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Sample records for local markov random

  1. Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.

    PubMed

    Song, Sanming; Si, Bailu; Herrmann, J Michael; Feng, Xisheng

    2016-05-01

    A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption. It adapts, however, the parameters in a block-by-block style with a simple Hebbian learning rule. Experiments with given label fields show that the LAE is able to converge in far less time than required for a scan. It is also possible to derive an estimate for LAE based on a Cramer–Rao bound that is similar to the classical maximum pseudolikelihood method. As a general algorithm, LAE can be used to estimate the parameters in anisotropic label fields. Furthermore, LAE is not limited to the classical Potts model and can be applied to other types of Potts models by simple label field transformations and straightforward learning rule extensions. Experimental results on image segmentations demonstrate the efficiency and generality of the LAE algorithm.

  2. Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.

    PubMed

    Song, Sanming; Si, Bailu; Herrmann, J Michael; Feng, Xisheng

    2016-05-01

    A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption. It adapts, however, the parameters in a block-by-block style with a simple Hebbian learning rule. Experiments with given label fields show that the LAE is able to converge in far less time than required for a scan. It is also possible to derive an estimate for LAE based on a Cramer–Rao bound that is similar to the classical maximum pseudolikelihood method. As a general algorithm, LAE can be used to estimate the parameters in anisotropic label fields. Furthermore, LAE is not limited to the classical Potts model and can be applied to other types of Potts models by simple label field transformations and straightforward learning rule extensions. Experimental results on image segmentations demonstrate the efficiency and generality of the LAE algorithm. PMID:27019491

  3. Markov random field surface reconstruction.

    PubMed

    Paulsen, Rasmus R; Baerentzen, Jakob Andreas; Larsen, Rasmus

    2010-01-01

    A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaptation of Markov Random Field regularization of a distance field. The Markov Random Field formulation allows us to integrate both knowledge about the type of surface we wish to reconstruct (the prior) and knowledge about data (the observation model) in an orthogonal fashion. Local models that account for both scene-specific knowledge and physical properties of the scanning device are described. Furthermore, how the optimal distance field can be computed is demonstrated using conjugate gradients, sparse Cholesky factorization, and a multiscale iterative optimization scheme. The method is demonstrated on a set of scanned human heads and, both in terms of accuracy and the ability to close holes, the proposed method is shown to have similar or superior performance when compared to current state-of-the-art algorithms.

  4. A Novel Microaneurysms Detection Method Based on Local Applying of Markov Random Field.

    PubMed

    Ganjee, Razieh; Azmi, Reza; Moghadam, Mohsen Ebrahimi

    2016-03-01

    Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential step in automatic detection of DR. In this paper, a new MAs detection method is presented. The proposed approach consists of two main steps. In the first step, the MA candidates are detected based on local applying of Markov random field model (MRF). In the second step, these candidate regions are categorized to identify the correct MAs using 23 features based on shape, intensity and Gaussian distribution of MAs intensity. The proposed method is evaluated on DIARETDB1 which is a standard and publicly available database in this field. Evaluation of the proposed method on this database resulted in the average sensitivity of 0.82 for a confidence level of 75 as a ground truth. The results show that our method is able to detect the low contrast MAs with the background while its performance is still comparable to other state of the art approaches.

  5. Markov speckle for efficient random bit generation.

    PubMed

    Horstmeyer, Roarke; Chen, Richard Y; Judkewitz, Benjamin; Yang, Changhuei

    2012-11-19

    Optical speckle is commonly observed in measurements using coherent radiation. While lacking experimental validation, previous work has often assumed that speckle's random spatial pattern follows a Markov process. Here, we present a derivation and experimental confirmation of conditions under which this assumption holds true. We demonstrate that a detected speckle field can be designed to obey the first-order Markov property by using a Cauchy attenuation mask to modulate scattered light. Creating Markov speckle enables the development of more accurate and efficient image post-processing algorithms, with applications including improved de-noising, segmentation and super-resolution. To show its versatility, we use the Cauchy mask to maximize the entropy of a detected speckle field with fixed average speckle size, allowing cryptographic applications to extract a maximum number of useful random bits from speckle images.

  6. Modeling stereopsis via Markov random field.

    PubMed

    Ming, Yansheng; Hu, Zhanyi

    2010-08-01

    Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity of two regions and a potential function that models the continuity constraint. In our model, the likelihood function is constructed on the basis of the disparity energy model because complex cells are considered as front-end disparity encoders in the visual pathway. Our likelihood function is also relevant to several psychological findings. The potential function in our model is constrained by the psychological finding that the strength of the cooperative interaction minimizing relative disparity decreases as the separation between stimuli increases. Our model is tested on three kinds of stereo images. In simulations on images with repetitive patterns, we demonstrate that our model could account for the human depth percepts that were previously explained by the second-order mechanism. In simulations on random dot stereograms and natural scene images, we demonstrate that false matches introduced by the disparity energy model can be reliably removed using our model. A comparison with the coarse-to-fine model shows that our model is able to compute the absolute disparity of small objects with larger relative disparity. We also relate our model to several physiological findings. The hypothesized neurons of the model are selective for absolute disparity and have facilitative extra receptive field. There are plenty of such neurons in the visual cortex. In conclusion, we think that stereopsis can be implemented by neural networks resembling MRF.

  7. The infinite hidden Markov random field model.

    PubMed

    Chatzis, Sotirios P; Tsechpenakis, Gabriel

    2010-06-01

    Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters derived by the image segmentation procedure. Existing methods, including likelihood- or entropy-based criteria, and reversible Markov chain Monte Carlo methods, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (DP, infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori; infinite mixture models based on the original DP or spatially constrained variants of it have been applied in unsupervised image segmentation applications showing promising results. Under this motivation, to resolve the aforementioned issues of HMRF models, in this paper, we introduce a nonparametric Bayesian formulation for the HMRF model, the infinite HMRF model, formulated on the basis of a joint Dirichlet process mixture (DPM) and Markov random field (MRF) construction. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally demonstrate its advantages over competing methodologies.

  8. Learning Heterogeneous Hidden Markov Random Fields

    PubMed Central

    Liu, Jie; Zhang, Chunming; Burnside, Elizabeth; Page, David

    2014-01-01

    Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study. PMID:25404989

  9. Defect Detection Using Hidden Markov Random Fields

    SciTech Connect

    Dogandzic, Aleksandar; Eua-anant, Nawanat; Zhang Benhong

    2005-04-09

    We derive an approximate maximum a posteriori (MAP) method for detecting NDE defect signals using hidden Markov random fields (HMRFs). In the proposed HMRF framework, a set of spatially distributed NDE measurements is assumed to form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. Here, the random field describes the defect signals to be estimated or detected. The HMRF models incorporate measurement locations into the statistical analysis, which is important in scenarios where the same defect affects measurements at multiple locations. We also discuss initialization of the proposed HMRF detector and apply to simulated eddy-current data and experimental ultrasonic C-scan data from an inspection of a cylindrical Ti 6-4 billet.

  10. Unmixing hyperspectral images using Markov random fields

    SciTech Connect

    Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves

    2011-03-14

    This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.

  11. Computation of image spatial entropy using quadrilateral Markov random field.

    PubMed

    Razlighi, Qolamreza R; Kehtarnavaz, Nasser; Nosratinia, Aria

    2009-12-01

    Shannon entropy is a powerful tool in image analysis, but its reliable computation from image data faces an inherent dimensionality problem that calls for a low-dimensional and closed form model for the pixel value distributions. The most promising such models are Markovian, however, the conventional Markov random field is hampered by noncausality and its causal versions are also not free of difficulties. For example, the Markov mesh random field has its own limitations due to the strong diagonal dependency in its local neighboring system. A new model, named quadrilateral Markov random field (QMRF) is introduced in this paper in order to overcome these limitations. A property of QMRF with neighboring size of 2 is then used to decompose an image prior into a product of 2-D joint pdfs in which they are estimated using a joint histogram under the homogeneity assumption. In addition, the paper includes an extension of the introduced method to the computation of image spatial mutual information. Comparisons on synthesized images as well as two applications with real images are presented to motivate the developments in this paper and demonstrate the advantages in the performance of the introduced method over the existing ones.

  12. Multiple testing for neuroimaging via hidden Markov random field.

    PubMed

    Shu, Hai; Nan, Bin; Koeppe, Robert

    2015-09-01

    Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three-dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation-maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer's or another dementia, and normal controls in the FDG-PET imaging study of the Alzheimer's Disease Neuroimaging Initiative.

  13. Multiple testing for neuroimaging via hidden Markov random field.

    PubMed

    Shu, Hai; Nan, Bin; Koeppe, Robert

    2015-09-01

    Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, which aims to minimize the false nondiscovery rate subject to a constraint on the false discovery rate, to three-dimensional neuroimaging data using a hidden Markov random field model. A generalized expectation-maximization algorithm for maximizing the penalized likelihood is proposed for estimating the model parameters. Extensive simulations show that the proposed approach is more powerful than conventional false discovery rate procedures. We apply the method to the comparison between mild cognitive impairment, a disease status with increased risk of developing Alzheimer's or another dementia, and normal controls in the FDG-PET imaging study of the Alzheimer's Disease Neuroimaging Initiative. PMID:26012881

  14. Fusion moves for Markov random field optimization.

    PubMed

    Lempitsky, Victor; Rother, Carsten; Roth, Stefan; Blake, Andrew

    2010-08-01

    The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation).

  15. Leukocytes segmentation using Markov random fields.

    PubMed

    Reta, C; Gonzalez, J A; Diaz, R; Guichard, J S

    2011-01-01

    The segmentation of leukocytes and their components plays an important role in the extraction of geometric, texture, and morphological characteristics used to diagnose different diseases. This paper presents a novel method to segment leukocytes and their respective nucleus and cytoplasm from microscopic bone marrow leukemia cell images. Our method uses color and texture contextual information of image pixels to extract cellular elements from images, which show heterogeneous color and texture staining and high-cell population. The CIEL ( ∗ ) a ( ∗ ) b ( ∗ ) color space is used to extract color features, whereas a 2D Wold Decomposition model is applied to extract structural and stochastic texture features. The color and texture contextual information is incorporated into an unsupervised binary Markov Random Field segmentation model. Experimental results show the performance of the proposed method on both synthetic and real leukemia cell images. An average accuracy of 95% was achieved in the segmentation of real cell images by comparing those results with manually segmented cell images.

  16. Markov random-field-based anomaly screening algorithm

    NASA Astrophysics Data System (ADS)

    Bello, Martin G.

    1995-06-01

    A novel anomaly screening algorithm is described which makes use of a regression diagnostic associated with the fitting of Markov Random Field (MRF) models. This regression diagnostic quantifies the extent to which a given neighborhood of pixels is atypical, relative to local background characteristics. The screening algorithm consists first in the calculation of an MRF-based anomoly statistic values. Next, 'blob' features, such as pixel count and maximal pixel intensity are calculated, and ranked over the image, in order to 'filter' the blobs to some final subset of most likely candidates. Receiver operating characteristics obtained from applying the above described screening algorithm to the detection of minelike targets in high- and low-frequency side-scan sonar imagery are presented together with results obtained from other screening algorithms for comparison, demonstrating performance comparable to trained human operators. In addition, real-time implementation considerations associated with each algorithmic component of the described procedure are identified.

  17. Computationally tractable stochastic image modeling based on symmetric Markov mesh random fields.

    PubMed

    Yousefi, Siamak; Kehtarnavaz, Nasser; Cao, Yan

    2013-06-01

    In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced image model comprises the product of several local 1D density and 2D joint density functions of pixels in an image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for image modeling purposes compared to the conventional Markov random field models.

  18. Measuring marine oil spill extent by Markov Random Fields

    NASA Astrophysics Data System (ADS)

    Moctezuma, Miguel; Parmiggiani, Flavio; Lopez Lopez, Ludwin

    2014-10-01

    The Deepwater Horizon oil spill of the Gulf of Mexico in the spring of 2010 was the largest accidental marine oil spill in the history of the petroleum industry. An immediate request, after the accident, was to detect the oil slick and to measure its extent: SAR images were the obvious tool to be employed for the task. This paper presents a processing scheme based on Markov Random Fields (MRF) theory. MRF theory describes the global information by probability terms involving local neighborhood representations of the SAR backscatter data. The random degradation introduced by speckle noise is dealt with a pre-processing stage which applies a nonlinear diffusion filter. Spatial context attributes are structured by the Bayes equation derived from a Maximum-A-Posteriori (MAP) estimation. The probability terms define an objective function of a MRF model whose goal is to detect contours and fine structures. The markovian segmentation problem is solved with a numerical optimization method. The scheme was applied to an Envisat/ASAR image over the Gulf of Mexico of May 9, 2010, when the oil spill was already fully developed. The final result was obtained with 51 recursion cycles, where, at each step, the segmentation consists of a 3-class label field (open sea and two oil slick thicknesses). Both the MRF model and the parameters of the stochastic optimization procedure will be provided, together with the area measurement of the two kinds of oil slick.

  19. Utilizing Gaussian Markov random field properties of Bayesian animal models.

    PubMed

    Steinsland, Ingelin; Jensen, Henrik

    2010-09-01

    In this article, we demonstrate how Gaussian Markov random field properties give large computational benefits and new opportunities for the Bayesian animal model. We make inference by computing the posteriors for important quantitative genetic variables. For the single-trait animal model, a nonsampling-based approximation is presented. For the multitrait model, we set up a robust and fast Markov chain Monte Carlo algorithm. The proposed methodology was used to analyze quantitative genetic properties of morphological traits of a wild house sparrow population. Results for single- and multitrait models were compared.

  20. A Markov Random Field Groupwise Registration Framework for Face Recognition.

    PubMed

    Liao, Shu; Shen, Dinggang; Chung, Albert C S

    2014-04-01

    In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.

  1. A Markov Random Field Groupwise Registration Framework for Face Recognition

    PubMed Central

    Liao, Shu; Shen, Dinggang; Chung, Albert C.S.

    2014-01-01

    In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison. PMID:25506109

  2. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

  3. Fuzzy Markov random fields versus chains for multispectral image segmentation.

    PubMed

    Salzenstein, Fabien; Collet, Christophe

    2006-11-01

    This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data. PMID:17063681

  4. Markov-random-field modeling for linear seismic tomography.

    PubMed

    Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro

    2014-10-01

    We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.

  5. Markov-random-field modeling for linear seismic tomography

    NASA Astrophysics Data System (ADS)

    Kuwatani, Tatsu; Nagata, Kenji; Okada, Masato; Toriumi, Mitsuhiro

    2014-10-01

    We apply the Markov-random-field model to linear seismic tomography and propose a method to estimate the hyperparameters for the smoothness and the magnitude of the noise. Optimal hyperparameters can be determined analytically by minimizing the free energy function, which is defined by marginalizing the evaluation function. In synthetic inversion tests under various settings, the assumed velocity structures are successfully reconstructed, which shows the effectiveness and robustness of the proposed method. The proposed mathematical framework can be applied to inversion problems in various fields in the natural sciences.

  6. Markov random fields for static foreground classification in surveillance systems

    NASA Astrophysics Data System (ADS)

    Fitzsimons, Jack K.; Lu, Thomas T.

    2014-09-01

    We present a novel technique for classifying static foreground in automated airport surveillance systems between abandoned and removed objects by representing the image as a Markov Random Field. The proposed algorithm computes and compares the net probability of the region of interest before and after the event occurs, hence finding which fits more naturally with their respective backgrounds. Having tested on a dataset from the PETS 2006, PETS 2007, AVSS20074, CVSG, VISOR, CANDELA and WCAM datasets, the algorithm has shown capable of matching the results of the state-of-the-art, is highly parallel and has a degree of robustness to noise and illumination changes.

  7. Sub-Markov Random Walk for Image Segmentation.

    PubMed

    Dong, Xingping; Shen, Jianbing; Shao, Ling; Van Gool, Luc

    2016-02-01

    A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.

  8. Submodular Relaxation for Inference in Markov Random Fields.

    PubMed

    Osokin, Anton; Vetrov, Dmitry P

    2015-07-01

    In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is known to be NP-hard in general and its practical importance motivates numerous approximate algorithms. We propose a submodular relaxation approach (SMR) based on a Lagrangian relaxation of the initial problem. Unlike the dual decomposition approach of Komodakis et al. [29] SMR does not decompose the graph structure of the initial problem but constructs a submodular energy that is minimized within the Lagrangian relaxation. Our approach is applicable to both pairwise and high-order MRFs and allows to take into account global potentials of certain types. We study theoretical properties of the proposed approach and evaluate it experimentally.

  9. A Markov random field approach for microstructure synthesis

    NASA Astrophysics Data System (ADS)

    Kumar, A.; Nguyen, L.; DeGraef, M.; Sundararaghavan, V.

    2016-03-01

    We test the notion that many microstructures have an underlying stationary probability distribution. The stationary probability distribution is ubiquitous: we know that different windows taken from a polycrystalline microstructure are generally ‘statistically similar’. To enable computation of such a probability distribution, microstructures are represented in the form of undirected probabilistic graphs called Markov Random Fields (MRFs). In the model, pixels take up integer or vector states and interact with multiple neighbors over a window. Using this lattice structure, algorithms are developed to sample the conditional probability density for the state of each pixel given the known states of its neighboring pixels. The sampling is performed using reference experimental images. 2D microstructures are artificially synthesized using the sampled probabilities. Statistical features such as grain size distribution and autocorrelation functions closely match with those of the experimental images. The mechanical properties of the synthesized microstructures were computed using the finite element method and were also found to match the experimental values.

  10. Phase unwrapping using region-based markov random field model.

    PubMed

    Dong, Ying; Ji, Jim

    2010-01-01

    Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method.

  11. Causal Markov random field for brain MR image segmentation.

    PubMed

    Razlighi, Qolamreza R; Orekhov, Aleksey; Laine, Andrew; Stern, Yaakov

    2012-01-01

    We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique distributions existing in its neighboring structure. 20 manually labeled human brain MR images are used to train and assess the MAP-QMRF classifier using the jackknife validation method. Comparing the results of the proposed classifier and FreeSurfer on the Dice overlap measure shows an average gain of 1.8%. We have performed a power analysis to demonstrate that this increase in segmentation accuracy substantially reduces the number of samples required to detect a 5% change in volume of a brain region.

  12. CAUSAL MARKOV RANDOM FIELD FOR BRAIN MR IMAGE SEGMENTATION

    PubMed Central

    Razlighi, Qolamreza R.; Orekhov, Aleksey; Laine, Andrew; Stern, Yaakov

    2013-01-01

    We propose a new Bayesian classifier, based on the recently introduced causal Markov random field (MRF) model, Quadrilateral MRF (QMRF). We use a second order inhomogeneous anisotropic QMRF to model the prior and likelihood probabilities in the maximum a posteriori (MAP) classifier, named here as MAP-QMRF. The joint distribution of QMRF is given in terms of the product of two dimensional clique distributions existing in its neighboring structure. 20 manually labeled human brain MR images are used to train and assess the MAP-QMRF classifier using the jackknife validation method. Comparing the results of the proposed classifier and FreeSurfer on the Dice overlap measure shows an average gain of 1.8%. We have performed a power analysis to demonstrate that this increase in segmentation accuracy substantially reduces the number of samples required to detect a 5% change in volume of a brain region. PMID:23366607

  13. Phase unwrapping using region-based markov random field model.

    PubMed

    Dong, Ying; Ji, Jim

    2010-01-01

    Phase unwrapping is a classical problem in Magnetic Resonance Imaging (MRI), Interferometric Synthetic Aperture Radar and Sonar (InSAR/InSAS), fringe pattern analysis, and spectroscopy. Although many methods have been proposed to address this problem, robust and effective phase unwrapping remains a challenge. This paper presents a novel phase unwrapping method using a region-based Markov Random Field (MRF) model. Specifically, the phase image is segmented into regions within which the phase is not wrapped. Then, the phase image is unwrapped between different regions using an improved Highest Confidence First (HCF) algorithm to optimize the MRF model. The proposed method has desirable theoretical properties as well as an efficient implementation. Simulations and experimental results on MRI images show that the proposed method provides similar or improved phase unwrapping than Phase Unwrapping MAx-flow/min-cut (PUMA) method and ZpM method. PMID:21096819

  14. Spatial Markov model of anomalous transport through random lattice networks.

    PubMed

    Kang, Peter K; Dentz, Marco; Le Borgne, Tanguy; Juanes, Ruben

    2011-10-28

    Flow through lattice networks with quenched disorder exhibits a strong correlation in the velocity field, even if the link transmissivities are uncorrelated. This feature, which is a consequence of the divergence-free constraint, induces anomalous transport of passive particles carried by the flow. We propose a Lagrangian statistical model that takes the form of a continuous time random walk with correlated velocities derived from a genuinely multidimensional Markov process in space. The model captures the anomalous (non-Fickian) longitudinal and transverse spreading, and the tail of the mean first-passage time observed in the Monte Carlo simulations of particle transport. We show that reproducing these fundamental aspects of transport in disordered systems requires honoring the correlation in the Lagrangian velocity.

  15. INSTRUCTIONAL CONFERENCE ON THE THEORY OF STOCHASTIC PROCESSES: Controlled random sequences and Markov chains

    NASA Astrophysics Data System (ADS)

    Yushkevich, A. A.; Chitashvili, R. Ya

    1982-12-01

    CONTENTSIntroduction Chapter I. Foundations of the general theory of controlled random sequences and Markov chains with the expected reward criterion § 1. Controlled random sequences, Markov chains, and models § 2. Necessary and sufficient conditions for optimality § 3. The Bellman equation for the value function and the existence of (ε-) optimal strategies Chapter II. Some problems in the theory of controlled homogeneous Markov chains § 4. Description of the solutions of the Bellman equation, a characterization of the value function, and the Bellman operator § 5. Sufficiency of stationary strategies in homogeneous Markov models § 6. The lexicographic Bellman equation References

  16. MRFalign: protein homology detection through alignment of Markov random fields.

    PubMed

    Ma, Jianzhu; Wang, Sheng; Wang, Zhiyong; Xu, Jinbo

    2014-03-01

    Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5. PMID:24675572

  17. Learning dynamic hybrid Markov random field for image labeling.

    PubMed

    Zhou, Quan; Zhu, Jun; Liu, Wenyu

    2013-06-01

    Using shape information has gained increasing concerns in the task of image labeling. In this paper, we present a dynamic hybrid Markov random field (DHMRF), which explicitly captures middle-level object shape and low-level visual appearance (e.g., texture and color) for image labeling. Each node in DHMRF is described by either a deformable template or an appearance model as visual prototype. On the other hand, the edges encode two types of intersections: co-occurrence and spatial layered context, with respect to the labels and prototypes of connected nodes. To learn the DHMRF model, an iterative algorithm is designed to automatically select the most informative features and estimate model parameters. The algorithm achieves high computational efficiency since a branch-and-bound schema is introduced to estimate model parameters. Compared with previous methods, which usually employ implicit shape cues, our DHMRF model seamlessly integrates color, texture, and shape cues to inference labeling output, and thus produces more accurate and reliable results. Extensive experiments validate its superiority over other state-of-the-art methods in terms of recognition accuracy and implementation efficiency on: 1) the MSRC 21-class dataset, and 2) the lotus hill institute 15-class dataset.

  18. Glaucoma progression detection using nonlocal Markov random field prior.

    PubMed

    Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A; Balasubramanian, Madhusudhanan; Weinreb, Robert N; Zangwill, Linda M

    2014-10-01

    Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.

  19. An invariance principle for reversible Markov processes. Applications to random motions in random environments

    SciTech Connect

    De Masi, A.; Ferrari, P.A.; Goldstein, S.; Wick, W.D. )

    1989-05-01

    The authors present an invariance principle for antisymmetric functions of a reversible Markov process which immediately implies convergence to Brownian motion for a wide class of random motions in random environments. They apply it to establish convergence to Brownian motion (i) for a walker moving in the infinite cluster of the two-dimensional bond percolation model, (ii) for a d-dimensional walker moving in a symmetric random environment under very mild assumptions on the distribution of the environment, (iii) for a tagged particle in a d-dimensional symmetric lattice gas which allows interchanges, (iv) for a tagged particle in a d-dimensional system of interacting Brownian particles. Their formulation also leads naturally to bounds on the diffusion constant.

  20. Comparing quantum versus Markov random walk models of judgements measured by rating scales

    PubMed Central

    Wang, Z.; Busemeyer, J. R.

    2016-01-01

    Quantum and Markov random walk models are proposed for describing how people evaluate stimuli using rating scales. To empirically test these competing models, we conducted an experiment in which participants judged the effectiveness of public health service announcements from either their own personal perspective or from the perspective of another person. The order of the self versus other judgements was manipulated, which produced significant sequential effects. The quantum and Markov models were fitted to the data using the same number of parameters, and the model comparison strongly supported the quantum over the Markov model. PMID:26621984

  1. Spatio-temporal contextual classification based on Markov random field model. [for thematic mapping

    NASA Technical Reports Server (NTRS)

    Jeon, Byeungwoo; Landgrebe, D. A.

    1991-01-01

    A contextural classifier based on a Markov random field model, which can utilize both spatial and temporal contexts, is investigated. Spatial and temporal neighbors are defined, and the class assignment of each pixel is assumed to be dependent only on the measurement vectors of itself and those of its spatial and temporal neighbors according to the Markov random field property. Only interpixel class dependency context is used in the classification. The joint prior probability of the classes of each pixel and its spatial and temporal neighbors are modeled by a Gibbs random field. The classification is performed in a recursive manner. Experiments with multi-temporal Thematic Mapper data show promising results.

  2. Automatic prone to supine haustral fold matching in CT colonography using a Markov random field model.

    PubMed

    Hampshire, Thomas; Roth, Holger; Hu, Mingxing; Boone, Darren; Slabaugh, Greg; Punwani, Shonit; Halligan, Steve; Hawkes, David

    2011-01-01

    CT colonography is routinely performed with the patient prone and supine to differentiate fixed colonic pathology from mobile faecal residue. We propose a novel method to automatically establish correspondence. Haustral folds are detected using a graph cut method applied to a surface curvature-based metric, where image patches are generated using endoluminal CT colonography surface rendering. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints, are used with a Markov Random Field (MRF) model to estimate the fold labelling assignment. The method achieved fold matching accuracy of 83.1% and 88.5% with and without local colonic collapse. Moreover, it improves an existing surface-based registration algorithm, decreasing mean registration error from 9.7mm to 7.7mm in cases exhibiting collapse.

  3. Classification method for disease risk mapping based on discrete hidden Markov random fields.

    PubMed

    Charras-Garrido, Myriam; Abrial, David; Goër, Jocelyn De; Dachian, Sergueï; Peyrard, Nathalie

    2012-04-01

    Risk mapping in epidemiology enables areas with a low or high risk of disease contamination to be localized and provides a measure of risk differences between these regions. Risk mapping models for pooled data currently used by epidemiologists focus on the estimated risk for each geographical unit. They are based on a Poisson log-linear mixed model with a latent intrinsic continuous hidden Markov random field (HMRF) generally corresponding to a Gaussian autoregressive spatial smoothing. Risk classification, which is necessary to draw clearly delimited risk zones (in which protection measures may be applied), generally must be performed separately. We propose a method for direct classified risk mapping based on a Poisson log-linear mixed model with a latent discrete HMRF. The discrete hidden field (HF) corresponds to the assignment of each spatial unit to a risk class. The risk values attached to the classes are parameters and are estimated. When mapping risk using HMRFs, the conditional distribution of the observed field is modeled with a Poisson rather than a Gaussian distribution as in image segmentation. Moreover, abrupt changes in risk levels are rare in disease maps. The spatial hidden model should favor smoothed out risks, but conventional discrete Markov random fields (e.g. the Potts model) do not impose this. We therefore propose new potential functions for the HF that take into account class ordering. We use a Monte Carlo version of the expectation-maximization algorithm to estimate parameters and determine risk classes. We illustrate the method's behavior on simulated and real data sets. Our method appears particularly well adapted to localize high-risk regions and estimate the corresponding risk levels.

  4. Entropy, complexity, and Markov diagrams for random walk cancer models.

    PubMed

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

    2014-12-19

    The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

  5. Entropy, complexity, and Markov diagrams for random walk cancer models

    NASA Astrophysics Data System (ADS)

    Newton, Paul K.; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter

    2014-12-01

    The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.

  6. Maximum a posteriori blind image deconvolution with Huber-Markov random-field regularization.

    PubMed

    Xu, Zhimin; Lam, Edmund Y

    2009-05-01

    We propose a maximum a posteriori blind deconvolution approach using a Huber-Markov random-field model. Compared with the conventional maximum-likelihood method, our algorithm not only suppresses noise effectively but also significantly alleviates the artifacts produced by the deconvolution process. The performance of this method is demonstrated by computer simulations.

  7. Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach

    NASA Astrophysics Data System (ADS)

    Albora, A. Muhittin; Ucan, Osman N.; Aydogan, Davut

    2007-05-01

    In this study, a Markov Random Field (MRF) approach is used to locate source boundary positions which are difficult to identify from Bouguer gravity and magnetic maps. As a generalized form of Markov Chains, the MRF approach is an unsupervised statistical model based algorithm and is applied to the analysis of images, particularly in the detection of visual patterns or textures. Here, we present a dynamic programming based on the MRF approach for boundary detection of noisy and super-positioned potential anomalies, which are produced by various geological structures. In the MRF method, gravity and magnetic maps are considered as two-dimensional (2-D) images with a matrix composed of N 1 × N 2 pixels. Each pixel value of the matrix is optimized in real time with no a priori processing by using two parameter sets; average steering vector (θ) and quantization level (M). They carry information about the correlation of neighboring pixels and the locality of their connections. We have chosen MRF as a processing approach for geophysical data since it is an unsupervised, efficient model for image enhancement, border detection and separation of 2-D potential anomalies. The main benefit of MRF is that an average steering vector and a quantization level are enough in evaluation of the potential anomaly maps. We have compared the MRF method to noise implemented synthetic potential field anomalies. After satisfactory results were found, the method has been applied to gravity and magnetic anomaly maps of Gelibolu Peninsula in Western Turkey. Here, we have observed Anafartalar thrust fault and another parallel fault northwest of Anafartalar thrust fault. We have modeled a geological structure including a lateral fault, which results in a higher susceptibility and anomaly amplitude increment. We have shown that the MRF method is effective to detect the broad-scale geological structures in the Gelibolu Peninsula, and thus to delineate the complex tectonic structure of Gelibolu

  8. Markov random field model-based edge-directed image interpolation.

    PubMed

    Li, Min; Nguyen, Truong Q

    2008-07-01

    This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.

  9. Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models.

    PubMed

    Wang, Hongyan; Zhou, Xiaobo

    2013-04-01

    By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice.

  10. Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models.

    PubMed

    Wang, Hongyan; Zhou, Xiaobo

    2013-04-01

    By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice. PMID:23237214

  11. The application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields.

    PubMed

    Zhang, J

    1996-01-01

    The Gibbs-Bogoliubov-Feynman (GBF) inequality of statistical mechanics is adopted, with an information-theoretic interpretation, as a general optimization framework for deriving and examining various mean field approximations for Markov random fields (MRF's). The efficacy of this approach is demonstrated through the compound Gauss-Markov (CGM) model, comparisons between different mean field approximations, and experimental results in image restoration.

  12. Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class.

    PubMed

    Liu, Xin; Langer, Deanna L; Haider, Masoom A; Yang, Yongyi; Wernick, Miles N; Yetik, Imam Samil

    2009-06-01

    Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a tissue of interest, some researchers are exploring the uses of multispectral MRI to guide prostate biopsies and radiation therapy. However, success with prostate cancer localization based on current imaging methods has been limited due to overlap in feature space of benign and malignant tissues using any one MRI method and the interobserver variability. In this paper, we present a new unsupervised segmentation method for prostate cancer detection, using fuzzy Markov random fields (fuzzy MRFs) for the segmentation of multispectral MR prostate images. Typically, both hard and fuzzy MRF models have two groups of parameters to be estimated: the MRF parameters and class parameters for each pixel in the image. To date, these two parameters have been treated separately, and estimated in an alternating fashion. In this paper, we develop a new method to estimate the parameters defining the Markovian distribution of the measured data, while performing the data clustering simultaneously. We perform computer simulations on synthetic test images and multispectral MR prostate datasets to demonstrate the efficacy and efficiency of the proposed method and also provide a comparison with some of the commonly used methods.

