in the long-time limit. In other words, the probability of observing a particular sequence of events, given a long trajectory with a specified entropy production rate σ, is the same as the probability of observing the time-reversed sequence of events, given a trajectory conditioned on the opposite entropy production, -σ, where both trajectories are sampled from the same underlying Markov process. To obtain our result, we use an equivalence between conditioned ("microcanonical") and biased ("canonical") ensembles of nonequilibrium trajectories. We provide an example to illustrate our findings. PMID:26986282
Conditional reversibility in nonequilibrium stochastic systems
NASA Astrophysics Data System (ADS)
Bonança, Marcus V. S.; Jarzynski, Christopher
2016-02-01
For discrete-state stochastic systems obeying Markovian dynamics, we establish the counterpart of the conditional reversibility theorem obtained by Gallavotti for deterministic systems [Ann. de l'Institut Henri Poincaré (A) 70, 429 (1999)]. Our result states that stochastic trajectories conditioned on opposite values of entropy production are related by time reversal, in the long-time limit. In other words, the probability of observing a particular sequence of events, given a long trajectory with a specified entropy production rate σ , is the same as the probability of observing the time-reversed sequence of events, given a trajectory conditioned on the opposite entropy production, -σ , where both trajectories are sampled from the same underlying Markov process. To obtain our result, we use an equivalence between conditioned ("microcanonical") and biased ("canonical") ensembles of nonequilibrium trajectories. We provide an example to illustrate our findings.
Stochastic model for protein flexibility analysis
NASA Astrophysics Data System (ADS)
Xia, Kelin; Wei, Guo-Wei
2013-12-01
Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature.
Stochastic cooling of a high energy collider
Blaskiewicz, M.; Brennan, J.M.; Lee, R.C.; Mernick, K.
2011-09-04
Gold beams in RHIC revolve more than a billion times over the course of a data acquisition session or store. During operations with these heavy ions the event rates in the detectors decay as the beams diffuse. A primary cause for this beam diffusion is small angle Coloumb scattering of the particles within the bunches. This intra-beam scattering (IBS) is particularly problematic at high energy because the negative mass effect removes the possibility of even approximate thermal equilibrium. Stochastic cooling can combat IBS. A theory of bunched beam cooling was developed in the early eighties and stochastic cooling systems for the SPS and the Tevatron were explored. Cooling for heavy ions in RHIC was also considered.
Controlling statistical moments of stochastic dynamical networks
NASA Astrophysics Data System (ADS)
Bielievtsov, Dmytro; Ladenbauer, Josef; Obermayer, Klaus
2016-07-01
We consider a general class of stochastic networks and ask which network nodes need to be controlled, and how, to stabilize and switch between desired metastable (target) states in terms of the first and second statistical moments of the system. We first show that it is sufficient to directly interfere with a subset of nodes which can be identified using information about the graph of the network only. Then we develop a suitable method for feedback control which acts on that subset of nodes and preserves the covariance structure of the desired target state. Finally, we demonstrate our theoretical results using a stochastic Hopfield network and a global brain model. Our results are applicable to a variety of (model) networks and further our understanding of the relationship between network structure and collective dynamics for the benefit of effective control.
Controlling statistical moments of stochastic dynamical networks.
Bielievtsov, Dmytro; Ladenbauer, Josef; Obermayer, Klaus
2016-07-01
We consider a general class of stochastic networks and ask which network nodes need to be controlled, and how, to stabilize and switch between desired metastable (target) states in terms of the first and second statistical moments of the system. We first show that it is sufficient to directly interfere with a subset of nodes which can be identified using information about the graph of the network only. Then we develop a suitable method for feedback control which acts on that subset of nodes and preserves the covariance structure of the desired target state. Finally, we demonstrate our theoretical results using a stochastic Hopfield network and a global brain model. Our results are applicable to a variety of (model) networks and further our understanding of the relationship between network structure and collective dynamics for the benefit of effective control. PMID:27575147
Entropy production of doubly stochastic quantum channels
NASA Astrophysics Data System (ADS)
Müller-Hermes, Alexander; Stilck França, Daniel; Wolf, Michael M.
2016-02-01
We study the entropy increase of quantum systems evolving under primitive, doubly stochastic Markovian noise and thus converging to the maximally mixed state. This entropy increase can be quantified by a logarithmic-Sobolev constant of the Liouvillian generating the noise. We prove a universal lower bound on this constant that stays invariant under taking tensor-powers. Our methods involve a new comparison method to relate logarithmic-Sobolev constants of different Liouvillians and a technique to compute logarithmic-Sobolev inequalities of Liouvillians with eigenvectors forming a projective representation of a finite abelian group. Our bounds improve upon similar results established before and as an application we prove an upper bound on continuous-time quantum capacities. In the last part of this work we study entropy production estimates of discrete-time doubly stochastic quantum channels by extending the framework of discrete-time logarithmic-Sobolev inequalities to the quantum case.
An exact accelerated stochastic simulation algorithm
Mjolsness, Eric; Orendorff, David; Chatelain, Philippe; Koumoutsakos, Petros
2009-01-01
An exact method for stochastic simulation of chemical reaction networks, which accelerates the stochastic simulation algorithm (SSA), is proposed. The present “ER-leap” algorithm is derived from analytic upper and lower bounds on the multireaction probabilities sampled by SSA, together with rejection sampling and an adaptive multiplicity for reactions. The algorithm is tested on a number of well-quantified reaction networks and is found experimentally to be very accurate on test problems including a chaotic reaction network. At the same time ER-leap offers a substantial speedup over SSA with a simulation time proportional to the 2∕3 power of the number of reaction events in a Galton–Watson process. PMID:19368432
Stochasticity in the yeast mating pathway
NASA Astrophysics Data System (ADS)
Wang, Hong-Li; Fu, Zheng-Ping; Xu, Xin-Hang; Ouyang, Qi
2009-05-01
We report stochastic simulations of the yeast mating signal transduction pathway. The effects of intrinsic and external noise, the influence of cell-to-cell difference in the pathway capacity, and noise propagation in the pathway have been examined. The stochastic temporal behaviour of the pathway is found to be robust to the influence of inherent fluctuations, and intrinsic noise propagates in the pathway in a uniform pattern when the yeasts are treated with pheromones of different stimulus strengths and of varied fluctuations. In agreement with recent experimental findings, extrinsic noise is found to play a more prominent role than intrinsic noise in the variability of proteins. The occurrence frequency for the reactions in the pathway are also examined and a more compact network is obtained by dropping most of the reactions of least occurrence.
Stochastic dynamic models and Chebyshev splines
Fan, Ruzong; Zhu, Bin; Wang, Yuedong
2015-01-01
In this article, we establish a connection between a stochastic dynamic model (SDM) driven by a linear stochastic differential equation (SDE) and a Chebyshev spline, which enables researchers to borrow strength across fields both theoretically and numerically. We construct a differential operator for the penalty function and develop a reproducing kernel Hilbert space (RKHS) induced by the SDM and the Chebyshev spline. The general form of the linear SDE allows us to extend the well-known connection between an integrated Brownian motion and a polynomial spline to a connection between more complex diffusion processes and Chebyshev splines. One interesting special case is connection between an integrated Ornstein–Uhlenbeck process and an exponential spline. We use two real data sets to illustrate the integrated Ornstein–Uhlenbeck process model and exponential spline model and show their estimates are almost identical. PMID:26045632
Stochastic discrete model of karstic networks
NASA Astrophysics Data System (ADS)
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
Langevin dynamics, entropic crowding, and stochastic cloaking.
Eliazar, Iddo
2011-12-01
We consider a pack of independent probes--within a spatially inhomogeneous thermal bath consisting of a vast number of randomly moving particles--which are subjected to an external force. The stochastic dynamics of the probes are governed by Langevin's equation. The probes attain a steady state distribution which, in general, is different than the concentration of the particles in the spatially inhomogeneous thermal bath. In this paper we explore the state of "entropic crowding" in which the probes' distribution and the particles' concentration coincide--thus yielding maximal relative entropies of one with respect to the other. Entropic crowding can be attained by two scenarios which are analyzed in detail: (i) "entropically crowding thermal baths"--in which the particles crowd uniformly around the probes; (ii) "entropically crowding Langevin forces"--in which the probes crowd uniformly amongst the particles. Entropic crowding is equivalent to the optimal stochastic cloaking of the probes within the spatially inhomogeneous thermal bath. PMID:22304065
Aquifer Structure Identification Using Stochastic Inversion
Harp, Dylan R; Dai, Zhenxue; Wolfsberg, Andrew V; Vrugt, Jasper A
2008-01-01
This study presents a stochastic inverse method for aquifer structure identification using sparse geophysical and hydraulic response data. The method is based on updating structure parameters from a transition probability model to iteratively modify the aquifer structure and parameter zonation. The method is extended to the adaptive parameterization of facies hydraulic parameters by including these parameters as optimization variables. The stochastic nature of the statistical structure parameters leads to nonconvex objective functions. A multi-method genetically adaptive evolutionary approach (AMALGAM-SO) was selected to perform the inversion given its search capabilities. Results are obtained as a probabilistic assessment of facies distribution based on indicator cokriging simulation of the optimized structural parameters. The method is illustrated by estimating the structure and facies hydraulic parameters of a synthetic example with a transient hydraulic response.
COSMIC DUST AGGREGATION WITH STOCHASTIC CHARGING
Matthews, Lorin S.; Hyde, Truell W.; Shotorban, Babak
2013-10-20
The coagulation of cosmic dust grains is a fundamental process which takes place in astrophysical environments, such as presolar nebulae and circumstellar and protoplanetary disks. Cosmic dust grains can become charged through interaction with their plasma environment or other processes, and the resultant electrostatic force between dust grains can strongly affect their coagulation rate. Since ions and electrons are collected on the surface of the dust grain at random time intervals, the electrical charge of a dust grain experiences stochastic fluctuations. In this study, a set of stochastic differential equations is developed to model these fluctuations over the surface of an irregularly shaped aggregate. Then, employing the data produced, the influence of the charge fluctuations on the coagulation process and the physical characteristics of the aggregates formed is examined. It is shown that dust with small charges (due to the small size of the dust grains or a tenuous plasma environment) is affected most strongly.
Stochastic magnetization dynamics in single domain particles
NASA Astrophysics Data System (ADS)
Giordano, Stefano; Dusch, Yannick; Tiercelin, Nicolas; Pernod, Philippe; Preobrazhensky, Vladimir
2013-06-01
Magnetic particles are largely utilized in several applications ranging from magnetorheological fluids to bioscience and from nanothechnology to memories or logic devices. The behavior of each single particle at finite temperature (under thermal stochastic fluctuations) plays a central role in determining the response of the whole physical system taken into consideration. Here, the magnetization evolution is studied through the Landau-Lifshitz-Gilbert formalism and the non-equilibrium statistical mechanics is introduced with the Langevin and Fokker-Planck methodologies. As result of the combination of such techniques we analyse the stochastic magnetization dynamics and we numerically determine the convergence time, measuring the velocity of attainment of thermodynamic equilibrium, as function of the system temperature.
Stochastic reasoning, free energy, and information geometry.
Ikeda, Shiro; Tanaka, Toshiyuki; Amari, Shun-ichi
2004-09-01
Belief propagation (BP) is a universal method of stochastic reasoning. It gives exact inference for stochastic models with tree interactions and works surprisingly well even if the models have loopy interactions. Its performance has been analyzed separately in many fields, such as AI, statistical physics, information theory, and information geometry. This article gives a unified framework for understanding BP and related methods and summarizes the results obtained in many fields. In particular, BP and its variants, including tree reparameterization and concave-convex procedure, are reformulated with information-geometrical terms, and their relations to the free energy function are elucidated from an information-geometrical viewpoint. We then propose a family of new algorithms. The stabilities of the algorithms are analyzed, and methods to accelerate them are investigated. PMID:15265322
Stochastic Radiative transfer and real cloudiness
Evans, F.
1995-09-01
Plane-parallel radiative transfer modeling of clouds in GCMs is thought to be an inadequate representation of the effects of real cloudiness. A promising new approach for studying the effects of cloud horizontal inhomogeneity is stochastic radiative transfer, which computes the radiative effects of ensembles of cloud structures described by probability distributions. This approach is appropriate because cloud information is inherently statistical, and it is the mean radiative effect of complex 3D cloud structure that is desired. 2 refs., 1 fig.
Variational approach to stochastic nonlinear problems
Phythian, R.; Curtis, W.D.
1986-03-01
A variational principle is formulated which enables the mean value and higher moments of the solution of a stochastic nonlinear differential equation to be expressed as stationary values of certain quantities. Approximations are generated by using suitable trial functions in this variational principle and some of these are investigated numerically for the case of a Bernoulli oscillator driven by white noise. Comparison with exact data available for this system show that the variational approach to such problems can be quite effective.
Linear System Control Using Stochastic Learning Automata
NASA Technical Reports Server (NTRS)
Ziyad, Nigel; Cox, E. Lucien; Chouikha, Mohamed F.
1998-01-01
This paper explains the use of a Stochastic Learning Automata (SLA) to control switching between three systems to produce the desired output response. The SLA learns the optimal choice of the damping ratio for each system to achieve a desired result. We show that the SLA can learn these states for the control of an unknown system with the proper choice of the error criteria. The results of using a single automaton are compared to using multiple automata.
Stochastic differential equations and turbulent dispersion
NASA Technical Reports Server (NTRS)
Durbin, P. A.
1983-01-01
Aspects of the theory of continuous stochastic processes that seem to contribute to an understanding of turbulent dispersion are introduced and the theory and philosophy of modelling turbulent transport is emphasized. Examples of eddy diffusion examined include shear dispersion, the surface layer, and channel flow. Modeling dispersion with finite-time scale is considered including the Langevin model for homogeneous turbulence, dispersion in nonhomogeneous turbulence, and the asymptotic behavior of the Langevin model for nonhomogeneous turbulence.
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.
Stochastic behavior of nanoscale dielectric wall buckling
NASA Astrophysics Data System (ADS)
Friedman, Lawrence H.; Levin, Igor; Cook, Robert F.
2016-03-01
The random buckling patterns of nanoscale dielectric walls are analyzed using a nonlinear multi-scale stochastic method that combines experimental measurements with simulations. The dielectric walls, approximately 200 nm tall and 20 nm wide, consist of compliant, low dielectric constant (low-k) fins capped with stiff, compressively stressed TiN lines that provide the driving force for buckling. The deflections of the buckled lines exhibit sinusoidal pseudoperiodicity with amplitude fluctuation and phase decorrelation arising from stochastic variations in wall geometry, properties, and stress state at length scales shorter than the characteristic deflection wavelength of about 1000 nm. The buckling patterns are analyzed and modeled at two length scales: a longer scale (up to 5000 nm) that treats randomness as a longer-scale measurable quantity, and a shorter-scale (down to 20 nm) that treats buckling as a deterministic phenomenon. Statistical simulation is used to join the two length scales. Through this approach, the buckling model is validated and material properties and stress states are inferred. In particular, the stress state of TiN lines in three different systems is determined, along with the elastic moduli of low-k fins and the amplitudes of the small-scale random fluctuations in wall properties—all in the as-processed state. The important case of stochastic effects giving rise to buckling in a deterministically sub-critical buckling state is demonstrated. The nonlinear multiscale stochastic analysis provides guidance for design of low-k structures with acceptable buckling behavior and serves as a template for how randomness that is common to nanoscale phenomena might be measured and analyzed in other contexts.
Optimization Testbed Cometboards Extended into Stochastic Domain
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.; Patnaik, Surya N.
2010-01-01
COMparative Evaluation Testbed of Optimization and Analysis Routines for the Design of Structures (CometBoards) is a multidisciplinary design optimization software. It was originally developed for deterministic calculation. It has now been extended into the stochastic domain for structural design problems. For deterministic problems, CometBoards is introduced through its subproblem solution strategy as well as the approximation concept in optimization. In the stochastic domain, a design is formulated as a function of the risk or reliability. Optimum solution including the weight of a structure, is also obtained as a function of reliability. Weight versus reliability traced out an inverted-S-shaped graph. The center of the graph corresponded to 50 percent probability of success, or one failure in two samples. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure that corresponded to unity for reliability. Weight can be reduced to a small value for the most failure-prone design with a compromised reliability approaching zero. The stochastic design optimization (SDO) capability for an industrial problem was obtained by combining three codes: MSC/Nastran code was the deterministic analysis tool, fast probabilistic integrator, or the FPI module of the NESSUS software, was the probabilistic calculator, and CometBoards became the optimizer. The SDO capability requires a finite element structural model, a material model, a load model, and a design model. The stochastic optimization concept is illustrated considering an academic example and a real-life airframe component made of metallic and composite materials.
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.
Stochastic Games with Average Payoff Criterion
Ghosh, M. K.; Bagchi, A.
1998-11-15
We study two-person stochastic games on a Polish state and compact action spaces and with average payoff criterion under a certain ergodicity condition. For the zero-sum game we establish the existence of a value and stationary optimal strategies for both players. For the nonzero-sum case the existence of Nash equilibrium in stationary strategies is established under certain separability conditions.
Continous-time stochastic Markov games
Nowak, A.
1994-12-31
We consider zero-sum game in which the players control a continuous-time stochastic jump process. The state space is assumed to be a Borel set. Sufficient conditions for the existence of optimal strategies for the players are to be presented. In the undiscounted case we will consider conditions which are related to geometric ergodicity. Some possible extentions to non-zero-sum games will be mentioned.
Circular analysis in complex stochastic systems
NASA Astrophysics Data System (ADS)
Valleriani, Angelo
2015-12-01
Ruling out observations can lead to wrong models. This danger occurs unwillingly when one selects observations, experiments, simulations or time-series based on their outcome. In stochastic processes, conditioning on the future outcome biases all local transition probabilities and makes them consistent with the selected outcome. This circular self-consistency leads to models that are inconsistent with physical reality. It is also the reason why models built solely on macroscopic observations are prone to this fallacy.
