最优传输理论在图像处理中的应用

最优传输问题是寻找概率测度间的最优传输变换的一类特殊的优化问题,近年来在众多领域得到了广泛的关注.针对传统最优传输问题存在的计算量过大、正则性缺失等问题,学者们提出了多种改进的最优传输模型和算法,用于处理实际中的各种问题.简述最优传输问题的基本理论和方法,介绍Wasserstein距离的概念及其衍生出的Wasserstein重心,探讨离散化最优传输模型及其在正则化等方面的改进,讨论求解最优传输问题的算法进展,综述Wasserstein距离在图像处理领域的简单应用,并展望有待进一步研究的工作.     

Stochastic shape sensitivity in powder metallurgy processing

Stochastic shape sensitivity in forming process of powder metallurgy materials is analyzed. For this purpose the rigid-poroplastic material model has been assumed. The theoretical formulation for stochastic shape sensitivity is described which presents probabilistic distributions taking into account random initial and boundary conditions. The control volume approach is discussed. Stochastic finite element equations for rigid – poroplastic materials are solved for the first two probabilistic moments. Numerical simulations were performed to illustrate shape sensitivity problems in the process of compression of rigid-poroplastic cylinder. The differences in deterministic and stochastic sensitivities are presented. The results derived can be used for the subsequent quantitative stochastic shape design as well as stochastic shape optimization.     

Parameter identification and optimization algorithm in microbial continuous culture

In this study, a novelty mathematical model is established to formulate the continuous culture of glycerol to 1,3-Propanediol (1,3-PD) by Klebsiella pneumoniae, in which the inhibition of 3-hydroxypropionaldehyde (3-HPA) to cells growth and activity of some enzymes (such as glycerol dehydratase (GDHt) and 1,3-PD oxidoreductase (PDOR)), and the passive diffusion and active transport of glycerol and 1,3-PD across cell membrane are all taken into consideration. Taking the mean relative error between the experimental data and calculated values as the performance index, a parameter identification model involving multiple nonlinear dynamic systems is presented. The identifiability of the parameter identification model is also proved. Finally, an improved particle swarm optimization (PSO) algorithm is constructed to find the optimal parameters for the systems under substrate limitation and excess conditions, respectively. Numerical results not only show that the established model can be used to describe the continuous fermentation reasonably, but also the improved PSO algorithm is valid.     

Robust stability in distribution of Boolean networks under multi-bits stochastic function perturbations

In this work, robust stability in distribution of Boolean networks (BNs) is studied under multi-bits probabilistic and markovian function perturbations. Firstly, definition of multi-bits stochastic function perturbations is given and an identification matrix is introduced to present each case. Then, by viewing each case as a switching subsystem, BNs under multi-bits stochastic function perturbations can be equivalently converted into stochastic switching systems. After constructing respective transition probability matrices which can unify multi-bits probabilistic and markovian function perturbations in a consolidated framework, robust stability in distribution can be handled. On such basis, necessary and sufficient conditions for robust stability in distribution of BNs under stochastic function perturbations are given respectively. Finally, two numerical examples are presented to verify the validity of our theoretical results.     

Moments of the critical parameters of the transport of particles in a random medium

The problem of estimating the moments of the critical values of transport in a medium with a random density that scatters, absorbs, and multiplies particles is solved. To this end, a special iterative process is used to estimate the first- and second-order derivatives of the critical parameters with respect to the density in different subregions of the medium. These estimates are used to implement linearization and homogenization methods. In addition, a simple probabilistic model of transport in an unbounded medium with the additional absorption probability depending on the random density is constructed. The computation results demonstrate the practical effectiveness of the estimates.     

Probabilistic modeling of random variables with inconsistent data

The aim of the present paper was to formulate probabilistic modeling for random variables with inconsistent data to facilitate accurate reliability assessment. Traditionally, random variables have some outputs available, based on which, some distribution is identified. However, as will be illustrated, the data relevant to those extreme events might not necessarily follow the same distribution as well as the other part, but they generally have small weights in the definition of the distribution due to their small quantity. The adoption of one single probabilistic distribution to describe random variables with such inconsistent data might cause great errors in the reliability assessment, especially for extreme events. One new formulation of probabilistic modeling is proposed here for such type of random variables. The inconsistency within the data set is identified and based on how the set is divided. Each division is described by the respective distribution and finally they are unified into one framework. The relevant problems in the modeling (e.g., the identification of the boundary between the divisions, the definition of the probability distributions, and the unification of the distributions into one framework) are presented and solved. The realization of the proposed approach in the practical numerical analysis is further investigated afterwards. Finally, two examples are presented to illustrate the application from different perspectives.     