  13. Synthesis of cervical tissue second harmonic generation images using Markov random field modeling.

    PubMed

    Yousefi, S; Kehtarnavaz, N; Gholipour, A

    2011-01-01

    This paper presents a statistical image modeling approach based on Markov random field to synthesize cervical tissue second harmonic generation (SHG) images. Binary images representing fiber and pore areas of the cervix tissue are first obtained from SHG images using an image processing pipeline consisting of noise removal, contrast enhancement and optimal thresholding. These binary images are modeled using a Markov random field whose parameters are estimated via the least squares method. The parameters are then used to synthesize fiber and pore areas of cervical tissue in the form of binary images. The effectiveness of the synthesis is demonstrated by reporting the classification outcome for two classes of cervical SHG images collected from mice at two different stages of normal pregnancy. The developed synthesis allows generation of realistic fiber and pore area binary images for cervical tissue studies.

  14. On the convergence of EM-like algorithms for image segmentation using Markov random fields.

    PubMed

    Roche, Alexis; Ribes, Delphine; Bach-Cuadra, Meritxell; Krüger, Gunnar

    2011-12-01

    Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

  15. Lossy cutset coding of bilevel images based on Markov random fields.

    PubMed

    Reyes, Matthew G; Neuhoff, David L; Pappas, Thrasyvoulos N

    2014-04-01

    An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.

  16. Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximum a posteriori model.

    PubMed

    Ge, Mei; Mainprize, James G; Mawdsley, Gordon E; Yaffe, Martin J

    2014-10-01

    Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semielliptic-shaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either "For Processing" or "For Presentation" image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM "For Processing" images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types.

  17. Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximum a posteriori model

    PubMed Central

    Ge, Mei; Mainprize, James G.; Mawdsley, Gordon E.; Yaffe, Martin J.

    2014-01-01

    Abstract. Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semielliptic-shaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either “For Processing” or “For Presentation” image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM “For Processing” images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types. PMID:26158068

  18. Local leaders in random networks

    NASA Astrophysics Data System (ADS)

    Blondel, Vincent D.; Guillaume, Jean-Loup; Hendrickx, Julien M.; de Kerchove, Cristobald; Lambiotte, Renaud

    2008-03-01

    We consider local leaders in random uncorrelated networks, i.e., nodes whose degree is higher than or equal to the degree of all their neighbors. An analytical expression is found for the probability for a node of degree k to be a local leader. This quantity is shown to exhibit a transition from a situation where high-degree nodes are local leaders to a situation where they are not, when the tail of the degree distribution behaves like the power law ˜k-γc with γc=3 . Theoretical results are verified by computer simulations, and the importance of finite-size effects is discussed.

  19. A Hypergraph-Based Reduction for Higher-Order Binary Markov Random Fields.

    PubMed

    Fix, Alexander; Gruber, Aritanan; Boros, Endre; Zabih, Ramin

    2015-07-01

    Higher-order Markov Random Fields, which can capture important properties of natural images, have become increasingly important in computer vision. While graph cuts work well for first-order MRF's, until recently they have rarely been effective for higher-order MRF's. Ishikawa's graph cut technique [1], [2] shows great promise for many higher-order MRF's. His method transforms an arbitrary higher-order MRF with binary labels into a first-order one with the same minima. If all the terms are submodular the exact solution can be easily found; otherwise, pseudoboolean optimization techniques can produce an optimal labeling for a subset of the variables. We present a new transformation with better performance than [1], [2], both theoretically and experimentally. While [1], [2] transforms each higher-order term independently, we use the underlying hypergraph structure of the MRF to transform a group of terms at once. For n binary variables, each of which appears in terms with k other variables, at worst we produce n non-submodular terms, while [1], [2] produces O(nk). We identify a local completeness property under which our method perform even better, and show that under certain assumptions several important vision problems (including common variants of fusion moves) have this property. We show experimentally that our method produces smaller weight of non-submodular edges, and that this metric is directly related to the effectiveness of QPBO [3]. Running on the same field of experts dataset used in [1], [2] we optimally label significantly more variables (96 versus 80 percent) and converge more rapidly to a lower energy. Preliminary experiments suggest that some other higher-order MRF's used in stereo [4] and segmentation [5] are also locally complete and would thus benefit from our work.

  20. Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics

    PubMed Central

    François, Olivier; Ancelet, Sophie; Guillot, Gilles

    2006-01-01

    We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set. PMID:16888334

  1. A hidden Markov random field model for genome-wide association studies.

    PubMed

    Li, Hongzhe; Wei, Zhi; Maris, John

    2010-01-01

    Genome-wide association studies (GWAS) are increasingly utilized for identifying novel susceptible genetic variants for complex traits, but there is little consensus on analysis methods for such data. Most commonly used methods include single single nucleotide polymorphism (SNP) analysis or haplotype analysis with Bonferroni correction for multiple comparisons. Since the SNPs in typical GWAS are often in linkage disequilibrium (LD), at least locally, Bonferroni correction of multiple comparisons often leads to conservative error control and therefore lower statistical power. In this paper, we propose a hidden Markov random field model (HMRF) for GWAS analysis based on a weighted LD graph built from the prior LD information among the SNPs and an efficient iterative conditional mode algorithm for estimating the model parameters. This model effectively utilizes the LD information in calculating the posterior probability that an SNP is associated with the disease. These posterior probabilities can then be used to define a false discovery controlling procedure in order to select the disease-associated SNPs. Simulation studies demonstrated the potential gain in power over single SNP analysis. The proposed method is especially effective in identifying SNPs with borderline significance at the single-marker level that nonetheless are in high LD with significant SNPs. In addition, by simultaneously considering the SNPs in LD, the proposed method can also help to reduce the number of false identifications of disease-associated SNPs. We demonstrate the application of the proposed HMRF model using data from a case-control GWAS of neuroblastoma and identify 1 new SNP that is potentially associated with neuroblastoma.

  2. Separable Markov random field model and its applications in low level vision.

    PubMed

    Sun, Jian; Tappen, Marshall F

    2013-01-01

    This brief proposes a continuously-valued Markov random field (MRF) model with separable filter bank, denoted as MRFSepa, which significantly reduces the computational complexity in the MRF modeling. In this framework, we design a novel gradient-based discriminative learning method to learn the potential functions and separable filter banks. We learn MRFSepa models with 2-D and 3-D separable filter banks for the applications of gray-scale/color image denoising and color image demosaicing. By implementing MRFSepa model on graphics processing unit, we achieve real-time image denoising and fast image demosaicing with high-quality results.

  3. Theory of Distribution Estimation of Hyperparameters in Markov Random Field Models

    NASA Astrophysics Data System (ADS)

    Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato

    2016-06-01

    We investigated the performance of distribution estimation of hyperparameters in Markov random field models proposed by Nakanishi-Ohno et al., http://doi.org/10.1088/1751-8113/47/4/045001, J. Phys. A 47, 045001 (2014) when used to evaluate the confidence of data. We analytically calculated the configurational average, with respect to data, of the negative logarithm of the posterior distribution, which is called free energy based on an analogy with statistical mechanics. This configurational average of free energy shrinks as the amount of data increases. Our results theoretically confirm the numerical results from that previous study.

  4. Pixel partition method using Markov random field for measurements of closely spaced objects by optical sensors

    NASA Astrophysics Data System (ADS)

    Wang, Xueying; Li, Jun; Sheng, Weidong; An, Wei; Du, Qinfeng

    2015-10-01

    ABSTRACT In Space-based optical system, during the tracking for closely spaced objects (CSOs), the traditional method with a constant false alarm rate(CFAR) detecting brings either more clutter measurements or the loss of target information. CSOs can be tracked as Extended targets because their features on optical sensor's pixel-plane. A pixel partition method under the framework of Markov random field(MRF) is proposed, simulation results indicate: the method proposed provides higher pixel partition performance than traditional method, especially when the signal-noise-rate is poor.

  5. Robust phase sensitive inversion recovery imaging using a Markov random field model.

    PubMed

    Garach, Ravindra M; Ji, Jim X; Ying, Lei; Ma, Jingfei

    2004-01-01

    This paper presents a novel method for phase sensitive inversion recovery (PSIR) imaging for improved T/sub 1/ contrast. This method models the phase of the complex magnetic resonance image using a statistical model based on Markov random fields. A computationally efficient optimization method is developed. Computer simulations and in-vivo brain imaging experiments show that the proposed method can produce PSIR images with enhanced T/sub 1/ contrast and it is robust against high levels of data noise even when rapid phase variations are presented.

  6. Bayesian Markov Random Field analysis for protein function prediction based on network data.

    PubMed

    Kourmpetis, Yiannis A I; van Dijk, Aalt D J; Bink, Marco C A M; van Ham, Roeland C H J; ter Braak, Cajo J F

    2010-02-24

    Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S. cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.

  7. Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

    PubMed

    Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

    2009-09-01

    A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

  8. A New Markov Random Field Segmentation Method for Breast Lesion Segmentation in MR images.

    PubMed

    Azmi, Reza; Norozi, Narges

    2011-07-01

    Breast cancer is a major public health problem for women in the Iran and many other parts of the world. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a pivotal role in breast cancer care, including detection, diagnosis, and treatment monitoring. But segmentation of these images which is seriously affected by intensity inhomogeneities created by radio-frequency coils is a challenging task. Markov Random Field (MRF) is used widely in medical image segmentation especially in MR images. It is because this method can model intensity inhomogeneities occurring in these images. But this method has two critical weaknesses: Computational complexity and sensitivity of the results to the models parameters. To overcome these problems, in this paper, we present Improved-Markov Random Field (I-MRF) method for breast lesion segmentation in MR images. Unlike the conventional MRF, in the proposed approach, we don't use the Iterative Conditional Mode (ICM) method or Simulated Annealing (SA) for class membership estimation of each pixel (lesion and non-lesion). The prior distribution of the class membership is modeled as a ratio of two conditional probability distributions in a neighborhood which is defined for each pixel: probability distribution of similar pixels and non-similar ones. Since our proposed approach don't use an iterative method for maximizing the posterior probability, above mentioned problems are solved. Experimental results show that performance of segmentation in this approach is higher than conventional MRF in terms of accuracy, precision, and Computational complexity.

  9. Automatic segmentation of breast MR images through a Markov random field statistical model.

    PubMed

    Ribes, S; Didierlaurent, D; Decoster, N; Gonneau, E; Risser, L; Feillel, V; Caselles, O

    2014-10-01

    An algorithm dedicated to automatic segmentation of breast magnetic resonance images is presented in this paper. Our approach is based on a pipeline that includes a denoising step and statistical segmentation. The noise removal preprocessing relies on an anisotropic diffusion scheme, whereas the statistical segmentation is conducted through a Markov random field model. The continuous updating of all parameters governing the diffusion process enables automatic denoising, and the partial volume effect is also addressed during the labeling step. To assess the relevance, the Jaccard similarity coefficient was computed. Experiments were conducted on synthetic data and breast magnetic resonance images extracted from a high-risk population. The relevance of the approach for the dataset is highlighted, and we demonstrate accuracy superior to that of traditional clustering algorithms. The results emphasize the benefits of both denoising guided by input data and the inclusion of spatial dependency through a Markov random field. For example, the Jaccard coefficient for the clinical data was increased by 114%, 109%, and 140% with respect to a K-means algorithm and, respectively, for the adipose, glandular and muscle and skin components. Moreover, the agreement between the manual segmentations provided by an experienced radiologist and the automatic segmentations performed with this algorithm was good, with Jaccard coefficients equal to 0.769, 0.756, and 0.694 for the above-mentioned classes.

  10. Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

    PubMed

    Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

    2009-09-01

    A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms. PMID:19446435

  11. Single-image super-resolution based on Markov random field and contourlet transform

    NASA Astrophysics Data System (ADS)

    Wu, Wei; Liu, Zheng; Gueaieb, Wail; He, Xiaohai

    2011-04-01

    Learning-based methods are well adopted in image super-resolution. In this paper, we propose a new learning-based approach using contourlet transform and Markov random field. The proposed algorithm employs contourlet transform rather than the conventional wavelet to represent image features and takes into account the correlation between adjacent pixels or image patches through the Markov random field (MRF) model. The input low-resolution (LR) image is decomposed with the contourlet transform and fed to the MRF model together with the contourlet transform coefficients from the low- and high-resolution image pairs in the training set. The unknown high-frequency components/coefficients for the input low-resolution image are inferred by a belief propagation algorithm. Finally, the inverse contourlet transform converts the LR input and the inferred high-frequency coefficients into the super-resolved image. The effectiveness of the proposed method is demonstrated with the experiments on facial, vehicle plate, and real scene images. A better visual quality is achieved in terms of peak signal to noise ratio and the image structural similarity measurement.

  12. Investigation of Laplace transforms for Erlangen distribution of the first passage of zero level of the semi-Markov random process with positive tendency and negative jump

    NASA Astrophysics Data System (ADS)

    Maden, Selahattin; Nasirova, Tamilla I.; Karimova, Ulviyya Y.

    2016-08-01

    Real physics and some technical issues describing with semi-Markov random process. We would like to describe one type of semi-Markov random process in introducing paper. The first passage of the zero level of this process will be included as a random variable. The parameters of the distribution will be calculated on the basis of the final results.

  13. Entropy and long-range memory in random symbolic additive Markov chains.

    PubMed

    Melnik, S S; Usatenko, O V

    2016-06-01

    The goal of this paper is to develop an estimate for the entropy of random symbolic sequences with elements belonging to a finite alphabet. As a plausible model, we use the high-order additive stationary ergodic Markov chain with long-range memory. Supposing that the correlations between random elements of the chain are weak, we express the conditional entropy of the sequence by means of the symbolic pair correlation function. We also examine an algorithm for estimating the conditional entropy of finite symbolic sequences. We show that the entropy contains two contributions, i.e., the correlation and the fluctuation. The obtained analytical results are used for numerical evaluation of the entropy of written English texts and DNA nucleotide sequences. The developed theory opens the way for constructing a more consistent and sophisticated approach to describe the systems with strong short-range and weak long-range memory. PMID:27415245

  14. Entropy and long-range memory in random symbolic additive Markov chains

    NASA Astrophysics Data System (ADS)

    Melnik, S. S.; Usatenko, O. V.

    2016-06-01

    The goal of this paper is to develop an estimate for the entropy of random symbolic sequences with elements belonging to a finite alphabet. As a plausible model, we use the high-order additive stationary ergodic Markov chain with long-range memory. Supposing that the correlations between random elements of the chain are weak, we express the conditional entropy of the sequence by means of the symbolic pair correlation function. We also examine an algorithm for estimating the conditional entropy of finite symbolic sequences. We show that the entropy contains two contributions, i.e., the correlation and the fluctuation. The obtained analytical results are used for numerical evaluation of the entropy of written English texts and DNA nucleotide sequences. The developed theory opens the way for constructing a more consistent and sophisticated approach to describe the systems with strong short-range and weak long-range memory.

  15. Joint modeling of ChIP-seq data via a Markov random field model.

    PubMed

    Bao, Yanchun; Vinciotti, Veronica; Wit, Ernst; 't Hoen, Peter A C

    2014-04-01

    Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first-order Markov dependence and, for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modeling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared with existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.

  16. Multilayer Markov Random Field models for change detection in optical remote sensing images

    NASA Astrophysics Data System (ADS)

    Benedek, Csaba; Shadaydeh, Maha; Kato, Zoltan; Szirányi, Tamás; Zerubia, Josiane

    2015-09-01

    In this paper, we give a comparative study on three Multilayer Markov Random Field (MRF) based solutions proposed for change detection in optical remote sensing images, called Multicue MRF, Conditional Mixed Markov model, and Fusion MRF. Our purposes are twofold. On one hand, we highlight the significance of the focused model family and we set them against various state-of-the-art approaches through a thematic analysis and quantitative tests. We discuss the advantages and drawbacks of class comparison vs. direct approaches, usage of training data, various targeted application fields and different ways of Ground Truth generation, meantime informing the Reader in which roles the Multilayer MRFs can be efficiently applied. On the other hand we also emphasize the differences between the three focused models at various levels, considering the model structures, feature extraction, layer interpretation, change concept definition, parameter tuning and performance. We provide qualitative and quantitative comparison results using principally a publicly available change detection database which contains aerial image pairs and Ground Truth change masks. We conclude that the discussed models are competitive against alternative state-of-the-art solutions, if one uses them as pre-processing filters in multitemporal optical image analysis. In addition, they cover together a large range of applications, considering the different usage options of the three approaches.

  17. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling

    SciTech Connect

    Vrugt, Jasper A; Hyman, James M; Robinson, Bruce A; Higdon, Dave; Ter Braak, Cajo J F; Diks, Cees G H

    2008-01-01

    Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.

  18. Class-specific weighting for Markov random field estimation: application to medical image segmentation.

    PubMed

    Monaco, James P; Madabhushi, Anant

    2012-12-01

    Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: (1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, (2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and (3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation-achieved via the incorporation of multiplicative weights into the MAP cost function-which allows certain classes to be preferred over others. This creates a natural bias for specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the MPM

  19. Class-specific weighting for Markov random field estimation: application to medical image segmentation.

    PubMed

    Monaco, James P; Madabhushi, Anant

    2012-12-01

    Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: (1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, (2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and (3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation-achieved via the incorporation of multiplicative weights into the MAP cost function-which allows certain classes to be preferred over others. This creates a natural bias for specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the MPM

  20. Class-Specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation

    PubMed Central

    Monaco, James P.; Madabhushi, Anant

    2012-01-01

    Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/Specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/Specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: 1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, 2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and 3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation — achieved via the incorporation of multiplicative weights into the MAP cost function — which allows certain classes to be preferred over others. This creates a natural bias for Specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the

  1. Variational level set combined with Markov random field modeling for simultaneous intensity non-uniformity correction and segmentation of MR images.

    PubMed

    Shahvaran, Zahra; Kazemi, Kamran; Helfroush, Mohammad Sadegh; Jafarian, Nassim; Noorizadeh, Negar

    2012-08-15

    Noise and intensity non-uniformity are causing major difficulties in magnetic resonance (MR) image segmentation. This paper introduces a variational level set approach for simultaneous MR image segmentation and intensity non-uniformity correction. The proposed energy functional is based on local Gaussian intensity fitting with local means and variances. Furthermore, the proposed model utilizes Markov random fields to model the spatial correlation between neighboring pixels/voxels. The improvements achieved with our method are demonstrated by brain segmentation experiments with simulated and real magnetic resonance images with different noise and bias level. In particular, it is superior in term of accuracy as compared to LGDF and FSL-FAST methods.

  2. Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields

    PubMed Central

    Perdikaris, P.; Venturi, D.; Royset, J. O.; Karniadakis, G. E.

    2015-01-01

    We propose a new framework for design under uncertainty based on stochastic computer simulations and multi-level recursive co-kriging. The proposed methodology simultaneously takes into account multi-fidelity in models, such as direct numerical simulations versus empirical formulae, as well as multi-fidelity in the probability space (e.g. sparse grids versus tensor product multi-element probabilistic collocation). We are able to construct response surfaces of complex dynamical systems by blending multiple information sources via auto-regressive stochastic modelling. A computationally efficient machine learning framework is developed based on multi-level recursive co-kriging with sparse precision matrices of Gaussian–Markov random fields. The effectiveness of the new algorithms is demonstrated in numerical examples involving a prototype problem in risk-averse design, regression of random functions, as well as uncertainty quantification in fluid mechanics involving the evolution of a Burgers equation from a random initial state, and random laminar wakes behind circular cylinders. PMID:26345079

  3. A Markov random field approach for modeling spatio-temporal evolution of microstructures

    NASA Astrophysics Data System (ADS)

    Acar, Pinar; Sundararaghavan, Veera

    2016-10-01

    The following problem is addressed: ‘Can one synthesize microstructure evolution over a large area given experimental movies measured over smaller regions?’ Our input is a movie of microstructure evolution over a small sample window. A Markov random field (MRF) algorithm is developed that uses this data to estimate the evolution of microstructure over a larger region. Unlike the standard microstructure reconstruction problem based on stationary images, the present algorithm is also able to reconstruct time-evolving phenomena such as grain growth. Such an algorithm would decrease the cost of full-scale microstructure measurements by coupling mathematical estimation with targeted small-scale spatiotemporal measurements. The grain size, shape and orientation distribution statistics of synthesized polycrystalline microstructures at different times are compared with the original movie to verify the method.

  4. Markov random field model for segmenting large populations of lipid vesicles from micrographs.

    PubMed

    Zupanc, Jernej; Drobne, Damjana; Ster, Branko

    2011-12-01

    Giant unilamellar lipid vesicles, artificial replacements for cell membranes, are a promising tool for in vitro assessment of interactions between products of nanotechnologies and biological membranes. However, the effect of nanoparticles can not be derived from observations on a single specimen, vesicle populations should be observed instead. We propose an adaptation of the Markov random field image segmentation model which allows detection and segmentation of numerous vesicles in micrographs. The reliability of this model with different lighting, blur, and noise characteristics of micrographs is examined and discussed. Moreover, the automatic segmentation is tested on micrographs with thousands of vesicles and the result is compared to that of manual segmentation. The segmentation step presented is part of a methodology we are developing for bio-nano interaction assessment studies on lipid vesicles.

  5. Segmentation of the right ventricle using diffusion maps and Markov random fields.

    PubMed

    Moolan-Feroze, Oliver; Mirmehdi, Majid; Hamilton, Mark; Bucciarelli-Ducci, Chiara

    2014-01-01

    Accurate automated segmentation of the right ventricle is difficult due in part to the large shape variation found between patients. We explore the ability of manifold learning based shape models to represent the complexity of shape variation found within an RV dataset as compared to a typical PCA based model. This is empirically evaluated with the manifold model displaying a greater ability to represent complex shapes. Furthermore, we present a combined manifold shape model and Markov Random Field Segmentation framework. The novelty of this method is the iterative generation of targeted shape priors from the manifold using image information and a current estimate of the segmentation; a process that can be seen as a traversal across the manifold. We apply our method to the independently evaluated MICCAI 2012 RV Segmentation Challenge data set. Our method performs similarly or better than the state-of-the-art methods.

  6. Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET.

    PubMed

    Bousse, Alexandre; Pedemonte, Stefano; Thomas, Benjamin A; Erlandsson, Kjell; Ourselin, Sébastien; Arridge, Simon; Hutton, Brian F

    2012-10-21

    In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image.

  7. MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search.

    PubMed

    Daniels, Noah M; Gallant, Andrew; Ramsey, Norman; Cowen, Lenore J

    2015-01-01

    We introduce MRFy, a tool for protein remote homology detection that captures beta-strand dependencies in the Markov random field. Over a set of 11 SCOP beta-structural superfamilies, MRFy shows a 14 percent improvement in mean Area Under the Curve for the motif recognition problem as compared to HMMER, 25 percent improvement as compared to RAPTOR, 14 percent improvement as compared to HHPred, and a 18 percent improvement as compared to CNFPred and RaptorX. MRFy was implemented in the Haskell functional programming language, and parallelizes well on multi-core systems. MRFy is available, as source code as well as an executable, from http://mrfy.cs.tufts.edu/.

  8. Nakagami Markov random field as texture model for ultrasound RF envelope image.

    PubMed

    Bouhlel, N; Sevestre-Ghalila, S

    2009-06-01

    The aim of this paper is to propose a new Markov random field (MRF) model for the backscattered ultrasonic echo in order to get information about backscatter characteristics, such as the scatterer density, amplitude and spacing. The model combines the Nakagami distribution that describes the envelope of backscattered echo with spatial interaction using MRF. In this paper, the parameters of the model and the estimation parameter method are introduced. Computer simulation using ultrasound radio-frequency (RF) simulator and experiments on choroidal malignant melanoma have been undertaken to test the validity of the model. The relationship between the parameters of MRF model and the backscatter characteristics has been established. Furthermore, the ability of the model to distinguish between normal and abnormal tissue has been proved. All the results can show the success of the model.

  9. Theory of Distribution Estimation of Hyperparameters in Markov Random Field Models

    NASA Astrophysics Data System (ADS)

    Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato

    2016-06-01

    We investigated the performance of distribution estimation of hyperparameters in Markov random field models proposed by Nakanishi-Ohno et al., J. Phys. A 47, 045001 (2014) when used to evaluate the confidence of data. We analytically calculated the configurational average, with respect to data, of the negative logarithm of the posterior distribution, which is called free energy based on an analogy with statistical mechanics. This configurational average of free energy shrinks as the amount of data increases. Our results theoretically confirm the numerical results from that previous study.

  10. Smoothing Parameter Estimation for Markov Random Field Classification of non-Gaussian Distribution Image

    NASA Astrophysics Data System (ADS)

    Aghighi, H.; Trindet, J.; Wang, K.; Tarabalka, Y.; Lim, S.

    2014-09-01

    In the context of remote sensing image classification, Markov random fields (MRFs) have been used to combine both spectral and contextual information. The MRFs use a smoothing parameter to balance the contribution of the spectral versus spatial energies, which is often defined empirically. This paper proposes a framework to estimate the smoothing parameter using the probability estimates from support vector machines and the spatial class co-occurrence distribution. Furthermore, we construct a spatially weighted parameter to preserve the edges by using seven different edge detectors. The performance of the proposed methods is evaluated on two hyperspectral datasets recorded by the AVIRIS and ROSIS and a simulated ALOS PALSAR image. The experimental results demonstrated that the estimated smoothing parameter is optimal and produces a classified map with high accuracy. Moreover, we found that the Canny-based edge probability map preserved the contours better than others.

  11. Monte-Carlo analysis of rarefied-gas diffusion including variance reduction using the theory of Markov random walks

    NASA Technical Reports Server (NTRS)

    Perlmutter, M.

    1973-01-01

    Molecular diffusion through a rarefied gas is analyzed by using the theory of Markov random walks. The Markov walk is simulated on the computer by using random numbers to find the new states from the appropriate transition probabilities. As the sample molecule during its random walk passes a scoring position, which is a location at which the macroscopic diffusing flow variables such as molecular flux and molecular density are desired, an appropriate payoff is scored. The payoff is a function of the sample molecule velocity. For example, in obtaining the molecular flux across a scoring position, the random walk payoff is the net number of times the scoring position has been crossed in the positive direction. Similarly, when the molecular density is required, the payoff is the sum of the inverse velocity of the sample molecule passing the scoring position. The macroscopic diffusing flow variables are then found from the expected payoff of the random walks.

  12. The conditional independences between variables derived from two independent identically distributed Markov random fields when pairwise order is ignored.

    PubMed

    Thomas, Alun

    2010-09-01

    A result for the equivalence of conditional independence graphs of ordered and unordered vector random variables from first-order Markov models is extended to arbitrary forests. The result is relevant to estimating graphical models for linkage disequilibrium between genetic loci. It explains why, in terms of the conditional independence structure, it sometimes does not matter whether you consider haplotypes or genotypes.

  13. Estimating the granularity coefficient of a Potts-Markov random field within a Markov chain Monte Carlo algorithm.

    PubMed

    Pereyra, Marcelo; Dobigeon, Nicolas; Batatia, Hadj; Tourneret, Jean-Yves

    2013-06-01

    This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method, the estimation of β is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating β jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of β. On the other hand, choosing an incorrect value of β can degrade estimation performance significantly. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images.

  14. Markov-switching multifractal models as another class of random-energy-like models in one-dimensional space

    NASA Astrophysics Data System (ADS)

    Saakian, David B.

    2012-03-01

    We map the Markov-switching multifractal model (MSM) onto the random energy model (REM). The MSM is, like the REM, an exactly solvable model in one-dimensional space with nontrivial correlation functions. According to our results, four different statistical physics phases are possible in random walks with multifractal behavior. We also introduce the continuous branching version of the model, calculate the moments, and prove multiscaling behavior. Different phases have different multiscaling properties.

  15. Passive error concealment for wavelet-coded I-frames with an inhomogeneous Gauss-Markov random field model.

    PubMed

    Rombaut, Joost; Pizurica, Aleksandra; Philips, Wilfried

    2009-04-01

    In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.

  16. A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation.

    PubMed

    Cordero-Grande, Lucilio; Vegas-Sánchez-Ferrero, Gonzalo; Casaseca-de-la-Higuera, Pablo; Alberola-López, Carlos

    2012-04-01

    This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.

  17. Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.

    PubMed

    Roy, Pallab Kanti; Bhuiyan, Alauddin; Janke, Andrew; Desmond, Patricia M; Wong, Tien Yin; Abhayaratna, Walter P; Storey, Elsdon; Ramamohanarao, Kotagiri

    2015-10-01

    White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  19. Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.

    PubMed

    Roy, Pallab Kanti; Bhuiyan, Alauddin; Janke, Andrew; Desmond, Patricia M; Wong, Tien Yin; Abhayaratna, Walter P; Storey, Elsdon; Ramamohanarao, Kotagiri

    2015-10-01

    White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice. PMID:26398564

  20. Region-based Active Contour Model based on Markov Random Field to Segment Images with Intensity Non-Uniformity and Noise.

    PubMed

    Shahvaran, Zahra; Kazemi, Kamran; Helfroush, Mohammad Sadegh; Jafarian, Nassim

    2012-01-01

    This paper represents a new region-based active contour model that can be used to segment images with intensity non-uniformity and high-level noise. The main idea of our proposed method is to use Gaussian distributions with different means and variances with incorporation of intensity non-uniformity model for image segmentation. In order to integrate the spatial information between neighboring pixels in our proposed method, we use Markov Random Field. Our experiments on synthetic images and cerebral magnetic resonance images show the advantages of the proposed method over state-of-art methods, i.e. local Gaussian distribution fitting.

  1. Nonpoint source solute transport normal to aquifer bedding in heterogeneous, Markov chain random fields

    NASA Astrophysics Data System (ADS)

    Zhang, Hua; Harter, Thomas; Sivakumar, Bellie

    2006-06-01

    Facies-based geostatistical models have become important tools for analyzing flow and mass transport processes in heterogeneous aquifers. Yet little is known about the relationship between these latter processes and the parameters of facies-based geostatistical models. In this study, we examine the transport of a nonpoint source solute normal (perpendicular) to the major bedding plane of an alluvial aquifer medium that contains multiple geologic facies, including interconnected, high-conductivity (coarse textured) facies. We also evaluate the dependence of the transport behavior on the parameters of the constitutive facies model. A facies-based Markov chain geostatistical model is used to quantify the spatial variability of the aquifer system's hydrostratigraphy. It is integrated with a groundwater flow model and a random walk particle transport model to estimate the solute traveltime probability density function (pdf) for solute flux from the water table to the bottom boundary (the production horizon) of the aquifer. The cases examined include two-, three-, and four-facies models, with mean length anisotropy ratios for horizontal to vertical facies, ek, from 25:1 to 300:1 and with a wide range of facies volume proportions (e.g., from 5 to 95% coarse-textured facies). Predictions of traveltime pdfs are found to be significantly affected by the number of hydrostratigraphic facies identified in the aquifer. Those predictions of traveltime pdfs also are affected by the proportions of coarse-textured sediments, the mean length of the facies (particularly the ratio of length to thickness of coarse materials), and, to a lesser degree, the juxtapositional preference among the hydrostratigraphic facies. In transport normal to the sedimentary bedding plane, traveltime is not lognormally distributed as is often assumed. Also, macrodispersive behavior (variance of the traveltime) is found not to be a unique function of the conductivity variance. For the parameter range

  2. Context-aware patch-based image inpainting using Markov random field modeling.

    PubMed

    Ružić, Tijana; Pižurica, Aleksandra

    2015-01-01

    In this paper, we first introduce a general approach for context-aware patch-based image inpainting, where textural descriptors are used to guide and accelerate the search for well-matching (candidate) patches. A novel top-down splitting procedure divides the image into variable size blocks according to their context, constraining thereby the search for candidate patches to nonlocal image regions with matching context. This approach can be employed to improve the speed and performance of virtually any (patch-based) inpainting method. We apply this approach to the so-called global image inpainting with the Markov random field (MRF) prior, where MRF encodes a priori knowledge about consistency of neighboring image patches. We solve the resulting optimization problem with an efficient low-complexity inference method. Experimental results demonstrate the potential of the proposed approach in inpainting applications like scratch, text, and object removal. Improvement and significant acceleration of a related global MRF-based inpainting method is also evident.