A stochastic model for kinesin bidirectional stepping
Yao, Xiaojun; Zheng, Yujun
2014-02-28
In this paper, a hand-over-hand stochastic model for the dynamics of the conventional kinesin is constructed. In the model, both forward and backward motions are taken into consideration. First passage time distributions, average velocities, dwell times, and forward/backward step ratios are investigated based on the model. A good agreement between the results of the model and experimental data is achieved under a variety of external loads.
Circular analysis in complex stochastic systems
Valleriani, Angelo
2015-01-01
Ruling out observations can lead to wrong models. This danger occurs unwillingly when one selects observations, experiments, simulations or time-series based on their outcome. In stochastic processes, conditioning on the future outcome biases all local transition probabilities and makes them consistent with the selected outcome. This circular self-consistency leads to models that are inconsistent with physical reality. It is also the reason why models built solely on macroscopic observations are prone to this fallacy. PMID:26656656
STOCHASTIC COOLING STUDIES IN RHIC, II.
BLASKIEWICZ,M.BRENNAN,J.M.WEI,J.
2004-07-05
Intra-beam scattering (IBS) is unavoidable for highly charged heavy ions and causes emittance growth during the store for collision physics. A longitudinal bunched beam stochastic cooling system will confine the bunch within the RF bucket increasing the useful luminosity. We describe a series of measurements in RHIC that have been used to verify our understanding of the relevant physics and the cooling system architecture that is being prototyped.
Stochastic behavior of nanoscale dielectric wall buckling
Friedman, Lawrence H.; Levin, Igor; Cook, Robert F.
2016-01-01
The random buckling patterns of nanoscale dielectric walls are analyzed using a nonlinear multi-scale stochastic method that combines experimental measurements with simulations. The dielectric walls, approximately 200 nm tall and 20 nm wide, consist of compliant, low dielectric constant (low-k) fins capped with stiff, compressively stressed TiN lines that provide the driving force for buckling. The deflections of the buckled lines exhibit sinusoidal pseudoperiodicity with amplitude fluctuation and phase decorrelation arising from stochastic variations in wall geometry, properties, and stress state at length scales shorter than the characteristic deflection wavelength of about 1000 nm. The buckling patterns are analyzed and modeled at two length scales: a longer scale (up to 5000 nm) that treats randomness as a longer-scale measurable quantity, and a shorter-scale (down to 20 nm) that treats buckling as a deterministic phenomenon. Statistical simulation is used to join the two length scales. Through this approach, the buckling model is validated and material properties and stress states are inferred. In particular, the stress state of TiN lines in three different systems is determined, along with the elastic moduli of low-k fins and the amplitudes of the small-scale random fluctuations in wall properties—all in the as-processed state. The important case of stochastic effects giving rise to buckling in a deterministically sub-critical buckling state is demonstrated. The nonlinear multiscale stochastic analysis provides guidance for design of low-k structures with acceptable buckling behavior and serves as a template for how randomness that is common to nanoscale phenomena might be measured and analyzed in other contexts. PMID:27330220
Moment closure and the stochastic logistic model.
Nåsell, Ingemar
2003-03-01
The quasi-stationary distribution of the stochastic logistic model is studied in the parameter region where its body is approximately normal. Improved asymptotic approximations of its first three cumulants are derived. It is shown that the same results can be derived with the aid of the moment closure method. This indicates that the moment closure method leads to expressions for the cumulants that are asymptotic approximations of the cumulants of the quasi-stationary distribution. PMID:12615498
Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine
Lipinski, Kamil A.; Barber, Louise J.; Davies, Matthew N.; Ashenden, Matthew; Sottoriva, Andrea; Gerlinger, Marco
2016-01-01
The ability to predict the future behavior of an individual cancer is crucial for precision cancer medicine. The discovery of extensive intratumor heterogeneity and ongoing clonal adaptation in human tumors substantiated the notion of cancer as an evolutionary process. Random events are inherent in evolution and tumor spatial structures hinder the efficacy of selection, which is the only deterministic evolutionary force. This review outlines how the interaction of these stochastic and deterministic processes, which have been extensively studied in evolutionary biology, limits cancer predictability and develops evolutionary strategies to improve predictions. Understanding and advancing the cancer predictability horizon is crucial to improve precision medicine outcomes. PMID:26949746
Evaluation of stochastic reservoir operation optimization models
NASA Astrophysics Data System (ADS)
Celeste, Alcigeimes B.; Billib, Max
2009-09-01
This paper investigates the performance of seven stochastic models used to define optimal reservoir operating policies. The models are based on implicit (ISO) and explicit stochastic optimization (ESO) as well as on the parameterization-simulation-optimization (PSO) approach. The ISO models include multiple regression, two-dimensional surface modeling and a neuro-fuzzy strategy. The ESO model is the well-known and widely used stochastic dynamic programming (SDP) technique. The PSO models comprise a variant of the standard operating policy (SOP), reservoir zoning, and a two-dimensional hedging rule. The models are applied to the operation of a single reservoir damming an intermittent river in northeastern Brazil. The standard operating policy is also included in the comparison and operational results provided by deterministic optimization based on perfect forecasts are used as a benchmark. In general, the ISO and PSO models performed better than SDP and the SOP. In addition, the proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.
Discrete Deterministic and Stochastic Petri Nets
NASA Technical Reports Server (NTRS)
Zijal, Robert; Ciardo, Gianfranco
1996-01-01
Petri nets augmented with timing specifications gained a wide acceptance in the area of performance and reliability evaluation of complex systems exhibiting concurrency, synchronization, and conflicts. The state space of time-extended Petri nets is mapped onto its basic underlying stochastic process, which can be shown to be Markovian under the assumption of exponentially distributed firing times. The integration of exponentially and non-exponentially distributed timing is still one of the major problems for the analysis and was first attacked for continuous time Petri nets at the cost of structural or analytical restrictions. We propose a discrete deterministic and stochastic Petri net (DDSPN) formalism with no imposed structural or analytical restrictions where transitions can fire either in zero time or according to arbitrary firing times that can be represented as the time to absorption in a finite absorbing discrete time Markov chain (DTMC). Exponentially distributed firing times are then approximated arbitrarily well by geometric distributions. Deterministic firing times are a special case of the geometric distribution. The underlying stochastic process of a DDSPN is then also a DTMC, from which the transient and stationary solution can be obtained by standard techniques. A comprehensive algorithm and some state space reduction techniques for the analysis of DDSPNs are presented comprising the automatic detection of conflicts and confusions, which removes a major obstacle for the analysis of discrete time models.
Stochastic Flow Modeling for Resin Transfer Moulding
NASA Astrophysics Data System (ADS)
Desplentere, Frederik; Verpoest, Ignaas; Lomov, Stepan
2009-07-01
Liquid moulding processes suffer from inherently present scatter in the textile reinforcement properties. This variability can lead to unwanted filling patterns within the mould resulting in bad parts. If thermoplastic resins are used with the in-situ polymerisation technique, an additional difficulty appears. The time window to inject the material is small if industrial processing parameters are used (<5 minutes). To model the stochastic nature of RTM, Darcy's description of the mould filling process has been used with the permeability distribution of the preform given as a random field. The random field of the permeability is constructed as a correlated field with an exponential correlation function. Optical microscopy and X-ray micro-CT have been used to study the stochastic parameters of the geometry for 2D and 3D woven textile preforms. The parameters describing the random permeability field (average, standard deviation and correlation length) are identified based on the stochastic parameters of the geometry for the preforms, analytical estimations and CFD modelling of the permeability. In order to implement the random field for the permeability and the variability for the resin viscosity, an add-on to the mould filling simulation software PAM-RTM™ has been developed. This analysis has been validated on case studies.
Renormalization of stochastic lattice models: basic formulation.
Haselwandter, Christoph A; Vvedensky, Dimitri D
2007-10-01
We describe a general method for the multiscale analysis of stochastic lattice models. Beginning with a lattice Langevin formulation of site fluctuations, we derive stochastic partial differential equations by regularizing the transition rules of the model. Subsequent coarse graining is accomplished by calculating renormalization-group (RG) trajectories from initial conditions determined by the regularized atomistic models. The RG trajectories correspond to hierarchies of continuum equations describing lattice models over expanding length and time scales. These continuum equations retain a quantitative connection over different scales, as well as to the underlying atomistic dynamics. This provides a systematic method for the derivation of continuum equations from the transition rules of lattice models for any length and time scales. As an illustration we consider the one-dimensional (1D) Wolf-Villain (WV) model [Europhys. Lett. 13, 389 (1990)]. The RG analysis of this model, which we develop in detail, is generic and can be applied to a wide range of conservative lattice models. The RG trajectory of the 1D WV model shows a complex crossover sequence of linear and nonlinear stochastic differential equations, which is in excellent agreement with kinetic Monte Carlo simulations of this model. We conclude by discussing possible applications of the multiscale method described here to other nonequilibrium systems. PMID:17994944
Evaluating uncertainty in stochastic simulation models
McKay, M.D.
1998-02-01
This paper discusses fundamental concepts of uncertainty analysis relevant to both stochastic simulation models and deterministic models. A stochastic simulation model, called a simulation model, is a stochastic mathematical model that incorporates random numbers in the calculation of the model prediction. Queuing models are familiar simulation models in which random numbers are used for sampling interarrival and service times. Another example of simulation models is found in probabilistic risk assessments where atmospheric dispersion submodels are used to calculate movement of material. For these models, randomness comes not from the sampling of times but from the sampling of weather conditions, which are described by a frequency distribution of atmospheric variables like wind speed and direction as a function of height above ground. A common characteristic of simulation models is that single predictions, based on one interarrival time or one weather condition, for example, are not nearly as informative as the probability distribution of possible predictions induced by sampling the simulation variables like time and weather condition. The language of model analysis is often general and vague, with terms having mostly intuitive meaning. The definition and motivations for some of the commonly used terms and phrases offered in this paper lead to an analysis procedure based on prediction variance. In the following mathematical abstraction the authors present a setting for model analysis, relate practical objectives to mathematical terms, and show how two reasonable premises lead to a viable analysis strategy.
Stochastic calibration of an orographic percipitation model
Hay, L.E.
1998-01-01
In this study a stochastic approach to calibration of an orographic precipitation model (Rhea, 1978) was applied in the Gunnison River Basin of south-western Colorado. The stochastic approach to model calibration was used to determine: (1) the model parameter uncertainty and sensitivity; (2) the grid-cell resolution to run the model (10 or 5 km grids); (3) the model grid rotation increment; and (4) the basin subdivision by elevation band for parameter definition. Results from the stochastic calibration are location and data dependent. Uncertainty, sensitivity and range in the final parameter sets were found to vary by grid-cell resolution and elevation. Ten km grids were found to be a more robust model configuration than 5 km grids. Grid rotation increment, tested using only 10 km grids, indicated increments of less than 10 degrees to be superior. Basin subdivision into two elevation bands was found to produce 'optimal' results for both 10 and 5 km grids. ?? 1998 John Wiley & Sons, Ltd.
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.
Robust stochastic optimization for reservoir operation
NASA Astrophysics Data System (ADS)
Pan, Limeng; Housh, Mashor; Liu, Pan; Cai, Ximing; Chen, Xin
2015-01-01
Optimal reservoir operation under uncertainty is a challenging engineering problem. Application of classic stochastic optimization methods to large-scale problems is limited due to computational difficulty. Moreover, classic stochastic methods assume that the estimated distribution function or the sample inflow data accurately represents the true probability distribution, which may be invalid and the performance of the algorithms may be undermined. In this study, we introduce a robust optimization (RO) approach, Iterative Linear Decision Rule (ILDR), so as to provide a tractable approximation for a multiperiod hydropower generation problem. The proposed approach extends the existing LDR method by accommodating nonlinear objective functions. It also provides users with the flexibility of choosing the accuracy of ILDR approximations by assigning a desired number of piecewise linear segments to each uncertainty. The performance of the ILDR is compared with benchmark policies including the sampling stochastic dynamic programming (SSDP) policy derived from historical data. The ILDR solves both the single and multireservoir systems efficiently. The single reservoir case study results show that the RO method is as good as SSDP when implemented on the original historical inflows and it outperforms SSDP policy when tested on generated inflows with the same mean and covariance matrix as those in history. For the multireservoir case study, which considers water supply in addition to power generation, numerical results show that the proposed approach performs as well as in the single reservoir case study in terms of optimal value and distributional robustness.
Investigation of the stochastic model for sawteeth
NASA Astrophysics Data System (ADS)
Firpo, Marie-Christine; Ettoumi, Wahb; Farengo, Ricardo; Ferrari, Hugo; Garcia-Martinez, Pablo Luis; Lifschitz, Agustin
2013-10-01
Tokamak sawteeth have often been considered as a manifestation of magnetic reconnection in a laboratory plasma. However, measurements have repeatedly shown that the very fast crash phase may be associated with little reconnection, as the central q-profile remains below one and almost unchanged before and after the sawtooth collapse. One is thus left with the need to search for an explanation of the fastness of the sawtooth crash outside of the pure frame of magnetic reconnection. To account for incomplete reconnection, Lichtenberg argued in a seminal paper that the fast disruptive relaxation could be caused by the intrinsic large-scale stochasticity caused by overlapping magnetic islands. Nevertheless, the well known nickel trace experiments in JET [Wesson et al. PRL 1997] appeared to contradict the simple notion of stochasticity and thermal redistribution. Using a full orbit following code for the nickel ions, we demonstrate that the profile flattening of nickel ions during the sawtooth crash phase may be well reproduced using a stochastic model for the magnetic field and the electric field deduced from an ideal MHD hypothesis, but not in the case of integrable magnetic field lines. A chaotic indicator for the nickel motion quantifies the discrepancy between the two scenarios. Financial support from the ECOS-MINCyT Research Grant No. A09E02 is gratefully acknowledged.
Stochastic extinction dynamics of HIV-1
NASA Astrophysics Data System (ADS)
Schwartz, Ira; Forgoston, Eric; Weinberger, Leor
2012-02-01
We consider an HIV-1 within host model in which T cells are infected by the virus. Due to small numbers of molecules, stochastic effects play an important role in the dynamical outcomes in that two states are observed experimentally: a replication state in which the virus is active, or a dormant state leading to latency in which the virus becomes active after a delay. The two states are conjectured to be governed by the Tat gene protein transcription process, which does not possess two stable attractors. Rather, the active state is stable, while the dormant state is unstable. Therefore the dormant state can only be achieved through the dynamics of stochastic fluctuations in which noise organizes a path to dormancy. Here we use optimal path theory applied to a Tat gene stochastic model to show how random fluctuations generate the dormant state by deriving a path which optimizes the probability of achieving the dormant state. We explicitly show how the probability of achieving dormancy scales with the transition rate parameters.
Numerical Methods for Stochastic Partial Differential Equations
Sharp, D.H.; Habib, S.; Mineev, M.B.
1999-07-08
This is the final report of a Laboratory Directed Research and Development (LDRD) project at the Los Alamos National laboratory (LANL). The objectives of this proposal were (1) the development of methods for understanding and control of spacetime discretization errors in nonlinear stochastic partial differential equations, and (2) the development of new and improved practical numerical methods for the solutions of these equations. The authors have succeeded in establishing two methods for error control: the functional Fokker-Planck equation for calculating the time discretization error and the transfer integral method for calculating the spatial discretization error. In addition they have developed a new second-order stochastic algorithm for multiplicative noise applicable to the case of colored noises, and which requires only a single random sequence generation per time step. All of these results have been verified via high-resolution numerical simulations and have been successfully applied to physical test cases. They have also made substantial progress on a longstanding problem in the dynamics of unstable fluid interfaces in porous media. This work has lead to highly accurate quasi-analytic solutions of idealized versions of this problem. These may be of use in benchmarking numerical solutions of the full stochastic PDEs that govern real-world problems.
Transient absolute robustness in stochastic biochemical networks.
Enciso, German A
2016-08-01
Absolute robustness allows biochemical networks to sustain a consistent steady-state output in the face of protein concentration variability from cell to cell. This property is structural and can be determined from the topology of the network alone regardless of rate parameters. An important question regarding these systems is the effect of discrete biochemical noise in the dynamical behaviour. In this paper, a variable freezing technique is developed to show that under mild hypotheses the corresponding stochastic system has a transiently robust behaviour. Specifically, after finite time the distribution of the output approximates a Poisson distribution, centred around the deterministic mean. The approximation becomes increasingly accurate, and it holds for increasingly long finite times, as the total protein concentrations grow to infinity. In particular, the stochastic system retains a transient, absolutely robust behaviour corresponding to the deterministic case. This result contrasts with the long-term dynamics of the stochastic system, which eventually must undergo an extinction event that eliminates robustness and is completely different from the deterministic dynamics. The transiently robust behaviour may be sufficient to carry out many forms of robust signal transduction and cellular decision-making in cellular organisms. PMID:27581485
Linear stochastic degenerate Sobolev equations and applications†
NASA Astrophysics Data System (ADS)
Liaskos, Konstantinos B.; Pantelous, Athanasios A.; Stratis, Ioannis G.
2015-12-01
In this paper, a general class of linear stochastic degenerate Sobolev equations with additive noise is considered. This class of systems is the infinite-dimensional analogue of linear descriptor systems in finite dimensions. Under appropriate assumptions, the mild and strong well-posedness for the initial value problem are studied using elements of the semigroup theory and properties of the stochastic convolution. The final value problem is also examined and it is proved that this is uniquely strongly solvable and the solution is continuously dependent on the final data. Based on the results of the forward and backward problem, the conditions for the exact controllability are investigated for a special but important class of these equations. The abstract results are illustrated by applications in complex media electromagnetics, in the one-dimensional stochastic Dirac equation in the non-relativistic limit and in a potential application in input-output analysis in economics. Dedicated to Professor Grigoris Kalogeropoulos on the occasion of his seventieth birthday.