A New Method for Bounding Rates of Convergence of Empirical Spectral Distributions

The probabilistic properties of eigenvalues of random matrices whose dimension increases indefinitely has received considerable attention. One important aspect is the existence and identification of the limiting spectral distribution (LSD) of the empirical distribution of the eigenvalues. When the LSD exists, it is useful to know the rate at which the convergence holds. The main method to establish such rates is the use of Stieltjes transform. In this article we introduce a new technique of bounding the rates of convergence to the LSD. We show how our results apply to specific cases such as the Wigner matrix and the Sample Covariance matrix.     

Local probabilistic homogenization schemes for assessment of material uncertainties in solid foams

The present study is concerned with a probabilistic model for prediction of the uncertainties in the effective material response of long fiber reinforced thermoplastic materials. The model is based on a simple elasticity model using the rules of mixture together with an ensemble averaging procedure. Assuming the scatter band width of local fiber orientation angle and the local fiber density as random variables, a probabilistic model is obtained. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Probabilistic diagnosis of clustered faults for shared structures

The probabilistic diagnosis model is useful in many fields such as distributed network, digital system level testing and wafer fault testing. Some topologies and continuous defect units distributions are studied in our previous work. In this paper, we extend the model to arbitrary topology structure with share nodes and to the discrete defect distributions, such as Poission distribution and Binomial distribution. The results show high identification percentage of the nodes.     

Fully discrete approximation methods for the estimation of parabolic systems and boundary parameters

A spatially and temporally discrete numerical approximation scheme is developed for the identification of a class of semilinear parabolic systems with unknown boundary parameters. The identification problem is formulated as a least squares fit to data subject to an equivalent representation for the dynamics in the form of an abstract evolution equation. Finite-dimensional difference equation state approximations are constructed using a cubic spline-based, Galerkin method and the Padé rational function approximations to the exponential. A sequence of approximating identification problems result, the solutions of which are shown to exist and, in a certain sense, approximate solutions to the original identification problem. Numerical results for two examples, one involving the modeling of biological mixing in deep sea sediment cores, and the other, the estimation of transport parameters for indoor mixing, are discussed. In both examples, the identification is based upon actual experimental data.Parts of the research were carried out while the authors were visitors at the Institute for Computer Applications in Science and Engineering (ICASE), NASA Langley Research Center, Hampton, Virginia, which is operated under NASA Contracts No. NAS1-17070 and No. NAS1-17130.Research supported in part by NSF Grant MCS-8205355, AFOSR Contract 81-0198 and ARO Contract ARO-DAAG-29-K-0029.     

The Downs-Thomson Paradox: Existence, Uniqueness and Stability of User Equilibria

Consider a network where two routes are available for users wishing to travel from a source to a destination. On one route (which could be viewed as private transport) service slows as traffic increases. On the other (which could be viewed as public transport) the service frequency increases with demand. The Downs-Thomson paradox occurs when improvements in service produce an overall decline in performance as user equilibria adjust. Using the model proposed by Calvert [10], with a ⋅|M|1 queue corresponding to the private transport route, and a bulk-service infinite server queue modelling the public transport route, we give a complete analysis of this system in the setting of probabilistic routing. We obtain the user equilibria (which are not always unique), and determine their stability.AMS subject classification: 60K30, 90B15, 90B20, 91A10, 91A13This revised version was published online in June 2005 with corrected coverdate     

Representing probability measures using probabilistic processes

In the Type-2 Theory of Effectivity, one considers representations of topological spaces in which infinite words are used as “names” for the elements they represent. Given such a representation, we show that probabilistic processes on infinite words, under which each successive symbol is determined by a finite probabilistic choice, generate Borel probability measures on the represented space. Conversely, for several well-behaved types of space, every Borel probability measure is represented by a corresponding probabilistic process. Accordingly, we consider probabilistic processes as providing “probabilistic names” for Borel probability measures. We show that integration is computable with respect to the induced representation of measures.     