  3. Identifying protein interaction subnetworks by a bagging Markov random field-based method.

    PubMed

    Chen, Li; Xuan, Jianhua; Riggins, Rebecca B; Wang, Yue; Clarke, Robert

    2013-01-01

    Identification of differentially expressed subnetworks from protein-protein interaction (PPI) networks has become increasingly important to our global understanding of the molecular mechanisms that drive cancer. Several methods have been proposed for PPI subnetwork identification, but the dependency among network member genes is not explicitly considered, leaving many important hub genes largely unidentified. We present a new method, based on a bagging Markov random field (BMRF) framework, to improve subnetwork identification for mechanistic studies of breast cancer. The method follows a maximum a posteriori principle to form a novel network score that explicitly considers pairwise gene interactions in PPI networks, and it searches for subnetworks with maximal network scores. To improve their robustness across data sets, a bagging scheme based on bootstrapping samples is implemented to statistically select high confidence subnetworks. We first compared the BMRF-based method with existing methods on simulation data to demonstrate its improved performance. We then applied our method to breast cancer data to identify PPI subnetworks associated with breast cancer progression and/or tamoxifen resistance. The experimental results show that not only an improved prediction performance can be achieved by the BMRF approach when tested on independent data sets, but biologically meaningful subnetworks can also be revealed that are relevant to breast cancer and tamoxifen resistance.

  4. Incorporating biological pathways via a Markov random field model in genome-wide association studies.

    PubMed

    Chen, Min; Cho, Judy; Zhao, Hongyu

    2011-04-01

    Genome-wide association studies (GWAS) examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF) model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene-based method. We also illustrate the usefulness of our approach through its applications to a real data example.

  5. A Markov random field approach to group-wise registration/mosaicing with application to ultrasound.

    PubMed

    Kutarnia, Jason; Pedersen, Peder

    2015-08-01

    In this paper we present a group-wise non-rigid registration/mosaicing algorithm based on block-matching, which is developed within a probabilistic framework. The discrete form of its energy functional is linked to a Markov Random Field (MRF) containing double and triple cliques, which can be effectively optimized using modern MRF optimization algorithms popular in computer vision. Also, the registration problem is simplified by introducing a mosaicing function which partitions the composite volume into regions filled with data from unique, partially overlapping source volumes. Ultrasound confidence maps are incorporated into the registration framework in order to give accurate results in the presence of image artifacts. The algorithm is initially tested on simulated images where shadows have been generated. Also, validation results for the group-wise registration algorithm using real ultrasound data from an abdominal phantom are presented. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult. In addition, results are presented suggesting that a fusion approach to MRF registration can produce accurate displacement fields much faster than standard approaches.

  6. Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization.

    PubMed

    Sun, Jun; Palade, Vasile; Wu, Xiaojun; Fang, Wei

    2014-01-01

    Hidden Markov Models (HMMs) are powerful tools for multiple sequence alignment (MSA), which is known to be an NP-complete and important problem in bioinformatics. Learning HMMs is a difficult task, and many meta-heuristic methods, including particle swarm optimization (PSO), have been used for that. In this paper, a new variant of PSO, called the random drift particle swarm optimization (RDPSO) algorithm, is proposed to be used for HMM learning tasks in MSA problems. The proposed RDPSO algorithm, inspired by the free electron model in metal conductors in an external electric field, employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Moreover, in order to further enhance the algorithmic performance of the RDPSO, we incorporate a diversity control method into the algorithm and, thus, propose an RDPSO with diversity-guided search (RDPSO-DGS). The performances of the RDPSO, RDPSO-DGS and other algorithms are tested and compared by learning HMMs for MSA on two well-known benchmark data sets. The experimental results show that the HMMs learned by the RDPSO and RDPSO-DGS are able to generate better alignments for the benchmark data sets than other most commonly used HMM learning methods, such as the Baum-Welch and other PSO algorithms. The performance comparison with well-known MSA programs, such as ClustalW and MAFFT, also shows that the proposed methods have advantages in multiple sequence alignment.

  7. Service-oriented node scheduling scheme for wireless sensor networks using Markov random field model.

    PubMed

    Cheng, Hongju; Su, Zhihuang; Lloret, Jaime; Chen, Guolong

    2014-11-06

    Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.

  8. Impact of Markov Random Field optimizer on MRI-based tissue segmentation in the aging brain.

    PubMed

    Schwarz, Christopher G; Tsui, Alex; Fletcher, Evan; Singh, Baljeet; DeCarli, Charles; Carmichael, Owen

    2011-01-01

    Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly data sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Belief propagation (BP) and graph cuts (GC), used as the MRF optimizer component of a standardized segmentation system, provide high segmentation performance on aggregate that is competitive with end-to-end systems provided by SPM and FSL (FAST) as well as the more traditional MRF optimizer iterated conditional modes (ICM). However, the relative performance of each method varied strongly by performance criterion and differed between young and old brains. The findings emphasize the unique difficulties involved in segmenting the aging brain, and suggest that optimal algorithm components may depend in part on performance criteria.

  9. Markov random field based automatic alignment for low SNR imagesfor cryo electron tomography

    SciTech Connect

    Amat, Fernando; Moussavi, Farshid; Comolli, Luis R.; Elidan, Gal; Horowitz, Mark

    2007-07-21

    We present a method for automatic full precision alignmentof the images in a tomographic tilt series. Full-precision automaticalignment of cryo electron microscopy images has remained a difficultchallenge to date, due to the limited electron dose and low imagecontrast. These facts lead to poor signal to noise ratio (SNR) in theimages, which causes automatic feature trackers to generate errors, evenwith high contrast gold particles as fiducial features. To enable fullyautomatic alignment for full-precision reconstructions, we frame theproblem probabilistically as finding the most likely particle tracksgiven a set of noisy images, using contextual information to make thesolution more robust to the noise in each image. To solve this maximumlikelihood problem, we use Markov Random Fields (MRF) to establish thecorrespondence of features in alignment and robust optimization forprojection model estimation. The resultingalgorithm, called RobustAlignment and Projection Estimation for Tomographic Reconstruction, orRAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as goodas the manual approach by an expert user. We are able to automaticallymap complete and partial marker trajectories and thus obtain highlyaccurate image alignment. Our method has been applied to challenging cryoelectron tomographic datasets with low SNR from intact bacterial cells,as well as several plastic section and x-ray datasets.

  10. A wavelet-based Markov random field segmentation model in segmenting microarray experiments.

    PubMed

    Athanasiadis, Emmanouil; Cavouras, Dionisis; Kostopoulos, Spyros; Glotsos, Dimitris; Kalatzis, Ioannis; Nikiforidis, George

    2011-12-01

    In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.

  11. Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.

    PubMed

    Athanasiadis, Emmanouil I; Cavouras, Dionisis A; Glotsos, Dimitris Th; Georgiadis, Pantelis V; Kalatzis, Ioannis K; Nikiforidis, George C

    2009-11-01

    A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r2 : 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively).

  12. A Markov Random Field orientation prior for electronic cleansing in CT Colonography.

    PubMed

    Krishnan, Karthik; Desai, Nasir

    2015-01-01

    Tagging of the bowel content with an oral contrast facilitates CT Colonography with limited bowel preparation. Electronic colon cleansing (ECC) reconstructs the colon lumen, devoid of feces from a CT scan acquired with fecal fluid tagging. A popular method to estimate the stool composition in an image (with the purpose of removing it) is the well-established Expectation Maximization (EM) method. The tagged fluid residue appears as a contrast enhanced region with a largely horizontal interface with air above it. One of the issues is the partial volume (PV) effect that creates voxels with attenuations similar to that of the colon wall at the boundary of air and tagged fluid. We present here, a novel orientation prior formulated as a Markov Random Field that is included as part of the EM tissue segmentation framework to mitigate this PV effect at the air and tagged fluid layer. We show quantitative results on a simple synthetic dataset and qualitative results on patient data that highlight improvements due to the inclusion of the orientation prior.

  13. Oriented Markov random field based dendritic spine segmentation for fluorescence microscopy images.

    PubMed

    Cheng, Jie; Zhou, Xiaobo; Miller, Eric L; Alvarez, Veronica A; Sabatini, Bernardo L; Wong, Stephen T C

    2010-10-01

    Dendritic spines have been shown to be closely related to various functional properties of the neuron. Usually dendritic spines are manually labeled to analyze their morphological changes, which is very time-consuming and susceptible to operator bias, even with the assistance of computers. To deal with these issues, several methods have been recently proposed to automatically detect and measure the dendritic spines with little human interaction. However, problems such as degraded detection performance for images with larger pixel size (e.g. 0.125 μm/pixel instead of 0.08 μm/pixel) still exist in these methods. Moreover, the shapes of detected spines are also distorted. For example, the "necks" of some spines are missed. Here we present an oriented Markov random field (OMRF) based algorithm which improves spine detection as well as their geometric characterization. We begin with the identification of a region of interest (ROI) containing all the dendrites and spines to be analyzed. For this purpose, we introduce an adaptive procedure for identifying the image background. Next, the OMRF model is discussed within a statistical framework and the segmentation is solved as a maximum a posteriori estimation (MAP) problem, whose optimal solution is found by a knowledge-guided iterative conditional mode (KICM) algorithm. Compared with the existing algorithms, the proposed algorithm not only provides a more accurate representation of the spine shape, but also improves the detection performance by more than 50% with regard to reducing both the misses and false detection.

  14. Posterior-mean super-resolution with a causal Gaussian Markov random field prior.

    PubMed

    Katsuki, Takayuki; Torii, Akira; Inoue, Masato

    2012-07-01

    We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from given multiple low-resolution images. An MRF model with the line process supplies a preferable prior for natural images with edges. We improve the existing image transformation model, the compound MRF model, and its hyperparameter prior model. We also derive the optimal estimator--not the joint maximum a posteriori (MAP) or the marginalized maximum likelihood (ML) but the posterior mean (PM)--from the objective function of the L2-norm-based (mean square error) peak signal-to-noise ratio. Point estimates such as MAP and ML are generally not stable in ill-posed high-dimensional problems because of overfitting, whereas PM is a stable estimator because all the parameters in the model are evaluated as distributions. The estimator is numerically determined by using the variational Bayesian method. The variational Bayesian method is a widely used method that approximately determines a complicated posterior distribution, but it is generally hard to use because it needs the conjugate prior. We solve this problem with simple Taylor approximations. Experimental results have shown that the proposed method is more accurate or comparable to existing methods.

  15. Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model.

    PubMed

    Cordero-Grande, L; Vegas-Sánchez-Ferrero, G; Casaseca-de-la-Higuera, P; San-Román-Calvar, J Alberto; Revilla-Orodea, Ana; Martín-Fernández, M; Alberola-López, C

    2011-06-01

    A stochastic deformable model is proposed for the segmentation of the myocardium in Magnetic Resonance Imaging. The segmentation is posed as a probabilistic optimization problem in which the optimal time-dependent surface is obtained for the myocardium of the heart in a discrete space of locations built upon simple geometric assumptions. For this purpose, first, the left ventricle is detected by a set of image analysis tools gathered from the literature. Then, the segmentation solution is obtained by the Maximization of the Posterior Marginals for the myocardium location in a Markov Random Field framework which optimally integrates temporal-spatial smoothness with intensity and gradient related features in an unsupervised way by the Maximum Likelihood estimation of the parameters of the field. This scheme provides a flexible and robust segmentation method which has been able to generate results comparable to manually segmented images for some derived cardiac function parameters in a set of 43 patients affected in different degrees by an Acute Myocardial Infarction.

  16. Disease gene identification by using graph kernels and Markov random fields.

    PubMed

    Chen, BoLin; Li, Min; Wang, JianXin; Wu, Fang-Xiang

    2014-11-01

    Genes associated with similar diseases are often functionally related. This principle is largely supported by many biological data sources, such as disease phenotype similarities, protein complexes, protein-protein interactions, pathways and gene expression profiles. Integrating multiple types of biological data is an effective method to identify disease genes for many genetic diseases. To capture the gene-disease associations based on biological networks, a kernel-based MRF method is proposed by combining graph kernels and the Markov random field (MRF) method. In the proposed method, three kinds of kernels are employed to describe the overall relationships of vertices in five biological networks, respectively, and a novel weighted MRF method is developed to integrate those data. In addition, an improved Gibbs sampling procedure and a novel parameter estimation method are proposed to generate predictions from the kernel-based MRF method. Numerical experiments are carried out by integrating known gene-disease associations, protein complexes, protein-protein interactions, pathways and gene expression profiles. The proposed kernel-based MRF method is evaluated by the leave-one-out cross validation paradigm, achieving an AUC score of 0.771 when integrating all those biological data in our experiments, which indicates that our proposed method is very promising compared with many existing methods.

  17. Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

    PubMed

    Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping

    2014-01-01

    The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

  18. Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field

    NASA Astrophysics Data System (ADS)

    Shi, Junfei; Li, Lingling; Liu, Fang; Jiao, Licheng; Liu, Hongying; Yang, Shuyuan; Liu, Lu; Hao, Hongxia

    2016-04-01

    Markov random field (MRF) model is an effective tool for polarimetric synthetic aperture radar (PolSAR) image classification. However, due to the lack of suitable contextual information in conventional MRF methods, there is usually a contradiction between edge preservation and region homogeneity in the classification result. To preserve edge details and obtain homogeneous regions simultaneously, an adaptive MRF framework is proposed based on a polarimetric sketch map. The polarimetric sketch map can provide the edge positions and edge directions in detail, which can guide the selection of neighborhood structures. Specifically, the polarimetric sketch map is extracted to partition a PolSAR image into structural and nonstructural parts, and then adaptive neighborhoods are learned for two parts. For structural areas, geometric weighted neighborhood structures are constructed to preserve image details. For nonstructural areas, the maximum homogeneous regions are obtained to improve the region homogeneity. Experiments are taken on both the simulated and real PolSAR data, and the experimental results illustrate that the proposed method can obtain better performance on both region homogeneity and edge preservation than the state-of-the-art methods.

  19. Medical image retrieval and analysis by Markov random fields and multi-scale fractal dimension.

    PubMed

    Backes, André Ricardo; Gerhardinger, Leandro Cavaleri; Batista Neto, João do Espírito Santo; Bruno, Odemir Martinez

    2015-02-01

    Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval. It combines texture with complexity extracted from objects within the images. The approach consists of a texture segmentation step, modeled as a Markov Random Field process, followed by the estimation of the complexity of each computed region. The complexity is given by a Multi-scale Fractal Dimension. Experiments have been conducted using an MRI database in both pattern recognition and image retrieval contexts. The results show the accuracy of the proposed method in comparison with other traditional texture descriptors and also indicate how the performance changes as the level of complexity is altered.

  20. Network-based genomic discovery: application and comparison of Markov random field models.

    PubMed

    Wei, Peng; Pan, Wei

    2010-01-01

    As biological knowledge accumulates rapidly, gene networks encoding genome-wide gene-gene interactions have been constructed. As an improvement over the standard mixture model that tests all the genes iid a priori, Wei and Li (2007) and Wei and Pan (2008) proposed modeling a gene network as a Discrete- or Gaussian-Markov random field (DMRF or GMRF) respectively in a mixture model to analyze genomic data. However, how these methods compare in practical applications in not well understood and this is the aim here. We also propose two novel constraints in prior specifications for the GMRF model and a fully Bayesian approach to the DMRF model. We assess the accuracy of estimating the False Discovery Rate (FDR) by posterior probabilities in the context of MRF models. Applications to a ChIP-chip data set and simulated data show that the modified GMRF models has superior performance as compared with other models, while both MRF-based mixture models, with reasonable robustness to misspecified gene networks, outperform the standard mixture model.

  1. A comparative study of Gaussian geostatistical models and Gaussian Markov random field models1

    PubMed Central

    Song, Hae-Ryoung; Fuentes, Montserrat; Ghosh, Sujit

    2008-01-01

    Gaussian geostatistical models (GGMs) and Gaussian Markov random fields (GM-RFs) are two distinct approaches commonly used in spatial models for modeling point referenced and areal data, respectively. In this paper, the relations between GGMs and GMRFs are explored based on approximations of GMRFs by GGMs, and approximations of GGMs by GMRFs. Two new metrics of approximation are proposed: (i) the Kullback-Leibler discrepancy of spectral densities and (ii) the chi-squared distance between spectral densities. The distances between the spectral density functions of GGMs and GMRFs measured by these metrics are minimized to obtain the approximations of GGMs and GMRFs. The proposed methodologies are validated through several empirical studies. We compare the performance of our approach to other methods based on covariance functions, in terms of the average mean squared prediction error and also the computational time. A spatial analysis of a dataset on PM2.5 collected in California is presented to illustrate the proposed method. PMID:19337581

  2. A segmentation model using compound Markov random fields based on a boundary model.

    PubMed

    Wu, Jue; Chung, Albert C S

    2007-01-01

    Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 x 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.

  3. A new method for direction finding based on Markov random field model

    NASA Astrophysics Data System (ADS)

    Ota, Mamoru; Kasahara, Yoshiya; Goto, Yoshitaka

    2015-07-01

    Investigating the characteristics of plasma waves observed by scientific satellites in the Earth's plasmasphere/magnetosphere is effective for understanding the mechanisms for generating waves and the plasma environment that influences wave generation and propagation. In particular, finding the propagation directions of waves is important for understanding mechanisms of VLF/ELF waves. To find these directions, the wave distribution function (WDF) method has been proposed. This method is based on the idea that observed signals consist of a number of elementary plane waves that define wave energy density distribution. However, the resulting equations constitute an ill-posed problem in which a solution is not determined uniquely; hence, an adequate model must be assumed for a solution. Although many models have been proposed, we have to select the most optimum model for the given situation because each model has its own advantages and disadvantages. In the present study, we propose a new method for direction finding of the plasma waves measured by plasma wave receivers. Our method is based on the assumption that the WDF can be represented by a Markov random field model with inference of model parameters performed using a variational Bayesian learning algorithm. Using computer-generated spectral matrices, we evaluated the performance of the model and compared the results with those obtained from two conventional methods.

  4. Reconstruction for distributed video coding: a Markov random field approach with context-adaptive smoothness prior

    NASA Astrophysics Data System (ADS)

    Zhang, Yongsheng; Xiong, Hongkai; He, Zhihai; Yu, Songyu

    2010-07-01

    An important issue in Wyner-Ziv video coding is the reconstruction of Wyner-Ziv frames with decoded bit-planes. So far, there are two major approaches: the Maximum a Posteriori (MAP) reconstruction and the Minimum Mean Square Error (MMSE) reconstruction algorithms. However, these approaches do not exploit smoothness constraints in natural images. In this paper, we model a Wyner-Ziv frame by Markov random fields (MRFs), and produce reconstruction results by finding an MAP estimation of the MRF model. In the MRF model, the energy function consists of two terms: a data term, MSE distortion metric in this paper, measuring the statistical correlation between side-information and the source, and a smoothness term enforcing spatial coherence. In order to better describe the spatial constraints of images, we propose a context-adaptive smoothness term by analyzing the correspondence between the output of Slepian-Wolf decoding and successive frames available at decoders. The significance of the smoothness term varies in accordance with the spatial variation within different regions. To some extent, the proposed approach is an extension to the MAP and MMSE approaches by exploiting the intrinsic smoothness characteristic of natural images. Experimental results demonstrate a considerable performance gain compared with the MAP and MMSE approaches.

  5. Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

    NASA Astrophysics Data System (ADS)

    Pathak, Sayan D.; Haynor, David R.; Thompson, Carol L.; Lein, Ed; Hawrylycz, Michael

    2009-02-01

    Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.

  6. Combining Monte Carlo and mean-field-like methods for inference in hidden Markov random fields.

    PubMed

    Forbes, Florence; Fort, Gersende

    2007-03-01

    Issues involving missing data are typical settings where exact inference is not tractable as soon as nontrivial interactions occur between the missing variables. Approximations are required, and most of them are based either on simulation methods or on deterministic variational methods. While variational methods provide fast and reasonable approximate estimates in many scenarios, simulation methods offer more consideration of important theoretical issues such as accuracy of the approximation and convergence of the algorithms but at a much higher computational cost. In this work, we propose a new class of algorithms that combine the main features and advantages of both simulation and deterministic methods and consider applications to inference in hidden Markov random fields (HMRFs). These algorithms can be viewed as stochastic perturbations of variational expectation maximization (VEM) algorithms, which are not tractable for HMRF. We focus more specifically on one of these perturbations and we prove their (almost sure) convergence to the same limit set as the limit set of VEM. In addition, experiments on synthetic and real-world images show that the algorithm performance is very close and sometimes better than that of other existing simulation-based and variational EM-like algorithms.

  7. Detection and inpainting of facial wrinkles using texture orientation fields and Markov random field modeling.

    PubMed

    Batool, Nazre; Chellappa, Rama

    2014-09-01

    Facial retouching is widely used in media and entertainment industry. Professional software usually require a minimum level of user expertise to achieve the desirable results. In this paper, we present an algorithm to detect facial wrinkles/imperfection. We believe that any such algorithm would be amenable to facial retouching applications. The detection of wrinkles/imperfections can allow these skin features to be processed differently than the surrounding skin without much user interaction. For detection, Gabor filter responses along with texture orientation field are used as image features. A bimodal Gaussian mixture model (GMM) represents distributions of Gabor features of normal skin versus skin imperfections. Then, a Markov random field model is used to incorporate the spatial relationships among neighboring pixels for their GMM distributions and texture orientations. An expectation-maximization algorithm then classifies skin versus skin wrinkles/imperfections. Once detected automatically, wrinkles/imperfections are removed completely instead of being blended or blurred. We propose an exemplar-based constrained texture synthesis algorithm to inpaint irregularly shaped gaps left by the removal of detected wrinkles/imperfections. We present results conducted on images downloaded from the Internet to show the efficacy of our algorithms.

  8. False discovery rate control in magnetic resonance imaging studies via Markov random fields.

    PubMed

    Nguyen, Hien D; McLachlan, Geoffrey J; Cherbuin, Nicolas; Janke, Andrew L

    2014-08-01

    Magnetic resonance imaging (MRI) is widely used to study population effects of factors on brain morphometry. Inference from such studies often require the simultaneous testing of millions of statistical hypotheses. Such scale of inference is known to lead to large numbers of false positive results. Control of the false discovery rate (FDR) is commonly employed to mitigate against such outcomes. However, current methodologies in FDR control only account for the marginal significance of hypotheses, and are not able to explicitly account for spatial relationships, such as those between MRI voxels. In this article, we present novel methods that incorporate spatial dependencies into the process of controlling FDR through the use of Markov random fields. Our method is able to automatically estimate the relationships between spatially dependent hypotheses by means of maximum pseudo-likelihood estimation and the pseudo-likelihood information criterion. We show that our methods have desirable statistical properties with regards to FDR control and are able to outperform noncontexual methods in simulations of dependent hypothesis scenarios. Our method is applied to investigate the effects of aging on brain morphometry using data from the PATH study. Evidence of whole brain and component level effects that correspond to similar findings in the literature is found in our investigation.

  9. Segmentation of cone-beam CT using a hidden Markov random field with informative priors

    NASA Astrophysics Data System (ADS)

    Moores, M.; Hargrave, C.; Harden, F.; Mengersen, K.

    2014-03-01

    Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

  10. Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data

    PubMed Central

    Stingo, Francesco C.; Vannucci, Marina

    2011-01-01

    Motivation: Discriminant analysis is an effective tool for the classification of experimental units into groups. Here, we consider the typical problem of classifying subjects according to phenotypes via gene expression data and propose a method that incorporates variable selection into the inferential procedure, for the identification of the important biomarkers. To achieve this goal, we build upon a conjugate normal discriminant model, both linear and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. Furthermore, we incorporate into the model prior information on the relationships among the genes as described by a gene–gene network. We use a Markov random field (MRF) prior to map the network connections among genes. Our prior model assumes that neighboring genes in the network are more likely to have a joint effect on the relevant biological processes. Results: We use simulated data to assess performances of our method. In particular, we compare the MRF prior to a situation where independent Bernoulli priors are chosen for the individual predictors. We also illustrate the method on benchmark datasets for gene expression. Our simulation studies show that employing the MRF prior improves on selection accuracy. In real data applications, in addition to identifying markers and improving prediction accuracy, we show how the integration of existing biological knowledge into the prior model results in an increased ability to identify genes with strong discriminatory power and also aids the interpretation of the results. Contact: marina@rice.edu PMID:21159623

  11. Handwritten Chinese/Japanese text recognition using semi-Markov conditional random fields.

    PubMed

    Zhou, Xiang-Dong; Wang, Da-Han; Tian, Feng; Liu, Cheng-Lin; Nakagawa, Masaki

    2013-10-01

    This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

  12. Segmentation of 2D gel electrophoresis spots using a Markov random field

    NASA Astrophysics Data System (ADS)

    Hoeflich, Christopher S.; Corso, Jason J.

    2009-02-01

    We propose a statistical model-based approach for the segmentation of fragments of DNA as a first step in the automation of the primarily manual process of comparing two or more images resulting from the Restriction Landmark Genomic Scanning (RLGS) method. These 2D gel electrophoresis images are the product of the separation of DNA into fragments that appear as spots on X-ray films. The goal is to find instances where a spot appears in one image and not in another since a missing spot can be correlated with a region of DNA that has been affected by a disease such as cancer. The entire comparison process is typically done manually, which is tedious and very error prone. We pose the problem as the labeling of each image pixel as either a spot or non-spot and use a Markov Random Field (MRF) model and simulated annealing for inference. Neighboring spot labels are then connected to form spot regions. The MRF based model was tested on actual 2D gel electrophoresis images.

  13. Unsupervised change detection based on improved Markov random field technique using multichannel synthetic aperture radar images

    NASA Astrophysics Data System (ADS)

    Salehi, Sara; Valadan Zoej, Mohammad Javad

    2014-01-01

    Change detection represents an important remote sensing tool in environmental monitoring and disaster management. In this respect, multichannel synthetic aperture radar (SAR) data offer great potential because of their insensitivity to atmospheric and sun-illumination conditions (over optical multispectral data) and the improved discrimination capability they may provide compared to single-channel SAR. The problem of detecting the changes caused by flooding is addressed by a contextual unsupervised technique based on a Markovian data fusion approach. However, the isotropic formulation of Markov random field (MRF) models causes oversmoothing of spatial boundaries in the final change maps. In order to reduce this drawback, an edge-preserving MRF model is proposed and formulated by using energy functions that combine the edge information extracted from the produced edge maps using competitive fuzzy rules and Canny technique, the information conveyed by each SAR channel, and the spatial contextual information. The proposed technique is experimentally validated with semisimulated data and real ASAR-ENVISAT images. Change detection results obtained by the improved MRF model exhibited a higher accuracy than its predecessors for both semisimulated (average 12%) and real (average 6%) data.

  14. Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

    NASA Astrophysics Data System (ADS)

    Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

    2011-11-01

    Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

  15. Local Random Quantum Circuits are Approximate Polynomial-Designs

    NASA Astrophysics Data System (ADS)

    Brandão, Fernando G. S. L.; Harrow, Aram W.; Horodecki, Michał

    2016-09-01

    We prove that local random quantum circuits acting on n qubits composed of O( t 10 n 2) many nearest neighbor two-qubit gates form an approximate unitary t-design. Previously it was unknown whether random quantum circuits were a t-design for any t > 3. The proof is based on an interplay of techniques from quantum many-body theory, representation theory, and the theory of Markov chains. In particular we employ a result of Nachtergaele for lower bounding the spectral gap of frustration-free quantum local Hamiltonians; a quasi-orthogonality property of permutation matrices; a result of Oliveira which extends to the unitary group the path-coupling method for bounding the mixing time of random walks; and a result of Bourgain and Gamburd showing that dense subgroups of the special unitary group, composed of elements with algebraic entries, are ∞-copy tensor-product expanders. We also consider pseudo-randomness properties of local random quantum circuits of small depth and prove that circuits of depth O( t 10 n) constitute a quantum t-copy tensor-product expander. The proof also rests on techniques from quantum many-body theory, in particular on the detectability lemma of Aharonov, Arad, Landau, and Vazirani. We give applications of the results to cryptography, equilibration of closed quantum dynamics, and the generation of topological order. In particular we show the following pseudo-randomness property of generic quantum circuits: Almost every circuit U of size O( n k ) on n qubits cannot be distinguished from a Haar uniform unitary by circuits of size O( n ( k-9)/11) that are given oracle access to U.

  16. Local Random Quantum Circuits are Approximate Polynomial-Designs

    NASA Astrophysics Data System (ADS)

    Brandão, Fernando G. S. L.; Harrow, Aram W.; Horodecki, Michał

    2016-08-01

    We prove that local random quantum circuits acting on n qubits composed of O(t 10 n 2) many nearest neighbor two-qubit gates form an approximate unitary t-design. Previously it was unknown whether random quantum circuits were a t-design for any t > 3. The proof is based on an interplay of techniques from quantum many-body theory, representation theory, and the theory of Markov chains. In particular we employ a result of Nachtergaele for lower bounding the spectral gap of frustration-free quantum local Hamiltonians; a quasi-orthogonality property of permutation matrices; a result of Oliveira which extends to the unitary group the path-coupling method for bounding the mixing time of random walks; and a result of Bourgain and Gamburd showing that dense subgroups of the special unitary group, composed of elements with algebraic entries, are ∞-copy tensor-product expanders. We also consider pseudo-randomness properties of local random quantum circuits of small depth and prove that circuits of depth O(t 10 n) constitute a quantum t-copy tensor-product expander. The proof also rests on techniques from quantum many-body theory, in particular on the detectability lemma of Aharonov, Arad, Landau, and Vazirani. We give applications of the results to cryptography, equilibration of closed quantum dynamics, and the generation of topological order. In particular we show the following pseudo-randomness property of generic quantum circuits: Almost every circuit U of size O(n k ) on n qubits cannot be distinguished from a Haar uniform unitary by circuits of size O(n (k-9)/11) that are given oracle access to U.

  17. Analysis and Validation of Grid dem Generation Based on Gaussian Markov Random Field

    NASA Astrophysics Data System (ADS)

    Aguilar, F. J.; Aguilar, M. A.; Blanco, J. L.; Nemmaoui, A.; García Lorca, A. M.

    2016-06-01

    Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m2) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

  18. Localization length fluctuation in randomly layered media

    NASA Astrophysics Data System (ADS)

    Yuan, Haiming; Huang, Feng; Jiang, Xiangqian; Sun, Xiudong

    2016-10-01

    Localization properties of the two-component randomly layered media (RLM) are studied in detail both analytically and numerically. The localization length is found fluctuating around the analytical result obtained under the high-frequency limit. The fluctuation amplitude approaches zero with the increasing of disorder, which is characterized by the distribution width of random thickness. It is also found that the localization length over the mean thickness periodically varies with the distribution center of random thickness. For the multi-component RLM structure, the arrangement of material must be considered.