Decomposing generalized measurements into continuous stochastic processes
Varbanov, Martin; Brun, Todd A.
2007-09-15
One of the broadest concepts of measurement in quantum theory is the generalized measurement. Another paradigm of measurement--arising naturally in quantum optics, among other fields--is that of continuous-time measurements, which can be seen as the limit of a consecutive sequence of weak measurements. They are naturally described in terms of stochastic processes, or time-dependent random variables. We show that any generalized measurement can be decomposed as a sequence of weak measurements with a mathematical limit as a continuous stochastic process. We give an explicit construction for any generalized measurement, and prove that the resulting continuous evolution, in the long-time limit, collapses the state of the quantum system to one of the final states generated by the generalized measurement, being decomposed, with the correct probabilities. A prominent feature of the construction is the presence of a feedback mechanism--the instantaneous choice weak measurement at a given time depends on the outcomes of earlier measurements. For a generalized measurement with n outcomes, this information is captured by a real n-vector on an n-simplex, which obeys a simple classical stochastic evolution.
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.
Recursive stochastic effects in valley hybrid inflation
NASA Astrophysics Data System (ADS)
Levasseur, Laurence Perreault; Vennin, Vincent; Brandenberger, Robert
2013-10-01
Hybrid inflation is a two-field model where inflation ends because of a tachyonic instability, the duration of which is determined by stochastic effects and has important observational implications. Making use of the recursive approach to the stochastic formalism presented in [L. P. Levasseur, preceding article, Phys. Rev. D 88, 083537 (2013)], these effects are consistently computed. Through an analysis of backreaction, this method is shown to converge in the valley but points toward an (expected) instability in the waterfall. It is further shown that the quasistationarity of the auxiliary field distribution breaks down in the case of a short-lived waterfall. We find that the typical dispersion of the waterfall field at the critical point is then diminished, thus increasing the duration of the waterfall phase and jeopardizing the possibility of a short transition. Finally, we find that stochastic effects worsen the blue tilt of the curvature perturbations by an O(1) factor when compared with the usual slow-roll contribution.
Ayati, Moosa; Alwan, Mohamad; Liu Xinzhi; Khaloozadeh, Hamid
2011-11-30
State observation (estimation) is a very important issue in system analysis and control. This paper develops a new observer called Stochastic Adaptive Impulsive Observer (SAIO) for the state estimation of impulsive systems. The proposed observer is applicable to linear and nonlinear stochastic impulsive systems. In addition, the effect of parametric uncertainty is considered and unknown parameters of the system are estimated by suitable adaptation laws. Impulsive system theory, particularly stochastic Lyapunov-like function, is used to analyze the stability and convergence of the state estimations. The main advantages of the proposed observer are: 1) it gives continuous estimation from discrete time measurements of the system output, and 2) it is useful for state estimation when continuous measurements are impossible or expensive. Simulation results show the effectiveness of the proposed observer and we believe that it has many applications in control and estimation theories.
Stochastic-dynamic Modelling of Morphodynamics
NASA Astrophysics Data System (ADS)
Eppel, D. P.; Kapitza, H.
The numerical prediction of coastal sediment motion over time spans of years and decades is hampered by the sediment's ability, when stirred by waves and currents, to often react not uniquely to the external forcing but rather to show some kind of internal dynamics whose characteristics are not directly linked to the external forcing. Analytical stability analyses of the sediment-water system indicate that instabilities of tidally forced sediment layers in shallow seas can occur on spatial scales smaller than and not related to the scales of the tidal components. The finite growth of these un- stable amplitides can be described in terms of Ginzburg-Landau equations. Examples are the formation of ripples, sand waves and sand dunes or the formation of shore- face connected ridges. Among others, analyses of time series of coastal profiles from Duck, South Carolina extending over several decades gave evidence for self-organized behaviour suggesting that some important sediment-water systems can be perceived as dissipative dynamical structures. The consequences of such behaviour for predicting morphodynamics has been pointed out: one would expect that there exist time horizons beyond which predictions in the traditional deterministic sense are not possible. One would have to look for statistical quantities containing information of some relevance such as phase-space densities of solutions, attractor sets and the like. This contribution is part of an effort to address the prediction problem of morphody- namics through process-oriented models containing stochastic parameterizations for bottom shear stresses, critical shear stresses, etc.; process-based models because they are directly related to the physical processes but in a stochastic form because it is known that the physical processes contain strong stochastic components. The final outcome of such a program would be the generation of an ensemble of solutions by Monte Carlo integrations of the stochastic model
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Stochastic simulation of radium-223 dichloride therapy at the sub-cellular level
NASA Astrophysics Data System (ADS)
Gholami, Y.; Zhu, X.; Fulton, R.; Meikle, S.; El-Fakhri, G.; Kuncic, Z.
2015-08-01
Radium-223 dichloride (223Ra) is an alpha particle emitter and a natural bone-seeking radionuclide that is currently used for treating osteoblastic bone metastases associated with prostate cancer. The stochastic nature of alpha emission, hits and energy deposition poses some challenges for estimating radiation damage. In this paper we investigate the distribution of hits to cells by multiple alpha particles corresponding to a typical clinically delivered dose using a Monte Carlo model to simulate the stochastic effects. The number of hits and dose deposition were recorded in the cytoplasm and nucleus of each cell. Alpha particle tracks were also visualized. We found that the stochastic variation in dose deposited in cell nuclei (≃ 40%) can be attributed in part to the variation in LET with pathlength. We also found that ≃ 18% of cell nuclei receive less than one sigma below the average dose per cell (≃ 15.4 Gy). One possible implication of this is that the efficacy of cell kill in alpha particle therapy need not rely solely on ionization clustering on DNA but possibly also on indirect DNA damage through the production of free radicals and ensuing intracellular signaling.
Stochastic simulation of radium-223 dichloride therapy at the sub-cellular level.
Gholami, Y; Zhu, X; Fulton, R; Meikle, S; El-Fakhri, G; Kuncic, Z
2015-08-01
Radium-223 dichloride ((223)Ra) is an alpha particle emitter and a natural bone-seeking radionuclide that is currently used for treating osteoblastic bone metastases associated with prostate cancer. The stochastic nature of alpha emission, hits and energy deposition poses some challenges for estimating radiation damage. In this paper we investigate the distribution of hits to cells by multiple alpha particles corresponding to a typical clinically delivered dose using a Monte Carlo model to simulate the stochastic effects. The number of hits and dose deposition were recorded in the cytoplasm and nucleus of each cell. Alpha particle tracks were also visualized. We found that the stochastic variation in dose deposited in cell nuclei ([Formula: see text]40%) can be attributed in part to the variation in LET with pathlength. We also found that [Formula: see text]18% of cell nuclei receive less than one sigma below the average dose per cell ([Formula: see text]15.4 Gy). One possible implication of this is that the efficacy of cell kill in alpha particle therapy need not rely solely on ionization clustering on DNA but possibly also on indirect DNA damage through the production of free radicals and ensuing intracellular signaling. PMID:26216391
Stochastic effects in a seasonally forced epidemic model
NASA Astrophysics Data System (ADS)
Rozhnova, G.; Nunes, A.
2010-10-01
The interplay of seasonality, the system’s nonlinearities and intrinsic stochasticity, is studied for a seasonally forced susceptible-exposed-infective-recovered stochastic model. The model is explored in the parameter region that corresponds to childhood infectious diseases such as measles. The power spectrum of the stochastic fluctuations around the attractors of the deterministic system that describes the model in the thermodynamic limit is computed analytically and validated by stochastic simulations for large system sizes. Size effects are studied through additional simulations. Other effects such as switching between coexisting attractors induced by stochasticity often mentioned in the literature as playing an important role in the dynamics of childhood infectious diseases are also investigated. The main conclusion is that stochastic amplification, rather than these effects, is the key ingredient to understand the observed incidence patterns.
Stability of solutions to stochastic partial differential equations
NASA Astrophysics Data System (ADS)
Gess, Benjamin; Tölle, Jonas M.
2016-03-01
We provide a general framework for the stability of solutions to stochastic partial differential equations with respect to perturbations of the drift. More precisely, we consider stochastic partial differential equations with drift given as the subdifferential of a convex function and prove continuous dependence of the solutions with regard to random Mosco convergence of the convex potentials. In particular, we identify the concept of stochastic variational inequalities (SVI) as a well-suited framework to study such stability properties. The generality of the developed framework is then laid out by deducing Trotter type and homogenization results for stochastic fast diffusion and stochastic singular p-Laplace equations. In addition, we provide an SVI treatment for stochastic nonlocal p-Laplace equations and prove their convergence to the respective local models.
Problems of Mathematical Finance by Stochastic Control Methods
NASA Astrophysics Data System (ADS)
Stettner, Łukasz
The purpose of this paper is to present main ideas of mathematics of finance using the stochastic control methods. There is an interplay between stochastic control and mathematics of finance. On the one hand stochastic control is a powerful tool to study financial problems. On the other hand financial applications have stimulated development in several research subareas of stochastic control in the last two decades. We start with pricing of financial derivatives and modeling of asset prices, studying the conditions for the absence of arbitrage. Then we consider pricing of defaultable contingent claims. Investments in bonds lead us to the term structure modeling problems. Special attention is devoted to historical static portfolio analysis called Markowitz theory. We also briefly sketch dynamic portfolio problems using viscosity solutions to Hamilton-Jacobi-Bellman equation, martingale-convex analysis method or stochastic maximum principle together with backward stochastic differential equation. Finally, long time portfolio analysis for both risk neutral and risk sensitive functionals is introduced.
Method to describe stochastic dynamics using an optimal coordinate.
Krivov, Sergei V
2013-12-01
A general method to describe the stochastic dynamics of Markov processes is suggested. The method aims to solve three related problems: the determination of an optimal coordinate for the description of stochastic dynamics; the reconstruction of time from an ensemble of stochastic trajectories; and the decomposition of stationary stochastic dynamics into eigenmodes which do not decay exponentially with time. The problems are solved by introducing additive eigenvectors which are transformed by a stochastic matrix in a simple way - every component is translated by a constant distance. Such solutions have peculiar properties. For example, an optimal coordinate for stochastic dynamics with detailed balance is a multivalued function. An optimal coordinate for a random walk on a line corresponds to the conventional eigenvector of the one-dimensional Dirac equation. The equation for the optimal coordinate in a slowly varying potential reduces to the Hamilton-Jacobi equation for the action function. PMID:24483410
Planning under uncertainty solving large-scale stochastic linear programs
Infanger, G. . Dept. of Operations Research Technische Univ., Vienna . Inst. fuer Energiewirtschaft)
1992-12-01
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.
NASA Astrophysics Data System (ADS)
Zhang, Sumei; Wang, Lihe
2013-07-01
This study proposes a pricing model through allowing for stochastic interest rate and stochastic volatility in the double exponential jump-diffusion setting. The characteristic function of the proposed model is then derived. Fast numerical solutions for European call and put options pricing based on characteristic function and fast Fourier transform (FFT) technique are developed. Simulations show that our numerical technique is accurate, fast and easy to implement, the proposed model is suitable for modeling long-time real-market changes. The model and the proposed option pricing method are useful for empirical analysis of asset returns and risk management in firms.
NASA Astrophysics Data System (ADS)
Balaji, Bhashyam
2012-06-01
Conventional trackers provide the human operator with estimated target tracks. It is desirable to make higher level inference of the target behaviour/intent (e.g., trajectory inference) in an automated manner. One such approach is to use stochastic context-free grammars and the Earley-Stoelcke parsing algorithm. The problem of inference is reformulated as one of parsing. In this paper, the consistency of stochastic context-free grammars is reviewed. Some examples illustrating the constraints on SCFGs due to consistency are presented, including a toy SCFG that has been used to successfully parse real GMTI radar data.
Magnetic stochasticity in gyrokinetic simulations of plasma microturbulence
Nevins, W M; Wang, E; Candy, J
2010-02-12
Analysis of the magnetic field structure from electromagnetic simulations of tokamak ion temperature gradient turbulence demonstrates that the magnetic field can be stochastic even at very low plasma pressure. The degree of magnetic stochasticity is quantified by evaluating the magnetic diffusion coefficient. We find that the magnetic stochasticity fails to produce a dramatic increase in the electron heat conductivity because the magnetic diffusion coefficient remains small.
Stochastic resonance in the mechanoelectrical transduction of hair cells
NASA Astrophysics Data System (ADS)
Lindner, John F.; Bennett, Matthew; Wiesenfeld, Kurt
2005-11-01
In transducing mechanical stimuli into electrical signals, at least some hair cells in vertebrate auditory and vestibular systems respond optimally to weak periodic signals at natural, nonzero noise intensities. We understand this stochastic resonance by constructing a faithful mechanical model reflecting the hair cell geometry and described by a nonlinear stochastic differential equation. This Langevin description elucidates the mechanism of hair cell stochastic resonance while supporting the hypothesis that noise plays a functional role in hearing.
Stochastic properties of the plasma wave heating map
NASA Astrophysics Data System (ADS)
Nomura, Y.; Kamimura, T.; Ichikawa, Y. H.
1990-02-01
Diffusion coefficient for stochastic ion motion in a lower hybrid wave is derived analytically by use of the characteristic function method. The renormalization calculation is carried out successfully to account for effects of the higher order correlation. Numerical observation of the diffusion process confirms the expectation that the renormalized diffusion coefficient describes correctly the stochastic properties of the systems even in the region of the small stochastic parameter, except where the accelerator modes manifest their influence upon the chaotic orbits.
A stochastic model of AIDS and condom use
NASA Astrophysics Data System (ADS)
Dalal, Nirav; Greenhalgh, David; Mao, Xuerong
2007-01-01
In this paper we introduce stochasticity into a model of AIDS and condom use via the technique of parameter perturbation which is standard in stochastic population modelling. We show that the model established in this paper possesses non-negative solutions as desired in any population dynamics. We also carry out a detailed analysis on asymptotic stability both in probability one and in pth moment. Our results reveal that a certain type of stochastic perturbation may help to stabilise the underlying system.
Suboptimal stochastic controller for an n-body spacecraft
NASA Technical Reports Server (NTRS)
Larson, V.
1973-01-01
The problem is studied of determining a stochastic optimal controller for an n-body spacecraft. The approach used in obtaining the stochastic controller involves the application, interpretation, and combination of advanced dynamical principles and the theoretical aspects of modern control theory. The stochastic controller obtained for a complicated model of a spacecraft uses sensor angular measurements associated with the base body to obtain smoothed estimates of the entire state vector, can be easily implemented, and enables system performance to be significantly improved.
Stochastic Impulse Control of Non-Markovian Processes
Djehiche, Boualem; Hamadene, Said Hdhiri, Ibtissam
2010-02-15
We consider a class of stochastic impulse control problems of general stochastic processes i.e. not necessarily Markovian. Under fairly general conditions we establish existence of an optimal impulse control. We also prove existence of combined optimal stochastic and impulse control of a fairly general class of diffusions with random coefficients. Unlike, in the Markovian framework, we cannot apply quasi-variational inequalities techniques. We rather derive the main results using techniques involving reflected BSDEs and the Snell envelope.
Vaccination Control in a Stochastic SVIR Epidemic Model
Witbooi, Peter J.; Muller, Grant E.; Van Schalkwyk, Garth J.
2015-01-01
For a stochastic differential equation SVIR epidemic model with vaccination, we prove almost sure exponential stability of the disease-free equilibrium for ℛ0 < 1, where ℛ0 denotes the basic reproduction number of the underlying deterministic model. We study an optimal control problem for the stochastic model as well as for the underlying deterministic model. In order to solve the stochastic problem numerically, we use an approximation based on the solution of the deterministic model. PMID:26089961
Stochastic resonance enhanced by dichotomic noise in a bistable system
Rozenfeld, Robert; Neiman, Alexander; Schimansky-Geier, Lutz
2000-09-01
We study linear responses of a stochastic bistable system driven by dichotomic noise to a weak periodic signal. We show that the effect of stochastic resonance can be greatly enhanced in comparison with the conventional case when dichotomic forcing is absent, that is, both the signal-to-noise ratio and the spectral power amplification reach much greater values than in the standard stochastic resonance setup. (c) 2000 The American Physical Society.
Stochastic resonance in passive and active electronic circuits
Anishchenko, V.S.; Khovanov, I.A.; Shulgin, B.V.
1996-06-01
The phenomenon of stochastic resonance in a bistable system modeling overdamped oscillator is studied by numerical simulations and experiments. Experimental data are compared with theoretical results. Stochastic resonance in Chua{close_quote}s circuit is investigated in detail for different regimes of its own dynamics. The main characteristics of stochastic resonance for different regimes under the adiabatic approximation are compared. {copyright} {ital 1996 American Institute of Physics.}
Symmetries of stochastic differential equations: A geometric approach
NASA Astrophysics Data System (ADS)
De Vecchi, Francesco C.; Morando, Paola; Ugolini, Stefania
2016-06-01
A new notion of stochastic transformation is proposed and applied to the study of both weak and strong symmetries of stochastic differential equations (SDEs). The correspondence between an algebra of weak symmetries for a given SDE and an algebra of strong symmetries for a modified SDE is proved under suitable regularity assumptions. This general approach is applied to a stochastic version of a two dimensional symmetric ordinary differential equation and to the case of two dimensional Brownian motion.
A stochastic method for computing hadronic matrix elements
Alexandrou, Constantia; Constantinou, Martha; Dinter, Simon; Drach, Vincent; Jansen, Karl; Hadjiyiannakou, Kyriakos; Renner, Dru B.
2014-01-24
In this study, we present a stochastic method for the calculation of baryon 3-point functions which is an alternative to the typically used sequential method offering more versatility. We analyze the scaling of the error of the stochastically evaluated 3-point function with the lattice volume and find a favorable signal to noise ratio suggesting that the stochastic method can be extended to large volumes providing an efficient approach to compute hadronic matrix elements and form factors.