Numerical Evaluation of the Evans Function by Magnus Integration

We use Magnus methods to compute the Evans function for spectral problems as arise when determining the linear stability of travelling wave solutions to reaction-diffusion and related partial differential equations. In a typical application scenario, we need to repeatedly sample the solution to a system of linear non-autonomous ordinary differential equations for different values of one or more parameters as we detect and locate the zeros of the Evans function in the right half of the complex plane. In this situation, a substantial portion of the computational effort—the numerical evaluation of the iterated integrals which appear in the Magnus series—can be performed independent of the parameters and hence needs to be done only once. More importantly, for any given tolerance Magnus integrators possess lower bounds on the step size which are uniform across large regions of parameter space and which can be estimated a priori. We demonstrate, analytically as well as through numerical experiment, that these features render Magnus integrators extremely robust and, depending on the regime of interest, efficient in comparison with standard ODE solvers. AMS subject classification (2000) 65F20     

User equilibria for a parallel queueing system with state dependent routing

Consider a system of two queues in parallel, one of which is a ⋅|M|1 single-server infinite capacity queue, and the other a ⋅|G ^(N)|∞ batch service queue. A stream of general arrivals choose which queue to join, after observing the current state of the system, and so as to minimize their own expected delay. We show that a unique user equilibrium (user optimal policy) exists and that it possesses various monotonicity properties, using sample path and coupling arguments. This is a very simplified model of a transportation network with a choice of private and public modes of transport. Under probabilistic routing (which is equivalent to the assumption that users have knowledge only of the mean delays on routes), the network may exhibit the Downs–Thomson paradox observed in transportation networks with expected delay increasing as the capacity of the ⋅|M|1 queue (private transport) is increased. We give examples where state-dependent routing mitigates the Downs–Thomson effect observed under probabilistic routing, and providing additional information on the state of the system to users reduces delay considerably.     

Optimal Sensor Placement Method Considering the Importance of Structural Performance Degradation for the Allowable Loadings for Damage Identification

Considering the importance of damage for the structural performance and for decreasing the identification error, this paper proposes an optimal sensor placement method based on a weighted standard deviation norm (WSDN) index. The standard deviation of the identified damage parameters is solved using the series expansion theory and probabilistic method to quantify the effect of a measurement error on damage identification. The damage estimation weight (DEW) index, which can reflect the importance of each element in the structural capabilities, is established based on a performance-damage curve. A significant DEW for a specified element indicates that the element is important for the structure and that its identification error should be small. The WSDN index is obtained from the Hadamard product of the standard deviations (SDs) and DEWs. Thus, the identification error of the entire structure is measured using the weighting coefficient. The optimal sensor placement (OSP) procedure is performed by minimizing the WSDN index. The proposed method can clearly decrease the uncertainties of the identification results for the important elements. Other OSP criteria, including the condition number, information entropy, and standard deviation norm, which aim to decrease the identification error, are discussed in this paper for comparison with the proposed method. Two numerical examples and an experiment, which pertain to the deformation performance, buckling features, and dynamic characteristics, are discussed to verify the advantages of the proposed method.     

Imperfect debugging in software reliability: A Bayesian approach

The objective of studying software reliability is to assist software engineers in understanding more of the probabilistic nature of software failures during the debugging stages and to construct reliability models. In this paper, we consider modeling of a multiplicative failure rate whose components are evolving stochastically over testing stages and discuss its Bayesian estimation. In doing so, we focus on the modeling of parameters such as the fault detection rate per fault and the number of faults. We discuss how the proposed model can account for “imperfect debugging” under certain conditions. We use actual inter-failure data to carry out inference on model parameters via Markov chain Monte Carlo methods and present additional insights from Bayesian analysis.     

The Bayesian Change Point and Variable Selection Algorithm: Application to the δ18O Proxy Record of the Plio-Pleistocene

In this article, we introduce the Bayesian change point and variable selection algorithm that uses dynamic programming recursions to draw direct samples from a very high-dimensional space in a computationally efficient manner, and apply this algorithm to a geoscience problem that concerns the Earth's history of glaciation. Strong evidence exists for at least two changes in the behavior of the Earth's glaciers over the last five million years. Around 2.7 Ma, the extent of glacial cover on the Earth increased, but the frequency of glacial melting events remained constant at 41 kyr. A more dramatic change occurred around 1 Ma. For over three decades, the “Mid-Pleistocene Transition” has been described in the geoscience literature not only by a further increase in the magnitude of glacial cover, but also as the dividing point between the 41 kyr and the 100 kyr glacial worlds. Given such striking changes in the glacial record, it is clear that a model whose parameters can change through time is essential for the analysis of these data. The Bayesian change point algorithm provides a probabilistic solution to a data segmentation problem, while the exact Bayesian inference in regression procedure performs variable selection within each regime delineated by the change points. Together, they can model a time series in which the predictor variables as well as the parameters of the model are allowed to change with time. Our algorithm allows one to simultaneously perform variable selection and change point analysis in a computationally efficient manner. Supplementary materials including MATLAB code for the Bayesian change point and variable selection algorithm and the datasets described in this article are available online or by contacting the first author.