  19. Bayesian inference of local trees along chromosomes by the sequential Markov coalescent.

    PubMed

    Zheng, Chaozhi; Kuhner, Mary K; Thompson, Elizabeth A

    2014-05-01

    We propose a genealogy-sampling algorithm, Sequential Markov Ancestral Recombination Tree (SMARTree), that provides an approach to estimation from SNP haplotype data of the patterns of coancestry across a genome segment among a set of homologous chromosomes. To enable analysis across longer segments of genome, the sequence of coalescent trees is modeled via the modified sequential Markov coalescent (Marjoram and Wall, Genetics 7:16, 2006). To assess performance in estimating these local trees, our SMARTree implementation is tested on simulated data. Our base data set is of the SNPs in 10 DNA sequences over 50 kb. We examine the effects of longer sequences and of more sequences, and of a recombination and/or mutational hotspot. The model underlying SMARTree is an approximation to the full recombinant-coalescent distribution. However, in a small trial on simulated data, recovery of local trees was similar to that of LAMARC (Kuhner et al. Genetics 156:1393-1401, 2000a), a sampler which uses the full model. PMID:24817610

  20. Markov random field-based clustering applied to the segmentation of masses in digital mammograms.

    PubMed

    Suliga, M; Deklerck, R; Nyssen, E

    2008-09-01

    In this paper we propose a new pixel clustering model applied to the analysis of digital mammograms. The clustering represents here the first step in a more general method and aims at the creation of a concise data-set (clusters) for automatic detection and classification of masses, which are typically among the first symptoms analysed in early diagnosis of breast cancer. For the purpose of this work, a set of mammographic images has been employed, that are 12-bit gray level digital scans and as such, are inherently inhomogeneous and affected by the noise resulting from the film scanning. The image pixels are described only by their intensity (gray level), therefore, the available information is limited to one dimension. We propose a Markov random field (MRF)-based technique that is suitable for performing clustering in an environment which is described by poor or limited data. The proposed method is a statistical classification model, that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics, that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the output. Moreover, we define a probabilistic description of the model, that is characterised by a remarkable simplicity, such that its realisation can be easily and efficiently implemented in any high- or low-level programming language, thus allowing it to be run on virtually any kind of platform. Finally, we evaluate the algorithm against the classical K-means clustering routine. We point out similarities between the two methods and, moreover, show the advantages and superiority of the MRF scheme.

  1. SAR-based change detection using hypothesis testing and Markov random field modelling

    NASA Astrophysics Data System (ADS)

    Cao, W.; Martinis, S.

    2015-04-01

    The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

  2. A BAYESIAN HIERARCHICAL SPATIAL MODEL FOR DENTAL CARIES ASSESSMENT USING NON-GAUSSIAN MARKOV RANDOM FIELDS

    PubMed Central

    Jin, Ick Hoon; Yuan, Ying; Bandyopadhyay, Dipankar

    2016-01-01

    Research in dental caries generates data with two levels of hierarchy: that of a tooth overall and that of the different surfaces of the tooth. The outcomes often exhibit spatial referencing among neighboring teeth and surfaces, i.e., the disease status of a tooth or surface might be influenced by the status of a set of proximal teeth/surfaces. Assessments of dental caries (tooth decay) at the tooth level yield binary outcomes indicating the presence/absence of teeth, and trinary outcomes at the surface level indicating healthy, decayed, or filled surfaces. The presence of these mixed discrete responses complicates the data analysis under a unified framework. To mitigate complications, we develop a Bayesian two-level hierarchical model under suitable (spatial) Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. At the first level, we utilize an autologistic model to accommodate the spatial dependence for the tooth-level binary outcomes. For the second level and conditioned on a tooth being non-missing, we utilize a Potts model to accommodate the spatial referencing for the surface-level trinary outcomes. The regression models at both levels were controlled for plausible covariates (risk factors) of caries, and remain connected through shared parameters. To tackle the computational challenges in our Bayesian estimation scheme caused due to the doubly-intractable normalizing constant, we employ a double Metropolis-Hastings sampler. We compare and contrast our model performances to the standard non-spatial (naive) model using a small simulation study, and illustrate via an application to a clinical dataset on dental caries. PMID:27807470

  3. Improving Markov Random Field Based Super Resolution Mapping Through Fuzzy Parameter Integration

    NASA Astrophysics Data System (ADS)

    . Welikanna, D. R.; Tamura, M.; Tolpekin, V. A.; Susaki, J.; Maki, M.

    2012-07-01

    The objective of this study was to improve the Markov Random Field (MRF) based Super Resolution Mapping (SRM) technique to account for the vague land-cover interpretations (class mixture and the intermediate conditions) in an urban area. The algorithm has been improved to integrate the fuzzy mean and fuzzy covariance measurements, to a MRF based SRM scheme to optimize the classification results. The technique was tested on a WORLDVIEW-2 data set, acquired over a highway construction area, in Colombo, Sri Lanka. Based on the visual interpretation of the image, three major land-cover types of this area were identified for the study; those were vegetation, soil and exposed grass and impervious surface with low medium and high albedo. The membership values for each pixel were determined from training samples through Spectral Angle Mapper (SAM) technique. The compulsory fuzzy mean and the covariance measurements were derived using these membership grades, and subsequently was applied in MRF based SRM technique. The primary reference data was generated using Maximum Likelihood Classification (MLC) performed on the same data which was resampled to 1m resolution. The scale factor was set to be (S) = 2, to generate SRM of 1m resolution. The smoothening parameter (λ) which balances the prior and likelihood energy terms were tested in the range from 0.3 to 0.9. SRM were generated using fuzzy MRF and the conventional MRF models respectively. Results suggest that the fuzzy integrated model has improved the results with an overall accuracy of 85.60% and kappa value of 0.78 between the optimal results and the reference data, while in the conventional case it was 77.81% of overall accuracy with kappa being 0.65. Among the two MRF models, fuzzy parameter integrated model shows the highest agreement with class fractions from the reference image with a smallest average _MAE (MAE, Mean Absolute Error) of 0.03.

  4. Random nanolasing in the Anderson localized regime

    NASA Astrophysics Data System (ADS)

    Liu, J.; Garcia, P. D.; Ek, S.; Gregersen, N.; Suhr, T.; Schubert, M.; Mørk, J.; Stobbe, S.; Lodahl, P.

    2014-04-01

    The development of nanoscale optical devices for classical and quantum photonics is affected by unavoidable fabrication imperfections that often impose performance limitations. However, disorder may also enable new functionalities, for example in random lasers, where lasing relies on random multiple scattering. The applicability of random lasers has been limited due to multidirectional emission, lack of tunability, and strong mode competition with chaotic fluctuations due to a weak mode confinement. The regime of Anderson localization of light has been proposed for obtaining stable multimode random lasing, and initial work concerned macroscopic one-dimensional layered media. Here, we demonstrate on-chip random nanolasers where the cavity feedback is provided by the intrinsic disorder. The strong confinement achieved by Anderson localization reduces the spatial overlap between lasing modes, thus preventing mode competition and improving stability. This enables highly efficient, stable and broadband wavelength-controlled lasers with very small mode volumes. Furthermore, the complex interplay between gain, dispersion-controlled slow light, and disorder is demonstrated experimentally for a non-conservative random medium. The statistical analysis shows a way towards optimizing random-lasing performance by reducing the localization length, a universal parameter.

  5. Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms.

    PubMed

    Zhu, Yanjie; Tan, Yongqing; Hua, Yanqing; Zhang, Guozhen; Zhang, Jianguo

    2012-06-01

    Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  7. Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model.

    PubMed

    Lekadir, Karim; Lange, Matthias; Zimmer, Veronika A; Hoogendoorn, Corné; Frangi, Alejandro F

    2016-01-01

    The construction of subject-specific dense and realistic 3D meshes of the myocardial fibers is an important pre-requisite for the simulation of cardiac electrophysiology and mechanics. Current diffusion tensor imaging (DTI) techniques, however, provide only a sparse sampling of the 3D cardiac anatomy based on a limited number of 2D image slices. Moreover, heart motion affects the diffusion measurements, thus resulting in a significant amount of noisy fibers. This paper presents a Markov random field (MRF) approach for dense reconstruction of 3D cardiac fiber orientations from sparse DTI 2D slices. In the proposed MRF model, statistical constraints are used to relate the missing and the known fibers, while a consistency term is encoded to ensure that the obtained 3D meshes are locally continuous. The validation of the method using both synthetic and real DTI datasets demonstrates robust fiber reconstruction and denoising, as well as physiologically meaningful estimations of cardiac electrical activation.

  8. Envelope synthesis of a cylindrical outgoing wavelet in layered random elastic media based on the Markov approximation

    NASA Astrophysics Data System (ADS)

    Emoto, Kentaro; Sato, Haruo; Nishimura, Takeshi

    2013-08-01

    In the heterogeneous earth medium, short period seismograms of an earthquake are well characterized by their smooth envelopes with random phases. The Markov approximation has often been used for the practical synthesis of their envelopes for a given frequency band. It is a stochastic extension of the phase screen method to synthesize wave envelopes in media with random fluctuations under the condition that the wavelength is shorter than the correlation distance of the fluctuation. We propose an extension of the Markov approximation for the envelope synthesis to the case that an isotropically outgoing wavelet is radiated from a point source in horizontal layered random elastic media, where different layers have different randomness and different background velocities. In each layer, we solve the master equation for the two frequency mutual coherence function (TFMCF) which contains the information of the intensity in the frequency domain. Just below each layer boundary, we calculate the angular spectrum which is the expression of the TFMCF in the transverse wavenumber domain for up-going wavelets. The angular spectrum shows the ray angle distribution of intensities of scattered waves. Multiplying it by the square of transmission coefficients calculated from the background velocity contrast at the boundary, we evaluate the angular spectrum just above it. We neglect P to S (S to P) conversion scattering inside of each layer; however, we take into account the mode conversion at the layer boundary. Different from the vertical incidence of a plane wavelet, the wavefront expands with time and its curvature is modified at the layer boundary due to the Snell's law. Approximating the wavefront in the second layer by a circle for a small incidence angle, we may shift the real origin to the pseudo-origin of the wavefront circle, which leads to the change in geometrical spreading factor. Finally, we calculate the mean square envelope from the TFMCF by using an FFT. By

  9. A Markov Random Field Framework for Protein Side-Chain Resonance Assignment

    NASA Astrophysics Data System (ADS)

    Zeng, Jianyang; Zhou, Pei; Donald, Bruce Randall

    Nuclear magnetic resonance (NMR) spectroscopy plays a critical role in structural genomics, and serves as a primary tool for determining protein structures, dynamics and interactions in physiologically-relevant solution conditions. The current speed of protein structure determination via NMR is limited by the lengthy time required in resonance assignment, which maps spectral peaks to specific atoms and residues in the primary sequence. Although numerous algorithms have been developed to address the backbone resonance assignment problem [68,2,10,37,14,64,1,31,60], little work has been done to automate side-chain resonance assignment [43, 48, 5]. Most previous attempts in assigning side-chain resonances depend on a set of NMR experiments that record through-bond interactions with side-chain protons for each residue. Unfortunately, these NMR experiments have low sensitivity and limited performance on large proteins, which makes it difficult to obtain enough side-chain resonance assignments. On the other hand, it is essential to obtain almost all of the side-chain resonance assignments as a prerequisite for high-resolution structure determination. To overcome this deficiency, we present a novel side-chain resonance assignment algorithm based on alternative NMR experiments measuring through-space interactions between protons in the protein, which also provide crucial distance restraints and are normally required in high-resolution structure determination. We cast the side-chain resonance assignment problem into a Markov Random Field (MRF) framework, and extend and apply combinatorial protein design algorithms to compute the optimal solution that best interprets the NMR data. Our MRF framework captures the contact map information of the protein derived from NMR spectra, and exploits the structural information available from the backbone conformations determined by orientational restraints and a set of discretized side-chain conformations (i.e., rotamers). A Hausdorff

  10. A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

    NASA Astrophysics Data System (ADS)

    Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

    2016-07-01

    A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.

  11. A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

    DOE PAGESBeta

    Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

    2016-07-20

    A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

  12. Bayesian prestack seismic inversion with a self-adaptive Huber-Markov random-field edge protection scheme

    NASA Astrophysics Data System (ADS)

    Tian, Yu-Kun; Zhou, Hui; Chen, Han-Ming; Zou, Ya-Ming; Guan, Shou-Jun

    2013-12-01

    Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber-Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.

  13. Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models.

    PubMed

    Michalopoulos, Kostas; Zervakis, Michalis; Deiber, Marie-Pierre; Bourbakis, Nikolaos

    2016-09-01

    We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials. PMID:27255799

  14. Markov random field driven region-based active contour model (MaRACel): application to medical image segmentation.

    PubMed

    Xu, Jun; Monaco, James P; Madabhushi, Anant

    2010-01-01

    In this paper we present a Markov random field (MRF) driven region-based active contour model (MaRACel) for medical image segmentation. State-of-the-art region-based active contour (RAC) models assume that every spatial location in the image is statistically independent of the others, thereby ignoring valuable contextual information. To address this shortcoming we incorporate a MRF prior into the AC model, further generalizing Chan & Vese's (CV) and Rousson and Deriche's (RD) AC models. This incorporation requires a Markov prior that is consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analogue to the discrete Potts model. To demonstrate the effectiveness of MaRACel, we compare its performance to those of the CV and RD AC models in the following scenarios: (1) the qualitative segmentation of a cancerous lesion in a breast DCE-MR image and (2) the qualitative and quantitative segmentations of prostatic acini (glands) in 200 histopathology images. Across the 200 prostate needle core biopsy histology images, MaRACel yielded an average sensitivity, specificity, and positive predictive value of 71%, 95%, 74% with respect to the segmented gland boundaries; the CV and RD models have corresponding values of 19%, 81%, 20% and 53%, 88%, 56%, respectively.

  15. [Spine disc MR image analysis using improved independent component analysis based active appearance model and Markov random field].

    PubMed

    Hao, Shijie; Zhan, Shu; Jiang, Jianguo; Li, Hong; Ian, Rosse

    2010-02-01

    As there are not many research reports on segmentation and quantitative analysis of soft tissues in lumbar medical images, this paper presents an algorithm for segmenting and quantitatively analyzing discs in lumbar Magnetic Resonance Imaging (MRI). Vertebrae are first segmented using improved Independent component analysis based active appearance model (ICA-AAM), and lumbar curve is obtained with Minimum Description Length (MDL); based on these results, fast and unsupervised Markov Random Field (MRF) disc segmentation combining disc imaging features and intensity profile is further achieved; finally, disc herniation is quantitatively evaluated. The experiment proves that the proposed algorithm is fast and effective, thus providing doctors with aid in diagnosing and curing lumbar disc herniation.

  16. Document ink bleed-through removal with two hidden Markov random fields and a single observation field.

    PubMed

    Wolf, Christian

    2010-03-01

    We present a new method for blind document bleed-through removal based on separate Markov Random Field (MRF) regularization for the recto and for the verso side, where separate priors are derived from the full graph. The segmentation algorithm is based on Bayesian Maximum a Posteriori (MAP) estimation. The advantages of this separate approach are the adaptation of the prior to the contents creation process (e.g., superimposing two handwritten pages), and the improvement of the estimation of the recto pixels through an estimation of the verso pixels covered by recto pixels; moreover, the formulation as a binary labeling problem with two hidden labels per pixels naturally leads to an efficient optimization method based on the minimum cut/maximum flow in a graph. The proposed method is evaluated on scanned document images from the 18th century, showing an improvement of character recognition results compared to other restoration methods.

  17. A functional network estimation method of resting-state fMRI using a hierarchical Markov random field.

    PubMed

    Liu, Wei; Awate, Suyash P; Anderson, Jeffrey S; Fletcher, P Thomas

    2014-10-15

    We propose a hierarchical Markov random field model for estimating both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show that our proposed model is able to identify both group and subject functional networks with higher accuracy on synthetic data, more robustness, and inter-session consistency on the real data.

  18. A Functional Networks Estimation Method of Resting-State fMRI Using a Hierarchical Markov Random Field

    PubMed Central

    Liu, Wei; Awate, Suyash P.; Anderson, Jeffrey S.; Fletcher, P. Thomas

    2014-01-01

    We propose a hierarchical Markov random field model that estimates both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show our proposed model is able to identify both group and subject functional networks with higher accuracy, more robustness, and inter-session consistency. PMID:24954282

  19. Weighted maximum posterior marginals for random fields using an ensemble of conditional densities from multiple Markov chain Monte Carlo simulations.

    PubMed

    Monaco, James Peter; Madabhushi, Anant

    2011-07-01

    The ability of classification systems to adjust their performance (sensitivity/specificity) is essential for tasks in which certain errors are more significant than others. For example, mislabeling cancerous lesions as benign is typically more detrimental than mislabeling benign lesions as cancerous. Unfortunately, methods for modifying the performance of Markov random field (MRF) based classifiers are noticeably absent from the literature, and thus most such systems restrict their performance to a single, static operating point (a paired sensitivity/specificity). To address this deficiency we present weighted maximum posterior marginals (WMPM) estimation, an extension of maximum posterior marginals (MPM) estimation. Whereas the MPM cost function penalizes each error equally, the WMPM cost function allows misclassifications associated with certain classes to be weighted more heavily than others. This creates a preference for specific classes, and consequently a means for adjusting classifier performance. Realizing WMPM estimation (like MPM estimation) requires estimates of the posterior marginal distributions. The most prevalent means for estimating these--proposed by Marroquin--utilizes a Markov chain Monte Carlo (MCMC) method. Though Marroquin's method (M-MCMC) yields estimates that are sufficiently accurate for MPM estimation, they are inadequate for WMPM. To more accurately estimate the posterior marginals we present an equally simple, but more effective extension of the MCMC method (E-MCMC). Assuming an identical number of iterations, E-MCMC as compared to M-MCMC yields estimates with higher fidelity, thereby 1) allowing a far greater number and diversity of operating points and 2) improving overall classifier performance. To illustrate the utility of WMPM and compare the efficacies of M-MCMC and E-MCMC, we integrate them into our MRF-based classification system for detecting cancerous glands in (whole-mount or quarter) histological sections of the prostate.

  20. Shared random effects analysis of multi-state Markov models: application to a longitudinal study of transitions to dementia.

    PubMed

    Salazar, Juan C; Schmitt, Frederick A; Yu, Lei; Mendiondo, Marta M; Kryscio, Richard J

    2007-02-10

    Multi-state models are appealing tools for analysing data about the progression of a disease over time. In this paper, we consider a multi-state Markov chain with two competing absorbing states: dementia and death and three transient non-demented states: cognitively normal, amnestic mild cognitive impairment (amnestic MCI), and non-amnestic mild cognitive impairment (non-amnestic MCI). The likelihood function for the data is derived and estimates for the effects of the covariates on transitions are determined when the process can be viewed as a polytomous logistic regression model with shared random effects. The presence of a shared random effect not only complicates the formulation of the likelihood but also its evaluation and maximization. Three approaches for maximizing the likelihood are compared using a simulation study; the first method is based on the Gauss-quadrature technique, the second method is based on importance sampling ideas, and the third method is based on an expansion by Taylor series. The best approach is illustrated using a longitudinal study on a cohort of cognitively normal subjects, followed annually for conversion to mild cognitive impairment (MCI) and/or dementia, conducted at the Sanders Brown Center on Aging at the University of Kentucky. PMID:16345024

  1. Possibility between earthquake and explosion seismogram differentiation by discrete stochastic non-Markov processes and local Hurst exponent analysis.

    PubMed

    Yulmetyev, R; Gafarov, F; Hänggi, P; Nigmatullin, R; Kayumov, S

    2001-12-01

    The basic scientific point of this paper is to draw the attention of researchers to new possibilities of differentiation of similar signals having different nature. One example of such kinds of signals is presented by seismograms containing recordings of earthquakes (EQ's) and technogenic explosions (TE's). EQ's are among the most dramatic phenomena in nature. We propose here a discrete stochastic model for possible solution of a problem of strong EQ forecasting and differentiation of TE's from the weak EQ's. Theoretical analysis is performed by two independent methods: by using statistical theory of discrete non-Markov stochastic processes [Phys. Rev. E 62, 6178 (2000)] and the local Hurst exponent. The following Earth states have been considered among them: before (Ib) and during (I) strong EQ, during weak EQ (II) and during TE (III), and in a calm state of Earth's core (IV). The estimation of states I, II, and III has been made on the particular examples of Turkey (1999) EQ's, state IV has been taken as an example of Earth's state before underground TE. Time recordings of seismic signals of the first four dynamic orthogonal collective variables, six various planes of phase portrait of four-dimensional phase space of orthogonal variables and the local Hurst exponent have been calculated for the dynamic analysis of states of systems I-IV. The analysis of statistical properties of seismic time series I-IV has been realized with the help of a set of discrete time-dependent functions (time correlation function and first three memory functions), their power spectra, and the first three points in the statistical spectrum of non-Markovity parameters. In all systems studied we have found a bizarre combination of the following spectral characteristics: the fractal frequency spectra adjustable by phenomena of usual and restricted self-organized criticality, spectra of white and color noises and unusual alternation of Markov and non-Markov effects of long-range memory

  2. Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

    PubMed

    Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

  3. A Bayesian 3D data fusion and unsupervised joint segmentation approach for stochastic geological modelling using Hidden Markov random fields

    NASA Astrophysics Data System (ADS)

    Wang, Hui; Wellmann, Florian

    2016-04-01

    It is generally accepted that 3D geological models inferred from observed data will contain a certain amount of uncertainties. The uncertainty quantification and stochastic sampling methods are essential for gaining the insight into the geological variability of subsurface structures. In the community of deterministic or traditional modelling techniques, classical geo-statistical methods using boreholes (hard data sets) are still most widely accepted although suffering certain drawbacks. Modern geophysical measurements provide us regional data sets in 2D or 3D spaces either directly from sensors or indirectly from inverse problem solving using observed signal (soft data sets). We propose a stochastic modelling framework to extract subsurface heterogeneity from multiple and complementary types of data. In the presented work, subsurface heterogeneity is considered as the "hidden link" among multiple spatial data sets as well as inversion results. Hidden Markov random field models are employed to perform 3D segmentation which is the representation of the "hidden link". Finite Gaussian mixture models are adopted to characterize the statistical parameters of the multiple data sets. The uncertainties are quantified via a Gibbs sampling process under the Bayesian inferential framework. The proposed modelling framework is validated using two numerical examples. The model behavior and convergence are also well examined. It is shown that the presented stochastic modelling framework is a promising tool for the 3D data fusion in the communities of geological modelling and geophysics.

  4. A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data.

    PubMed

    Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu

    2016-04-01

    The statistical methodology developed in this study was motivated by our interest in studying neurodevelopment using the mouse brain RNA-Seq data set, where gene expression levels were measured in multiple layers in the somatosensory cortex across time in both female and male samples. We aim to identify differentially expressed genes between adjacent time points, which may provide insights on the dynamics of brain development. Because of the extremely small sample size (one male and female at each time point), simple marginal analysis may be underpowered. We propose a Markov random field (MRF)-based approach to capitalizing on the between layers similarity, temporal dependency and the similarity between sex. The model parameters are estimated by an efficient EM algorithm with mean field-like approximation. Simulation results and real data analysis suggest that the proposed model improves the power to detect differentially expressed genes than simple marginal analysis. Our method also reveals biologically interesting results in the mouse brain RNA-Seq data set.

  5. A model-based approach to gene clustering with missing observation reconstruction in a Markov random field framework.

    PubMed

    Blanchet, Juliette; Vignes, Matthieu

    2009-03-01

    The different measurement techniques that interrogate biological systems provide means for monitoring the behavior of virtually all cell components at different scales and from complementary angles. However, data generated in these experiments are difficult to interpret. A first difficulty arises from high-dimensionality and inherent noise of such data. Organizing them into meaningful groups is then highly desirable to improve our knowledge of biological mechanisms. A more accurate picture can be obtained when accounting for dependencies between components (e.g., genes) under study. A second difficulty arises from the fact that biological experiments often produce missing values. When it is not ignored, the latter issue has been solved by imputing the expression matrix prior to applying traditional analysis methods. Although helpful, this practice can lead to unsound results. We propose in this paper a statistical methodology that integrates individual dependencies in a missing data framework. More explicitly, we present a clustering algorithm dealing with incomplete data in a Hidden Markov Random Field context. This tackles the missing value issue in a probabilistic framework and still allows us to reconstruct missing observations a posteriori without imposing any pre-processing of the data. Experiments on synthetic data validate the gain in using our method, and analysis of real biological data shows its potential to extract biological knowledge.

  6. Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

    PubMed

    Lu, Yisu; Jiang, Jun; Yang, Wei; Feng, Qianjin; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

  7. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    NASA Astrophysics Data System (ADS)

    Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

    2015-04-01

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  8. Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

    PubMed Central

    Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi

    2015-01-01

    Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy. PMID:26630674

  9. Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.

    PubMed

    Robinson, Sean; Guyon, Laurent; Nevalainen, Jaakko; Toriseva, Mervi; Åkerfelt, Malin; Nees, Matthias

    2015-01-01

    Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.

  10. Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

    SciTech Connect

    Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

    2015-04-24

    Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

  11. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior

    PubMed Central

    Lu, Yisu; Jiang, Jun; Chen, Wufan

    2014-01-01

    Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064

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

    PubMed

    Lagzi, Fereshteh; Rotter, Stefan

    2014-01-01

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

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

    PubMed Central

    Lagzi, Fereshteh; Rotter, Stefan

    2014-01-01

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

  14. A Markov regression random-effects model for remission of functional disability in patients following a first stroke: a Bayesian approach.

    PubMed

    Pan, Shin-Liang; Wu, Hui-Min; Yen, Amy Ming-Fang; Chen, Tony Hsiu-Hsi

    2007-12-20

    Few attempts have been made to model the dynamics of stroke-related disability. It is possible though, using panel data and multi-state Markov regression models that incorporate measured covariates and latent variables (random effects). This study aimed to model a series of functional transitions (following a first stroke) using a three-state Markov model with or without considering random effects. Several proportional hazards parameterizations were considered. A Bayesian approach that utilizes the Markov Chain Monte Carlo (MCMC) and Gibbs sampling functionality of WinBUGS (a Windows-based Bayesian software package) was developed to generate the marginal posterior distributions of the various transition parameters (e.g. the transition rates and transition probabilities). Model building and comparisons was guided by reference to the deviance information criteria (DIC). Of the four proportional hazards models considered, exponential regression was preferred because it led to the smallest deviances. Adding random effects further improved the model fit. Of the covariates considered, only age, infarct size, and baseline functional status were significant. By using our final model we were able to make individual predictions about functional recovery in stroke patients. PMID:17676712

  15. Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model.

    PubMed

    Dwyer, Michael G; Bergsland, Niels; Zivadinov, Robert

    2014-04-15

    SIENA and similar techniques have demonstrated the utility of performing "direct" measurements as opposed to post-hoc comparison of cross-sectional data for the measurement of whole brain (WB) atrophy over time. However, gray matter (GM) and white matter (WM) atrophy are now widely recognized as important components of neurological disease progression, and are being actively evaluated as secondary endpoints in clinical trials. Direct measures of GM/WM change with advantages similar to SIENA have been lacking. We created a robust and easily-implemented method for direct longitudinal analysis of GM/WM atrophy, SIENAX multi-time-point (SIENAX-MTP). We built on the basic halfway-registration and mask composition components of SIENA to improve the raw output of FMRIB's FAST tissue segmentation tool. In addition, we created LFAST, a modified version of FAST incorporating a 4th dimension in its hidden Markov random field model in order to directly represent time. The method was validated by scan-rescan, simulation, comparison with SIENA, and two clinical effect size comparisons. All validation approaches demonstrated improved longitudinal precision with the proposed SIENAX-MTP method compared to SIENAX. For GM, simulation showed better correlation with experimental volume changes (r=0.992 vs. 0.941), scan-rescan showed lower standard deviations (3.8% vs. 8.4%), correlation with SIENA was more robust (r=0.70 vs. 0.53), and effect sizes were improved by up to 68%. Statistical power estimates indicated a potential drop of 55% in the number of subjects required to detect the same treatment effect with SIENAX-MTP vs. SIENAX. The proposed direct GM/WM method significantly improves on the standard SIENAX technique by trading a small amount of bias for a large reduction in variance, and may provide more precise data and additional statistical power in longitudinal studies.

  16. Application of hidden Markov random field approach for quantification of perfusion/diffusion mismatch in acute ischemic stroke.

    PubMed

    Dwyer, Michael G; Bergsland, Niels; Saluste, Erik; Sharma, Jitendra; Jaisani, Zeenat; Durfee, Jacqueline; Abdelrahman, Nadir; Minagar, Alireza; Hoque, Romy; Munschauer, Frederick E; Zivadinov, Robert

    2008-10-01

    The perfusion/diffusion 'mismatch model' in acute ischemic stroke provides the potential to more accurately understand the consequences of thrombolytic therapy on an individual patient basis. Few methods exist to quantify mismatch extent (ischemic penumbra) and none have shown a robust ability to predict infarcted tissue outcome. Hidden Markov random field (HMRF) approaches have been used successfully in many other applications. The aim of the study was to develop a method for rapid and reliable identification and quantification of perfusion/diffusion mismatch using an HMRF approach. An HMRF model was used in combination with automated contralateral identification to segment normal tissue from non-infarcted tissue with perfusion abnormality. The infarct was used as a seed point to initialize segmentation, along with the contralateral mirror tissue. The two seeds were then allowed to compete for ownership of all unclassified tissue. In addition, a novel method was presented for quantifying tissue salvageability by weighting the volume with the degree of hypoperfusion, allowing the penumbra voxels to contribute unequal potential damage estimates. Simulated and in vivo datasets were processed and compared with results from a conventional thresholding approach. Both simulated and in vivo experiments demonstrated a dramatic improvement in accuracy with the proposed technique. For the simulated dataset, the mean absolute error decreased from 171.9% with conventional thresholding to 2.9% for the delay-weighted HMRF approach. For the in vivo dataset, the mean absolute error decreased from 564.6% for thresholding to 34.2% for the delay-weighted HMRF approach. The described method represents a significant improvement over thresholding techniques.

  17. Inference in alpha rhythm phase and amplitude modeled on Markov random field using belief propagation from electroencephalograms.

    PubMed

    Naruse, Yasushi; Takiyama, Ken; Okada, Masato; Murata, Tsutomu

    2010-07-01

    Alpha rhythm is a major component of spontaneous electroencephalographic (EEG) data. We develop a novel method that can be used to estimate the instantaneous phases and amplitudes of the alpha rhythm with high accuracy by modeling the alpha rhythm phase and amplitude as Markov random field (MRF) models. By using a belief propagation technique, we construct an exact-inference algorithm that can be used to estimate instantaneous phases and amplitudes and calculate the marginal likelihood. Maximizing the marginal likelihood enables us to estimate the hyperparameters on the basis of type-II maximum likelihood estimation. We prove that the instantaneous phase and amplitude estimation by our method is consistent with that by the Hilbert transform, which has been commonly used to estimate instantaneous phases and amplitudes, of a signal filtered from observed data in the limited case that the observed data consist of only one frequency signal whose amplitude is constant and a Gaussian noise. Comparison of the performances of observation noise reduction by our method and by a Gaussian MRF model of alpha rhythm signal indicates that our method reduces observation noise more efficiently. Moreover, the instantaneous phase and amplitude estimates obtained using our method are more accurate than those obtained by the Hilbert transform. Application of our method to experimental EEG data also demonstrates that the relationship between the alpha rhythm phase and the reaction time emerges more clearly by using our method than the Hilbert transform. This indicates our method's practical usefulness. Therefore, applying our method to experimental EEG data will enable us to estimate the instantaneous phases and amplitudes of the alpha rhythm more precisely.