Solving the Langevin equation with stochastic algebraically correlated noise
NASA Astrophysics Data System (ADS)
Płoszajczak, M.; Srokowski, T.
1997-05-01
The long time tail in the velocity and force autocorrelation function has been found recently in molecular dynamics simulations of peripheral collisions of ions. Simulation of those slowly decaying correlations in the stochastic transport theory requires the development of new methods of generating stochastic force of arbitrarily long correlation times. In this paper we propose a Markovian process, the multidimensional kangaroo process, which permits the description of various algebraically correlated stochastic processes.
The Sharma-Parthasarathy stochastic two-body problem
Cresson, J.
2015-03-15
We study the Sharma-Parthasarathy stochastic two-body problem introduced by Sharma and Parthasarathy in [“Dynamics of a stochastically perturbed two-body problem,” Proc. R. Soc. A 463, 979-1003 (2007)]. In particular, we focus on the preservation of some fundamental features of the classical two-body problem like the Hamiltonian structure and first integrals in the stochastic case. Numerical simulations are performed which illustrate the dynamical behaviour of the osculating elements as the semi-major axis, the eccentricity, and the pericenter. We also derive a stochastic version of Gauss’s equations in the planar case.
Morton, D.P.
1994-01-01
Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems. Stochastic programming, Hydroelectric scheduling, Large-scale Systems.
Analysis of stochastically forced quasi-periodic attractors
Ryashko, Lev
2015-11-30
A problem of the analysis of stochastically forced quasi-periodic auto-oscillations of nonlinear dynamic systems is considered. A stationary distribution of random trajectories in the neighborhood of the corresponding deterministic attractor (torus) is studied. A parametric description of quadratic approximation of the quasipotential based on the stochastic sensitivity functions (SSF) technique is given. Using this technique, we analyse a dispersion of stochastic flows near the torus. For the case of two-torus in three-dimensional space, the stochastic sensitivity function is constructed.
H2/H∞ control for stochastic systems with delay
NASA Astrophysics Data System (ADS)
Qixia, Zhang
2015-12-01
This paper is concerned with the H2/H∞ control problem for stochastic linear systems with delay in state, control and external disturbance-dependent noise. A necessary and sufficient condition for the existence of a unique solution to the control problem is derived. The resulting solution is characterised by a kind of complex generalised forward-backward stochastic differential equations with stochastic delay equations as forward equations and anticipated backward stochastic differential equations as backward equations. Especially, we present the equivalent feedback solution via a new type of Riccati equations. To explain the theoretical results, we apply them to a population control problem.
A heterogeneous stochastic FEM framework for elliptic PDEs
Hou, Thomas Y. Liu, Pengfei
2015-01-15
We introduce a new concept of sparsity for the stochastic elliptic operator −div(a(x,ω)∇(⋅)), which reflects the compactness of its inverse operator in the stochastic direction and allows for spatially heterogeneous stochastic structure. This new concept of sparsity motivates a heterogeneous stochastic finite element method (HSFEM) framework for linear elliptic equations, which discretizes the equations using the heterogeneous coupling of spatial basis with local stochastic basis to exploit the local stochastic structure of the solution space. We also provide a sampling method to construct the local stochastic basis for this framework using the randomized range finding techniques. The resulting HSFEM involves two stages and suits the multi-query setting: in the offline stage, the local stochastic structure of the solution space is identified; in the online stage, the equation can be efficiently solved for multiple forcing functions. An online error estimation and correction procedure through Monte Carlo sampling is given. Numerical results for several problems with high dimensional stochastic input are presented to demonstrate the efficiency of the HSFEM in the online stage.
NASA Astrophysics Data System (ADS)
Chen, Chuchu; Hong, Jialin; Zhang, Liying
2016-02-01
Stochastic Maxwell equations with additive noise are a system of stochastic Hamiltonian partial differential equations intrinsically, possessing the stochastic multi-symplectic conservation law. It is shown that the averaged energy increases linearly with respect to the evolution of time and the flow of stochastic Maxwell equations with additive noise preserves the divergence in the sense of expectation. Moreover, we propose three novel stochastic multi-symplectic methods to discretize stochastic Maxwell equations in order to investigate the preservation of these properties numerically. We make theoretical discussions and comparisons on all of the three methods to observe that all of them preserve the corresponding discrete version of the averaged divergence. Meanwhile, we obtain the corresponding dissipative property of the discrete averaged energy satisfied by each method. Especially, the evolution rates of the averaged energies for all of the three methods are derived which are in accordance with the continuous case. Numerical experiments are performed to verify our theoretical results.
Stochastic analysis of virus transport in aquifers
Campbell, Rehmann L.L.; Welty, C.; Harvey, R.W.
1999-01-01
A large-scale model of virus transport in aquifers is derived using spectral perturbation analysis. The effects of spatial variability in aquifer hydraulic conductivity and virus transport (attachment, detachment, and inactivation) parameters on large-scale virus transport are evaluated. A stochastic mean model of virus transport is developed by linking a simple system of local-scale free-virus transport and attached-virus conservation equations from the current literature with a random-field representation of aquifer and virus transport properties. The resultant mean equations for free and attached viruses are found to differ considerably from the local-scale equations on which they are based and include effects such as a free-virus effective velocity that is a function of aquifer heterogeneity as well as virus transport parameters. Stochastic mean free-virus breakthrough curves are compared with local model output in order to observe the effects of spatial variability on mean one-dimensional virus transport in three-dimensionally heterogeneous porous media. Significant findings from this theoretical analysis include the following: (1) Stochastic model breakthrough occurs earlier than local model breakthrough, and this effect is most pronounced for the least conductive aquifers studied. (2) A high degree of aquifer heterogeneity can lead to virus breakthrough actually preceding that of a conservative tracer. (3) As the mean hydraulic conductivity is increased, the mean model shows less sensitivity to the variance of the natural-logarithm hydraulic conductivity and mean virus diameter. (4) Incorporation of a heterogeneous colloid filtration term results in higher predicted concentrations than a simple first-order adsorption term for a given mean attachment rate. (5) Incorporation of aquifer heterogeneity leads to a greater range of virus diameters for which significant breakthrough occurs. (6) The mean model is more sensitive to the inactivation rate of viruses
Deterministic versus stochastic trends: Detection and challenges
NASA Astrophysics Data System (ADS)
Fatichi, S.; Barbosa, S. M.; Caporali, E.; Silva, M. E.
2009-09-01
The detection of a trend in a time series and the evaluation of its magnitude and statistical significance is an important task in geophysical research. This importance is amplified in climate change contexts, since trends are often used to characterize long-term climate variability and to quantify the magnitude and the statistical significance of changes in climate time series, both at global and local scales. Recent studies have demonstrated that the stochastic behavior of a time series can change the statistical significance of a trend, especially if the time series exhibits long-range dependence. The present study examines the trends in time series of daily average temperature recorded in 26 stations in the Tuscany region (Italy). In this study a new framework for trend detection is proposed. First two parametric statistical tests, the Phillips-Perron test and the Kwiatkowski-Phillips-Schmidt-Shin test, are applied in order to test for trend stationary and difference stationary behavior in the temperature time series. Then long-range dependence is assessed using different approaches, including wavelet analysis, heuristic methods and by fitting fractionally integrated autoregressive moving average models. The trend detection results are further compared with the results obtained using nonparametric trend detection methods: Mann-Kendall, Cox-Stuart and Spearman's ρ tests. This study confirms an increase in uncertainty when pronounced stochastic behaviors are present in the data. Nevertheless, for approximately one third of the analyzed records, the stochastic behavior itself cannot explain the long-term features of the time series, and a deterministic positive trend is the most likely explanation.
Bayesian Estimation and Inference Using Stochastic Electronics
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
Stochastic multiscale modeling of polycrystalline materials
NASA Astrophysics Data System (ADS)
Wen, Bin
Mechanical properties of engineering materials are sensitive to the underlying random microstructure. Quantification of mechanical property variability induced by microstructure variation is essential for the prediction of extreme properties and microstructure-sensitive design of materials. Recent advances in high throughput characterization of polycrystalline microstructures have resulted in huge data sets of microstructural descriptors and image snapshots. To utilize these large scale experimental data for computing the resulting variability of macroscopic properties, appropriate mathematical representation of microstructures is needed. By exploring the space containing all admissible microstructures that are statistically similar to the available data, one can estimate the distribution/envelope of possible properties by employing efficient stochastic simulation methodologies along with robust physics-based deterministic simulators. The focus of this thesis is on the construction of low-dimensional representations of random microstructures and the development of efficient physics-based simulators for polycrystalline materials. By adopting appropriate stochastic methods, such as Monte Carlo and Adaptive Sparse Grid Collocation methods, the variability of microstructure-sensitive properties of polycrystalline materials is investigated. The primary outcomes of this thesis include: (1) Development of data-driven reduced-order representations of microstructure variations to construct the admissible space of random polycrystalline microstructures. (2) Development of accurate and efficient physics-based simulators for the estimation of material properties based on mesoscale microstructures. (3) Investigating property variability of polycrystalline materials using efficient stochastic simulation methods in combination with the above two developments. The uncertainty quantification framework developed in this work integrates information science and materials science, and
Hybrid stochastic simplifications for multiscale gene networks
Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu
2009-01-01
Background 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. Results 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. Conclusion 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. PMID:19735554
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326
A stochastic model for palaeomagnetic field variations
NASA Astrophysics Data System (ADS)
Buffett, Bruce A.; Ziegler, Leah; Constable, Cathy G.
2013-10-01
Regeneration of the Earth's magnetic field by convection in the liquid core produces a broad spectrum of time variation. Relative palaeointensity measurements in marine sediments provide a detailed record over the past 2 Myr, but an explicit reconstruction of the underlying dynamics is not feasible. A more practical alternative is to construct a stochastic model from estimates of the virtual axial dipole moment. The deterministic part of the model (drift term) describes time-averaged behaviour, whereas the random part (diffusion term) characterizes complex interactions over convective timescales. We recover estimates of the drift and diffusion terms from the SINT2000 model of Valet et al. and the PADM2M model of Ziegler et al. The results are used in numerical solutions of the Fokker-Planck equation to predict statistical properties of the palaeomagnetic field, including the average rates of magnetic reversals and excursions. A physical interpretation of the stochastic model suggests that the timescale for adjustments in the axial dipole moment is set by the dipole decay time τd. We obtain τd = 29 kyr from the stochastic models, which falls within the expected range for the Earth's core. We also predict the amplitude of convective fluctuations in the core, and establish a physical connection to the rates of magnetic reversals and excursions. Chrons lasting longer than 10 Myr are unlikely under present-day conditions. However, long chrons become more likely if the diffusion term is reduced by a factor of 2. Such a change is accomplished by reducing the velocity fluctuations in the core by a factor of √2, which could be attributed to a shift in the spatial pattern of heat flux from the core or a reduction in the total core heat flow.
ERIC Educational Resources Information Center
McMillan, Melville L.; Chan, Wing H.
2006-01-01
Efficiency scores are determined for Canadian universities using both data envelopment analysis and stochastic frontier methods for selected specifications. The outcomes are compared. There is considerable divergence in the efficiency scores and their rankings among methods and specifications. An analysis of rankings, however, reveals that the…
Stochastic resonance in mammalian neuronal networks
Gluckman, B.J.; So, P.; Netoff, T.I.; Spano, M.L.; Schiff, S.J. |
1998-09-01
We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization (increase then decrease) in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment. {copyright} {ital 1998 American Institute of Physics.}
Quantum stochastic thermodynamic on harmonic networks
NASA Astrophysics Data System (ADS)
Deffner, Sebastian
2016-01-01
Fluctuation theorems are symmetry relations for the probability to observe an amount of entropy production in a finite-time process. In a recent paper Pigeon et al (2016 New. J. Phys. 18 013009) derived fluctuation theorems for harmonic networks by means of the large deviation theory. Their novel approach is illustrated with various examples of experimentally relevant systems. As a main result, however, Pigeon et al provide new insight how to consistently formulate quantum stochastic thermodynamics, and provide new and robust tools for the study of the thermodynamics of quantum harmonic networks.
Stochastic rotation dynamics for nematic liquid crystals
Lee, Kuang-Wu Mazza, Marco G.
2015-04-28
We introduce a new mesoscopic model for nematic liquid crystals (LCs). We extend the particle-based stochastic rotation dynamics method, which reproduces the Navier-Stokes equation, to anisotropic fluids by including a simplified Ericksen-Leslie formulation of nematodynamics. We verify the applicability of this hybrid model by studying the equilibrium isotropic-nematic phase transition and nonequilibrium problems, such as the dynamics of topological defects and the rheology of sheared LCs. Our simulation results show that this hybrid model captures many essential aspects of LC physics at the mesoscopic scale, while preserving microscopic thermal fluctuations.
ON NONSTATIONARY STOCHASTIC MODELS FOR EARTHQUAKES.
Safak, Erdal; Boore, David M.
1986-01-01
A seismological stochastic model for earthquake ground-motion description is presented. Seismological models are based on the physical properties of the source and the medium and have significant advantages over the widely used empirical models. The model discussed here provides a convenient form for estimating structural response by using random vibration theory. A commonly used random process for ground acceleration, filtered white-noise multiplied by an envelope function, introduces some errors in response calculations for structures whose periods are longer than the faulting duration. An alternate random process, filtered shot-noise process, eliminates these errors.
Stochastic cooling requirements for a muon collider
Ruggiero, A.G.
1993-12-31
The most severe limitation to the muon production for a large-energy muon collider is the short time allowed for cooling the beam to dimensions small enough to provide reasonably high luminosity. The limitation is caused by the short lifetime of the particles. It appears to be desirable to accelerate the beam quickly in very short bunches. This paper describes the requirements of single-pass, fast stochastic cooling for very short bunches. Bandwidth, amplifier gain and Schottky power do not seem to be of major concern. Problems do arise with the ultimate low emittance that can be achieved, the value of which is seriously affected by the front-end noise.
Reversible Stochastically Gated Diffusion-Influenced Reactions.
Gopich, Irina V; Szabo, Attila
2016-08-25
An approximate but accurate theory is developed for the kinetics of reversible binding of a ligand to a macromolecule when either can stochastically fluctuate between reactive and unreactive conformations. The theory is based on a set of reaction-diffusion equations for the deviations of the pair distributions from their bulk values. The concentrations are shown to satisfy non-Markovian rate equations with memory kernels that are obtained by solving an irreversible geminate (i.e., two-particle) problem. The relaxation to equilibrium is not exponential but rather a power law. In the Markovian limit, the theory reduces to a set of ordinary rate equations with renormalized rate constants. PMID:26956646
The isolation limits of stochastic vibration
NASA Technical Reports Server (NTRS)
Knopse, C. R.; Allaire, P. E.
1993-01-01
The vibration isolation problem is formulated as a 1D kinematic problem. The geometry of the stochastic wall trajectories arising from the stroke constraint is defined in terms of their significant extrema. An optimal control solution for the minimum acceleration return path determines a lower bound on platform mean square acceleration. This bound is expressed in terms of the probability density function on the significant maxima and the conditional fourth moment of the first passage time inverse. The first of these is found analytically while the second is found using a Monte Carlo simulation. The rms acceleration lower bound as a function of available space is then determined through numerical quadrature.
Stochastic thermodynamics of active Brownian particles
NASA Astrophysics Data System (ADS)
Ganguly, Chandrima; Chaudhuri, Debasish
2013-09-01
Examples of self-propulsion in strongly fluctuating environments are abundant in nature, e.g., molecular motors and pumps operating in living cells. Starting from the Langevin equation of motion, we develop a stochastic thermodynamic description of noninteracting self-propelled particles using simple models of velocity-dependent forces. We derive fluctuation theorems for entropy production and a modified fluctuation-dissipation relation, characterizing the linear response in nonequilibrium steady states. We study these notions in a simple model of molecular motors, and in the Rayleigh-Helmholtz and energy-depot models of self-propelled particles.
On the stochastic fatigue crack growth problem
NASA Astrophysics Data System (ADS)
Enneking, Thomas Joseph
The research focuses on continuous and discrete stochastic models for fatigue crack growth which are based on Markov process theory. These models account for the random nature of fatigue crack growth which is not adequately explained by a deterministic approach. A hybrid finite element/finite difference solution methodology is developed and shown to be highly effective in determining the solution of the backward Kolmogorov equation and the Pontryagin-Vitt equation yielding the probabilistic description of the time to reach a critical crack size as a function of the initial crack size. Excellent comparisons are shown between this method, previous analytical studies, and experimental results. A significant reduction in computer processing time and storage is achieved with this approach. Alternatively, the forward Fokker-Planck-Kolmogorov equation is formulated, and a two-dimensional initial boundary value problem developed, to determine the distribution of crack sizes as a function of time. A two-dimensional finite element solution approach is used for problem solution. A major advantage of this problem formulation is that the entire probability density function is obtained as a function of cycle number. Studies of discrete Markov process models are also considered for the characterization of fatigue crack growth. A cell-to-cell mapping approach, which has been effectively utilized for other two-state problems in stochastic dynamics, is developed for the stochastic fatigue crack growth problem. In this approach the transitional probability matrix for crack transition from cell i to any other cell is determined using simulation with a two-state lognormal random process model. Repeated matrix multiplication is then used to determine the distribution of crack lengths at other times for a given initial flow size distribution. The effect of varying the initial fatigue quality may be evaluated without repeating the simulation of the probability transition matrix
Stochastic mean-field polycrystal plasticity methods
NASA Astrophysics Data System (ADS)
Tonks, Michael R.