  18. Inference in alpha rhythm phase and amplitude modeled on Markov random field using belief propagation from electroencephalograms

    NASA Astrophysics Data System (ADS)

    Naruse, Yasushi; Takiyama, Ken; Okada, Masato; Murata, Tsutomu

    2010-07-01

    Alpha rhythm is a major component of spontaneous electroencephalographic (EEG) data. We develop a novel method that can be used to estimate the instantaneous phases and amplitudes of the alpha rhythm with high accuracy by modeling the alpha rhythm phase and amplitude as Markov random field (MRF) models. By using a belief propagation technique, we construct an exact-inference algorithm that can be used to estimate instantaneous phases and amplitudes and calculate the marginal likelihood. Maximizing the marginal likelihood enables us to estimate the hyperparameters on the basis of type-II maximum likelihood estimation. We prove that the instantaneous phase and amplitude estimation by our method is consistent with that by the Hilbert transform, which has been commonly used to estimate instantaneous phases and amplitudes, of a signal filtered from observed data in the limited case that the observed data consist of only one frequency signal whose amplitude is constant and a Gaussian noise. Comparison of the performances of observation noise reduction by our method and by a Gaussian MRF model of alpha rhythm signal indicates that our method reduces observation noise more efficiently. Moreover, the instantaneous phase and amplitude estimates obtained using our method are more accurate than those obtained by the Hilbert transform. Application of our method to experimental EEG data also demonstrates that the relationship between the alpha rhythm phase and the reaction time emerges more clearly by using our method than the Hilbert transform. This indicates our method’s practical usefulness. Therefore, applying our method to experimental EEG data will enable us to estimate the instantaneous phases and amplitudes of the alpha rhythm more precisely.

  19. Time-varying Markov regression random-effect model with Bayesian estimation procedures: Application to dynamics of functional recovery in patients with stroke.

    PubMed

    Pan, Shin-Liang; Chen, Hsiu-Hsi

    2010-09-01

    The rates of functional recovery after stroke tend to decrease with time. Time-varying Markov processes (TVMP) may be more biologically plausible than time-invariant Markov process for modeling such data. However, analysis of such stochastic processes, particularly tackling reversible transitions and the incorporation of random effects into models, can be analytically intractable. We make use of ordinary differential equations to solve continuous-time TVMP with reversible transitions. The proportional hazard form was used to assess the effects of an individual's covariates on multi-state transitions with the incorporation of random effects that capture the residual variation after being explained by measured covariates under the concept of generalized linear model. We further built up Bayesian directed acyclic graphic model to obtain full joint posterior distribution. Markov chain Monte Carlo (MCMC) with Gibbs sampling was applied to estimate parameters based on posterior marginal distributions with multiple integrands. The proposed method was illustrated with empirical data from a study on the functional recovery after stroke. PMID:20600158

  20. Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling

    PubMed Central

    Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2016-01-01

    Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging. PMID:27555464

  1. Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling

    NASA Astrophysics Data System (ADS)

    Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2016-08-01

    Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.

  2. Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.

    PubMed

    Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2016-01-01

    Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging. PMID:27555464

  3. Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.

    PubMed

    Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David

    2016-08-24

    Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.

  4. Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots.

    PubMed

    Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

    2015-01-01

    One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914

  5. Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

    PubMed Central

    Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

    2015-01-01

    One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914

  6. Modelling local and global effects on the risk of contracting Tuberculosis using stochastic Markov-chain models.

    PubMed

    Hoad, K A; van't Hoog, A H; Rosen, D; Marston, B; Nyabiage, L; Williams, B G; Dye, C; Cheng, R C H

    2009-04-01

    For some diseases, the transmission of infection can cause spatial clustering of disease cases. This clustering has an impact on how one estimates the rate of the spread of the disease and on the design of control strategies. It is, however, difficult to assess such clustering, (local effects on transmission), using traditional statistical methods. A stochastic Markov-chain model that takes into account possible local or more dispersed global effects on the risk of contracting disease is introduced in the context of the transmission dynamics of tuberculosis. The model is used to analyse TB notifications collected in the Asembo and Gem Divisions of Nyanza Province in western Kenya by the Kenya Ministry of Health/National Leprosy and Tuberculosis Program and the Centers for Disease Control and Prevention. The model shows evidence of a pronounced local effect that is significantly greater than the global effect. We discuss a number of variations of the model which identify how this local effect depends on factors such as age and gender. Zoning/clustering of villages is used to identify the influence that zone size has on the model's ability to distinguish local and global effects. An important possible use of the model is in the design of a community randomised trial where geographical clusters of people are divided into two groups and the effectiveness of an intervention policy is assessed by applying it to one group but not the other. Here the model can be used to take the effect of case clustering into consideration in calculating the minimum difference in an outcome variable (e.g. disease prevalence) that can be detected with statistical significance. It thereby gauges the potential effectiveness of such a trial. Such a possible application is illustrated with the given time/spatial TB data set.

  7. Localization of Spinons in Random Majumdar-Ghosh Chains

    NASA Astrophysics Data System (ADS)

    Lavarélo, Arthur; Roux, Guillaume

    2013-02-01

    We study the effect of disorder on frustrated dimerized spin-1/2 chains at the Majumdar-Ghosh point. Using variational methods and density-matrix renormalization group approaches, we identify two localization mechanisms for spinons which are the deconfined fractional elementary excitations of these chains. The first one belongs to the Anderson localization class and dominates at the random Majumdar-Ghosh point. There, spinons remain gapped and localize in Lifshitz states whose localization length is analytically obtained. The other mechanism is a random confinement mechanism which induces an effective interaction between spinons and brings the chain into a gapless and partially polarized phase for arbitrarily small disorder.

  8. Exact localization and superresolution with noisy data and random illumination

    NASA Astrophysics Data System (ADS)

    Fannjiang, Albert C.

    2011-06-01

    This paper studies the problem of exact localization of multiple objects with noisy data. The crux of the proposed approach consists of random illumination. Two recovery methods are analyzed: the Lasso and the one-step thresholding (OST). For independent random probes, it is shown that both recovery methods can localize exactly s= O(m), up to a logarithmic factor, objects where m is the number of data. Moreover, when the number of random probes is large the Lasso with random illumination has a performance guarantee for superresolution, beating the Rayleigh resolution limit. Numerical evidence confirms the predictions and indicates that the performance of the Lasso is superior to that of the OST for the proposed setup with random illumination.

  9. Local theorems in strengthened form for lattice random variables.

    NASA Technical Reports Server (NTRS)

    Mason, J. D.

    1971-01-01

    Investigation of some conditions which are sufficient for a sequence of independent integral-valued lattice random variables to satisfy a local theorem in strengthened form. A number of theorems giving the conditions under which the investigated sequence satisfies a local theorem in strengthened form are proven with the aid of lemmas derived by Kruglov (1968).

  10. An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours.

    PubMed

    Martín-Fernández, Marcos; Alberola-López, Carlos

    2005-02-01

    In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out.

  11. Localization of Spinons in Random Majumdar-Ghosh Chains

    NASA Astrophysics Data System (ADS)

    Roux, Guillaume; Lavarelo, Arthur

    2014-03-01

    We study the effect of disorder on frustrated dimerized spin-1/2 chains at the Majumdar-Ghosh point. Using variational methods and density-matrix renormalization group approaches, we identify two localization mechanisms for spinons which are the deconfined fractional elementary excitations of these chains. The first one belongs to the Anderson localization class and dominates at the random Majumdar-Ghosh point. There, spinons remain gapped and localize in Lifshitz states whose localization length is analytically obtained. The other mechanism is a random confinement mechanism which induces an effective interaction between spinons and brings the initially gapped antiferromagnetic chain into a gapless and partially polarized phase for arbitrarily small disorder. This Imry-Ma mechanism induces domains which statistics is analyzed. Last, the connection to the real-space renormalization group method suited for the strong disorder limit is discussed.

  12. Designing for Local and Global Meanings of Randomness

    ERIC Educational Resources Information Center

    Paparistodemou, Efi; Noss, Richard

    2004-01-01

    This research aims to study the ways in which "local" events of randomness, based on experiencing the outcome of individual events, can be developed into "global" understandings that focus on an aggregated view of probability (e.g. probability of an event). The findings reported in the paper are part of a broader study that adopted a strategy of…

  13. Local theorems for nonidentically distributed lattice random variables.

    NASA Technical Reports Server (NTRS)

    Mason, J. D.

    1972-01-01

    Derivation of local limit theorems for a sequence X sub n of independent integral-valued lattice random variables involving only a finite number of distinct nondegenerate distributions. Given appropriate sequences A sub n and B sub n of constants such that 1/B sub n (X sub 1 +

  14. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model.

    PubMed

    Khayati, Rasoul; Vafadust, Mansur; Towhidkhah, Farzad; Nabavi, Massood

    2008-03-01

    In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.

  15. Prestack inversion based on anisotropic Markov random field-maximum posterior probability inversion and its application to identify shale gas sweet spots

    NASA Astrophysics Data System (ADS)

    Wang, Kang-Ning; Sun, Zan-Dong; Dong, Ning

    2015-12-01

    Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young's modulus and Poisson's ratio. Fracture-prone sweet spots can be predicted by prestack inversion, which is an ill-posed problem; thus, regularization is needed to obtain unique and stable solutions. To characterize gas-bearing shale sedimentary bodies, elastic parameter variations are regarded as an anisotropic Markov random field. Bayesian statistics are adopted for transforming prestack inversion to the maximum posterior probability. Two energy functions for the lateral and vertical directions are used to describe the distribution, and the expectation-maximization algorithm is used to estimate the hyperparameters of the prior probability of elastic parameters. Finally, the inversion yields clear geological boundaries, high vertical resolution, and reasonable lateral continuity using the conjugate gradient method to minimize the objective function. Antinoise and imaging ability of the method were tested using synthetic and real data.

  16. Fusing Markov random fields with anatomical knowledge and shape-based analysis to segment multiple sclerosis white matter lesions in magnetic resonance images of the brain

    NASA Astrophysics Data System (ADS)

    AlZubi, Stephan; Toennies, Klaus D.; Bodammer, N.; Hinrichs, Herman

    2002-05-01

    This paper proposes an image analysis system to segment multiple sclerosis lesions of magnetic resonance (MR) brain volumes consisting of 3 mm thick slices using three channels (images showing T1-, T2- and PD -weighted contrast). The method uses the statistical model of Markov Random Fields (MRF) both at low and high levels. The neighborhood system used in this MRF is defined in three types: (1) Voxel to voxel: a low-level heterogeneous neighborhood system is used to restore noisy images. (2) Voxel to segment: a fuzzy atlas, which indicates the probability distribution of each tissue type in the brain, is registered elastically with the MRF. It is used by the MRF as a-priori knowledge to correct miss-classified voxels. (3) Segment to segment: Remaining lesion candidates are processed by a feature based classifier that looks at unary and neighborhood information to eliminate more false positives. An expert's manual segmentation was compared with the algorithm.

  17. Extended State to Localization in Random Aperiodic Chains

    NASA Astrophysics Data System (ADS)

    Gao, Hui-Fen; Tao, Rui-Bao

    2006-11-01

    The electronic states in Thus-Morse chain (TMC) and generalized Fibonacci chain (GFC) are studied by solving eigenequation and using transfer matrix method. Two model Hamiltonians are studied. One contains the nearest neighbor (n.n.) hopping terms only and the other has additionally next nearest neighbor (n.n.n.) hopping terms. Based on the transfer matrix method, a criterion of transition from the extended to the localized states is suggested for GFC and TMC. The numerical calculation shows the existence of both extended and localized states in pure aperiodic system. A random potential is introduced to the diagonal term of the Hamiltonian and then the extended states are always changed to be localized. The exponents related to the localization length as a function of randomness are calculated. For different kinds of aperiodic chain, the critical value of randomness for the transition from extended to the localized states are found to be zero, consistent with the case of ordinary one-dimensional systems.

  18. Localization of disordered bosons and magnets in random fields

    SciTech Connect

    Yu, Xiaoquan; Müller, Markus

    2013-10-15

    We study localization properties of disordered bosons and spins in random fields at zero temperature. We focus on two representatives of different symmetry classes, hard-core bosons (XY magnets) and Ising magnets in random transverse fields, and contrast their physical properties. We describe localization properties using a locator expansion on general lattices. For 1d Ising chains, we find non-analytic behavior of the localization length as a function of energy at ω=0, ξ{sup −1}(ω)=ξ{sup −1}(0)+A|ω|{sup α}, with α vanishing at criticality. This contrasts with the much smoother behavior predicted for XY magnets. We use these results to approach the ordering transition on Bethe lattices of large connectivity K, which mimic the limit of high dimensionality. In both models, in the paramagnetic phase with uniform disorder, the localization length is found to have a local maximum at ω=0. For the Ising model, we find activated scaling at the phase transition, in agreement with infinite randomness studies. In the Ising model long range order is found to arise due to a delocalization and condensation initiated at ω=0, without a closing mobility gap. We find that Ising systems establish order on much sparser (fractal) subgraphs than XY models. Possible implications of these results for finite-dimensional systems are discussed. -- Highlights: •Study of localization properties of disordered bosons and spins in random fields. •Comparison between XY magnets (hard-core bosons) and Ising magnets. •Analysis of the nature of the magnetic transition in strong quenched disorder. •Ising magnets: activated scaling, no closing mobility gap at the transition. •Ising order emerges on sparser (fractal) support than XY order.

  19. Beyond Anderson localization in 1D: anomalous localization of microwaves in random waveguides.

    PubMed

    Fernández-Marín, A A; Méndez-Bermúdez, J A; Carbonell, J; Cervera, F; Sánchez-Dehesa, J; Gopar, V A

    2014-12-01

    Experimental evidence demonstrating that anomalous localization of waves can be induced in a controllable manner is reported. A microwave waveguide with dielectric slabs randomly placed is used to confirm the presence of anomalous localization. If the random spacing between slabs follows a distribution with a power-law tail (Lévy-type distribution), unconventional properties in the microwave-transmission fluctuations take place revealing the presence of anomalous localization. We study both theoretically and experimentally the complete distribution of the transmission through random waveguides characterized by α=1/2 ("Lévy waveguides") and α=3/4, α being the exponent of the power-law tail of the Lévy-type distribution. As we show, the transmission distributions are determined by only two parameters, both of them experimentally accessible. Effects of anomalous localization on the transmission are compared with those from the standard Anderson localization.

  20. Local Spin Relaxation within the Random Heisenberg Chain

    NASA Astrophysics Data System (ADS)

    Herbrych, J.; Kokalj, J.; Prelovšek, P.

    2013-10-01

    Finite-temperature local dynamical spin correlations Snn(ω) are studied numerically within the random spin-1/2 antiferromagnetic Heisenberg chain. The aim is to explain measured NMR spin-lattice relaxation times in BaCu2(Si0.5Ge0.5)2O7, which is the realization of a random spin chain. In agreement with experiments we find that the distribution of relaxation times within the model shows a very large span similar to the stretched-exponential form. The distribution is strongly reduced with increasing T, but stays finite also in the high-T limit. Anomalous dynamical correlations can be associated with the random singlet concept but not directly with static quantities. Our results also reveal the crucial role of the spin anisotropy (interaction), since the behavior is in contrast with the ones for the XX model, where we do not find any significant T dependence of the distribution.

  1. Non-local MRI denoising using random sampling.

    PubMed

    Hu, Jinrong; Zhou, Jiliu; Wu, Xi

    2016-09-01

    In this paper, we propose a random sampling non-local mean (SNLM) algorithm to eliminate noise in 3D MRI datasets. Non-local means (NLM) algorithms have been implemented efficiently for MRI denoising, but are always limited by high computational complexity. Compared to conventional methods, which raster through the entire search window when computing similarity weights, the proposed SNLM algorithm randomly selects a small subset of voxels which dramatically decreases the computational burden, together with competitive denoising result. Moreover, structure tensor which encapsulates high-order information was introduced as an optimal sampling pattern for further improvement. Numerical experiments demonstrated that the proposed SNLM method can get a good balance between denoising quality and computation efficiency. At a relative sampling ratio (i.e. ξ=0.05), SNLM can remove noise as effectively as full NLM, meanwhile the running time can be reduced to 1/20 of NLM's. PMID:27114338

  2. Buckling mode localization in randomly disordered multispan continuous beams

    NASA Astrophysics Data System (ADS)

    Xie, Wei-Chau

    1995-06-01

    Buckling mode localization in large randomly disordered multispan continuous beams is studied. When the cross-sections of each span are uniform, an exact formulation is employed to establish the equations of equilibrium in terms of the angles of rotations at the supports. When the cross-sections of each span are not uniform, a finite element method is applied to set up the governing equations. Two approaches are applied to determine the localization factors, which characterize the average exponential rates of growth or decay of amplitudes of deformation. The first method applies a transfer matrix formulation and Furstenberg's theorem on the asymptotic behavior of products of random matrices. The second method uses a Green's function formulation for a linear eigenvalue problem of a block tridiagonal form.

  3. Simultaneous evaluation of abstinence and relapse using a Markov chain model in smokers enrolled in a two-year randomized trial

    PubMed Central

    2012-01-01

    Background GEE and mixed models are powerful tools to compare treatment effects in longitudinal smoking cessation trials. However, they are not capable of assessing the relapse (from abstinent back to smoking) simultaneously with cessation, which can be studied by transition models. Methods We apply a first-order Markov chain model to analyze the transition of smoking status measured every 6 months in a 2-year randomized smoking cessation trial, and to identify what factors are associated with the transition from smoking to abstinent and from abstinent to smoking. Missing values due to non-response are assumed non-ignorable and handled by the selection modeling approach. Results Smokers receiving high-intensity disease management (HDM), of male gender, lower daily cigarette consumption, higher motivation and confidence to quit, and having serious attempts to quit were more likely to become abstinent (OR = 1.48, 1.66, 1.03, 1.15, 1.09 and 1.34, respectively) in the next 6 months. Among those who were abstinent, lower income and stronger nicotine dependence (OR = 1.72 for ≤ vs. > 40 K and OR = 1.75 for first cigarette ≤ vs. > 5 min) were more likely to have relapse in the next 6 months. Conclusions Markov chain models allow investigation of dynamic smoking-abstinence behavior and suggest that relapse is influenced by different factors than cessation. The knowledge of treatments and covariates in transitions in both directions may provide guidance for designing more effective interventions on smoking cessation and relapse prevention. Trial Registration clinicaltrials.gov identifier: NCT00440115 PMID:22770436

  4. RRW: repeated random walks on genome-scale protein networks for local cluster discovery

    PubMed Central

    Macropol, Kathy; Can, Tolga; Singh, Ambuj K

    2009-01-01

    Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439

  5. Localization of wave packets in one-dimensional random potentials

    NASA Astrophysics Data System (ADS)

    Valdes, Juan Pablo Ramírez; Wellens, Thomas

    2016-06-01

    We study the expansion of an initially strongly confined wave packet in a one-dimensional weak random potential with short correlation length. At long times, the expansion of the wave packet comes to a halt due to destructive interferences leading to Anderson localization. We develop an analytical description for the disorder-averaged localized density profile. For this purpose, we employ the diagrammatic method of Berezinskii which we extend to the case of wave packets, present an analytical expression of the Lyapunov exponent which is valid for small as well as for high energies, and, finally, develop a self-consistent Born approximation in order to analytically calculate the energy distribution of our wave packet. By comparison with numerical simulations, we show that our theory describes well the complete localized density profile, not only in the tails but also in the center.

  6. Many-body localization in the quantum random energy model

    NASA Astrophysics Data System (ADS)

    Laumann, Chris; Pal, Arijeet

    2014-03-01

    The quantum random energy model is a canonical toy model for a quantum spin glass with a well known phase diagram. We show that the model exhibits a many-body localization-delocalization transition at finite energy density which significantly alters the interpretation of the statistical ``frozen'' phase at lower temperature in isolated quantum systems. The transition manifests in many-body level statistics as well as the long time dynamics of on-site observables. CRL thanks the Perimeter Institute for hospitality and support.

  7. Local random potentials of high differentiability to model the Landscape

    SciTech Connect

    Battefeld, T.; Modi, C.

    2015-03-09

    We generate random functions locally via a novel generalization of Dyson Brownian motion, such that the functions are in a desired differentiability class C{sup k}, while ensuring that the Hessian is a member of the Gaussian orthogonal ensemble (other ensembles might be chosen if desired). Potentials in such higher differentiability classes (k≥2) are required/desirable to model string theoretical landscapes, for instance to compute cosmological perturbations (e.g., k=2 for the power-spectrum) or to search for minima (e.g., suitable de Sitter vacua for our universe). Since potentials are created locally, numerical studies become feasible even if the dimension of field space is large (D∼100). In addition to the theoretical prescription, we provide some numerical examples to highlight properties of such potentials; concrete cosmological applications will be discussed in companion publications.

  8. Semi-Markov Graph Dynamics

    PubMed Central

    Raberto, Marco; Rapallo, Fabio; Scalas, Enrico

    2011-01-01

    In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first ingredient is a Markov chain on the space of possible graphs. The second ingredient is a semi-Markov counting process of renewal type. The model consists in subordinating the Markov chain to the semi-Markov counting process. In simple words, this means that the chain transitions occur at random time instants called epochs. The model is quite rich and its possible connections with algebraic geometry are briefly discussed. Moreover, for the sake of simplicity, we focus on the space of undirected graphs with a fixed number of nodes. However, in an example, we present an interbank market model where it is meaningful to use directed graphs or even weighted graphs. PMID:21887245

  9. Integration of Gibbs Markov random field and Hopfield-type neural networks for unsupervised change detection in remotely sensed multitemporal images.

    PubMed

    Ghosh, Ashish; Subudhi, Badri Narayan; Bruzzone, Lorenzo

    2013-08-01

    In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.

  10. Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework

    SciTech Connect

    Kulkarni, Ramaprasad; Tuller, Markus; Fink, Wolfgang; Wildschild, Dorthe

    2012-07-27

    Advancements in noninvasive imaging methods such as X-ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore-scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable three-dimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet-dry or dual-energy scans, image alignment, and subtraction analysis, commonly applied in X-ray micro-CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three-dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuff obtained with synchrotron X-ray micro-CT demonstrate great potential of the MRF image model for three-dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.

  11. A multichannel Markov random field approach for automated segmentation of breast cancer tumor in DCE-MRI data using kinetic observation model.

    PubMed

    Ashraf, Ahmed B; Gavenonis, Sara; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

    2011-01-01

    We present a multichannel extension of Markov random fields (MRFs) for incorporating multiple feature streams in the MRF model. We prove that for making inference queries, any multichannel MRF can be reduced to a single channel MRF provided features in different channels are conditionally independent given the hidden variable, Using this result we incorporate kinetic feature maps derived from breast DCE MRI into the observation model of MRF for tumor segmentation. Our algorithm achieves an ROC AUC of 0.97 for tumor segmentation, We present a comparison against the commonly used approach of fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES). These previous methods give a lower AUC of 0.86 and 0.60 respectively, indicating the superiority of our algorithm. Finally, we investigate the effect of superior segmentation on predicting breast cancer recurrence using kinetic DCE MRI features from the segmented tumor regions. A linear prediction model shows significant prediction improvement when segmenting the tumor using the proposed method, yielding a correlation coefficient r = 0.78 (p < 0.05) to validated cancer recurrence probabilities, compared to 0.63 and 0.45 when using FCM and FCM-VES respectively.

  12. Image encryption algorithm based on the random local phase encoding in gyrator transform domains

    NASA Astrophysics Data System (ADS)

    Liu, Zhengjun; Yang, Meng; Liu, Wei; Li, She; Gong, Min; Liu, Wanyu; Liu, Shutian

    2012-09-01

    A random local phase encoding method is presented for encrypting a secret image. Some random polygons are introduced to control the local regions of random phase encoding. The data located in the random polygon is encoded by random phase encoding. The random phase data is the main key in this encryption method. The different random phases calculated by using a monotonous function are employed. The random data defining random polygon serves as an additional key for enhancing the security of the image encryption scheme. Numerical simulations are given for demonstrating the performance of the proposed encryption approach.

  13. No-signaling, perfect bipartite dichotomic correlations and local randomness

    SciTech Connect

    Seevinck, M. P.

    2011-03-28

    The no-signaling constraint on bi-partite correlations is reviewed. It is shown that in order to obtain non-trivial Bell-type inequalities that discern no-signaling correlations from more general ones, one must go beyond considering expectation values of products of observables only. A new set of nontrivial no-signaling inequalities is derived which have a remarkably close resemblance to the CHSH inequality, yet are fundamentally different. A set of inequalities by Roy and Singh and Avis et al., which is claimed to be useful for discerning no-signaling correlations, is shown to be trivially satisfied by any correlation whatsoever. Finally, using the set of newly derived no-signaling inequalities a result with potential cryptographic consequences is proven: if different parties use identical devices, then, once they have perfect correlations at spacelike separation between dichotomic observables, they know that because of no-signaling the local marginals cannot but be completely random.

  14. Propagation of correlations in local random quantum circuits

    NASA Astrophysics Data System (ADS)

    Santra, Siddhartha; Balu, Radhakrishnan

    2016-08-01

    We derive a dynamical bound on the propagation of correlations in local random quantum circuits—lattice spin systems where piecewise quantum operations—in space and time—occur with classical probabilities. Correlations are quantified by the Frobenius norm of the commutator of two positive operators acting on disjoint regions of a one-dimensional circular chain of length L. For a time t=O(L) , correlations spread ballistically to spatial distances D=t , growing at best, diffusively with time for any distance within that radius with extensively suppressed distance- dependent corrections. For t=Ω (L^2) , all parts of the system get almost equally correlated with exponentially suppressed distance- dependent corrections and approach the maximum amount of correlations that may be established asymptotically.

  15. A Markov random field based approach to the identification of meat and bone meal in feed by near-infrared spectroscopic imaging.

    PubMed

    Jiang, Xunpeng; Yang, Zengling; Han, Lujia

    2014-07-01

    Contaminated meat and bone meal (MBM) in animal feedstuff has been the source of bovine spongiform encephalopathy (BSE) disease in cattle, leading to a ban in its use, so methods for its detection are essential. In this study, five pure feed and five pure MBM samples were used to prepare two sets of sample arrangements: set A for investigating the discrimination of individual feed/MBM particles and set B for larger numbers of overlapping particles. The two sets were used to test a Markov random field (MRF)-based approach. A Fourier transform infrared (FT-IR) imaging system was used for data acquisition. The spatial resolution of the near-infrared (NIR) spectroscopic image was 25 μm × 25 μm. Each spectrum was the average of 16 scans across the wavenumber range 7,000-4,000 cm(-1), at intervals of 8 cm(-1). This study introduces an innovative approach to analyzing NIR spectroscopic images: an MRF-based approach has been developed using the iterated conditional mode (ICM) algorithm, integrating initial labeling-derived results from support vector machine discriminant analysis (SVMDA) and observation data derived from the results of principal component analysis (PCA). The results showed that MBM covered by feed could be successfully recognized with an overall accuracy of 86.59% and a Kappa coefficient of 0.68. Compared with conventional methods, the MRF-based approach is capable of extracting spectral information combined with spatial information from NIR spectroscopic images. This new approach enhances the identification of MBM using NIR spectroscopic imaging.

  16. A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk.

    PubMed

    Ashraf, Ahmed B; Gavenonis, Sara C; Daye, Dania; Mies, Carolyn; Rosen, Mark A; Kontos, Despina

    2013-04-01

    We present a methodological framework for multichannel Markov random fields (MRFs). We show that conditional independence allows loopy belief propagation to solve a multichannel MRF as a single channel MRF. We use conditional mutual information to search for features that satisfy conditional independence assumptions. Using this framework we incorporate kinetic feature maps derived from breast dynamic contrast enhanced magnetic resonance imaging as observation channels in MRF for tumor segmentation. Our algorithm based on multichannel MRF achieves an receiver operating characteristic area under curve (AUC) of 0.97 for tumor segmentation when using a radiologist's manual delineation as ground truth. Single channel MRF based on the best feature chosen from the same pool of features as used by the multichannel MRF achieved a lower AUC of 0.89. We also present a comparison against the well established normalized cuts segmentation algorithm along with commonly used approaches for breast tumor segmentation including fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES). These previous methods give a lower AUC of 0.92, 0.88, and 0.60, respectively. Finally, we also investigate the role of superior segmentation in feature extraction and tumor characterization. Specifically, we examine the effect of improved segmentation on predicting the probability of breast cancer recurrence as determined by a validated tumor gene expression assay. We demonstrate that an support vector machine classifier trained on kinetic statistics extracted from tumors as segmented by our algorithm gives a significant improvement in distinguishing between women with high and low recurrence risk, giving an AUC of 0.88 as compared to 0.79, 0.76, 0.75, and 0.66 when using normalized cuts, single channel MRF, FCM, and FCM-VES, respectively, for segmentation.

  17. New automated Markov-Gibbs random field based framework for myocardial wall viability quantification on agent enhanced cardiac magnetic resonance images.

    PubMed

    Elnakib, Ahmed; Beache, Garth M; Gimel'farb, Georgy; El-Baz, Ayman

    2012-10-01

    A novel automated framework for detecting and quantifying viability from agent enhanced cardiac magnetic resonance images is proposed. The framework identifies the pathological tissues based on a joint Markov-Gibbs random field (MGRF) model that accounts for the 1st-order visual appearance of the myocardial wall (in terms of the pixel-wise intensities) and the 2nd-order spatial interactions between pixels. The pathological tissue is quantified based on two metrics: the percentage area in each segment with respect to the total area of the segment, and the trans-wall extent of the pathological tissue. This transmural extent is estimated using point-to-point correspondences based on a Laplace partial differential equation. Transmural extent was validated using a simulated phantom. We tested the proposed framework on 14 datasets (168 images) and validated against manual expert delineation of the pathological tissue by two observers. Mean Dice similarity coefficients (DSC) of 0.90 and 0.88 were obtained for the observers, approaching the ideal value, 1. The Bland-Altman statistic of infarct volumes estimated by manual versus the MGRF estimation revealed little bias difference, and most values fell within the 95% confidence interval, suggesting very good agreement. Using the DSC measure we documented statistically significant superior segmentation performance for our MGRF method versus established intensity-based methods (greater DSC, and smaller standard deviation). Our Laplace method showed good operating characteristics across the full range of extent of transmural infarct, outperforming conventional methods. Phantom validation and experiments on patient data confirmed the robustness and accuracy of the proposed framework.

  18. Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields: Stochastic inversion of AVA and CSEM data

    SciTech Connect

    Chen, J.; Hoversten, G.M.

    2011-09-15

    Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.

  19. Musical Markov Chains

    NASA Astrophysics Data System (ADS)

    Volchenkov, Dima; Dawin, Jean René

    A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.

  20. Randomized discrepancy bounded local search for transmission expansion planning

    SciTech Connect

    Bent, Russell W; Daniel, William B

    2010-11-23

    In recent years the transmission network expansion planning problem (TNEP) has become increasingly complex. As the TNEP is a non-linear and non-convex optimization problem, researchers have traditionally focused on approximate models of power flows to solve the TNEP. Existing approaches are often tightly coupled to the approximation choice. Until recently these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (e.g. large amounts of limited control, renewable generation) and necessitates new approaches. Recent work on deterministic Discrepancy Bounded Local Search (DBLS) has shown it to be quite effective in addressing this question. DBLS encapsulates the complexity of power flow modeling in a black box that may be queried for information about the quality of proposed expansions. In this paper, we propose a randomization strategy that builds on DBLS and dramatically increases the computational efficiency of the algorithm.

  1. Random walks in nonuniform environments with local dynamic interactions

    NASA Astrophysics Data System (ADS)

    Baker, Christopher M.; Hughes, Barry D.; Landman, Kerry A.

    2013-10-01

    We consider a class of lattice random walk models in which the random walker is initially confined to a finite connected set of allowed sites but has the opportunity to enlarge this set by colliding with its boundaries, each such collision having a given probability of breaking through. The model is motivated by an analogy to cell motility in tissue, where motile cells have the ability to remodel extracellular matrix, but is presented here as a generic model for stochastic erosion. For the one-dimensional case, we report some exact analytic results, some mean-field type analytic approximate results and simulations. We compute exactly the mean and variance of the time taken to enlarge the interval from a single site to a given size. The problem of determining the statistics of the interval length and the walker's position at a given time is more difficult and we report several interesting observations from simulations. Our simulations include the case in which the initial interval length is random and the case in which the initial state of the lattice is a random mixture of allowed and forbidden sites, with the walker placed at random on an allowed site. To illustrate the extension of these ideas to higher-dimensional systems, we consider the erosion of the simple cubic lattice commencing from a single site and report simulations of measures of cluster size and shape and the mean-square displacement of the walker.

  2. The defect-induced localization in many positions of the quantum random walk

    PubMed Central

    Chen, Tian; Zhang, Xiangdong

    2016-01-01

    We study the localization of probability distribution in a discrete quantum random walk on an infinite chain. With a phase defect introduced in any position of the quantum random walk (QRW), we have found that the localization of the probability distribution in the QRW emerges. Different localized behaviors of the probability distribution in the QRW are presented when the defect occupies different positions. Given that the coefficients of the localized stationary eigenstates relies on the coin operator, we reveal that when the defect occupies different positions, the amplitude of localized probability distribution in the QRW exhibits a non-trivial dependence on the coin operator. PMID:27216697

  3. Local dependence in random graph models: characterization, properties and statistical inference

    PubMed Central

    Schweinberger, Michael; Handcock, Mark S.

    2015-01-01

    Summary Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing random graph models with local dependence, many conventional exponential family random graph models induce strong dependence and are not amenable to statistical inference. We take first steps to characterize local dependence in random graph models, inspired by the notion of finite neighbourhoods in spatial statistics and M-dependence in time series, and we show that local dependence endows random graph models with desirable properties which make them amenable to statistical inference. We show that random graph models with local dependence satisfy a natural domain consistency condition which every model should satisfy, but conventional exponential family random graph models do not satisfy. In addition, we establish a central limit theorem for random graph models with local dependence, which suggests that random graph models with local dependence are amenable to statistical inference. We discuss how random graph models with local dependence can be constructed by exploiting either observed or unobserved neighbourhood structure. In the absence of observed neighbourhood structure, we take a Bayesian view and express the uncertainty about the neighbourhood structure by specifying a prior on a set of suitable neighbourhood structures. We present simulation results and applications to two real world networks with ‘ground truth’. PMID:26560142

  4. Elucid—exploring the local universe with the reconstructed initial density field. I. Hamiltonian Markov chain Monte Carlo method with particle mesh dynamics

    SciTech Connect

    Wang, Huiyuan; Mo, H. J.; Yang, Xiaohu; Lin, W. P.; Jing, Y. P.

    2014-10-10

    Simulating the evolution of the local universe is important for studying galaxies and the intergalactic medium in a way free of cosmic variance. Here we present a method to reconstruct the initial linear density field from an input nonlinear density field, employing the Hamiltonian Markov Chain Monte Carlo (HMC) algorithm combined with Particle-mesh (PM) dynamics. The HMC+PM method is applied to cosmological simulations, and the reconstructed linear density fields are then evolved to the present day with N-body simulations. These constrained simulations accurately reproduce both the amplitudes and phases of the input simulations at various z. Using a PM model with a grid cell size of 0.75 h {sup –1} Mpc and 40 time steps in the HMC can recover more than half of the phase information down to a scale k ∼ 0.85 h Mpc{sup –1} at high z and to k ∼ 3.4 h Mpc{sup –1} at z = 0, which represents a significant improvement over similar reconstruction models in the literature, and indicates that our model can reconstruct the formation histories of cosmic structures over a large dynamical range. Adopting PM models with higher spatial and temporal resolutions yields even better reconstructions, suggesting that our method is limited more by the availability of computer resource than by principle. Dynamic models of structure evolution adopted in many earlier investigations can induce non-Gaussianity in the reconstructed linear density field, which in turn can cause large systematic deviations in the predicted halo mass function. Such deviations are greatly reduced or absent in our reconstruction.

  5. Random vectorial fields representing the local structure of turbulence

    NASA Astrophysics Data System (ADS)

    Chevillard, Laurent; Robert, Raoul; Vargas, Vincent

    2011-12-01

    We propose a method to build up a random homogeneous, isotropic and incompressible turbulent velocity field that mimics turbulence in the inertial range. The underlying Gaussian field is given by a modified Biot-Savart law. The long range correlated nature of turbulence is then incorporated heuristically using a non linear transformation inspired by the recent fluid deformation imposed by the Euler equations. The resulting velocity field shows a non vanishing mean energy transfer towards the small scales and realistic alignment properties of vorticity with the eigenframe of the deformation rate.

  6. Markov Tracking for Agent Coordination

    NASA Technical Reports Server (NTRS)

    Washington, Richard; Lau, Sonie (Technical Monitor)

    1998-01-01

    Partially observable Markov decision processes (POMDPs) axe an attractive representation for representing agent behavior, since they capture uncertainty in both the agent's state and its actions. However, finding an optimal policy for POMDPs in general is computationally difficult. In this paper we present Markov Tracking, a restricted problem of coordinating actions with an agent or process represented as a POMDP Because the actions coordinate with the agent rather than influence its behavior, the optimal solution to this problem can be computed locally and quickly. We also demonstrate the use of the technique on sequential POMDPs, which can be used to model a behavior that follows a linear, acyclic trajectory through a series of states. By imposing a "windowing" restriction that restricts the number of possible alternatives considered at any moment to a fixed size, a coordinating action can be calculated in constant time, making this amenable to coordination with complex agents.

  7. Handling target obscuration through Markov chain observations

    NASA Astrophysics Data System (ADS)

    Kouritzin, Michael A.; Wu, Biao

    2008-04-01

    Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.

  8. Local hidden variable models for entangled quantum States using finite shared randomness.

    PubMed

    Bowles, Joseph; Hirsch, Flavien; Quintino, Marco Túlio; Brunner, Nicolas

    2015-03-27

    The statistics of local measurements performed on certain entangled states can be reproduced using a local hidden variable (LHV) model. While all known models make use of an infinite amount of shared randomness, we show that essentially all entangled states admitting a LHV model can be simulated with finite shared randomness. Our most economical model simulates noisy two-qubit Werner states using only log_{2}(12)≃3.58 bits of shared randomness. We also discuss the case of positive operator valued measures, and the simulation of nonlocal states with finite shared randomness and finite communication. Our work represents a first step towards quantifying the cost of LHV models for entangled quantum states.

  9. Anderson localization of electromagnetic waves in randomly-stratified magnetodielectric media with uniform impedance.

    PubMed

    Kim, Kihong

    2015-06-01

    The propagation and the Anderson localization of electromagnetic waves in a randomly-stratified slab, where both the dielectric permittivity and the magnetic permeability depend on one spatial coordinate in a random manner, is theoretically studied. The case where the wave impedance is uniform, while the refractive index is random, is considered in detail. The localization length and the disorder-averaged transmittance of s and p waves incident obliquely on the slab are calculated as a function of the incident angle θ and the strength of randomness in a numerically precise manner, using the invariant imbedding method. It is found that the waves incident perpendicularly on the slab are delocalized, while those incident obliquely are localized. As the incident angle increases from zero, the localization length decreases from infinity monotonically to some finite value. The localization length is found to depend on the incident angle as θ-4 and a simple analytical formula, which works quite well for weak disorder and small incident angles, is derived. The localization length does not depend on the wave polarization, but the disorder-averaged transmittance generally does.

  10. Wegner estimates, Lifshitz tails, and Anderson localization for Gaussian random magnetic fields

    NASA Astrophysics Data System (ADS)

    Ueki, Naomasa

    2016-07-01

    The Wegner estimate for the Hamiltonian of the Anderson model for the special Gaussian random magnetic field is extended to more general magnetic fields. The Lifshitz tail upper bounds of the integrated density of states as analyzed by Nakamura are reviewed and extended so that Gaussian random magnetic fields can be treated. By these and multiscale analysis, the Anderson localization at low energies is proven.

  11. Algorithms for Discovery of Multiple Markov Boundaries

    PubMed Central

    Statnikov, Alexander; Lytkin, Nikita I.; Lemeire, Jan; Aliferis, Constantin F.

    2013-01-01

    Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have been proposed to identify a single Markov boundary of the response variable. Even though faithful distributions and, more broadly, distributions that satisfy the intersection property always have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries from such data. However, there are currently no sound and efficient algorithms that can accomplish this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries in a distribution. The broad applicability as well as efficiency of the new algorithmic family is demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art algorithms/variants in 15 data sets from a diversity of application domains. PMID:25285052

  12. Local polynomial chaos expansion for linear differential equations with high dimensional random inputs

    SciTech Connect

    Chen, Yi; Jakeman, John; Gittelson, Claude; Xiu, Dongbin

    2015-01-08

    In this paper we present a localized polynomial chaos expansion for partial differential equations (PDE) with random inputs. In particular, we focus on time independent linear stochastic problems with high dimensional random inputs, where the traditional polynomial chaos methods, and most of the existing methods, incur prohibitively high simulation cost. Furthermore, the local polynomial chaos method employs a domain decomposition technique to approximate the stochastic solution locally. In each subdomain, a subdomain problem is solved independently and, more importantly, in a much lower dimensional random space. In a postprocesing stage, accurate samples of the original stochastic problems are obtained from the samples of the local solutions by enforcing the correct stochastic structure of the random inputs and the coupling conditions at the interfaces of the subdomains. Overall, the method is able to solve stochastic PDEs in very large dimensions by solving a collection of low dimensional local problems and can be highly efficient. In our paper we present the general mathematical framework of the methodology and use numerical examples to demonstrate the properties of the method.

  13. Hidden Markov model using Dirichlet process for de-identification.

    PubMed

    Chen, Tao; Cullen, Richard M; Godwin, Marshall

    2015-12-01

    For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance. PMID:26407642

  14. Anderson Localization of a Bose-Einstein Condensate in a 3D Random Potential

    SciTech Connect

    Skipetrov, S. E.; Minguzzi, A.; Tiggelen, B. A. van; Shapiro, B.

    2008-04-25

    We study the effect of Anderson localization on the expansion of a Bose-Einstein condensate, released from a harmonic trap, in a 3D random potential. We use scaling arguments and the self-consistent theory of localization to show that the long-time behavior of the condensate density is controlled by a single parameter equal to the ratio of the mobility edge and the chemical potential of the condensate. We find that the two critical exponents of the localization transition determine the evolution of the condensate density in time and space.

  15. Phasic Triplet Markov Chains.

    PubMed

    El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar

    2014-11-01

    Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data. PMID:26353069

  16. Local search methods based on variable focusing for random K-satisfiability.

    PubMed

    Lemoy, Rémi; Alava, Mikko; Aurell, Erik

    2015-01-01

    We introduce variable focused local search algorithms for satisfiabiliity problems. Usual approaches focus uniformly on unsatisfied clauses. The methods described here work by focusing on random variables in unsatisfied clauses. Variants are considered where variables are selected uniformly and randomly or by introducing a bias towards picking variables participating in several unsatistified clauses. These are studied in the case of the random 3-SAT problem, together with an alternative energy definition, the number of variables in unsatisfied constraints. The variable-based focused Metropolis search (V-FMS) is found to be quite close in performance to the standard clause-based FMS at optimal noise. At infinite noise, instead, the threshold for the linearity of solution times with instance size is improved by picking preferably variables in several UNSAT clauses. Consequences for algorithmic design are discussed. PMID:25679737

  17. Local search methods based on variable focusing for random K -satisfiability

    NASA Astrophysics Data System (ADS)

    Lemoy, Rémi; Alava, Mikko; Aurell, Erik

    2015-01-01

    We introduce variable focused local search algorithms for satisfiabiliity problems. Usual approaches focus uniformly on unsatisfied clauses. The methods described here work by focusing on random variables in unsatisfied clauses. Variants are considered where variables are selected uniformly and randomly or by introducing a bias towards picking variables participating in several unsatistified clauses. These are studied in the case of the random 3-SAT problem, together with an alternative energy definition, the number of variables in unsatisfied constraints. The variable-based focused Metropolis search (V-FMS) is found to be quite close in performance to the standard clause-based FMS at optimal noise. At infinite noise, instead, the threshold for the linearity of solution times with instance size is improved by picking preferably variables in several UNSAT clauses. Consequences for algorithmic design are discussed.

  18. The random-motion theorem in a local cosmology with dark energy

    NASA Astrophysics Data System (ADS)

    Chernin, A. D.; Dolgachev, V. P.; Domozhilova, L. M.; Teerikorpi, P.; Valtonen, M. Yu.

    2010-03-01

    It is shown that the random-motion theorem in cosmology proven in the early 1960s can be generalized to take into account the presence of a uniform dark-energy background. The role of the dark energy is substantial: its repulsive force exceeds the gravitational force due to darkmatter and baryons, both on the scale of the Universe as a whole and on local scales of about 1 Mpc. The generalized random-motion theorem has the form of a differential equation relating the kinetic energy of the random motion and the potential energy of the particles due to their own gravitational field and the repulsive dark-energy field. One consequence of the generalized theorem is a virial relation containing the potential energy in the repulsive field.

  19. Localization transition in random Lévy matrices: multifractality of eigenvectors in the localized phase and at criticality

    NASA Astrophysics Data System (ADS)

    Monthus, Cécile

    2016-09-01

    For random Lévy matrices of size N× N , where matrix elements are drawn with some heavy-tailed distribution P≤ft({{H}ij}\\right)\\propto {{N} -1}|{{H}ij}{{|}-1-μ} with 0<μ <2 (infinite variance), there exists an extensive number of finite eigenvalues E  =  O(1), while the maximal eigenvalue grows as {{E}\\text{max}}∼ {{N}\\frac{1μ}} . Here we study the localization properties of the corresponding eigenvectors via some strong disorder perturbative expansion that remains consistent within the localized phase and that yields their inverse participation ratios (IPR) Y q as a function of the continuous parameter 0. In the region 0<μ <1 , we find that all eigenvectors are localized but display some multifractality: the IPR are finite above some threshold q  >  q c but diverge in the region 0  <  q  <  q c near the origin. In the region 1<μ <2 , only the sub-extensive fraction {{N}\\frac{3{2+μ}}} of the biggest eigenvalues corresponding to the region |E|≥slant {{N}\\frac{(μ -1)μ (2+μ )}}} remains localized, while the extensive number of other states of smaller energy are delocalized. For the extensive number of finite eigenvalues E  =  O(1), the localization/delocalization transition thus takes place at the critical value {{μ\\text{c}}=1 corresponding to Cauchy matrices: the IPR Y q of the corresponding critical eigenstates follow the strong-multifractality spectrum characterized by the generalized fractal dimensions {{D}\\text{criti}}(q)=\\frac{1-2q}{1-q}θ ≤ft(0≤slant q≤slant \\frac{1}{2}\\right) , which has been found previously in various other Localization problems in spaces of effective infinite dimensionality.

  20. Localization at the edge of a 2D topological insulator by Kondo impurities with random anisotropies.

    PubMed

    Altshuler, B L; Aleiner, I L; Yudson, V I

    2013-08-23

    We consider chiral electrons moving along the one-dimensional helical edge of a two-dimensional topological insulator and interacting with a disordered chain of Kondo impurities. Assuming the electron-spin couplings of random anisotropies, we map this system to the problem of the pinning of the charge density wave by the disordered potential. This mapping proves that arbitrary weak anisotropic disorder in coupling of chiral electrons with spin impurities leads to the Anderson localization of the edge states.

  1. Markov reward processes

    NASA Technical Reports Server (NTRS)

    Smith, R. M.

    1991-01-01

    Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

  2. Random matrix theory and cross-correlations in global financial indices and local stock market indices

    NASA Astrophysics Data System (ADS)

    Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

    2013-02-01

    We analyzed cross-correlations between price fluctuations of global financial indices (20 daily stock indices over the world) and local indices (daily indices of 200 companies in the Korean stock market) by using random matrix theory (RMT). We compared eigenvalues and components of the largest and the second largest eigenvectors of the cross-correlation matrix before, during, and after the global financial the crisis in the year 2008. We find that the majority of its eigenvalues fall within the RMT bounds [ λ -, λ +], where λ - and λ + are the lower and the upper bounds of the eigenvalues of random correlation matrices. The components of the eigenvectors for the largest positive eigenvalues indicate the identical financial market mode dominating the global and local indices. On the other hand, the components of the eigenvector corresponding to the second largest eigenvalue are positive and negative values alternatively. The components before the crisis change sign during the crisis, and those during the crisis change sign after the crisis. The largest inverse participation ratio (IPR) corresponding to the smallest eigenvector is higher after the crisis than during any other periods in the global and local indices. During the global financial the crisis, the correlations among the global indices and among the local stock indices are perturbed significantly. However, the correlations between indices quickly recover the trends before the crisis.

  3. Simulation study of localization of electromagnetic waves in two-dimensional random dipolar systems.

    PubMed

    Wang, Ken Kang-Hsin; Ye, Zhen

    2003-12-01

    We study the propagation and scattering of electromagnetic waves by random arrays of dipolar cylinders in a uniform medium. A set of self-consistent equations, incorporating all orders of multiple scattering of the electromagnetic waves, is derived from first principles and then solved numerically for electromagnetic fields. For certain ranges of frequencies, spatially localized electromagnetic waves appear in such a simple but realistic disordered system. Dependence of localization on the frequency, radiation damping, and filling factor is shown. The spatial behavior of the total, coherent, and diffusive waves is explored in detail, and found to comply with a physical intuitive picture. A phase diagram characterizing localization is presented, in agreement with previous investigations on other systems.

  4. Continuous time random walks for non-local radial solute transport

    NASA Astrophysics Data System (ADS)

    Dentz, Marco; Kang, Peter K.; Le Borgne, Tanguy

    2015-08-01

    This study formulates and analyzes continuous time random walk (CTRW) models in radial flow geometries for the quantification of non-local solute transport induced by heterogeneous flow distributions and by mobile-immobile mass transfer processes. To this end we derive a general CTRW framework in radial coordinates starting from the random walk equations for radial particle positions and times. The particle density, or solute concentration is governed by a non-local radial advection-dispersion equation (ADE). Unlike in CTRWs for uniform flow scenarios, particle transition times here depend on the radial particle position, which renders the CTRW non-stationary. As a consequence, the memory kernel characterizing the non-local ADE, is radially dependent. Based on this general formulation, we derive radial CTRW implementations that (i) emulate non-local radial transport due to heterogeneous advection, (ii) model multirate mass transfer (MRMT) between mobile and immobile continua, and (iii) quantify both heterogeneous advection in a mobile region and mass transfer between mobile and immobile regions. The expected solute breakthrough behavior is studied using numerical random walk particle tracking simulations. This behavior is analyzed by explicit analytical expressions for the asymptotic solute breakthrough curves. We observe clear power-law tails of the solute breakthrough for broad (power-law) distributions of particle transit times (heterogeneous advection) and particle trapping times (MRMT model). The combined model displays two distinct time regimes. An intermediate regime, in which the solute breakthrough is dominated by the particle transit times in the mobile zones, and a late time regime that is governed by the distribution of particle trapping times in immobile zones. These radial CTRW formulations allow for the identification of heterogeneous advection and mobile-immobile processes as drivers of anomalous transport, under conditions relevant for field tracer

  5. MODELING PAVEMENT DETERIORATION PROCESSES BY POISSON HIDDEN MARKOV MODELS

    NASA Astrophysics Data System (ADS)

    Nam, Le Thanh; Kaito, Kiyoyuki; Kobayashi, Kiyoshi; Okizuka, Ryosuke

    In pavement management, it is important to estimate lifecycle cost, which is composed of the expenses for repairing local damages, including potholes, and repairing and rehabilitating the surface and base layers of pavements, including overlays. In this study, a model is produced under the assumption that the deterioration process of pavement is a complex one that includes local damages, which occur frequently, and the deterioration of the surface and base layers of pavement, which progresses slowly. The variation in pavement soundness is expressed by the Markov deterioration model and the Poisson hidden Markov deterioration model, in which the frequency of local damage depends on the distribution of pavement soundness, is formulated. In addition, the authors suggest a model estimation method using the Markov Chain Monte Carlo (MCMC) method, and attempt to demonstrate the applicability of the proposed Poisson hidden Markov deterioration model by studying concrete application cases.

  6. Markov Chain Monte Carlo Bayesian Learning for Neural Networks

    NASA Technical Reports Server (NTRS)

    Goodrich, Michael S.

    2011-01-01

    Conventional training methods for neural networks involve starting al a random location in the solution space of the network weights, navigating an error hyper surface to reach a minimum, and sometime stochastic based techniques (e.g., genetic algorithms) to avoid entrapment in a local minimum. It is further typically necessary to preprocess the data (e.g., normalization) to keep the training algorithm on course. Conversely, Bayesian based learning is an epistemological approach concerned with formally updating the plausibility of competing candidate hypotheses thereby obtaining a posterior distribution for the network weights conditioned on the available data and a prior distribution. In this paper, we developed a powerful methodology for estimating the full residual uncertainty in network weights and therefore network predictions by using a modified Jeffery's prior combined with a Metropolis Markov Chain Monte Carlo method.

  7. Dynamical Localization for Discrete and Continuous Random Schrödinger Operators

    NASA Astrophysics Data System (ADS)

    Germinet, F.; De Bièvre, S.

    We show for a large class of random Schrödinger operators Ho on and on that dynamical localization holds, i.e. that, with probability one, for a suitable energy interval I and for q a positive real, Here ψ is a function of sufficiently rapid decrease, and PI(Ho) is the spectral projector of Ho corresponding to the interval I. The result is obtained through the control of the decay of the eigenfunctions of Ho and covers, in the discrete case, the Anderson tight-binding model with Bernoulli potential (dimension ν = 1) or singular potential (ν > 1), and in the continuous case Anderson as well as random Landau Hamiltonians.

  8. Multiple scattering, radiative transfer, and weak localization in discrete random media: Unified microphysical approach

    NASA Astrophysics Data System (ADS)

    Mishchenko, Michael I.

    2008-06-01

    The radiative transfer theory has been extensively used in geophysics, remote sensing, and astrophysics for more than a century, but its physical basis had remained uncertain until quite recently. This ambiguous situation has finally changed, and the theory of radiative transfer in random particulate media has become a legitimate branch of Maxwell's electromagnetics. This tutorial review is intended to provide an accessible outline of recent basic developments. It discusses elastic electromagnetic scattering by random many-particle groups and summarizes the unified microphysical approach to radiative transfer and the effect of weak localization of electromagnetic waves (otherwise known as coherent backscattering). It explains the exact meaning of such fundamental concepts as single and multiple scattering, demonstrates how the theories of radiative transfer and weak localization originate in the Maxwell equations, and exposes and corrects certain misconceptions of the traditional phenomenological approach to radiative transfer. It also discusses the challenges facing the theories of multiple scattering, radiative transfer, and weak localization in the context of geophysical applications.

  9. Local anesthetic infusion pumps improve postoperative pain after inguinal hernia repair: a randomized trial.

    PubMed

    Sanchez, Barry; Waxman, Kenneth; Tatevossian, Raymond; Gamberdella, Marla; Read, Bruce

    2004-11-01

    Pain after an open inguinal hernia repair may be significant. In fact, some surgeons feel that the pain after open repair justifies a laparoscopic approach. The purpose of this study was to determine if the use of local anesthetic infusion pumps would reduce postoperative pain after open inguinal hernia repair. We performed a prospective, double-blind randomized study of 45 open plug and patch inguinal hernia repairs. Patients were randomized to receive either 0.25 per cent bupivicaine or saline solution via an elastomeric infusion pump (ON-Q) for 48 hours, at 2 cc/h. The catheters were placed in the subcutaneous tissue and removed on postoperative day 3. Both groups were prescribed hydrocodone to use in the postoperative period at the prescribed dosage as needed for pain. Interviews were conducted on postoperative days 3 and 7, and patient's questionnaires, including pain scores, amount of pain medicine used, and any complications, were collected accordingly. During the first 5 postoperative days, postoperative pain was assessed using a visual analog scale. Twenty-three repairs were randomized to the bupivicaine group and 22 repairs randomized to the placebo group. In the bupivicaine group, there was a significant decrease in postoperative pain on postoperative days 2 through 5 with P values <0.05. This significant difference continued through postoperative day 5, 2 days after the infusion pumps were removed. Patients who had bupivicaine instilled in their infusion pump had statistically significant lower subjective pain scores on postoperative days 2 through 5. This significant difference continued even after the infusion pumps were removed. Local anesthetic infusion pumps significantly decreased the amount of early postoperative pain. Pain relief persisted for 2 days after catheter and pump removal. PMID:15586515

  10. Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening.

    PubMed

    Kamimura, H A S; Wang, S; Wu, S-Y; Karakatsani, M E; Acosta, C; Carneiro, A A O; Konofagou, E E

    2015-10-01

    Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n  =  15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5–1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were mm3, mm3and mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were V.s, V.s and V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue. PMID:26394091

  11. How breadth of degree distribution influences network robustness: comparing localized and random attacks.

    PubMed

    Yuan, Xin; Shao, Shuai; Stanley, H Eugene; Havlin, Shlomo

    2015-09-01

    The stability of networks is greatly influenced by their degree distributions and in particular by their breadth. Networks with broader degree distributions are usually more robust to random failures but less robust to localized attacks. To better understand the effect of the breadth of the degree distribution we study two models in which the breadth is controlled and compare their robustness against localized attacks (LA) and random attacks (RA). We study analytically and by numerical simulations the cases where the degrees in the networks follow a bi-Poisson distribution, P(k)=αe^{-λ_{1}}λ_{1}^{k}/k!+(1-α)e^{-λ_{2}}λ_{2}^{k}/k!,α∈[0,1], and a Gaussian distribution, P(k)=Aexp(-(k-μ)^{2}/2σ^{2}), with a normalization constant A where k≥0. In the bi-Poisson distribution the breadth is controlled by the values of α, λ_{1}, and λ_{2}, while in the Gaussian distribution it is controlled by the standard deviation, σ. We find that only when α=0 or α=1, i.e., degrees obeying a pure Poisson distribution, are LA and RA the same. In all other cases networks are more vulnerable under LA than under RA. For a Gaussian distribution with an average degree μ fixed, we find that when σ^{2} is smaller than μ the network is more vulnerable against random attack. When σ^{2} is larger than μ, however, the network becomes more vulnerable against localized attack. Similar qualitative results are also shown for interdependent networks. PMID:26465441

  12. How breadth of degree distribution influences network robustness: Comparing localized and random attacks

    NASA Astrophysics Data System (ADS)

    Yuan, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo

    2015-09-01

    The stability of networks is greatly influenced by their degree distributions and in particular by their breadth. Networks with broader degree distributions are usually more robust to random failures but less robust to localized attacks. To better understand the effect of the breadth of the degree distribution we study two models in which the breadth is controlled and compare their robustness against localized attacks (LA) and random attacks (RA). We study analytically and by numerical simulations the cases where the degrees in the networks follow a bi-Poisson distribution, P (k ) =α e-λ1λ/1kk ! +(1 -α ) e-λ2λ/2kk ! ,α ∈[0 ,1 ] , and a Gaussian distribution, P (k ) =A exp(-(k/-μ) 22 σ2 ), with a normalization constant A where k ≥0 . In the bi-Poisson distribution the breadth is controlled by the values of α , λ1, and λ2, while in the Gaussian distribution it is controlled by the standard deviation, σ . We find that only when α =0 or α =1 , i.e., degrees obeying a pure Poisson distribution, are LA and RA the same. In all other cases networks are more vulnerable under LA than under RA. For a Gaussian distribution with an average degree μ fixed, we find that when σ2 is smaller than μ the network is more vulnerable against random attack. When σ2 is larger than μ , however, the network becomes more vulnerable against localized attack. Similar qualitative results are also shown for interdependent networks.

  13. Local random quantum circuits: Ensemble completely positive maps and swap algebras

    SciTech Connect

    Zanardi, Paolo

    2014-08-15

    We define different classes of local random quantum circuits (L-RQC) and show that (a) statistical properties of L-RQC are encoded into an associated family of completely positive maps and (b) average purity dynamics can be described by the action of these maps on operator algebras of permutations (swap algebras). An exactly solvable one-dimensional case is analyzed to illustrate the power of the swap algebra formalism. More in general, we prove short time area-law bounds on average purity for uncorrelated L-RQC and infinite time results for both the uncorrelated and correlated cases.

  14. In vivo MRI based prostate cancer localization with random forests and auto-context model.

    PubMed

    Qian, Chunjun; Wang, Li; Gao, Yaozong; Yousuf, Ambereen; Yang, Xiaoping; Oto, Aytekin; Shen, Dinggang

    2016-09-01

    Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method. PMID:27048995

  15. Classification of interstitial lung disease patterns using local DCT features and random forest.

    PubMed

    Anthimopoulos, M; Christodoulidis, S; Christe, A; Mougiakakou, S

    2014-01-01

    Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

  16. Absence of localized acoustic waves in a scale-free correlated random system.

    PubMed

    Costa, A E B; de Moura, F A B F

    2011-02-16

    We numerically study the propagation of acoustic waves in a one-dimensional medium with a scale-free long-range correlated elasticity distribution. The random elasticity distribution is assumed to have a power spectrum S(k) ∼ 1/k(α). By using a transfer-matrix method we solve the discrete version of the scalar wave equation and compute the localization length. In addition, we apply a second-order finite-difference method for both the time and spatial variables and study the nature of the waves that propagate in the chain. Our numerical data indicate the presence of extended acoustic waves for a high degree of correlations. In contrast with local correlations, we numerically demonstrate that scale-free correlations promote a stable phase of free acoustic waves in the thermodynamic limit. PMID:21406919

  17. Acoustic localization in weakly compressible elastic media containing random air bubbles.

    PubMed

    Liang, Bin; Cheng, Jian-chun

    2007-01-01

    We study theoretically the propagation of longitudinal wave in weakly compressible elastic media containing random air bubbles by using a self-consistent method. By inspecting the scattering cross section of an individual bubble and estimating the mean free paths of the elastic wave propagating in the bubbly weakly compressible media, the mode conversion is numerically proved negligible as the longitudinal wave is scattered by the bubbles. On the basis of the bubble dynamic equation, the wave propagation is solved rigorously with the multiple scattering effects incorporated. In a range of frequency slightly above the bubble resonance frequency, the acoustic localization in such a class of media is theoretically identified with even a very small volume fraction of bubbles. We present a method by analyzing the spatial correlation of wave field to identify the phenomenon of localization, which turns out to be effective. The sensibility of the features of localization to the structure parameters is numerically investigated. The spatial distribution of acoustic energy is also studied and the results show that the waves are trapped within a spatial domain adjacent to the source when localization occurs.

  18. Inelastic collapse and near-wall localization of randomly accelerated particles

    NASA Astrophysics Data System (ADS)

    Belan, S.; Chernykh, A.; Lebedev, V.; Falkovich, G.

    2016-05-01

    Inelastic collapse of stochastic trajectories of a randomly accelerated particle moving in half-space z >0 has been discovered by McKean [J. Math. Kyoto Univ. 2, 227 (1963)] and then independently rediscovered by Cornell et al. [Phys. Rev. Lett. 81, 1142 (1998), 10.1103/PhysRevLett.81.1142]. The essence of this phenomenon is that the particle arrives at the wall at z =0 with zero velocity after an infinite number of inelastic collisions if the restitution coefficient β of particle velocity is smaller than the critical value βc=exp(-π /√{3 }) . We demonstrate that inelastic collapse takes place also in a wide class of models with spatially inhomogeneous random forcing and, what is more, that the critical value βc is universal. That class includes an important case of inertial particles in wall-bounded random flows. To establish how inelastic collapse influences the particle distribution, we derive the exact equilibrium probability density function ρ (z ,v ) for the particle position and velocity. The equilibrium distribution exists only at β <βc and indicates that inelastic collapse does not necessarily imply near-wall localization.