To accommodate multiple length scales, mean-field polycrystal plasticity models treat each material point as an aggregate of N crystals. The crystal velocity gradients Lc are approximated and then used to evaluate the crystal stresses T c. The Tc are averaged to determine the material point stress T. Commonly, the Lc are approximated with the fully constrained model (FCM) based on the Taylor hypothesis which equates Lc to the macro-scale velocity gradient L. Herein, we present two stochastic models that relax the FCM constraint. Through various applications we show that these computationally efficient stochastic models provide realistic response predictions. We first investigate the texture evolution in a planar polycrystal with our stochastic Taylor model (STM), in which we define L c as a realization of a normal distribution with mean equal to L. Our STM predictions agree with crystal plasticity finite element method (CPFEM) predictions, demonstrating the development of a steady-state texture that is not predicted by the FCM. The computational cost of the STM is comparable to the FCM, i.e. substantially less than the CPFEM. We develop the STM for 3-D polycrystals based on CPFEM analysis results which show that Lc follows a normal distribution. In addition to the STM, we develop the stochastic no-constraints model (SNCM), which differs from the STM in the manner with which the Lc distribution means are determined. Calibration and validation of the models are performed using tantalum compression experiment data. Both models predict the compression textures more accurately than the FCM, and the SNCM predicts them more accurately than the STM. The STM is slightly more computationally expensive than the FCM, while the SNCM is three times more expensive. Finally, we incorporate the STM in a finite element simulation of the Taylor impact of two tantalum specimens. Our simulation predictions mimic the texture and deformation data measured from a powder metallurgy
Stochastic monotony signature and biomedical applications.
Demongeot, Jacques; Galli Carminati, Giuliana; Carminati, Federico; Rachdi, Mustapha
2015-12-01
We introduce a new concept, the stochastic monotony signature of a function, made of the sequence of the signs that indicate if the function is increasing or constant (sign +), or decreasing (sign -). If the function results from the averaging of successive observations with errors, the monotony sign is a random binary variable, whose density is studied under two hypotheses for the distribution of errors: uniform and Gaussian. Then, we describe a simple statistical test allowing the comparison between the monotony signatures of two functions (e.g., one observed and the other as reference) and we apply the test to four biomedical examples, coming from genetics, psychology, gerontology, and morphogenesis. PMID:26563556
A stochastic evolutionary model for survival dynamics
NASA Astrophysics Data System (ADS)
Fenner, Trevor; Levene, Mark; Loizou, George
2014-09-01
The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in different contexts. Here we propose a generative model to capture the essential dynamics of survival analysis, traditionally employed in clinical trials and reliability analysis in engineering. In our model, the only implicit assumption made is that the longer an actor has been in the system, the more likely it is to have failed. We derive a power-law distribution for the process and provide preliminary empirical evidence for the validity of the model from two well-known survival analysis data sets.
Hidden Markov models for stochastic thermodynamics
NASA Astrophysics Data System (ADS)
Bechhoefer, John
2015-07-01
The formalism of state estimation and hidden Markov models can simplify and clarify the discussion of stochastic thermodynamics in the presence of feedback and measurement errors. After reviewing the basic formalism, we use it to shed light on a recent discussion of phase transitions in the optimized response of an information engine, for which measurement noise serves as a control parameter. The HMM formalism also shows that the value of additional information displays a maximum at intermediate signal-to-noise ratios. Finally, we discuss how systems open to information flow can apparently violate causality; the HMM formalism can quantify the performance gains due to such violations.
BUNCHED BEAM STOCHASTIC COOLING PROJECT FOR RHIC.
BRENNAN, J.M.; BASKIEWICZ, M.M.
2005-09-18
The main performance limitation for RHIC is emittance growth caused by IntraBeam Scattering during the store. We have developed a longitudinal bunched-beam stochastic cooling system in the 5-8 GHz band which will be used to counteract IBS longitudinal emittance growth and prevent de-bunching during the store. Solutions to the technical problems of achieving sufficient kicker voltage and overcoming the electronic saturation effects caused by coherent components within the Schottky spectrum are described. Results from tests with copper ions in RHIC during the FY05 physics run, including the observation of signal suppression, are presented.
Bunched Beam Stochastic Cooling Project for RHIC
Brennan, J. M.; Blaskiewicz, M.
2006-03-20
The main performance limitation for RHIC is emittance growth caused by IntraBeam Scattering during the store. We have developed a longitudinal bunched-beam stochastic cooling system in the 5-8 GHz band which will be used to counteract IBS longitudinal emittance growth and prevent de-bunching during the store. Solutions to the technical problems of achieving sufficient kicker voltage and overcoming the electronic saturation effects caused by coherent components within the Schottky spectrum are described. Results from tests with copper ions in RHIC during the FY05 physics run, including the observation of signal suppression, are presented.
Stochastic Euler-Poincaré reduction
NASA Astrophysics Data System (ADS)
Arnaudon, Marc; Chen, Xin; Cruzeiro, Ana Bela
2014-08-01
We prove a Euler-Poincaré reduction theorem for stochastic processes taking values on a Lie group, which is a generalization of the reduction argument for the deterministic case [J. E. Marsden and T. S. Ratiu, Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems, Texts in Applied Mathematics (Springer, 2003)]. We also show examples of its application to SO(3) and to the group of diffeomorphisms, which includes the Navier-Stokes equation on a bounded domain and the Camassa-Holm equation.
Stochastic Euler-Poincaré reduction
Arnaudon, Marc; Chen, Xin; Cruzeiro, Ana Bela
2014-08-15
We prove a Euler-Poincaré reduction theorem for stochastic processes taking values on a Lie group, which is a generalization of the reduction argument for the deterministic case [J. E. Marsden and T. S. Ratiu, Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems, Texts in Applied Mathematics (Springer, 2003)]. We also show examples of its application to SO(3) and to the group of diffeomorphisms, which includes the Navier-Stokes equation on a bounded domain and the Camassa-Holm equation.
Two stochastic models useful in petroleum exploration
NASA Technical Reports Server (NTRS)
Kaufman, G. M.; Bradley, P. G.
1972-01-01
A model of the petroleum exploration process that tests empirically the hypothesis that at an early stage in the exploration of a basin, the process behaves like sampling without replacement is proposed along with a model of the spatial distribution of petroleum reserviors that conforms to observed facts. In developing the model of discovery, the following topics are discussed: probabilitistic proportionality, likelihood function, and maximum likelihood estimation. In addition, the spatial model is described, which is defined as a stochastic process generating values of a sequence or random variables in a way that simulates the frequency distribution of areal extent, the geographic location, and shape of oil deposits
Scattering of light by stochastically rough particles
NASA Technical Reports Server (NTRS)
Peltoniemi, Jouni I.; Lumme, Kari; Muinonen, Karri; Irvine, William M.
1989-01-01
The single particle phase function and the linear polarization for large stochastically deformed spheres have been calculated by Monte Carlo simulation using the geometrical optics approximation. The radius vector of a particle is assumed to obey a bivariate lognormal distribution with three free parameters: mean radius, its standard deviation and the coherence length of the autocorrelation function. All reflections/refractions which include sufficient energy have been included. Real and imaginary parts of the refractive index can be varied without any restrictions. Results and comparisons with some earlier less general theories are presented. Applications of this theory to the photometric properties of atmosphereless bodies and interplanetary dust are discussed.
A guide to differences between stochastic point-source and stochastic finite-fault simulations
Atkinson, G.M.; Assatourians, K.; Boore, D.M.; Campbell, K.; Motazedian, D.
2009-01-01
Why do stochastic point-source and finite-fault simulation models not agree on the predicted ground motions for moderate earthquakes at large distances? This question was posed by Ken Campbell, who attempted to reproduce the Atkinson and Boore (2006) ground-motion prediction equations for eastern North America using the stochastic point-source program SMSIM (Boore, 2005) in place of the finite-source stochastic program EXSIM (Motazedian and Atkinson, 2005) that was used by Atkinson and Boore (2006) in their model. His comparisons suggested that a higher stress drop is needed in the context of SMSIM to produce an average match, at larger distances, with the model predictions of Atkinson and Boore (2006) based on EXSIM; this is so even for moderate magnitudes, which should be well-represented by a point-source model. Why? The answer to this question is rooted in significant differences between point-source and finite-source stochastic simulation methodologies, specifically as implemented in SMSIM (Boore, 2005) and EXSIM (Motazedian and Atkinson, 2005) to date. Point-source and finite-fault methodologies differ in general in several important ways: (1) the geometry of the source; (2) the definition and application of duration; and (3) the normalization of finite-source subsource summations. Furthermore, the specific implementation of the methods may differ in their details. The purpose of this article is to provide a brief overview of these differences, their origins, and implications. This sets the stage for a more detailed companion article, "Comparing Stochastic Point-Source and Finite-Source Ground-Motion Simulations: SMSIM and EXSIM," in which Boore (2009) provides modifications and improvements in the implementations of both programs that narrow the gap and result in closer agreement. These issues are important because both SMSIM and EXSIM have been widely used in the development of ground-motion prediction equations and in modeling the parameters that control
Paul, Subhadip; Roy, Prasun Kumar
2016-02-01
Radiation therapy is one of the important treatment procedures of cancer. The day-to-day delivered dose to the tissue in radiation therapy often deviates from the planned fixed dose per fraction. This day-to-day variation of radiation dose is stochastic. Here, we have developed the mathematical formulation to represent the day-to-day stochastic dose variation effect in radiation therapy. Our analysis shows that that the fixed dose delivery approximation under-estimates the biological effective dose, even if the average delivered dose per fraction is equal to the planned dose per fraction. The magnitude of the under-estimation effect relies upon the day-to-day stochastic dose variation level, the dose fraction size and the values of the radiobiological parameters of the tissue. We have further explored the application of our mathematical formulation for adaptive dose calculation. Our analysis implies that, compared to the premise of the Linear Quadratic Linear (LQL) framework, the Linear Quadratic framework based analytical formulation under-estimates the required dose per fraction necessary to produce the same biological effective dose as originally planned. Our study provides analytical formulation to calculate iso-effect in adaptive radiation therapy considering day-to-day stochastic dose deviation from planned dose and also indicates the potential utility of LQL framework in this context. PMID:26776265
A hierarchical exact accelerated stochastic simulation algorithm
Orendorff, David; Mjolsness, Eric
2012-01-01
A new algorithm, “HiER-leap” (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled “blocks” and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms. PMID:23231214
The Stochastic Search Dynamics of Interneuron Migration
Britto, Joanne M.; Johnston, Leigh A.; Tan, Seong-Seng
2009-01-01
Abstract Migration is a dynamic process in which a cell searches the environment and translates acquired information into somal advancement. In particular, interneuron migration during development is accomplished by two distinct processes: the extension of neurites tipped with growth cones; and nucleus translocation, termed nucleokinesis. The primary purpose of our study is to investigate neurite branching and nucleokinesis using high-resolution time-lapse confocal microscopy and computational modeling. We demonstrate that nucleokinesis is accurately modeled by a spring-dashpot system and that neurite branching is independent of the nucleokinesis event, and displays the dynamics of a stochastic birth-death process. This is in contrast to traditional biological descriptions, which suggest a closer relationship between the two migratory mechanisms. Our models are validated on independent data sets acquired using two different imaging protocols, and are shown to be robust to alterations in guidance cues and cellular migratory mechanisms, through treatment with brain-derived neurotrophic factor, neurotrophin-4, and blebbistatin. We postulate that the stochastic branch dynamics exhibited by interneurons undergoing guidance-directed migration permit efficient exploration of the environment. PMID:19651028
Environmental stochasticity controls soil erosion variability
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
Infinite-degree-corrected stochastic block model.
Herlau, Tue; Schmidt, Mikkel N; Mørup, Morten
2014-09-01
In stochastic block models, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statistically similar link probabilities within and between groups. A recent extension by Karrer and Newman [Karrer and Newman, Phys. Rev. E 83, 016107 (2011)] incorporates a node degree correction to model degree heterogeneity within each group. Although this demonstrably leads to better performance on several networks, it is not obvious whether modeling node degree is always appropriate or necessary. We formulate the degree corrected stochastic block model as a nonparametric Bayesian model, incorporating a parameter to control the amount of degree correction that can then be inferred from data. Additionally, our formulation yields principled ways of inferring the number of groups as well as predicting missing links in the network that can be used to quantify the model's predictive performance. On synthetic data we demonstrate that including the degree correction yields better performance on both recovering the true group structure and predicting missing links when degree heterogeneity is present, whereas performance is on par for data with no degree heterogeneity within clusters. On seven real networks (with no ground truth group structure available) we show that predictive performance is about equal whether or not degree correction is included; however, for some networks significantly fewer clusters are discovered when correcting for degree, indicating that the data can be more compactly explained by clusters of heterogenous degree nodes. PMID:25314493
Stochastic population dynamics under resource constraints
NASA Astrophysics Data System (ADS)
Gavane, Ajinkya S.; Nigam, Rahul
2016-06-01
This paper investigates the population growth of a certain species in which every generation reproduces thrice over a period of predefined time, under certain constraints of resources needed for survival of population. We study the survival period of a species by randomizing the reproduction probabilities within a window at same predefined ages and the resources are being produced by the working force of the population at a variable rate. This randomness in the reproduction rate makes the population growth stochastic in nature and one cannot predict the exact form of evolution. Hence we study the growth by running simulations for such a population and taking an ensemble averaged over 500 to 5000 such simulations as per the need. While the population reproduces in a stochastic manner, we have implemented a constraint on the amount of resources available for the population. This is important to make the simulations more realistic. The rate of resource production then is tuned to find the rate which suits the survival of the species. We also compute the mean life time of the species corresponding to different resource production rate. Study for these outcomes in the parameter space defined by the reproduction probabilities and rate of resource production is carried out.
Environmental stochasticity controls soil erosion variability.
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
Stochastic particle acceleration and statistical closures
Dimits, A.M.; Krommes, J.A.
1985-10-01
In a recent paper, Maasjost and Elsasser (ME) concluded, from the results of numerical experiments and heuristic arguments, that the Bourret and the direct-interaction approximation (DIA) are ''of no use in connection with the stochastic acceleration problem'' because (1) their predictions were equivalent to that of the simpler Fokker-Planck (FP) theory, and (2) either all or none of the closures were in good agreement with the data. Here some analytically tractable cases are studied and used to test the accuracy of these closures. The cause of the discrepancy (2) is found to be the highly non-Gaussian nature of the force used by ME, a point not stressed by them. For the case where the force is a position-independent Ornstein-Uhlenbeck (i.e., Gaussian) process, an effective Kubo number K can be defined. For K << 1 an FP description is adequate, and conclusion (1) of ME follows; however, for K greater than or equal to 1 the DIA behaves much better qualitatively than the other two closures. For the non-Gaussian stochastic force used by ME, all common approximations fail, in agreement with (2).
Stochastic model for tumor growth with immunization
NASA Astrophysics Data System (ADS)
Bose, Thomas; Trimper, Steffen
2009-05-01
We analyze a stochastic model for tumor cell growth with both multiplicative and additive colored noises as well as nonzero cross correlations in between. Whereas the death rate within the logistic model is altered by a deterministic term characterizing immunization, the birth rate is assumed to be stochastically changed due to biological motivated growth processes leading to a multiplicative internal noise. Moreover, the system is subjected to an external additive noise which mimics the influence of the environment of the tumor. The stationary probability distribution Ps is derived depending on the finite correlation time, the immunization rate, and the strength of the cross correlation. Ps offers a maximum which becomes more pronounced for increasing immunization rate. The mean-first-passage time is also calculated in order to find out under which conditions the tumor can suffer extinction. Its characteristics are again controlled by the degree of immunization and the strength of the cross correlation. The behavior observed can be interpreted in terms of a biological model of tumor evolution.
A Stochastic Cratering Model for Asteroid Surfaces
NASA Technical Reports Server (NTRS)
Richardson, J. E.; Melosh, H. J.; Greenberg, R. J.
2005-01-01
The observed cratering records on asteroid surfaces (four so far: Gaspra, Ida, Mathilde, and Eros [1-4]) provide us with important clues to their past bombardment histories. Previous efforts toward interpreting these records have led to two basic modeling styles for reproducing the statistics of the observed crater populations. The first, and most direct, method is to use Monte Carlo techniques [5] to stochastically populate a matrix-model test surface with craters as a function of time [6,7]. The second method is to use a more general, parameterized approach to duplicate the statistics of the observed crater population [8,9]. In both methods, several factors must be included beyond the simple superposing of circular features: (1) crater erosion by subsequent impacts, (2) infilling of craters by impact ejecta, and (3) crater degradation and era- sure due to the seismic effects of subsequent impacts. Here we present an updated Monte Carlo (stochastic) modeling approach, designed specifically with small- to medium-sized asteroids in mind.
Optimality of collective choices: a stochastic approach.
Nicolis, S C; Detrain, C; Demolin, D; Deneubourg, J L
2003-09-01
Amplifying communication is a characteristic of group-living animals. This study is concerned with food recruitment by chemical means, known to be associated with foraging in most ant colonies but also with defence or nest moving. A stochastic approach of collective choices made by ants faced with different sources is developed to account for the fluctuations inherent to the recruitment process. It has been established that ants are able to optimize their foraging by selecting the most rewarding source. Our results not only confirm that selection is the result of a trail modulation according to food quality but also show the existence of an optimal quantity of laid pheromone for which the selection of a source is at the maximum, whatever the difference between the two sources might be. In terms of colony size, large colonies more easily focus their activity on one source. Moreover, the selection of the rich source is more efficient if many individuals lay small quantities of pheromone, instead of a small group of individuals laying a higher trail amount. These properties due to the stochasticity of the recruitment process can be extended to other social phenomena in which competition between different sources of information occurs. PMID:12909251
Efficient stochastic superparameterization for geophysical turbulence.