  19. Inelastic collapse and near-wall localization of randomly accelerated particles.

    PubMed

    Belan, S; Chernykh, A; Lebedev, V; Falkovich, G

    2016-05-01

    Inelastic collapse of stochastic trajectories of a randomly accelerated particle moving in half-space z>0 has been discovered by McKean [J. Math. Kyoto Univ. 2, 227 (1963)] and then independently rediscovered by Cornell et al. [Phys. Rev. Lett. 81, 1142 (1998)PRLTAO0031-900710.1103/PhysRevLett.81.1142]. The essence of this phenomenon is that the particle arrives at the wall at z=0 with zero velocity after an infinite number of inelastic collisions if the restitution coefficient β of particle velocity is smaller than the critical value β_{c}=exp(-π/sqrt[3]). We demonstrate that inelastic collapse takes place also in a wide class of models with spatially inhomogeneous random forcing and, what is more, that the critical value β_{c} is universal. That class includes an important case of inertial particles in wall-bounded random flows. To establish how inelastic collapse influences the particle distribution, we derive the exact equilibrium probability density function ρ(z,v) for the particle position and velocity. The equilibrium distribution exists only at β<β_{c} and indicates that inelastic collapse does not necessarily imply near-wall localization. PMID:27300879

  20. Equivalent Markov processes under gauge group.

    PubMed

    Caruso, M; Jarne, C

    2015-11-01

    We have studied Markov processes on denumerable state space and continuous time. We found that all these processes are connected via gauge transformations. We have used this result before as a method to resolve equations, included the case in a previous work in which the sample space is time-dependent [Phys. Rev. E 90, 022125 (2014)]. We found a general solution through dilation of the state space, although the prior probability distribution of the states defined in this new space takes smaller values with respect to that in the initial problem. The gauge (local) group of dilations modifies the distribution on the dilated space to restore the original process. In this work, we show how the Markov process in general could be linked via gauge (local) transformations, and we present some illustrative examples for this result.

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

    PubMed Central

    He, Xiaoqi; Zheng, Zizhao; Hu, Chao

    2015-01-01

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

  2. Random local binary pattern based label learning for multi-atlas segmentation

    NASA Astrophysics Data System (ADS)

    Zhu, Hancan; Cheng, Hewei; Fan, Yong

    2015-03-01

    Multi-atlas segmentation method has attracted increasing attention in the field of medical image segmentation. It segments the target image by combining warped atlas labels according to a label fusion strategy, usually based on the intensity information of the target and atlas images. However, it has been demonstrated that image intensity information itself is not discriminative enough for distinguishing different subcortical structures in brain magnetic resonance (MR) images. Recent advance in multi-atlas based segmentation has witnessed success of label fusion methods built on informative image features. The key component in these methods is the image feature extraction. Conventional image feature extraction methods, such as textural feature extraction, are built on manually designed image filters and their performance varies when applied to different segmentation problems. In this paper, we propose a random local binary pattern (RLBP) method to generate image features in a random fashion. Based on RLBP features, we use a local learning strategy to fuse labels in multi-atlas based segmentation. Our method has been validated for segmenting hippocampus from MR images. The experiment results have demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods.

  3. Anisotropy of the monomer random walk in a polymer melt: local-order and connectivity effects

    NASA Astrophysics Data System (ADS)

    Bernini, S.; Leporini, D.

    2016-05-01

    The random walk of a bonded monomer in a polymer melt is anisotropic due to local order and bond connectivity. We investigate both effects by molecular-dynamics simulations on melts of fully-flexible linear chains ranging from dimers (M  =  2) up to entangled polymers (M  =  200). The corresponding atomic liquid is also considered a reference system. To disentangle the influence of the local geometry and the bond arrangements, and to reveal their interplay, we define suitable measures of the anisotropy emphasising either the former or the latter aspect. Connectivity anisotropy, as measured by the correlation between the initial bond orientation and the direction of the subsequent monomer displacement, shows a slight enhancement due to the local order at times shorter than the structural relaxation time. At intermediate times—when the monomer displacement is comparable to the bond length—a pronounced peak and then decays slowly as t ‑1/2, becoming negligible when the displacement is as large as about five bond lengths, i.e. about four monomer diameters or three Kuhn lengths. Local-geometry anisotropy, as measured by the correlation between the initial orientation of a characteristic axis of the Voronoi cell and the subsequent monomer dynamics, is affected at shorter times than the structural relaxation time by the cage shape with antagonistic disturbance by the connectivity. Differently, at longer times, the connectivity favours the persistence of the local-geometry anisotropy, which vanishes when the monomer displacement exceeds the bond length. Our results strongly suggest that the sole consideration of the local order is not enough to understand the microscopic origin of the rattling amplitude of the trapped monomer in the cage of the neighbours.

  4. Anisotropy of the monomer random walk in a polymer melt: local-order and connectivity effects.

    PubMed

    Bernini, S; Leporini, D

    2016-05-11

    The random walk of a bonded monomer in a polymer melt is anisotropic due to local order and bond connectivity. We investigate both effects by molecular-dynamics simulations on melts of fully-flexible linear chains ranging from dimers (M  =  2) up to entangled polymers (M  =  200). The corresponding atomic liquid is also considered a reference system. To disentangle the influence of the local geometry and the bond arrangements, and to reveal their interplay, we define suitable measures of the anisotropy emphasising either the former or the latter aspect. Connectivity anisotropy, as measured by the correlation between the initial bond orientation and the direction of the subsequent monomer displacement, shows a slight enhancement due to the local order at times shorter than the structural relaxation time. At intermediate times-when the monomer displacement is comparable to the bond length-a pronounced peak and then decays slowly as t (-1/2), becoming negligible when the displacement is as large as about five bond lengths, i.e. about four monomer diameters or three Kuhn lengths. Local-geometry anisotropy, as measured by the correlation between the initial orientation of a characteristic axis of the Voronoi cell and the subsequent monomer dynamics, is affected at shorter times than the structural relaxation time by the cage shape with antagonistic disturbance by the connectivity. Differently, at longer times, the connectivity favours the persistence of the local-geometry anisotropy, which vanishes when the monomer displacement exceeds the bond length. Our results strongly suggest that the sole consideration of the local order is not enough to understand the microscopic origin of the rattling amplitude of the trapped monomer in the cage of the neighbours. PMID:27070080

  5. Anisotropy of the monomer random walk in a polymer melt: local-order and connectivity effects.

    PubMed

    Bernini, S; Leporini, D

    2016-05-11

    The random walk of a bonded monomer in a polymer melt is anisotropic due to local order and bond connectivity. We investigate both effects by molecular-dynamics simulations on melts of fully-flexible linear chains ranging from dimers (M  =  2) up to entangled polymers (M  =  200). The corresponding atomic liquid is also considered a reference system. To disentangle the influence of the local geometry and the bond arrangements, and to reveal their interplay, we define suitable measures of the anisotropy emphasising either the former or the latter aspect. Connectivity anisotropy, as measured by the correlation between the initial bond orientation and the direction of the subsequent monomer displacement, shows a slight enhancement due to the local order at times shorter than the structural relaxation time. At intermediate times-when the monomer displacement is comparable to the bond length-a pronounced peak and then decays slowly as t (-1/2), becoming negligible when the displacement is as large as about five bond lengths, i.e. about four monomer diameters or three Kuhn lengths. Local-geometry anisotropy, as measured by the correlation between the initial orientation of a characteristic axis of the Voronoi cell and the subsequent monomer dynamics, is affected at shorter times than the structural relaxation time by the cage shape with antagonistic disturbance by the connectivity. Differently, at longer times, the connectivity favours the persistence of the local-geometry anisotropy, which vanishes when the monomer displacement exceeds the bond length. Our results strongly suggest that the sole consideration of the local order is not enough to understand the microscopic origin of the rattling amplitude of the trapped monomer in the cage of the neighbours.

  6. Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening

    NASA Astrophysics Data System (ADS)

    Kamimura, H. A. S.; Wang, S.; Wu, S.-Y.; Karakatsani, M. E.; Acosta, C.; Carneiro, A. A. O.; Konofagou, E. E.

    2015-10-01

    Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n  =  15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5-1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were 9.38+/- 5.71 mm3, 8.91+/- 3.91 mm3and 35.47+/- 5.10 mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were 55.40+/- 28.43 V.s, 63.87+/- 29.97 V.s and 356.52+/- 257.15 V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue.

  7. Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening

    PubMed Central

    Kamimura, HAS; Wang, S; Wu, S-Y; Karakatsani, ME; Acosta, C; Carneiro, AAO; Konofagou, EE

    2015-01-01

    Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n=15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5-1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were 9.38±5.71 mm3, 8.91±3.91 mm3 and 35.47 ± 5.10 mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were 55.40±28.43 V.s, 63.87±29.97 V.s and 356.52±257.15 V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue. PMID:26394091

  8. Many-body localization phase transition: A simplified strong-randomness approximate renormalization group

    NASA Astrophysics Data System (ADS)

    Zhang, Liangsheng; Zhao, Bo; Devakul, Trithep; Huse, David A.

    2016-06-01

    We present a simplified strong-randomness renormalization group (RG) that captures some aspects of the many-body localization (MBL) phase transition in generic disordered one-dimensional systems. This RG can be formulated analytically and is mathematically equivalent to a domain coarsening model that has been previously solved. The critical fixed-point distribution and critical exponents (that satisfy the Chayes inequality) are thus obtained analytically or to numerical precision. This reproduces some, but not all, of the qualitative features of the MBL phase transition that are indicated by previous numerical work and approximate RG studies: our RG might serve as a "zeroth-order" approximation for future RG studies. One interesting feature that we highlight is that the rare Griffiths regions are fractal. For thermal Griffiths regions within the MBL phase, this feature might be qualitatively correctly captured by our RG. If this is correct beyond our approximations, then these Griffiths effects are stronger than has been previously assumed.

  9. Many-body localization in a quantum simulator with programmable random disorder

    NASA Astrophysics Data System (ADS)

    Smith, J.; Lee, A.; Richerme, P.; Neyenhuis, B.; Hess, P. W.; Hauke, P.; Heyl, M.; Huse, D. A.; Monroe, C.

    2016-10-01

    When a system thermalizes it loses all memory of its initial conditions. Even within a closed quantum system, subsystems usually thermalize using the rest of the system as a heat bath. Exceptions to quantum thermalization have been observed, but typically require inherent symmetries or noninteracting particles in the presence of static disorder. However, for strong interactions and high excitation energy there are cases, known as many-body localization (MBL), where disordered quantum systems can fail to thermalize. We experimentally generate MBL states by applying an Ising Hamiltonian with long-range interactions and programmable random disorder to ten spins initialized far from equilibrium. Using experimental and numerical methods we observe the essential signatures of MBL: initial-state memory retention, Poissonian distributed energy level spacings, and evidence of long-time entanglement growth. Our platform can be scaled to more spins, where a detailed modelling of MBL becomes impossible.

  10. k-core percolation on complex networks: Comparing random, localized, and targeted attacks.

    PubMed

    Yuan, Xin; Dai, Yang; Stanley, H Eugene; Havlin, Shlomo

    2016-06-01

    The type of malicious attack inflicting on networks greatly influences their stability under ordinary percolation in which a node fails when it becomes disconnected from the giant component. Here we study its generalization, k-core percolation, in which a node fails when it loses connection to a threshold k number of neighbors. We study and compare analytically and by numerical simulations of k-core percolation the stability of networks under random attacks (RA), localized attacks (LA) and targeted attacks (TA), respectively. By mapping a network under LA or TA into an equivalent network under RA, we find that in both single and interdependent networks, TA exerts the greatest damage to the core structure of a network. We also find that for Erdős-Rényi (ER) networks, LA and RA exert equal damage to the core structure, whereas for scale-free (SF) networks, LA exerts much more damage than RA does to the core structure. PMID:27415275

  11. Predicting local Soil- and Land-units with Random Forest in the Senegalese Sahel

    NASA Astrophysics Data System (ADS)

    Grau, Tobias; Brandt, Martin; Samimi, Cyrus

    2013-04-01

    MODIS (MCD12Q1) or Globcover are often the only available global land-cover products, however ground-truthing in the Sahel of Senegal has shown that most classes do have any agreement with actual land-cover making those products unusable in any local application. We suggest a methodology, which models local Wolof land- and soil-types in an area in the Senegalese Ferlo around Linguère at different scales. In a first step, interviews with the local population were conducted to ascertain the local denotation of soil units, as well as their agricultural use and woody vegetation mainly growing on them. "Ndjor" are soft sand soils with mainly Combretum glutinosum trees. They are suitable for groundnuts and beans while millet is grown on hard sand soils ("Bardjen") dominated by Balanites aegyptiaca and Acacia tortilis. "Xur" are clayey depressions with a high diversity of tree species. Lateritic pasture sites with dense woody vegetation (mostly Pterocarpus lucens and Guiera senegalensis) have never been used for cropping and are called "All". In a second step, vegetation and soil parameters of 85 plots (~1 ha) were surveyed in the field. 28 different soil parameters are clustered into 4 classes using the WARD algorithm. Here, 81% agree with the local classification. Then, an ordination (NMDS) with 2 dimensions and a stress-value of 9.13% was calculated using the 28 soil parameters. It shows several significant relationships between the soil classes and the fitted environmental parameters which are derived from field data, a digital elevation model, Landsat and RapidEye imagery as well as TRMM rainfall data. Landsat's band 5 reflectance values (1.55 - 1.75 µm) of mean dry season image (2000-2010) has a R² of 0.42 and is the most important of 9 significant variables (5%-level). A random forest classifier is then used to extrapolate the 4 classes to the whole study area based on the 9 significant environmental parameters. At a resolution of 30 m the OBB (out-of-bag) error

  12. Phase transitions in Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Bechhoefer, John; Lathouwers, Emma

    In Hidden Markov Models (HMMs), a Markov process is not directly accessible. In the simplest case, a two-state Markov model ``emits'' one of two ``symbols'' at each time step. We can think of these symbols as noisy measurements of the underlying state. With some probability, the symbol implies that the system is in one state when it is actually in the other. The ability to judge which state the system is in sets the efficiency of a Maxwell demon that observes state fluctuations in order to extract heat from a coupled reservoir. The state-inference problem is to infer the underlying state from such noisy measurements at each time step. We show that there can be a phase transition in such measurements: for measurement error rates below a certain threshold, the inferred state always matches the observation. For higher error rates, there can be continuous or discontinuous transitions to situations where keeping a memory of past observations improves the state estimate. We can partly understand this behavior by mapping the HMM onto a 1d random-field Ising model at zero temperature. We also present more recent work that explores a larger parameter space and more states. Research funded by NSERC, Canada.

  13. Adjuvant chemo- and hormonal therapy in locally advanced breast cancer: a randomized clinical study

    SciTech Connect

    Schaake-Koning, C.; van der Linden, E.H.; Hart, G.; Engelsman, E.

    1985-10-01

    Between 1977 and 1980, 118 breast cancer patients with locally advanced disease, T3B-4, any N, M0 or T1-3, tumor positive axillary apex biopsy, were randomized to one of three arms: I: radiotherapy (RT) to the breast and adjacent lymph node areas; II: RT followed by 12 cycles of cyclophosphamide, methotrexate, 5 fluorouracil (CMF) and tamoxifen during the chemotherapy period; III: 2 cycles of adriamycin and vincristine (AV), alternated with 2 cycles of CMF, then RT, followed by another 4 cycles of AV, alternated with 4 CMF; tamoxifen during the entire treatment period. The median follow-up period was 5 1/2 years. The adjuvant chemo- and hormonal therapy did not improve the overall survival; the 5-year survival was 37% for all three treatment arms. There was no statistically significant difference in RFS between the three modalities, nor when arm I was compared to arm II and III together. LR was not statistically different over the three treatment arms. In 18 of the 24 patients with LR, distant metastases appeared within a few months from the local recurrence. The menopausal status did not influence the treatment results. Dose reduction in more than 4 cycles of chemotherapy was accompanied by better results. In conclusion: adjuvant chemo- and hormonal therapy did not improve RFS and overall survival. These findings do not support the routine use of adjuvant chemo- and endocrine therapy for inoperable breast cancer.

  14. Dynamical decoupling of local transverse random telegraph noise in a two-qubit gate

    NASA Astrophysics Data System (ADS)

    D'Arrigo, A.; Falci, G.; Paladino, E.

    2015-10-01

    Achieving high-fidelity universal two-qubit gates is a central requisite of any implementation of quantum information processing. The presence of spurious fluctuators of various physical origin represents a limiting factor for superconducting nanodevices. Operating qubits at optimal points, where the qubit-fluctuator interaction is transverse with respect to the single qubit Hamiltonian, considerably improved single qubit gates. Further enhancement has been achieved by dynamical decoupling (DD). In this article we investigate DD of transverse random telegraph noise acting locally on each of the qubits forming an entangling gate. Our analysis is based on the exact numerical solution of the stochastic Schrödinger equation. We evaluate the gate error under local periodic, Carr-Purcell and Uhrig DD sequences. We find that a threshold value of the number, n, of pulses exists above which the gate error decreases with a sequence-specific power-law dependence on n. Below threshold, DD may even increase the error with respect to the unconditioned evolution, a behaviour reminiscent of the anti-Zeno effect.

  15. Power spectral ensity of markov texture fields

    NASA Technical Reports Server (NTRS)

    Shanmugan, K. S.; Holtzman, J. C.

    1984-01-01

    Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.

  16. Markov counting models for correlated binary responses.

    PubMed

    Crawford, Forrest W; Zelterman, Daniel

    2015-07-01

    We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology. PMID:25792624

  17. Markov Chain Analysis of Musical Dice Games

    NASA Astrophysics Data System (ADS)

    Volchenkov, D.; Dawin, J. R.

    2012-07-01

    A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.

  18. Markov counting models for correlated binary responses.

    PubMed

    Crawford, Forrest W; Zelterman, Daniel

    2015-07-01

    We propose a class of continuous-time Markov counting processes for analyzing correlated binary data and establish a correspondence between these models and sums of exchangeable Bernoulli random variables. Our approach generalizes many previous models for correlated outcomes, admits easily interpretable parameterizations, allows different cluster sizes, and incorporates ascertainment bias in a natural way. We demonstrate several new models for dependent outcomes and provide algorithms for computing maximum likelihood estimates. We show how to incorporate cluster-specific covariates in a regression setting and demonstrate improved fits to well-known datasets from familial disease epidemiology and developmental toxicology.

  19. Markov chain for estimating human mitochondrial DNA mutation pattern

    NASA Astrophysics Data System (ADS)

    Vantika, Sandy; Pasaribu, Udjianna S.

    2015-12-01

    The Markov chain was proposed to estimate the human mitochondrial DNA mutation pattern. One DNA sequence was taken randomly from 100 sequences in Genbank. The nucleotide transition matrix and mutation transition matrix were estimated from this sequence. We determined whether the states (mutation/normal) are recurrent or transient. The results showed that both of them are recurrent.

  20. Decentralized learning in Markov games.

    PubMed

    Vrancx, Peter; Verbeeck, Katja; Nowé, Ann

    2008-08-01

    Learning automata (LA) were recently shown to be valuable tools for designing multiagent reinforcement learning algorithms. One of the principal contributions of the LA theory is that a set of decentralized independent LA is able to control a finite Markov chain with unknown transition probabilities and rewards. In this paper, we propose to extend this algorithm to Markov games--a straightforward extension of single-agent Markov decision problems to distributed multiagent decision problems. We show that under the same ergodic assumptions of the original theorem, the extended algorithm will converge to a pure equilibrium point between agent policies.

  1. Effect of local anesthesia on atypical odontalgia--a randomized controlled trial.

    PubMed

    List, Thomas; Leijon, Göran; Helkimo, Martti; Oster, Anders; Svensson, Peter

    2006-06-01

    The aim of the study was to evaluate the analgesic effect of lidocaine in a double-blind, controlled multi-center study on patients with atypical odontalgia (AO)--a possible orofacial neuropathic pain condition. Thirty-five consecutive AO patients (range 31-81 years) with a mean pain duration of 7.2 years (range 1-30 years) were recruited from four different orofacial pain clinics in Sweden. In a randomized cross-over design, 1.5 ml local anesthesia (20mg/ml lidocaine and 12.5 microg/ml adrenaline) or 1.5 ml saline (9 mg/ml NaCl solution) (placebo) was injected to block the painful area. The VAS pain scores showed an overall effect of time (ANOVA: P<0.001) and treatment (ANOVA: P=0.018) with a significant interaction between the factors (ANOVA: P<0.001). Overall, VAS pain relief was significantly greater at 15-120 min following the lidocaine injections compared to the placebo injections (Tukey: P<0.05). All patients demonstrated significant disturbances in somatosensory function on the painful side compared to the non-painful side as revealed by quantitative sensory tests, however, only one significant inverse correlation was found between percentage pain relief and the magnitude of brush-evoked allodynia (Spearman: P<0.01). In conclusion, AO patients experienced significant, but not complete, pain relief from administration of local anesthetics compared with placebo. The findings indicate that the spontaneous pain in AO patients only to some extent is dependent on peripheral afferent inputs and that sensitization of higher order neurons may be involved in the pathophysiology of AO.

  2. Markov and non-Markov processes in complex systems by the dynamical information entropy

    NASA Astrophysics Data System (ADS)

    Yulmetyev, R. M.; Gafarov, F. M.

    1999-12-01

    We consider the Markov and non-Markov processes in complex systems by the dynamical information Shannon entropy (DISE) method. The influence and important role of the two mutually dependent channels of entropy alternation (creation or generation of correlation) and anti-correlation (destroying or annihilation of correlation) have been discussed. The developed method has been used for the analysis of the complex systems of various natures: slow neutron scattering in liquid cesium, psychology (short-time numeral and pattern human memory and effect of stress on the dynamical taping-test), random dynamics of RR-intervals in human ECG (problem of diagnosis of various disease of the human cardio-vascular systems), chaotic dynamics of the parameters of financial markets and ecological systems.

  3. Band gaps and localization of surface water waves over large-scale sand waves with random fluctuations.

    PubMed

    Zhang, Yu; Li, Yan; Shao, Hao; Zhong, Yaozhao; Zhang, Sai; Zhao, Zongxi

    2012-06-01

    Band structure and wave localization are investigated for sea surface water waves over large-scale sand wave topography. Sand wave height, sand wave width, water depth, and water width between adjacent sand waves have significant impact on band gaps. Random fluctuations of sand wave height, sand wave width, and water depth induce water wave localization. However, random water width produces a perfect transmission tunnel of water waves at a certain frequency so that localization does not occur no matter how large a disorder level is applied. Together with theoretical results, the field experimental observations in the Taiwan Bank suggest band gap and wave localization as the physical mechanism of sea surface water wave propagating over natural large-scale sand waves.

  4. Metrics for Labeled Markov Systems

    NASA Technical Reports Server (NTRS)

    Desharnais, Josee; Jagadeesan, Radha; Gupta, Vineet; Panangaden, Prakash

    1999-01-01

    Partial Labeled Markov Chains are simultaneously generalizations of process algebra and of traditional Markov chains. They provide a foundation for interacting discrete probabilistic systems, the interaction being synchronization on labels as in process algebra. Existing notions of process equivalence are too sensitive to the exact probabilities of various transitions. This paper addresses contextual reasoning principles for reasoning about more robust notions of "approximate" equivalence between concurrent interacting probabilistic systems. The present results indicate that:We develop a family of metrics between partial labeled Markov chains to formalize the notion of distance between processes. We show that processes at distance zero are bisimilar. We describe a decision procedure to compute the distance between two processes. We show that reasoning about approximate equivalence can be done compositionally by showing that process combinators do not increase distance. We introduce an asymptotic metric to capture asymptotic properties of Markov chains; and show that parallel composition does not increase asymptotic distance.

  5. Randomized clinical trial of local anesthetic versus a combination of local anesthetic with self-hypnosis in the management of pediatric procedure-related pain.

    PubMed

    Liossi, Christina; White, Paul; Hatira, Popi

    2006-05-01

    A prospective controlled trial was conducted to compare the efficacy of an analgesic cream (eutectic mixture of local anesthetics, or EMLA) with a combination of EMLA with hypnosis in the relief of lumbar puncture-induced pain and anxiety in 45 pediatric cancer patients (age 6-16 years). The study also explored whether young patients can be taught and can use hypnosis independently as well as whether the therapeutic benefit depends on hypnotizability. Patients were randomized to 1 of 3 groups: local anesthetic, local anesthetic plus hypnosis, and local anesthetic plus attention. Results confirmed that patients in the local anesthetic plus hypnosis group reported less anticipatory anxiety and less procedure-related pain and anxiety and that they were rated as demonstrating less behavioral distress during the procedure. The level of hypnotizability was significantly associated with the magnitude of treatment benefit, and this benefit was maintained when patients used hypnosis independently. PMID:16719602

  6. Near-field-assisted localization: effect of size and filling factor of randomly distributed zinc oxide nanoneedles on multiple scattering and localization of light

    NASA Astrophysics Data System (ADS)

    Silies, Martin; Mascheck, Manfred; Leipold, David; Kollmann, Heiko; Schmidt, Slawa; Sartor, Janos; Yatsui, Takashi; Kitamura, Kokoro; Ohtsu, Motoicho; Kalt, Heinz; Runge, Erich; Lienau, Christoph

    2016-07-01

    We investigate the influence of the diameter and the filling factor of randomly arranged ZnO nanoneedles on the multiple scattering and localization of light in disordered dielectrics. Coherent, ultra-broadband second-harmonic (SH) microscopy is used to probe the spatial localization of light in representative nm-sized ZnO arrays of needles. We observe strong fluctuations of the SH intensity inside different ZnO needle geometries. Comparison of the SH intensity distributions with predictions based on a one-parameter scaling model indicate that SH fluctuations can be taken as a quantitative measure for the degree of localization. Interestingly, the strongest localization signatures are found for densely packed arrays of thin needles with diameters in the range of only 30 nm range, despite the small scattering cross section of these needles. FDTD simulations indicate that in this case coupling of electric near-fields between neighbouring needles governs the localization.

  7. Markov transitions and the propagation of chaos

    SciTech Connect

    Gottlieb, A.

    1998-12-01

    The propagation of chaos is a central concept of kinetic theory that serves to relate the equations of Boltzmann and Vlasov to the dynamics of many-particle systems. Propagation of chaos means that molecular chaos, i.e., the stochastic independence of two random particles in a many-particle system, persists in time, as the number of particles tends to infinity. We establish a necessary and sufficient condition for a family of general n-particle Markov processes to propagate chaos. This condition is expressed in terms of the Markov transition functions associated to the n-particle processes, and it amounts to saying that chaos of random initial states propagates if it propagates for pure initial states. Our proof of this result relies on the weak convergence approach to the study of chaos due to Sztitman and Tanaka. We assume that the space in which the particles live is homomorphic to a complete and separable metric space so that we may invoke Prohorov's theorem in our proof. We also s how that, if the particles can be in only finitely many states, then molecular chaos implies that the specific entropies in the n-particle distributions converge to the entropy of the limiting single-particle distribution.

  8. Experimental study of the relationship between local particle-size distributions and local ordering in random close packing.

    PubMed

    Kurita, Rei

    2015-12-01

    We experimentally study the structural properties of a sediment of size distributed colloids. By determining each particle size using a size estimation algorithm, we are able to investigate the relationship between local environment and local ordering. Our results show that ordered environments of particles tend to generate where the local particle-size distribution is within 5%. In addition, we show that particles whose size is close to the average size have 12 coordinate neighbors, which matches the coordination number of the fcc and hcp crystals. On the other hand, bcc structures are observed around larger particles. Our results represent experiments to show a size dependence of the specific ordering in colloidal systems.

  9. Lower bound for the spatial extent of localized modes in photonic-crystal waveguides with small random imperfections

    NASA Astrophysics Data System (ADS)

    Faggiani, Rémi; Baron, Alexandre; Zang, Xiaorun; Lalouat, Loïc; Schulz, Sebastian A.; O'Regan, Bryan; Vynck, Kevin; Cluzel, Benoît; de Fornel, Frédérique; Krauss, Thomas F.; Lalanne, Philippe

    2016-06-01

    Light localization due to random imperfections in periodic media is paramount in photonics research. The group index is known to be a key parameter for localization near photonic band edges, since small group velocities reinforce light interaction with imperfections. Here, we show that the size of the smallest localized mode that is formed at the band edge of a one-dimensional periodic medium is driven instead by the effective photon mass, i.e. the flatness of the dispersion curve. Our theoretical prediction is supported by numerical simulations, which reveal that photonic-crystal waveguides can exhibit surprisingly small localized modes, much smaller than those observed in Bragg stacks thanks to their larger effective photon mass. This possibility is demonstrated experimentally with a photonic-crystal waveguide fabricated without any intentional disorder, for which near-field measurements allow us to distinctly observe a wavelength-scale localized mode despite the smallness (~1/1000 of a wavelength) of the fabrication imperfections.

  10. On Markov parameters in system identification

    NASA Technical Reports Server (NTRS)

    Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

    1991-01-01

    A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

  11. Adiabatic condition and the quantum hitting time of Markov chains

    SciTech Connect

    Krovi, Hari; Ozols, Maris; Roland, Jeremie

    2010-08-15

    We present an adiabatic quantum algorithm for the abstract problem of searching marked vertices in a graph, or spatial search. Given a random walk (or Markov chain) P on a graph with a set of unknown marked vertices, one can define a related absorbing walk P{sup '} where outgoing transitions from marked vertices are replaced by self-loops. We build a Hamiltonian H(s) from the interpolated Markov chain P(s)=(1-s)P+sP{sup '} and use it in an adiabatic quantum algorithm to drive an initial superposition over all vertices to a superposition over marked vertices. The adiabatic condition implies that, for any reversible Markov chain and any set of marked vertices, the running time of the adiabatic algorithm is given by the square root of the classical hitting time. This algorithm therefore demonstrates a novel connection between the adiabatic condition and the classical notion of hitting time of a random walk. It also significantly extends the scope of previous quantum algorithms for this problem, which could only obtain a full quadratic speedup for state-transitive reversible Markov chains with a unique marked vertex.

  12. Assessment of optimized Markov models in protein fold classification.

    PubMed

    Lampros, Christos; Simos, Thomas; Exarchos, Themis P; Exarchos, Konstantinos P; Papaloukas, Costas; Fotiadis, Dimitrios I

    2014-08-01

    Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%. PMID:25152041

  13. Markov Analysis of Sleep Dynamics

    NASA Astrophysics Data System (ADS)

    Kim, J. W.; Lee, J.-S.; Robinson, P. A.; Jeong, D.-U.

    2009-05-01

    A new approach, based on a Markov transition matrix, is proposed to explain frequent sleep and wake transitions during sleep. The matrix is determined by analyzing hypnograms of 113 obstructive sleep apnea patients. Our approach shows that the statistics of sleep can be constructed via a single Markov process and that durations of all states have modified exponential distributions, in contrast to recent reports of a scale-free form for the wake stage and an exponential form for the sleep stage. Hypnograms of the same subjects, but treated with Continuous Positive Airway Pressure, are analyzed and compared quantitatively with the pretreatment ones, suggesting potential clinical applications.