Grooms, Ian; Majda, Andrew J
2013-03-19
Efficient computation of geophysical turbulence, such as occurs in the atmosphere and ocean, is a formidable challenge for the following reasons: the complex combination of waves, jets, and vortices; significant energetic backscatter from unresolved small scales to resolved large scales; a lack of dynamical scale separation between large and small scales; and small-scale instabilities, conditional on the large scales, which do not saturate. Nevertheless, efficient methods are needed to allow large ensemble simulations of sufficient size to provide meaningful quantifications of uncertainty in future predictions and past reanalyses through data assimilation and filtering. Here, a class of efficient stochastic superparameterization algorithms is introduced. In contrast to conventional superparameterization, the method here (i) does not require the simulation of nonlinear eddy dynamics on periodic embedded domains, (ii) includes a better representation of unresolved small-scale instabilities, and (iii) allows efficient representation of a much wider range of unresolved scales. The simplest algorithm implemented here radically improves efficiency by representing small-scale eddies at and below the limit of computational resolution by a suitable one-dimensional stochastic model of random-direction plane waves. In contrast to heterogeneous multiscale methods, the methods developed here do not require strong scale separation or conditional equilibration of local statistics. The simplest algorithm introduced here shows excellent performance on a difficult test suite of prototype problems for geophysical turbulence with waves, jets, and vortices, with a speedup of several orders of magnitude compared with direct simulation. PMID:23487800
Environmental stochasticity controls soil erosion variability
NASA Astrophysics Data System (ADS)
Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone
2016-03-01
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.
Stochasticity and Stereotypy in the Ciona Notochord
Carlson, Maia; Reeves, Wendy; Veeman, Michael
2015-01-01
Fate mapping with single cell resolution has typically been confined to embryos with completely stereotyped development. The lineages giving rise to the 40 cells of the Ciona notochord are invariant, but the intercalation of those cells into a single-file column is not. Here we use genetic labeling methods to fate map the Ciona notochord with both high resolution and large sample sizes. We find that the ordering of notochord cells into a single column is not random, but instead shows a distinctive signature characteristic of mediolaterally-biased intercalation. We find that patterns of cell intercalation in the notochord are somewhat stochastic but far more stereotyped than previously believed. Cell behaviors vary by lineage, with the secondary notochord lineage being much more constrained than the primary lineage. Within the primary lineage, patterns of intercalation reflect the geometry of the intercalating tissue. We identify the latest point at which notochord morphogenesis is largely stereotyped, which is shortly before the onset of mediolateral intercalation and immediately after the final cell divisions in the primary lineage. These divisions are consistently oriented along the AP axis. Our results indicate that the interplay between stereotyped and stochastic cell behaviors in morphogenesis can only be assessed by fate mapping experiments that have both cellular resolution and large sample sizes. PMID:25459659
Efficient stochastic superparameterization for geophysical turbulence
Grooms, Ian; Majda, Andrew J.
2013-01-01
Efficient computation of geophysical turbulence, such as occurs in the atmosphere and ocean, is a formidable challenge for the following reasons: the complex combination of waves, jets, and vortices; significant energetic backscatter from unresolved small scales to resolved large scales; a lack of dynamical scale separation between large and small scales; and small-scale instabilities, conditional on the large scales, which do not saturate. Nevertheless, efficient methods are needed to allow large ensemble simulations of sufficient size to provide meaningful quantifications of uncertainty in future predictions and past reanalyses through data assimilation and filtering. Here, a class of efficient stochastic superparameterization algorithms is introduced. In contrast to conventional superparameterization, the method here (i) does not require the simulation of nonlinear eddy dynamics on periodic embedded domains, (ii) includes a better representation of unresolved small-scale instabilities, and (iii) allows efficient representation of a much wider range of unresolved scales. The simplest algorithm implemented here radically improves efficiency by representing small-scale eddies at and below the limit of computational resolution by a suitable one-dimensional stochastic model of random-direction plane waves. In contrast to heterogeneous multiscale methods, the methods developed here do not require strong scale separation or conditional equilibration of local statistics. The simplest algorithm introduced here shows excellent performance on a difficult test suite of prototype problems for geophysical turbulence with waves, jets, and vortices, with a speedup of several orders of magnitude compared with direct simulation. PMID:23487800
Stochastic simulation of the transducin GTPase cycle.
Felber, S; Breuer, H P; Petruccione, F; Honerkamp, J; Hofmann, K P
1996-01-01
On rod disc membranes, single photoactivated rhodopsin (R*) molecules catalytically activate many copies of the G-protein (Gt), which in turn binds and activates the effector (phosphodiesterase). We have performed master equation simulations of the underlying diffusional protein interactions on a rectangular 1-micron2 model membrane, divided into 15 x 15 cells. Mono- and bimolecular reactions occur within cells, and diffusional transitions occur between (neighboring) cells. Reaction and diffusion constants yield the related probabilities for the stochastic transitions. The calculated kinetics of active effector form a response that is essentially determined by the stochastic lifetime distribution of R* (with characteristic time tau R*) and the reaction constants of Gt activation. Only a short tau R* (approximately 0.3 s) and a high catalytic rate (3000-4000 Gt s-1 R*-1) are consistent with electrophysiological data. Although R* shut-off limits the rise of the response, the lifetime distribution of free R* is not translated into a corresponding variability of the response peaks, because 1) the lifetime distribution of catalytically engaged R* is distorted, 2) small responses are enlarged by an overshoot of active effector, and 3) larger responses tend to undergo saturation. Comparison of these results to published photocurrent waveforms may open ways to understand the relative uniformity of the rod response. Images FIGURE 2 PMID:8968576
Patchwork sampling of stochastic differential equations.
Kürsten, Rüdiger; Behn, Ulrich
2016-03-01
We propose a method to sample stationary properties of solutions of stochastic differential equations, which is accurate and efficient if there are rarely visited regions or rare transitions between distinct regions of the state space. The method is based on a complete, nonoverlapping partition of the state space into patches on which the stochastic process is ergodic. On each of these patches we run simulations of the process strictly truncated to the corresponding patch, which allows effective simulations also in rarely visited regions. The correct weight for each patch is obtained by counting the attempted transitions between all different patches. The results are patchworked to cover the whole state space. We extend the concept of truncated Markov chains which is originally formulated for processes which obey detailed balance to processes not fulfilling detailed balance. The method is illustrated by three examples, describing the one-dimensional diffusion of an overdamped particle in a double-well potential, a system of many globally coupled overdamped particles in double-well potentials subject to additive Gaussian white noise, and the overdamped motion of a particle on the circle in a periodic potential subject to a deterministic drift and additive noise. In an appendix we explain how other well-known Markov chain Monte Carlo algorithms can be related to truncated Markov chains. PMID:27078484
Stochastic basins of attraction for metastable states
NASA Astrophysics Data System (ADS)
Serdukova, Larissa; Zheng, Yayun; Duan, Jinqiao; Kurths, Jürgen
2016-07-01
Basin of attraction of a stable equilibrium point is an effective concept for stability analysis in deterministic systems; however, it does not contain information on the external perturbations that may affect it. Here we introduce the concept of stochastic basin of attraction (SBA) by incorporating a suitable probabilistic notion of basin. We define criteria for the size of the SBA based on the escape probability, which is one of the deterministic quantities that carry dynamical information and can be used to quantify dynamical behavior of the corresponding stochastic basin of attraction. SBA is an efficient tool to describe the metastable phenomena complementing the known exit time, escape probability, or relaxation time. Moreover, the geometric structure of SBA gives additional insight into the system's dynamical behavior, which is important for theoretical and practical reasons. This concept can be used not only in models with small noise intensity but also with noise whose amplitude is proportional or in general is a function of an order parameter. As an application of our main results, we analyze a three potential well system perturbed by two types of noise: Brownian motion and non-Gaussian α-stable Lévy motion. Our main conclusions are that the thermal fluctuations stabilize the metastable system with an asymmetric three-well potential but have the opposite effect for a symmetric one. For Lévy noise with larger jumps and lower jump frequencies ( α = 0.5 ) metastability is enhanced for both symmetric and asymmetric potentials.
Active motion assisted by correlated stochastic torques.
Weber, Christian; Radtke, Paul K; Schimansky-Geier, Lutz; Hänggi, Peter
2011-07-01
The stochastic dynamics of an active particle undergoing a constant speed and additionally driven by an overall fluctuating torque is investigated. The random torque forces are expressed by a stochastic differential equation for the angular dynamics of the particle determining the orientation of motion. In addition to a constant torque, the particle is supplemented by random torques, which are modeled as an Ornstein-Uhlenbeck process with given correlation time τ(c). These nonvanishing correlations cause a persistence of the particles' trajectories and a change of the effective spatial diffusion coefficient. We discuss the mean square displacement as a function of the correlation time and the noise intensity and detect a nonmonotonic dependence of the effective diffusion coefficient with respect to both correlation time and noise strength. A maximal diffusion behavior is obtained if the correlated angular noise straightens the curved trajectories, interrupted by small pirouettes, whereby the correlated noise amplifies a straightening of the curved trajectories caused by the constant torque. PMID:21867138
Single-particle stochastic heat engine.
Rana, Shubhashis; Pal, P S; Saha, Arnab; Jayannavar, A M
2014-10-01
We have performed an extensive analysis of a single-particle stochastic heat engine constructed by manipulating a Brownian particle in a time-dependent harmonic potential. The cycle consists of two isothermal steps at different temperatures and two adiabatic steps similar to that of a Carnot engine. The engine shows qualitative differences in inertial and overdamped regimes. All the thermodynamic quantities, including efficiency, exhibit strong fluctuations in a time periodic steady state. The fluctuations of stochastic efficiency dominate over the mean values even in the quasistatic regime. Interestingly, our system acts as an engine provided the temperature difference between the two reservoirs is greater than a finite critical value which in turn depends on the cycle time and other system parameters. This is supported by our analytical results carried out in the quasistatic regime. Our system works more reliably as an engine for large cycle times. By studying various model systems, we observe that the operational characteristics are model dependent. Our results clearly rule out any universal relation between efficiency at maximum power and temperature of the baths. We have also verified fluctuation relations for heat engines in time periodic steady state. PMID:25375477
Stochastic Event-Driven Molecular Dynamics
Donev, Aleksandar Garcia, Alejandro L.; Alder, Berni J.
2008-02-01
A novel Stochastic Event-Driven Molecular Dynamics (SEDMD) algorithm is developed for the simulation of polymer chains suspended in a solvent. SEDMD combines event-driven molecular dynamics (EDMD) with the Direct Simulation Monte Carlo (DSMC) method. The polymers are represented as chains of hard-spheres tethered by square wells and interact with the solvent particles with hard-core potentials. The algorithm uses EDMD for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in EDMD, rather, the momentum and energy exchange in the solvent is determined stochastically using DSMC. The coupling between the solvent and the solute is consistently represented at the particle level retaining hydrodynamic interactions and thermodynamic fluctuations. However, unlike full MD simulations of both the solvent and the solute, in SEDMD the spatial structure of the solvent is ignored. The SEDMD algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard-wall subjected to uniform shear. SEDMD closely reproduces results obtained using traditional EDMD simulations with two orders of magnitude greater efficiency. Results question the existence of periodic (cycling) motion of the polymer chain.
Stochastic dynamics of macromolecular-assembly networks.
NASA Astrophysics Data System (ADS)
Saiz, Leonor; Vilar, Jose
2006-03-01
The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current computational methods strongly limited for understanding how the physical interactions between cellular components give rise to systemic properties of cells. Here we present a stochastic approach to study the dynamics of networks formed by macromolecular complexes in terms of the molecular interactions of their components [1]. Exploiting key thermodynamic concepts, this approach makes it possible to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular networks. As prototype systems, we consider the lac operon and phage λ induction switches, which rely on the formation of DNA loops by proteins [2] and on the integration of these protein-DNA complexes into intracellular networks. This cross-scale approach offers an effective starting point to move forward from network diagrams, such as those of protein-protein and DNA-protein interaction networks, to the actual dynamics of cellular processes. [1] L. Saiz and J.M.G. Vilar, submitted (2005). [2] J.M.G. Vilar and L. Saiz, Current Opinion in Genetics & Development, 15, 136-144 (2005).
Optimal Control via Self-Generated Stochasticity
NASA Technical Reports Server (NTRS)
Zak, Michail
2011-01-01
The problem of global maxima of functionals has been examined. Mathematical roots of local maxima are the same as those for a much simpler problem of finding global maximum of a multi-dimensional function. The second problem is instability even if an optimal trajectory is found, there is no guarantee that it is stable. As a result, a fundamentally new approach is introduced to optimal control based upon two new ideas. The first idea is to represent the functional to be maximized as a limit of a probability density governed by the appropriately selected Liouville equation. Then, the corresponding ordinary differential equations (ODEs) become stochastic, and that sample of the solution that has the largest value will have the highest probability to appear in ODE simulation. The main advantages of the stochastic approach are that it is not sensitive to local maxima, the function to be maximized must be only integrable but not necessarily differentiable, and global equality and inequality constraints do not cause any significant obstacles. The second idea is to remove possible instability of the optimal solution by equipping the control system with a self-stabilizing device. The applications of the proposed methodology will optimize the performance of NASA spacecraft, as well as robot performance.
Patchwork sampling of stochastic differential equations
NASA Astrophysics Data System (ADS)
Kürsten, Rüdiger; Behn, Ulrich
2016-03-01
We propose a method to sample stationary properties of solutions of stochastic differential equations, which is accurate and efficient if there are rarely visited regions or rare transitions between distinct regions of the state space. The method is based on a complete, nonoverlapping partition of the state space into patches on which the stochastic process is ergodic. On each of these patches we run simulations of the process strictly truncated to the corresponding patch, which allows effective simulations also in rarely visited regions. The correct weight for each patch is obtained by counting the attempted transitions between all different patches. The results are patchworked to cover the whole state space. We extend the concept of truncated Markov chains which is originally formulated for processes which obey detailed balance to processes not fulfilling detailed balance. The method is illustrated by three examples, describing the one-dimensional diffusion of an overdamped particle in a double-well potential, a system of many globally coupled overdamped particles in double-well potentials subject to additive Gaussian white noise, and the overdamped motion of a particle on the circle in a periodic potential subject to a deterministic drift and additive noise. In an appendix we explain how other well-known Markov chain Monte Carlo algorithms can be related to truncated Markov chains.
Simulating stochastic dynamics using large time steps.
Corradini, O; Faccioli, P; Orland, H
2009-12-01
We present an approach to investigate the long-time stochastic dynamics of multidimensional classical systems, in contact with a heat bath. When the potential energy landscape is rugged, the kinetics displays a decoupling of short- and long-time scales and both molecular dynamics or Monte Carlo (MC) simulations are generally inefficient. Using a field theoretic approach, we perform analytically the average over the short-time stochastic fluctuations. This way, we obtain an effective theory, which generates the same long-time dynamics of the original theory, but has a lower time-resolution power. Such an approach is used to develop an improved version of the MC algorithm, which is particularly suitable to investigate the dynamics of rare conformational transitions. In the specific case of molecular systems at room temperature, we show that elementary integration time steps used to simulate the effective theory can be chosen a factor approximately 100 larger than those used in the original theory. Our results are illustrated and tested on a simple system, characterized by a rugged energy landscape. PMID:20365123
Constructive stochastic temporal reasoning in situation assessment
Kirillov, V.P.
1994-08-01
Situation assessment tasks (SA-tasks) are important in many applications, dealing with real-time environment. Due to poor structure of these tasks, the knowledge-based approach has been studied intensively by many authors. Regrettably, little progress up to now has been achieved because the existing techniques usually neglect the constructive nature of a human expert`s reasoning in a process of a SA-task solving. The latter feature manifests itself in the unconscious creation by an expert of new temporal/spatial patterns which, in a general case, are not present in an explicit form in the processed evidence data. Due to that, the development of a new method enabling one to model constructive reasoning, is rather complicated, the scope of this work has been restricted to the temporal aspect only. In the paper, a mathematical framework, which may be used in a rule-based expert system, reasoning about events in a constructive manner, is presented. Time instances and intervals are treated in detail, using a stochastic approach to represent imprecision. The human expert`s temporal knowledge is considered imprecise in the stochastical sense, as well. Both forward and backward modes of reasoning are studied. Sufficient conditions for consistency of the knowledge model are also derived. The method has been implemented by the author in a feasibility demonstration prototype expert system. 14 refs.
Phylogenetic Stochastic Mapping Without Matrix Exponentiation
Irvahn, Jan; Minin, Vladimir N.
2014-01-01
Abstract Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organism carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov chain (CTMC) and works well for small state spaces. The computations slow down considerably for larger state spaces (e.g., space of codons), because current methodology relies on exponentiating CTMC infinitesimal rate matrices—an operation whose computational complexity grows as the size of the CTMC state space cubed. In this work, we introduce a new approach, based on a CTMC technique called uniformization, which does not use matrix exponentiation for phylogenetic stochastic mapping. Our method is based on a new Markov chain Monte Carlo (MCMC) algorithm that targets the distribution of trait histories conditional on the trait data observed at the tips of the tree. The computational complexity of our MCMC method grows as the size of the CTMC state space squared. Moreover, in contrast to competing matrix exponentiation methods, if the rate matrix is sparse, we can leverage this sparsity and increase the computational efficiency of our algorithm further. Using simulated data, we illustrate advantages of our MCMC algorithm and investigate how large the state space needs to be for our method to outperform matrix exponentiation approaches. We show that even on the moderately large state space of codons our MCMC method can be significantly faster than currently used matrix exponentiation methods. PMID:24918812
Continuous data assimilation with stochastically noisy data
NASA Astrophysics Data System (ADS)
Bessaih, Hakima; Olson, Eric; Titi, Edriss S.
2015-03-01
We analyse the performance of a data-assimilation algorithm based on a linear feedback control when used with observational data that contains measurement errors. Our model problem consists of dynamics governed by the two-dimensional incompressible Navier-Stokes equations, observational measurements given by finite volume elements or nodal points of the velocity field and measurement errors which are represented by stochastic noise. Under these assumptions, the data-assimilation algorithm consists of a system of stochastically forced Navier-Stokes equations. The main result of this paper provides explicit conditions on the observation density (resolution) which guarantee explicit asymptotic bounds, as the time tends to infinity, on the error between the approximate solution and the actual solutions which is corresponding to these measurements, in terms of the variance of the noise in the measurements. Specifically, such bounds are given for the limit supremum, as the time tends to infinity, of the expected value of the L2-norm and of the H1 Sobolev norm of the difference between the approximating solution and the actual solution. Moreover, results on the average time error in mean are stated. birthday.
Neuronal Spike Trains and Stochastic Point Processes
Perkel, Donald H.; Gerstein, George L.; Moore, George P.
1967-01-01
In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains. PMID:4292791
Stochastic basins of attraction for metastable states.
Serdukova, Larissa; Zheng, Yayun; Duan, Jinqiao; Kurths, Jürgen
2016-07-01
Basin of attraction of a stable equilibrium point is an effective concept for stability analysis in deterministic systems; however, it does not contain information on the external perturbations that may affect it. Here we introduce the concept of stochastic basin of attraction (SBA) by incorporating a suitable probabilistic notion of basin. We define criteria for the size of the SBA based on the escape probability, which is one of the deterministic quantities that carry dynamical information and can be used to quantify dynamical behavior of the corresponding stochastic basin of attraction. SBA is an efficient tool to describe the metastable phenomena complementing the known exit time, escape probability, or relaxation time. Moreover, the geometric structure of SBA gives additional insight into the system's dynamical behavior, which is important for theoretical and practical reasons. This concept can be used not only in models with small noise intensity but also with noise whose amplitude is proportional or in general is a function of an order parameter. As an application of our main results, we analyze a three potential well system perturbed by two types of noise: Brownian motion and non-Gaussian α-stable Lévy motion. Our main conclusions are that the thermal fluctuations stabilize the metastable system with an asymmetric three-well potential but have the opposite effect for a symmetric one. For Lévy noise with larger jumps and lower jump frequencies ( α=0.5) metastability is enhanced for both symmetric and asymmetric potentials. PMID:27475077
Stochastic properties of strongly coupled plasmas.
Morozov, I V; Norman, G E; Valuev, A A
2001-03-01
Stochastic properties of equilibrium strongly coupled plasmas are investigated by a molecular dynamics method. The Krylov-Kolmogorov entropy K and the dynamical memory time t(m) are calculated both for electrons and ions with mass ratios 10-10(5). Two values of K entropy for ions are discovered corresponding to electron and ion time scales. The dependence of the K entropy on the number of particles, the nonideality parameter, and the form of the interaction potential is investigated. The problem of the accuracy of molecular dynamics simulations is discussed. A universal relation between Kt(m) and the fluctuation of the total energy of the system is obtained. The relation does not depend on the numerical integration scheme, temperature, density, and the interparticle interaction potential, so that it may be applied to arbitrary dynamic systems. Transition from dynamic to stochastic correlation is treated for both electron and ion velocity autocorrelation functions, for Langmuir and ion-sound plasma wave dynamic structure factors. We point to quantum uncertainty as a physical reason which limits dynamic (Newton) correlation for times greater than t(m). PMID:11308773
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.
Digital simulation and modeling of nonlinear stochastic systems
Richardson, J M; Rowland, J R
1981-04-01
Digitally generated solutions of nonlinear stochastic systems are not unique but depend critically on the numerical integration algorithm used. Some theoretical and practical implications of this dependence are examined. The Ito-Stratonovich controversy concerning the solution of nonlinear stochastic systems is shown to be more than a theoretical debate on maintaining Markov properties as opposed to utilizing the computational rules of ordinary calculus. The theoretical arguments give rise to practical considerations in the formation and solution of discrete models from continuous stochastic systems. Well-known numerical integration algorithms are shown not only to provide different solutions for the same stochastic system but also to correspond to different stochastic integral definitions. These correspondences are proved by considering first and second moments of solutions that result from different integration algorithms and then comparing the moments to those arising from various stochastic integral definitions. This algorithm-dependence of solutions is in sharp contrast to the deterministic and linear stochastic cases in which unique solutions are determined by any convergent numerical algorithm. Consequences of the relationship between stochastic system solutions and simulation procedures are presented for a nonlinear filtering example. Monte Carlo simulations and statistical tests are applied to the example to illustrate the determining role which computational procedures play in generating solutions.
Stochastic resonance with a mesoscopic reaction-diffusion system.
Mahara, Hitoshi; Yamaguchi, Tomohiko; Parmananda, P
2014-06-01
In a mesoscopic reaction-diffusion system with an Oregonator reaction model, we show that intrinsic noise can drive a resonant stable pattern in the presence of the initial subthreshold perturbations. Both spatially periodic and aperiodic stochastic resonances are demonstrated by employing the Gillespies stochastic simulation algorithm. The mechanisms for these phenomena are discussed. PMID:25019857
Two Different Approaches to Nonzero-Sum Stochastic Differential Games
Rainer, Catherine
2007-06-15
We make the link between two approaches to Nash equilibria for nonzero-sum stochastic differential games: the first one using backward stochastic differential equations and the second one using strategies with delay. We prove that, when both exist, the two notions of Nash equilibria coincide.
Stochastic Human Exposure and Dose Simulation Model for Wood Preservatives
SHEDS-Wood (Stochastic Human Exposure and Dose Simulation Model for Wood Preservatives) is a physically-based stochastic model that was developed to quantify exposure and dose of children to wood preservatives on treated playsets and residential decks. Probabilistic inputs are co...
Stochastic fate selection in HIV-infected patients.
Weinberger, Ariel D; Weinberger, Leor S
2013-10-24
Classic studies proposed that stochastic variability ("noise") can drive biological fate switching, enhancing evolutionary success. Now, Ho et al. report that HIV's reactivation from dormant (latently infected) patient cells-the major barrier to an HIV cure-is inherently stochastic. Eradicating an incompletely inducible (probabilistic) viral phenotype will require inventive approaches. PMID:24243007
Geometric quadratic stochastic operator on countable infinite set
Ganikhodjaev, Nasir; Hamzah, Nur Zatul Akmar
2015-02-03
In this paper we construct the family of Geometric quadratic stochastic operators defined on the countable sample space of nonnegative integers and investigate their trajectory behavior. Such operators can be reinterpreted in terms of of evolutionary operator of free population. We show that Geometric quadratic stochastic operators are regular transformations.
Stochastic fuzzy differential equations of a nonincreasing type
NASA Astrophysics Data System (ADS)
Malinowski, Marek T.
2016-04-01
Stochastic fuzzy differential equations constitute an apparatus in modeling dynamic systems operating in fuzzy environment and governed by stochastic noises. In this paper we introduce a new kind of such the equations. Namely, the stochastic fuzzy differential of nonincreasing type are considered. The fuzzy stochastic processes which are solutions to these equations have trajectories with nonincreasing fuzziness in their values. In our previous papers, as a first natural extension of crisp stochastic differential equations, stochastic fuzzy differential equations of nondecreasing type were studied. In this paper we show that under suitable conditions each of the equations has a unique solution which possesses property of continuous dependence on data of the equation. To prove existence of the solutions we use sequences of successive approximate solutions. An estimation of an error of the approximate solution is established as well. Some examples of equations are solved and their solutions are simulated to illustrate the theory of stochastic fuzzy differential equations. All the achieved results apply to stochastic set-valued differential equations.
On Scaling Modes and Balancing Stochastic, Discretization, and Modeling Error
NASA Astrophysics Data System (ADS)
Brown, J.
2015-12-01
We consider accuracy-cost tradeoffs and the problem of finding Pareto optimal configurations for stochastic forward and inverse problems. As the target accuracy is changed, we should use different physical models, stochastic models, discretizations, and solution algorithms. In this spectrum, we see different scientifically-relevant scaling modes, thus different opportunities and limitations on parallel computers and emerging architectures.
Spontaneous Stochasticity and Anomalous Dissipation for Burgers Equation
NASA Astrophysics Data System (ADS)
Eyink, Gregory L.; Drivas, Theodore D.
2015-01-01
We develop a Lagrangian approach to conservation-law anomalies in weak solutions of inviscid Burgers equation, motivated by previous work on the Kraichnan model of turbulent scalar advection. We show that the entropy solutions of Burgers possess Markov stochastic processes of (generalized) Lagrangian trajectories backward in time for which the Burgers velocity is a backward martingale. This property is shown to guarantee dissipativity of conservation-law anomalies for general convex functions of the velocity. The backward stochastic Burgers flows with these properties are not unique, however. We construct infinitely many such stochastic flows, both by a geometric construction and by the zero-noise limit of the Constantin-Iyer stochastic representation of viscous Burgers solutions. The latter proof yields the spontaneous stochasticity of Lagrangian trajectories backward in time for Burgers, at unit Prandtl number. It is conjectured that existence of a backward stochastic flow with the velocity as martingale is an admissibility condition which selects the unique entropy solution for Burgers. We also study linear transport of passive densities and scalars by inviscid Burgers flows. We show that shock solutions of Burgers exhibit spontaneous stochasticity backward in time for all finite Prandtl numbers, implying conservation-law anomalies for linear transport. We discuss the relation of our results for Burgers with incompressible Navier-Stokes turbulence, especially Lagrangian admissibility conditions for Euler solutions and the relation between turbulent cascade directions and time-asymmetry of Lagrangian stochasticity.
Stochastic Schroedinger equations with general complex Gaussian noises
Bassi, Angelo
2003-06-01
Within the framework of non-Markovian stochastic Schroedinger equations, we generalize the results of [W. T. Strunz, Phys. Lett. A 224, 25 (1996)] to the case of general complex Gaussian noises; we analyze the two important cases of purely real and purely imaginary stochastic processes.
... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ...
Colorectal cancer; Cancer - colon; Rectal cancer; Cancer - rectum; Adenocarcinoma - colon; Colon - adenocarcinoma ... In the United States, colorectal cancer is one of the leading causes of deaths due to cancer. Early diagnosis can often lead to a complete cure. Almost ...
... Partners & Collaborators Spotlight on Scientists Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & ... Collaborators Spotlight on Scientists NCI Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & ...
... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ...
... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ... Role in Cancer Research Intramural Research Extramural Research Bioinformatics and Cancer NCI-Designated Cancer Centers Frederick National ...
... Partners & Collaborators Spotlight on Scientists Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & ... Collaborators Spotlight on Scientists NCI Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & ...
Terminator Detection by Support Vector Machine Utilizing aStochastic Context-Free Grammar
Francis-Lyon, Patricia; Cristianini, Nello; Holbrook, Stephen
2006-12-30
A 2-stage detector was designed to find rho-independent transcription terminators in the Escherichia coli genome. The detector includes a Stochastic Context Free Grammar (SCFG) component and a Support Vector Machine (SVM) component. To find terminators, the SCFG searches the intergenic regions of nucleotide sequence for local matches to a terminator grammar that was designed and trained utilizing examples of known terminators. The grammar selects sequences that are the best candidates for terminators and assigns them a prefix, stem-loop, suffix structure using the Cocke-Younger-Kasaami (CYK) algorithm, modified to incorporate energy affects of base pairing. The parameters from this inferred structure are passed to the SVM classifier, which distinguishes terminators from non-terminators that score high according to the terminator grammar. The SVM was trained with negative examples drawn from intergenic sequences that include both featureless and RNA gene regions (which were assigned prefix, stem-loop, suffix structure by the SCFG), so that it successfully distinguishes terminators from either of these. The classifier was found to be 96.4% successful during testing.
Estimability and stochastic observability of quantised linear systems
NASA Astrophysics Data System (ADS)
Zhang, Hui; Shen, Ying
2016-04-01
The estimability and stochastic observability of quantised discrete-time linear dynamic systems are discussed from information theoretic viewpoint. Algebraic conditions of estimability and stochastic observability for quantised linear Gaussian systems, i.e., certain Gramians having full rank, are proposed based on the measure of mutual information. The obtained conditions of estimability and observability are consistent with the intuition and provide us with valuable hints on quantiser design. It is shown analytically that the Gramians of quantised systems converge to that of unquantised systems when the quantisation intervals turn to zero, and a well-designed quantiser can preserve the estimability and stochastic observability of the original system even if it is as coarse as one bit. Furthermore, the relation between estimability and stochastic observability is established for quantised stochastically autonomous systems. The analytical results are verified by illustrative simulations.
Improving the detection sensitivity of chromatography by stochastic resonance.
Zhang, Wei; Guo, Jianru; Xiang, Bingren; Fan, Hongyan; Xu, Fengguo
2014-05-01
Improving the detection sensitivity of analytical instruments has been a challenging task for chemometricians since undetectability has been almost unavoidable in trace analysis, even under optimized experimental conditions and with the use of modern instruments. Various chemometrics methods have been developed which attempt to address this detection problem but with limited success (e.g., fast Fourier transform and wavelet transform). However, the application of stochastic resonance (SR) creates an entirely new and effective methodology. Stochastic resonance is a phenomenon which is manifested in non-linear systems where a weak signal can be amplified and optimized with the assistance of noise. In this review, we summarize the use of basic SR, optimization of parameters and its modifications, including periodic modulation stochastic resonance (PSRA), linear modulation stochastic resonance (LSRA), single-well potential stochastic resonance (SSR) and the Duffing oscillator algorithm (DOA) for amplifying sub-threshold small signals. We also review the advantages and the disadvantages of various SR procedures. PMID:24622614
Stochastic receding horizon control: application to an octopedal robot
NASA Astrophysics Data System (ADS)
Shah, Shridhar K.; Tanner, Herbert G.
2013-06-01
Miniature autonomous systems are being developed under ARL's Micro Autonomous Systems and Technology (MAST). These systems can only be fitted with a small-size processor, and their motion behavior is inherently uncertain due to manufacturing and platform-ground interactions. One way to capture this uncertainty is through a stochastic model. This paper deals with stochastic motion control design and implementation for MAST- specific eight-legged miniature crawling robots, which have been kinematically modeled as systems exhibiting the behavior of a Dubin's car with stochastic noise. The control design takes the form of stochastic receding horizon control, and is implemented on a Gumstix Overo Fire COM with 720 MHz processor and 512 MB RAM, weighing 5.5 g. The experimental results show the effectiveness of this control law for miniature autonomous systems perturbed by stochastic noise.
Empirical insights into the stochasticity of small RNA sequencing
NASA Astrophysics Data System (ADS)
Qin, Li-Xuan; Tuschl, Thomas; Singer, Samuel
2016-04-01
The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.
A stochastic method for stand-alone photovoltaic system sizing
Cabral, Claudia Valeria Tavora; Filho, Delly Oliveira; Martins, Jose Helvecio; Toledo, Olga Moraes
2010-09-15
Photovoltaic systems utilize solar energy to generate electrical energy to meet load demands. Optimal sizing of these systems includes the characterization of solar radiation. Solar radiation at the Earth's surface has random characteristics and has been the focus of various academic studies. The objective of this study was to stochastically analyze parameters involved in the sizing of photovoltaic generators and develop a methodology for sizing of stand-alone photovoltaic systems. Energy storage for isolated systems and solar radiation were analyzed stochastically due to their random behavior. For the development of the methodology proposed stochastic analysis were studied including the Markov chain and beta probability density function. The obtained results were compared with those for sizing of stand-alone using from the Sandia method (deterministic), in which the stochastic model presented more reliable values. Both models present advantages and disadvantages; however, the stochastic one is more complex and provides more reliable and realistic results. (author)
Empirical insights into the stochasticity of small RNA sequencing
Qin, Li-Xuan; Tuschl, Thomas; Singer, Samuel
2016-01-01
The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data. PMID:27052356
Chen, Bor-Sen; Tsai, Kun-Wei; Li, Cheng-Wei
2015-01-01
Molecular biologists have long recognized carcinogenesis as an evolutionary process that involves natural selection. Cancer is driven by the somatic evolution of cell lineages. In this study, the evolution of somatic cancer cell lineages during carcinogenesis was modeled as an equilibrium point (ie, phenotype of attractor) shifting, the process of a nonlinear stochastic evolutionary biological network. This process is subject to intrinsic random fluctuations because of somatic genetic and epigenetic variations, as well as extrinsic disturbances because of carcinogens and stressors. In order to maintain the normal function (ie, phenotype) of an evolutionary biological network subjected to random intrinsic fluctuations and extrinsic disturbances, a network robustness scheme that incorporates natural selection needs to be developed. This can be accomplished by selecting certain genetic and epigenetic variations to modify the network structure to attenuate intrinsic fluctuations efficiently and to resist extrinsic disturbances in order to maintain the phenotype of the evolutionary biological network at an equilibrium point (attractor). However, during carcinogenesis, the remaining (or neutral) genetic and epigenetic variations accumulate, and the extrinsic disturbances become too large to maintain the normal phenotype at the desired equilibrium point for the nonlinear evolutionary biological network. Thus, the network is shifted to a cancer phenotype at a new equilibrium point that begins a new evolutionary process. In this study, the natural selection scheme of an evolutionary biological network of carcinogenesis was derived from a robust negative feedback scheme based on the nonlinear stochastic Nash game strategy. The evolvability and phenotypic robustness criteria of the evolutionary cancer network were also estimated by solving a Hamilton–Jacobi inequality – constrained optimization problem. The simulation revealed that the phenotypic shift of the lung cancer
Chen, Bor-Sen; Tsai, Kun-Wei; Li, Cheng-Wei
2015-01-01
Molecular biologists have long recognized carcinogenesis as an evolutionary process that involves natural selection. Cancer is driven by the somatic evolution of cell lineages. In this study, the evolution of somatic cancer cell lineages during carcinogenesis was modeled as an equilibrium point (ie, phenotype of attractor) shifting, the process of a nonlinear stochastic evolutionary biological network. This process is subject to intrinsic random fluctuations because of somatic genetic and epigenetic variations, as well as extrinsic disturbances because of carcinogens and stressors. In order to maintain the normal function (ie, phenotype) of an evolutionary biological network subjected to random intrinsic fluctuations and extrinsic disturbances, a network robustness scheme that incorporates natural selection needs to be developed. This can be accomplished by selecting certain genetic and epigenetic variations to modify the network structure to attenuate intrinsic fluctuations efficiently and to resist extrinsic disturbances in order to maintain the phenotype of the evolutionary biological network at an equilibrium point (attractor). However, during carcinogenesis, the remaining (or neutral) genetic and epigenetic variations accumulate, and the extrinsic disturbances become too large to maintain the normal phenotype at the desired equilibrium point for the nonlinear evolutionary biological network. Thus, the network is shifted to a cancer phenotype at a new equilibrium point that begins a new evolutionary process. In this study, the natural selection scheme of an evolutionary biological network of carcinogenesis was derived from a robust negative feedback scheme based on the nonlinear stochastic Nash game strategy. The evolvability and phenotypic robustness criteria of the evolutionary cancer network were also estimated by solving a Hamilton-Jacobi inequality - constrained optimization problem. The simulation revealed that the phenotypic shift of the lung cancer
... Cancer - perineum; Cancer - vulvar; Genital warts - vulvar cancer; HPV - vulvar cancer ... is rare. Risk factors include: Human papilloma virus (HPV, or genital warts ) infection in women under age ...
Heydari, M.H.; Hooshmandasl, M.R.; Cattani, C.; Maalek Ghaini, F.M.
2015-02-15
Because of the nonlinearity, closed-form solutions of many important stochastic functional equations are virtually impossible to obtain. Thus, numerical solutions are a viable alternative. In this paper, a new computational method based on the generalized hat basis functions together with their stochastic operational matrix of Itô-integration is proposed for solving nonlinear stochastic Itô integral equations in large intervals. In the proposed method, a new technique for computing nonlinear terms in such problems is presented. The main advantage of the proposed method is that it transforms problems under consideration into nonlinear systems of algebraic equations which can be simply solved. Error analysis of the proposed method is investigated and also the efficiency of this method is shown on some concrete examples. The obtained results reveal that the proposed method is very accurate and efficient. As two useful applications, the proposed method is applied to obtain approximate solutions of the stochastic population growth models and stochastic pendulum problem.
Spatial Moran models, II: cancer initiation in spatially structured tissue
Foo, J; Leder, K
2016-01-01
We study the accumulation and spread of advantageous mutations in a spatial stochastic model of cancer initiation on a lattice. The parameters of this general model can be tuned to study a variety of cancer types and genetic progression pathways. This investigation contributes to an understanding of how the selective advantage of cancer cells together with the rates of mutations driving cancer, impact the process and timing of carcinogenesis. These results can be used to give insights into tumor heterogeneity and the “cancer field effect,” the observation that a malignancy is often surrounded by cells that have undergone premalignant transformation. PMID:26126947
Stochastic Particle Tracking in Fractured Sedimentary Formations
NASA Astrophysics Data System (ADS)
Willmann, Matthias; Kinzelbach, Wolfgang
2014-05-01
Particle tracking simulations are very useful tools to assess transport behavior in deep subsurface formations. Unfortunately, those formations are often fractured. And particle tracking accounting for fracture and matrix transport simultaneously are conceptually complex and difficult to implement. Major problems are that particles moved within the matrix might jump over fractures, and the unclear nature of exchange between fractures and matrix. Due to these difficulties transport simulations are most often reduced to pure fracture transport. The matrix contribution is either ignored or approximated by using a retention mechanism like matrix diffusion. In crystalline rocks this appears to be a reasonable assumption, but in sedimentary rocks should be studied without any a priori assumption on the type of matrix transport. For sedimentary rocks advective transport within the matrix is expected to influence strongly the overall transport behavior. We developed a stochastic particle tracking method that models transport explicitly in both fractures and matrix. Similar to most flow simulators we conceptualize transport as a superposition of two separate domains, the fracture and the matrix domain, which exchange particles between them. But in our case this exchange is possible at each position within the fractures and not only at the nodes of the fracture. We restrict ourselves to an orthogonal grid for the matrix and here we allow only that fractures lay on the sides of individual matrix cells. This leads to step-like fractures with an enlarged path length inside the fractures. But it also enables us to use the Pollock method without major modifications. Now we calculate the position where a particle leaves an individual matrix cell. At a cell face a check is performed whether a fracture is present. This avoids a time consuming search for fractures at each point of the particle path. The time a particle stays in a fracture before being released again to the matrix
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
Statistic versus stochastic characterization of persistent droughts
NASA Astrophysics Data System (ADS)
Gonzalez-Perez, J.; Valdes, J. B.
2005-12-01
Droughts are one of more devastating natural disasters. A drought event is always related with deficiency in precipitation over a time period. As longer are the drought periods, larger are the damages associated with, following a potential relationship. Additionally, the extension covered by an event also increases its impact, because it makes difficult to compensate the deficit from neighbourhood water resources. Therefore, the characterization of a drought by its persistent deficit, and the area over which it extends are main points to be carried on. The Standardized Precipitation Index (SPI) provides a statistical characterization of the deficits. Its computation, for different aggregation time scales, allows a persistence evaluation. Another more recent statistic that may be applied in drought characterization is the extreme persistent probability function (e.p.f.), which characterizes the persistence of extreme realizations in a random sequence. This work presents an analysis of the differences in performance of the SPI and the e.p.f. in the statistical characterization of a drought event. The inclusion of the persistency directly in the statistic gives to the e.p.f. an advantage over the SPI. Furthermore, the relationship between the e.p.f. and its mean frequency of recurrence is known. Thus, the e.p.f. may be applied to provide either statistic or stochastic characterization of a drought event. Both criteria were compared, showing that the stochastic characterization produces a better drought indicator. The stochastic characterization using the e.p.f. as a criterion yields the new Drought Frequency Index (DFI). The index is applicable to any random water related variable to identify drought events. Its main advantages over the SPI are the direct inclusion of persistence, and its larger robustness to the time scale. To incorporate the spatial extension in the characterization of a drought event, the new DFI may also be evaluated to characterize the drought
Improving Sensorimotor Function Using Stochastic Vestibular Stimulation
NASA Technical Reports Server (NTRS)
Galvan, R. C.; Clark, T. K.; Merfeld, D. M.; Bloomberg, J. J.; Mulavara, A. P.; Oman, C. M.
2014-01-01
Astronauts experience sensorimotor changes during spaceflight, particularly during G-transition phases. Post flight sensorimotor changes may include postural and gait instability, spatial disorientation, and visual performance decrements, all of which can degrade operational capabilities of the astronauts and endanger the crew. Crewmember safety would be improved if these detrimental effects of spaceflight could be mitigated by a sensorimotor countermeasure and even further if adaptation to baseline could be facilitated. The goal of this research is to investigate the potential use of stochastic vestibular stimulation (SVS) as a technology to improve sensorimotor function. We hypothesize that low levels of SVS will improve sensorimotor performance through stochastic resonance (SR). The SR phenomenon occurs when the response of a nonlinear system to a weak input signal is optimized by the application of a particular nonzero level of noise. Two studies have been initiated to investigate the beneficial effects and potential practical usage of SVS. In both studies, electrical vestibular stimulation is applied via electrodes on the mastoid processes using a constant current stimulator. The first study aims to determine the repeatability of the effect of vestibular stimulation on sensorimotor performance and perception in order to better understand the practical use of SVS. The beneficial effect of low levels of SVS on balance performance has been shown in the past. This research uses the same balance task repeated multiple times within a day and across days to study the repeatability of the stimulation effects. The balance test consists of 50 sec trials in which the subject stands with his or her feet together, arms crossed, and eyes closed on compliant foam. Varying levels of SVS, ranging from 0-700 micro A, are applied across different trials. The subject-specific optimal SVS level is that which results in the best balance performance as measured by inertial
Stochastic inverse consistency in medical image registration.
Yeung, Sai Kit; Shi, Pengcheng
2005-01-01
An essential goal in medical image registration is, the forward and reverse mapping matrices should be inverse to each other, i.e., inverse consistency. Conventional approaches enforce consistency in deterministic fashions, through incorporation of sub-objective cost function to impose source-destination symmetric property during the registration process. Assuming that the initial forward and reverse matching matrices have been computed and used as the inputs to our system, this paper presents a stochastic framework which yields perfect inverse consistency with the simultaneous considerations of the errors underneath the registration matrices and the imperfectness of the consistent constraint. An iterative generalized total least square (GTLS) strategy has been developed such that the inverse consistency is optimally imposed. PMID:16685959
Mortality, redundancy, and diversity in stochastic search.
Meerson, Baruch; Redner, S
2015-05-15
We investigate a stochastic search process in one dimension under the competing roles of mortality, redundancy, and diversity of the searchers. This picture represents a toy model for the fertilization of an oocyte by sperm. A population of N independent and mortal diffusing searchers all start at x=L and attempt to reach the target at x=0. When mortality is irrelevant, the search time scales as τ_{D}/lnN for lnN≫1, where τ_{D}~L^{2}/D is the diffusive time scale. Conversely, when the mortality rate μ of the searchers is sufficiently large, the search time scales as sqrt[τ_{D}/μ], independent of N. When searchers have distinct and high mortalities, a subpopulation with a nontrivial optimal diffusivity is most likely to reach the target. We also discuss the effect of chemotaxis on the search time and its fluctuations. PMID:26024200
Algorithm refinement for the stochastic Burgers' equation
Bell, John B.; Foo, Jasmine; Garcia, Alejandro L. . E-mail: algarcia@algarcia.org
2007-04-10
In this paper, we develop an algorithm refinement (AR) scheme for an excluded random walk model whose mean field behavior is given by the viscous Burgers' equation. AR hybrids use the adaptive mesh refinement framework to model a system using a molecular algorithm where desired while allowing a computationally faster continuum representation to be used in the remainder of the domain. The focus in this paper is the role of fluctuations on the dynamics. In particular, we demonstrate that it is necessary to include a stochastic forcing term in Burgers' equation to accurately capture the correct behavior of the system. The conclusion we draw from this study is that the fidelity of multiscale methods that couple disparate algorithms depends on the consistent modeling of fluctuations in each algorithm and on a coupling, such as algorithm refinement, that preserves this consistency.
The theory of hybrid stochastic algorithms
Kennedy, A.D. . Supercomputer Computations Research Inst.)
1989-11-21
These lectures introduce the family of Hybrid Stochastic Algorithms for performing Monte Carlo calculations in Quantum Field Theory. After explaining the basic concepts of Monte Carlo integration we discuss the properties of Markov processes and one particularly useful example of them: the Metropolis algorithm. Building upon this framework we consider the Hybrid and Langevin algorithms from the viewpoint that they are approximate versions of the Hybrid Monte Carlo method; and thus we are led to consider Molecular Dynamics using the Leapfrog algorithm. The lectures conclude by reviewing recent progress in these areas, explaining higher-order integration schemes, the asymptotic large-volume behaviour of the various algorithms, and some simple exact results obtained by applying them to free field theory. It is attempted throughout to give simple yet correct proofs of the various results encountered. 38 refs.
Branching process in a stochastic extremal model
NASA Astrophysics Data System (ADS)
Manna, S. S.
2009-08-01
We considered a stochastic version of the Bak-Sneppen model (SBSM) of ecological evolution where the number M of sites mutated in a mutation event is restricted to only two. Here the mutation zone consists of only one site and this site is randomly selected from the neighboring sites at every mutation event in an annealed fashion. The critical behavior of the SBSM is found to be the same as the BS model in dimensions d=1 and 2. However on the scale-free graphs the critical fitness value is nonzero even in the thermodynamic limit but the critical behavior is mean-field like. Finally ⟨M⟩ has been made even smaller than two by probabilistically updating the mutation zone, which also shows the original BS model behavior. We conjecture that a SBSM on any arbitrary graph with any small branching factor greater than unity will lead to a self-organized critical state.
Branching process in a stochastic extremal model.
Manna, S S
2009-08-01
We considered a stochastic version of the Bak-Sneppen model (SBSM) of ecological evolution where the number M of sites mutated in a mutation event is restricted to only two. Here the mutation zone consists of only one site and this site is randomly selected from the neighboring sites at every mutation event in an annealed fashion. The critical behavior of the SBSM is found to be the same as the BS model in dimensions d=1 and 2. However on the scale-free graphs the critical fitness value is nonzero even in the thermodynamic limit but the critical behavior is mean-field like. Finally M has been made even smaller than two by probabilistically updating the mutation zone, which also shows the original BS model behavior. We conjecture that a SBSM on any arbitrary graph with any small branching factor greater than unity will lead to a self-organized critical state. PMID:19792102
Stochastic search with Poisson and deterministic resetting
NASA Astrophysics Data System (ADS)
Bhat, Uttam; De Bacco, Caterina; Redner, S.
2016-08-01
We investigate a stochastic search process in one, two, and three dimensions in which N diffusing searchers that all start at x 0 seek a target at the origin. Each of the searchers is also reset to its starting point, either with rate r, or deterministically, with a reset time T. In one dimension and for a small number of searchers, the search time and the search cost are minimized at a non-zero optimal reset rate (or time), while for sufficiently large N, resetting always hinders the search. In general, a single searcher leads to the minimum search cost in one, two, and three dimensions. When the resetting is deterministic, several unexpected feature arise for N searchers, including the search time being independent of T for 1/T\\to 0 and the search cost being independent of N over a suitable range of N. Moreover, deterministic resetting typically leads to a lower search cost than in Poisson resetting.
Stochastic bifurcations in a prototypical thermoacoustic system
NASA Astrophysics Data System (ADS)
Gopalakrishnan, E. A.; Tony, J.; Sreelekha, E.; Sujith, R. I.
2016-08-01
We study the influence of noise in a prototypical thermoacoustic system, which represents a nonlinear self-excited bistable oscillator. We analyze the time series of unsteady pressure obtained from a horizontal Rijke tube and a mathematical model to identify the effect of noise. We report the occurrence of stochastic bifurcations in a thermoacoustic system by tracking the changes in the stationary amplitude distribution. We observe a complete suppression of a bistable zone in the presence of high intensity noise. We find that the complete suppression of the bistable zone corresponds to the nonexistence of phenomenological (P) bifurcations. This is a study in thermoacoustics to identify the parameter regimes pertinent to P bifurcation using the stationary amplitude distribution obtained by solving the Fokker-Planck equation.
Optimal stochastic transport in inhomogeneous thermal environments
NASA Astrophysics Data System (ADS)
Bo, Stefano; Aurell, Erik; Eichhorn, Ralf; Celani, Antonio
2013-07-01
We consider the optimization of the average entropy production in inhomogeneous temperature environments within the framework of stochastic thermodynamics. For systems modeled by Langevin equations (e.g. a colloidal particle in a heat bath) it has been recently shown that a space-dependent temperature breaks the time reversal symmetry of the fast velocity degrees of freedom resulting in an anomalous contribution to the entropy production of the overdamped dynamics. We show that optimization of entropy production is determined by an auxiliary deterministic problem formally analogous to motion on a curved manifold in a potential. The “anomalous contribution” to entropy plays the role of the potential and the inverse of the diffusion tensor is the metric. We also find that entropy production is not minimized by adiabatically slow, quasi-static protocols but there is a finite optimal duration for the transport process. As an example we discuss the case of a linearly space-dependent diffusion coefficient.
Stochastic Inversion of 2D Magnetotelluric Data
2010-07-01
The algorithm is developed to invert 2D magnetotelluric (MT) data based on sharp boundary parametrization using a Bayesian framework. Within the algorithm, we consider the locations and the resistivity of regions formed by the interfaces are as unknowns. We use a parallel, adaptive finite-element algorithm to forward simulate frequency-domain MT responses of 2D conductivity structure. Those unknown parameters are spatially correlated and are described by a geostatistical model. The joint posterior probability distribution function ismore » explored by Markov Chain Monte Carlo (MCMC) sampling methods. The developed stochastic model is effective for estimating the interface locations and resistivity. Most importantly, it provides details uncertainty information on each unknown parameter. Hardware requirements: PC, Supercomputer, Multi-platform, Workstation; Software requirements C and Fortan; Operation Systems/version is Linux/Unix or Windows« less
A stochastic lattice model for locust outbreak
NASA Astrophysics Data System (ADS)
Kizaki, Shinya; Katori, Makoto
The locust is a kind of grasshoppers. Gregarious locusts form swarms and can migrate over large distances and they spread and damage a large area (locust outbreak). When the density is low, each of locusts behaves as an individual insect (solitary phase). As locusts become crowded, they become to act as a part of a group (gregarious phase) as a result of interactions among them. Modeling of this phenomenon is a challenging problem of statistical physics. We introduce a stochastic cellular automaton model of locust population-dynamics on lattices. Change of environmental conditions by seasonal migration is a key factor in gregarisation of locusts and we take it into account by changing the lattice size periodically. We study this model by computer simulations and discuss the locust outbreak as a cooperative phenomena.
A Stochastic Tikhonov Theorem in Infinite Dimensions
Buckdahn, Rainer Guatteri, Giuseppina
2006-03-15
The present paper studies the problem of singular perturbation in the infinite-dimensional framework and gives a Hilbert-space-valued stochastic version of the Tikhonov theorem. We consider a nonlinear system of Hilbert-space-valued equations for a 'slow' and a 'fast' variable; the system is strongly coupled and driven by linear unbounded operators generating a C{sub 0}-semigroup and independent cylindrical Brownian motions. Under well-established assumptions to guarantee the existence and uniqueness of mild solutions, we deduce the required stability of the system from a dissipativity condition on the drift of the fast variable. We avoid differentiability assumptions on the coefficients which would be unnatural in the infinite-dimensional framework.