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

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

  15. On a Result for Finite Markov Chains

    ERIC Educational Resources Information Center

    Kulathinal, Sangita; Ghosh, Lagnojita

    2006-01-01

    In an undergraduate course on stochastic processes, Markov chains are discussed in great detail. Textbooks on stochastic processes provide interesting properties of finite Markov chains. This note discusses one such property regarding the number of steps in which a state is reachable or accessible from another state in a finite Markov chain with M…

  16. Relative survival multistate Markov model.

    PubMed

    Huszti, Ella; Abrahamowicz, Michal; Alioum, Ahmadou; Binquet, Christine; Quantin, Catherine

    2012-02-10

    Prognostic studies often have to deal with two important challenges: (i) separating effects of predictions on different 'competing' events and (ii) uncertainty about cause of death. Multistate Markov models permit multivariable analyses of competing risks of, for example, mortality versus disease recurrence. On the other hand, relative survival methods help estimate disease-specific mortality risks even in the absence of data on causes of death. In this paper, we propose a new Markov relative survival (MRS) model that attempts to combine these two methodologies. Our MRS model extends the existing multistate Markov piecewise constant intensities model to relative survival modeling. The intensity of transitions leading to death in the MRS model is modeled as the sum of an estimable excess hazard of mortality from the disease of interest and an 'offset' defined as the expected hazard of all-cause 'natural' mortality obtained from relevant life-tables. We evaluate the new MRS model through simulations, with a design based on registry-based prognostic studies of colon cancer. Simulation results show almost unbiased estimates of prognostic factor effects for the MRS model. We also applied the new MRS model to reassess the role of prognostic factors for mortality in a study of colorectal cancer. The MRS model considerably reduces the bias observed with the conventional Markov model that does not permit accounting for unknown causes of death, especially if the 'true' effects of a prognostic factor on the two types of mortality differ substantially.

  17. Local Injection of Deferoxamine Improves Neovascularization in Ischemic Diabetic Random Flap by Increasing HIF-1α and VEGF Expression

    PubMed Central

    Zhang, Yun; Xiong, Zhuyou; Li, Guangzao; Cui, Lei

    2014-01-01

    Background Although the systemic administration of deferoxamine (DFO) is protective in experimental models of normal ischemic flap and diabetic wound, its effect on diabetic flap ischemia using a local injection remains unknown. Objective To explore the feasibility of local injection of DFO to improve the survival of ischemic random skin flaps in streptozotocin (STZ)-induced diabetic mice. Methods Ischemic random skin flaps were made in 125 mice. Animals were divided into the DFO-treated (n = 20), PBS-treated (n = 16) and untreated (n = 16) groups. Surviving area, vessel density, and expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) were evaluated on the seventh day after local injection. Results The viability of DFO-treated flap was significantly enhanced, with increased regional blood perfusion and capillary density compared with those in the two control groups. Fluorescence-activated cell sorting (FACS) analysis demonstrated a marked increase in systemic Flk-1+/CD11b− endothelial progenitor cells (EPCs) in DFO-treated mice. Furthermore, the expression of VEGF and HIF-1α was increased not only in diabetic flap tissue, but also in dermal fibroblasts cultured under hyperglycemic and hypoxic conditions. Conclusions Local injection of DFO could exert preventive effects against skin flap necrosis in STZ-induced diabetic mice by elevating the expression of HIF-1α and VEGF, increased EPC mobilization, which all contributed to promote ischemic diabetic flap survival. PMID:24963878

  18. Microwave conductance in random waveguides in the cross-over to Anderson localization and single-parameter scaling.

    PubMed

    Shi, Zhou; Wang, Jing; Genack, Azriel Z

    2014-02-25

    The nature of transport of electrons and classical waves in disordered systems depends upon the proximity to the Anderson localization transition between freely diffusing and localized waves. The suppression of average transport and the enhancement of relative fluctuations in conductance in one-dimensional samples with lengths greatly exceeding the localization length, L>ξ, are related in the single-parameter scaling (SPS) theory of localization. However, the difficulty of producing an ensemble of statistically equivalent samples in which the electron wave function is temporally coherent has so-far precluded the experimental demonstration of SPS. Here we demonstrate SPS in random multichannel systems for the transmittance T of microwave radiation, which is the analog of the dimensionless conductance. We show that for L∼4ξ, a single eigenvalue of the transmission matrix (TM) dominates transmission, and the distribution of the T is Gaussian with a variance equal to the average of −ln T, as conjectured by SPS. For samples in the cross-over to localization, L∼ξ, we find a one-sided distribution for T. This anomalous distribution is explained in terms of a charge model for the eigenvalues of the TM τ in which the Coulomb interaction between charges mimics the repulsion between the eigenvalues of TM. We show in the localization limit that the joint distribution of T and the effective number of transmission eigenvalues determines the probability distributions of intensity and total transmission for a single-incident channel.

  19. Transport of localized and extended excitations in chains embedded with randomly distributed linear and nonlinear n -mers

    NASA Astrophysics Data System (ADS)

    López-González, Dany; Molina, Mario I.

    2016-03-01

    We examine the transport of extended and localized excitations in one-dimensional linear chains populated by linear and nonlinear symmetric identical n -mers (with n =3 , 4, 5, and 6), randomly distributed. First, we examine the transmission of plane waves across a single linear n -mer, paying attention to its resonances, and looking for parameters that allow resonances to merge. Within this parameter regime we examine the transmission of plane waves through a disordered and nonlinear segment composed by n -mers randomly placed inside a linear chain. It is observed that nonlinearity tends to inhibit the transmission, which decays as a power law at long segment lengths. This behavior still holds when the n -mer parameters do not obey the resonance condition. On the other hand, the mean square displacement exponent of an initially localized excitation does not depend on nonlinearity at long propagation distances z , and shows a superdiffusive behavior ˜z1.8 for all n -mers, when parameters obey the resonance merging condition; otherwise the exponent reverts back to the random dimer model value ˜z1.5 .

  20. Localization of a two-component Bose-Einstein condensate in a one-dimensional random potential

    NASA Astrophysics Data System (ADS)

    Xi, Kui-Tian; Li, Jinbin; Shi, Da-Ning

    2015-02-01

    We consider a weakly interacting two-component Bose-Einstein condensate (BEC) in a one-dimensional random speckle potential. The problem is studied with solutions of Gross-Pitaevskii (GP) equations by means of numerical method in Crank-Nicolson scheme. Properties of various cases owing to the competition of disorder and repulsive interactions of a cigar-shaped two-component BEC are discussed in detail. It is shown that in the central region, phase separation of a two-component BEC is not only affected by the intra- and inter-component interactions, but also influenced by the strength of the random speckle potential. Due to the strong disorder of the potential, the criterion of phase separation which is independent of the trap strength in an ordered potential, such as a harmonic potential, is no longer available. The influence of different random numbers generated by distinct processes on localization of BEC in the random potential is also investigated, as well as the configurations of the density profiles in the tail regions.

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

    SciTech Connect

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

    2015-06-15

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

  2. Lower bound for the spatial extent of localized modes in photonic-crystal waveguides with small random imperfections.

    PubMed

    Faggiani, Rémi; Baron, Alexandre; Zang, Xiaorun; Lalouat, Loïc; Schulz, Sebastian A; O'Regan, Bryan; Vynck, Kevin; Cluzel, Benoît; de Fornel, Frédérique; Krauss, Thomas F; Lalanne, Philippe

    2016-01-01

    Light localization due to random imperfections in periodic media is paramount in photonics research. The group index is known to be a key parameter for localization near photonic band edges, since small group velocities reinforce light interaction with imperfections. Here, we show that the size of the smallest localized mode that is formed at the band edge of a one-dimensional periodic medium is driven instead by the effective photon mass, i.e. the flatness of the dispersion curve. Our theoretical prediction is supported by numerical simulations, which reveal that photonic-crystal waveguides can exhibit surprisingly small localized modes, much smaller than those observed in Bragg stacks thanks to their larger effective photon mass. This possibility is demonstrated experimentally with a photonic-crystal waveguide fabricated without any intentional disorder, for which near-field measurements allow us to distinctly observe a wavelength-scale localized mode despite the smallness (~1/1000 of a wavelength) of the fabrication imperfections. PMID:27246902

  3. Lower bound for the spatial extent of localized modes in photonic-crystal waveguides with small random imperfections

    PubMed Central

    Faggiani, Rémi; Baron, Alexandre; Zang, Xiaorun; Lalouat, Loïc; Schulz, Sebastian A.; O’Regan, Bryan; Vynck, Kevin; Cluzel, Benoît; de Fornel, Frédérique; Krauss, Thomas F.; Lalanne, Philippe

    2016-01-01

    Light localization due to random imperfections in periodic media is paramount in photonics research. The group index is known to be a key parameter for localization near photonic band edges, since small group velocities reinforce light interaction with imperfections. Here, we show that the size of the smallest localized mode that is formed at the band edge of a one-dimensional periodic medium is driven instead by the effective photon mass, i.e. the flatness of the dispersion curve. Our theoretical prediction is supported by numerical simulations, which reveal that photonic-crystal waveguides can exhibit surprisingly small localized modes, much smaller than those observed in Bragg stacks thanks to their larger effective photon mass. This possibility is demonstrated experimentally with a photonic-crystal waveguide fabricated without any intentional disorder, for which near-field measurements allow us to distinctly observe a wavelength-scale localized mode despite the smallness (~1/1000 of a wavelength) of the fabrication imperfections. PMID:27246902

  4. Wave propagation through random media: A local method of small perturbations based on the Helmholtz equation

    NASA Technical Reports Server (NTRS)

    Grosse, Ralf

    1990-01-01

    Propagation of sound through the turbulent atmosphere is a statistical problem. The randomness of the refractive index field causes sound pressure fluctuations. Although no general theory to predict sound pressure statistics from given refractive index statistics exists, there are several approximate solutions to the problem. The most common approximation is the parabolic equation method. Results obtained by this method are restricted to small refractive index fluctuations and to small wave lengths. While the first condition is generally met in the atmosphere, it is desirable to overcome the second. A generalization of the parabolic equation method with respect to the small wave length restriction is presented.

  5. Relativistic Weierstrass random walks.

    PubMed

    Saa, Alberto; Venegeroles, Roberto

    2010-08-01

    The Weierstrass random walk is a paradigmatic Markov chain giving rise to a Lévy-type superdiffusive behavior. It is well known that special relativity prevents the arbitrarily high velocities necessary to establish a superdiffusive behavior in any process occurring in Minkowski spacetime, implying, in particular, that any relativistic Markov chain describing spacetime phenomena must be essentially Gaussian. Here, we introduce a simple relativistic extension of the Weierstrass random walk and show that there must exist a transition time t{c} delimiting two qualitative distinct dynamical regimes: the (nonrelativistic) superdiffusive Lévy flights, for tt{c} . Implications of this crossover between different diffusion regimes are discussed for some explicit examples. The study of such an explicit and simple Markov chain can shed some light on several results obtained in much more involved contexts. PMID:20866862

  6. Relativistic Weierstrass random walks.

    PubMed

    Saa, Alberto; Venegeroles, Roberto

    2010-08-01

    The Weierstrass random walk is a paradigmatic Markov chain giving rise to a Lévy-type superdiffusive behavior. It is well known that special relativity prevents the arbitrarily high velocities necessary to establish a superdiffusive behavior in any process occurring in Minkowski spacetime, implying, in particular, that any relativistic Markov chain describing spacetime phenomena must be essentially Gaussian. Here, we introduce a simple relativistic extension of the Weierstrass random walk and show that there must exist a transition time t{c} delimiting two qualitative distinct dynamical regimes: the (nonrelativistic) superdiffusive Lévy flights, for tt{c} . Implications of this crossover between different diffusion regimes are discussed for some explicit examples. The study of such an explicit and simple Markov chain can shed some light on several results obtained in much more involved contexts.

  7. Plume mapping via hidden Markov methods.

    PubMed

    Farrell, J A; Pang, Shuo; Li, Wei

    2003-01-01

    This paper addresses the problem of mapping likely locations of a chemical source using an autonomous vehicle operating in a fluid flow. The paper reviews biological plume-tracing concepts, reviews previous strategies for vehicle-based plume tracing, and presents a new plume mapping approach based on hidden Markov methods (HMM). HMM provide efficient algorithms for predicting the likelihood of odor detection versus position, the likelihood of source location versus position, the most likely path taken by the odor to a given location, and the path between two points most likely to result in odor detection. All four are useful for solving the odor source localization problem using an autonomous vehicle. The vehicle is assumed to be capable of detecting above threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. The fluid flow is assumed to vary with space and time, and to have a high Reynolds number (Re>10). PMID:18238238

  8. Reconstructing local population dynamics in noisy metapopulations--the role of random catastrophes and Allee effects.

    PubMed

    Hart, Edmund M; Avilés, Leticia

    2014-01-01

    Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity--demographic, environmental, and random catastrophes--affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity--positive and negative environmental fluctuations--caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are

  9. Localized Piezoelectric Alveolar Decortication for Orthodontic Treatment in Adults: A Randomized Controlled Trial.

    PubMed

    Charavet, C; Lecloux, G; Bruwier, A; Rompen, E; Maes, N; Limme, M; Lambert, F

    2016-08-01

    This randomized controlled trial aimed to evaluate the benefits and clinical outcomes of piezocision, which is a minimally invasive approach to corticotomy that is used in orthodontic treatments. Twenty-four adult patients presenting with mild overcrowdings were randomly allocated to either a control group that was treated with conventional orthodontics or a test group that received piezo-assisted orthodontics. The piezocisions were performed 1 wk week after the placement of the orthodontic appliances. Neither grafting material nor sutures were used. All patients were followed every 2 wk, and archwires were changed only when they were no longer active. The periods required for the completion of the overall orthodontic treatments were calculated, and the periodontal parameters were evaluated at baseline and at the end of the orthodontic treatment. Patient-centered outcomes were assessed with a visual analog scale; analgesic use following the procedures was also recorded. The patient characteristics were similar between the 2 groups. The overall treatment time was significantly reduced by 43% in the piezocision group as compared with the control group. In both groups, periodontal parameters (i.e., recession depth, pocket depth, plaque index, and papilla bleeding index) remained unchanged between the baseline and treatment completion time points. No increase in root resorption was observed in either group. Scars were observed in 50% of the patients in the piezocision group. Analgesic consumption was similar following orthodontic appliance placement and piezocision surgery. Patient satisfaction was significantly better in the piezocision group than in the control group. In these conditions, the piezocision technique seemed to be effective in accelerating orthodontic tooth movement. No gingival recessions were observed. The risk of residual scars might limit the indications for piezocision in patients with a high smile line (ClinicalTrials.gov NCT02590835). PMID

  10. Localized Piezoelectric Alveolar Decortication for Orthodontic Treatment in Adults: A Randomized Controlled Trial.

    PubMed

    Charavet, C; Lecloux, G; Bruwier, A; Rompen, E; Maes, N; Limme, M; Lambert, F

    2016-08-01

    This randomized controlled trial aimed to evaluate the benefits and clinical outcomes of piezocision, which is a minimally invasive approach to corticotomy that is used in orthodontic treatments. Twenty-four adult patients presenting with mild overcrowdings were randomly allocated to either a control group that was treated with conventional orthodontics or a test group that received piezo-assisted orthodontics. The piezocisions were performed 1 wk week after the placement of the orthodontic appliances. Neither grafting material nor sutures were used. All patients were followed every 2 wk, and archwires were changed only when they were no longer active. The periods required for the completion of the overall orthodontic treatments were calculated, and the periodontal parameters were evaluated at baseline and at the end of the orthodontic treatment. Patient-centered outcomes were assessed with a visual analog scale; analgesic use following the procedures was also recorded. The patient characteristics were similar between the 2 groups. The overall treatment time was significantly reduced by 43% in the piezocision group as compared with the control group. In both groups, periodontal parameters (i.e., recession depth, pocket depth, plaque index, and papilla bleeding index) remained unchanged between the baseline and treatment completion time points. No increase in root resorption was observed in either group. Scars were observed in 50% of the patients in the piezocision group. Analgesic consumption was similar following orthodontic appliance placement and piezocision surgery. Patient satisfaction was significantly better in the piezocision group than in the control group. In these conditions, the piezocision technique seemed to be effective in accelerating orthodontic tooth movement. No gingival recessions were observed. The risk of residual scars might limit the indications for piezocision in patients with a high smile line (ClinicalTrials.gov NCT02590835).

  11. Reconstructing Local Population Dynamics in Noisy Metapopulations—The Role of Random Catastrophes and Allee Effects

    PubMed Central

    Hart, Edmund M.; Avilés, Leticia

    2014-01-01

    Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity—demographic, environmental, and random catastrophes— affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity—positive and negative environmental fluctuations—caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are

  12. A nonrandomized cohort and a randomized study of local control of large hepatocarcinoma by targeting intratumoral lactic acidosis

    PubMed Central

    Chao, Ming; Wu, Hao; Jin, Kai; Li, Bin; Wu, Jianjun; Zhang, Guangqiang; Yang, Gong; Hu, Xun

    2016-01-01

    Study design: Previous works suggested that neutralizing intratumoral lactic acidosis combined with glucose deprivation may deliver an effective approach to control tumor. We did a pilot clinical investigation, including a nonrandomized (57 patients with large HCC) and a randomized controlled (20 patients with large HCC) studies. Methods: The patients were treated with transarterial chemoembolization (TACE) with or without bicarbonate local infusion into tumor. Results: In the nonrandomized controlled study, geometric mean of viable tumor residues (VTR) in TACE with bicarbonate was 6.4-fold lower than that in TACE without bicarbonate (7.1% [95% CI: 4.6%–10.9%] vs 45.6% [28.9%–72.0%]; p<0.0001). This difference was recapitulated by a subsequent randomized controlled study. TACE combined with bicarbonate yielded a 100% objective response rate (ORR), whereas the ORR treated with TACE alone was 44.4% (nonrandomized) and 63.6% (randomized). The survival data suggested that bicarbonate may bring survival benefit. Conclusion: Bicarbonate markedly enhances the anticancer activity of TACE. Clinical trail registration: ChiCTR-IOR-14005319. DOI: http://dx.doi.org/10.7554/eLife.15691.001 PMID:27481188

  13. Full eigenvalues of the Markov matrix for scale-free polymer networks

    NASA Astrophysics Data System (ADS)

    Zhang, Zhongzhi; Guo, Xiaoye; Lin, Yuan

    2014-08-01

    Much important information about the structural and dynamical properties of complex systems can be extracted from the eigenvalues and eigenvectors of a Markov matrix associated with random walks performed on these systems, and spectral methods have become an indispensable tool in the complex system analysis. In this paper, we study the Markov matrix of a class of scale-free polymer networks. We present an exact analytical expression for all the eigenvalues and determine explicitly their multiplicities. We then use the obtained eigenvalues to derive an explicit formula for the random target access time for random walks on the studied networks. Furthermore, based on the link between the eigenvalues of the Markov matrix and the number of spanning trees, we confirm the validity of the obtained eigenvalues and their corresponding degeneracies.

  14. High-order hidden Markov model for piecewise linear processes and applications to speech recognition.

    PubMed

    Lee, Lee-Min; Jean, Fu-Rong

    2016-08-01

    The hidden Markov models have been widely applied to systems with sequential data. However, the conditional independence of the state outputs will limit the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a high-order hidden Markov model for piecewise linear processes is proposed to better approximate the behavior of a real process. A parameter estimation method based on the expectation-maximization algorithm was derived for the proposed model. Experiments on speech recognition of noisy Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate compared to a baseline hidden Markov model. PMID:27586781

  15. Quality control of radiation therapy in multi-institutional randomized clinical trial for localized prostate cancer

    SciTech Connect

    Hafermann, M.D.; Gibbons, R.P.; Murphy, G.P.

    1988-02-01

    The National Prostatic Cancer Project (NPCP) from 1978 through 1985 compared definitive radiation therapy for Stages B2, C, D1 lesions in those who received only radiation treatment to those who received two years of additional cyclophosphamide (Cytoxan) or estramustine phosphate (Emcyt) chemotherapy. Two hundred fifty-four patients were entered and 229 evaluated for compliance of the spatial localization of the prostate through review of the simulation and port films. In 78 per cent this was satisfactory, whereas in 12 per cent it was unsatisfactory, and another 10 per cent were not evaluable. The principle cause of an unsatisfactory rating was failure to adequately cover the prostatic target volume, especially the apex which was found to be variable in location. Routine use of retrograde urethrocystography is urged as part of the localization method in patients to receive definitive external beam radiation therapy for prostate cancer. The role and impact of quality assurance programs for radiotherapy in cooperative clinical study groups is reviewed and discussed.

  16. Constructing 1/ωα noise from reversible Markov chains

    NASA Astrophysics Data System (ADS)

    Erland, Sveinung; Greenwood, Priscilla E.

    2007-09-01

    This paper gives sufficient conditions for the output of 1/ωα noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/ωα condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/ω noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/ωα noise which also has a long memory.

  17. Multivariate longitudinal data analysis with mixed effects hidden Markov models.

    PubMed

    Raffa, Jesse D; Dubin, Joel A

    2015-09-01

    Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. PMID:25761965

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

    PubMed

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

    1999-06-01

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

  19. Incorporating dose-rate effects in Markov radiation cell survival models.

    PubMed

    Sachs, R K; Hlatky, L; Hahnfeldt, P; Chen, P L

    1990-11-01

    Markov models for the survival of cells subjected to ionizing radiation take stochastic fluctuations into account more systematically than do non-Markov counterparts. Albright's Markov RMR (repair-misrepair) model (Radiat. Res. 118, 1-20, 1989) and Curtis's Markov LPL (lethal-potentially lethal) model [in Quantitative Mathematical Models in Radiation Biology (J. Kiefer, Ed.), pp. 127-146. Springer, New York, 1989], which assume acute irradiation, are here generalized to finite dose rates. Instead of treating irradiation as an instantaneous event we introduce an irradiation period T and analyze processes during the interval T as well as afterward. Albright's RMR transition matrix is used throughout for computing the time development of repair and misrepair. During irradiation an additional matrix is added to describe the evolving radiation damage. Albright's and Curtis's Markov models are recovered as limiting cases by taking T----0 with total dose fixed; the opposite limit, of low dose rates, is also analyzed. Deviations from Poisson behavior in the statistical distributions of lesions are calculated. Other continuous-time Markov chain models ("compartmental models") are discussed briefly, for example, models which incorporate cell proliferation and saturable repair models. It is found that for low dose rates the Markov RMR and LPL models give lower survivals compared to the original non-Markov versions. For acute irradiation and high doses, the Markov models predict higher survivals. In general, theoretical extrapolations which neglect some random fluctuations have a systematic bias toward overoptimism when damage to irradiated tumors is compared with damage to surrounding tissues. PMID:2247602

  20. Markov and semi-Markov processes as a failure rate

    NASA Astrophysics Data System (ADS)

    Grabski, Franciszek

    2016-06-01

    In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.

  1. Effect of hydralazine on duration of soft tissue local anesthesia following dental treatment: a randomized clinical trial.

    PubMed

    Fakheran Esfahani, Omid; Pouraboutaleb, Mohammad Fazel; Khorami, Behnam

    2015-01-01

    Prolonged numbness following routine dental treatments can cause difficulties in speaking and swallowing and may result in inadvertent biting of soft tissues. Local injection of vasodilator agents may represent a solution to this problem. The aim of this study was to evaluate the effect of submucosal injection of hydralazine hydrochloride (HCl) on the duration of oral soft tissue anesthesia after routine dental treatment. This randomized, single-blinded, controlled clinical trial included 50 patients who received inferior alveolar nerve block (2% lidocaine with 1:100,000 epinephrine) for simple restorative treatment. Upon completion of the dental treatment, patients randomly received a hydralazine HCl or sham injection in the same site as the local anesthetic injection. The reversal time to normal sensation of soft tissues (lips, tongue, and perioral skin) was evaluated and reported every 5 minutes by the patients, who followed an assessment protocol that they were taught in advance of treatment. Median recovery times in the hydralazine group and the sham group were 81.4 (SD, 3.6) and 221.8 (SD, 6.3) minutes, respectively. Based on Kaplan-Meier survival analysis, the duration of soft tissue anesthesia in the 2 groups was significantly different (P < 0.0001). By 1 hour after the reversal injection, 76% of subjects receiving hydralazine HCl had returned to normal intraoral and perioral sensation, but none of the subjects in the sham group reported normal sensation. Based on these results, submucosal injection of hydralazine HCl can be considered a safe and effective method to reduce the duration of local anesthetic-induced soft tissue numbness and the related functional problems.

  2. Experiments with central-limit properties of spatial samples from locally covariant random fields

    USGS Publications Warehouse

    Barringer, T.H.; Smith, T.E.

    1992-01-01

    When spatial samples are statistically dependent, the classical estimator of sample-mean standard deviation is well known to be inconsistent. For locally dependent samples, however, consistent estimators of sample-mean standard deviation can be constructed. The present paper investigates the sampling properties of one such estimator, designated as the tau estimator of sample-mean standard deviation. In particular, the asymptotic normality properties of standardized sample means based on tau estimators are studied in terms of computer experiments with simulated sample-mean distributions. The effects of both sample size and dependency levels among samples are examined for various value of tau (denoting the size of the spatial kernel for the estimator). The results suggest that even for small degrees of spatial dependency, the tau estimator exhibits significantly stronger normality properties than does the classical estimator of standardized sample means. ?? 1992.

  3. A Hidden Markov Approach to Modeling Interevent Earthquake Times

    NASA Astrophysics Data System (ADS)

    Chambers, D.; Ebel, J. E.; Kafka, A. L.; Baglivo, J.

    2003-12-01

    A hidden Markov process, in which the interevent time distribution is a mixture of exponential distributions with different rates, is explored as a model for seismicity that does not follow a Poisson process. In a general hidden Markov model, one assumes that a system can be in any of a finite number k of states and there is a random variable of interest whose distribution depends on the state in which the system resides. The system moves probabilistically among the states according to a Markov chain; that is, given the history of visited states up to the present, the conditional probability that the next state is a specified one depends only on the present state. Thus the transition probabilities are specified by a k by k stochastic matrix. Furthermore, it is assumed that the actual states are unobserved (hidden) and that only the values of the random variable are seen. From these values, one wishes to estimate the sequence of states, the transition probability matrix, and any parameters used in the state-specific distributions. The hidden Markov process was applied to a data set of 110 interevent times for earthquakes in New England from 1975 to 2000. Using the Baum-Welch method (Baum et al., Ann. Math. Statist. 41, 164-171), we estimate the transition probabilities, find the most likely sequence of states, and estimate the k means of the exponential distributions. Using k=2 states, we found the data were fit well by a mixture of two exponential distributions, with means of approximately 5 days and 95 days. The steady state model indicates that after approximately one fourth of the earthquakes, the waiting time until the next event had the first exponential distribution and three fourths of the time it had the second. Three and four state models were also fit to the data; the data were inconsistent with a three state model but were well fit by a four state model.

  4. 70 Gy Versus 80 Gy in Localized Prostate Cancer: 5-Year Results of GETUG 06 Randomized Trial;Prostate cancer; Dose escalation; Conformal radiotherapy; Randomized trial

    SciTech Connect

    Beckendorf, Veronique; Guerif, Stephane; Le Prise, Elisabeth; Cosset, Jean-Marc; Bougnoux, Agnes; Chauvet, Bruno; Salem, Naji; Chapet, Olivier; Bourdain, Sylvain; Bachaud, Jean-Marc; Maingon, Philippe; Hannoun-Levi, Jean-Michel; Malissard, Luc; Simon, Jean-Marc; Pommier, Pascal; Hay, Men; Dubray, Bernard; Lagrange, Jean-Leon; Luporsi, Elisabeth; Bey, Pierre

    2011-07-15

    Purpose: To perform a randomized trial comparing 70 and 80 Gy radiotherapy for prostate cancer. Patients and Methods: A total of 306 patients with localized prostate cancer were randomized. No androgen deprivation was allowed. The primary endpoint was biochemical relapse according to the modified 1997-American Society for Therapeutic Radiology and Oncology and Phoenix definitions. Toxicity was graded using the Radiation Therapy Oncology Group 1991 criteria and the late effects on normal tissues-subjective, objective, management, analytic scales (LENT-SOMA) scales. The patients' quality of life was scored using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire 30-item cancer-specific and 25-item prostate-specific modules. Results: The median follow-up was 61 months. According to the 1997-American Society for Therapeutic Radiology and Oncology definition, the 5-year biochemical relapse rate was 39% and 28% in the 70- and 80-Gy arms, respectively (p = .036). Using the Phoenix definition, the 5-year biochemical relapse rate was 32% and 23.5%, respectively (p = .09). The subgroup analysis showed a better biochemical outcome for the higher dose group with an initial prostate-specific antigen level >15 ng/mL. At the last follow-up date, 26 patients had died, 10 of their disease and none of toxicity, with no differences between the two arms. According to the Radiation Therapy Oncology Group scale, the Grade 2 or greater rectal toxicity rate was 14% and 19.5% for the 70- and 80-Gy arms (p = .22), respectively. The Grade 2 or greater urinary toxicity was 10% at 70 Gy and 17.5% at 80 Gy (p = .046). Similar results were observed using the LENT-SOMA scale. Bladder toxicity was more frequent at 80 Gy than at 70 Gy (p = .039). The quality-of-life questionnaire results before and 5 years after treatment were available for 103 patients with no differences found between the 70- and 80-Gy arms. Conclusion: High-dose radiotherapy provided a

  5. Adjunctive Systemic and Local Antimicrobial Therapy in the Surgical Treatment of Peri-implantitis: A Randomized Controlled Clinical Trial.

    PubMed

    Carcuac, O; Derks, J; Charalampakis, G; Abrahamsson, I; Wennström, J; Berglundh, T

    2016-01-01

    The aim of the present randomized controlled clinical trial was to investigate the adjunctive effect of systemic antibiotics and the local use of chlorhexidine for implant surface decontamination in the surgical treatment of peri-implantitis. One hundred patients with severe peri-implantitis were recruited. Surgical therapy was performed with or without adjunctive systemic antibiotics or the local use of chlorhexidine for implant surface decontamination. Treatment outcomes were evaluated at 1 y. A binary logistic regression analysis was used to identify factors influencing the probability of treatment success, that is, probing pocket depth ≤5 mm, absence of bleeding/suppuration on probing, and no additional bone loss. Treatment success was obtained in 45% of all implants but was higher in implants with a nonmodified surface (79%) than those with a modified surface (34%). The local use of chlorhexidine had no overall effect on treatment outcomes. While adjunctive systemic antibiotics had no impact on treatment success at implants with a nonmodified surface, a positive effect on treatment success was observed at implants with a modified surface. The likelihood for treatment success using adjunctive systemic antibiotics in patients with implants with a modified surface, however, was low. As the effect of adjunctive systemic antibiotics depended on implant surface characteristics, recommendations for their use in the surgical treatment of peri-implantitis should be based on careful assessments of the targeted implant (ClinicalTrials.gov NCT01857804).

  6. Clinical and Radiographic Evaluation of Immediate Loaded Dental Implants With Local Application of Melatonin: A Preliminary Randomized Controlled Clinical Trial.

    PubMed

    El-Gammal, Mona Y; Salem, Ahmed S; Anees, Mohamed M; Tawfik, Mohamed A

    2016-04-01

    Immediate loading of dental implants in situations where low bone density exist, such as the posterior maxillary region, became possible recently after the introduction of biomimetic agents. This 1-year preliminary clinical trial was carried out to clinically and radiographically evaluate immediate-loaded 1-piece implants with local application of melatonin in the osteotomy site as a biomimetic material. 14 patients with missing maxillary premolars were randomized to receive 14 implants of 1-piece type that were subjected to immediate loading after 2 weeks of initial placement. Group I included 7 implants with acid-etched surface while group II included 7 implants with acid-etched surface combined with local application of melatonin gel at the osteotomy site. Patients were recalled for follow up at 1, 3, 6, and 12 months after loading. All implants were considered successful after 12 months of follow-up. Significant difference (P < 0.05) was found between both groups at 1 month of implant loading when considering the implant stability. At 1 and 3 months there were significant differences in the marginal bone level between the 2 groups. These results suggest that the local application of melatonin at the osteotomy site is associated with good stability and minimal bone resorption. However, more studies for longer follow-up periods are required to confirm the effect of melatonin hormone on osseointegration of dental implants.

  7. Local rhamnosoft, ceramides and L-isoleucine in atopic eczema: a randomized, placebo controlled trial

    PubMed Central

    Marseglia, Alessia; Licari, Amelia; Agostinis, Fabio; Barcella, Antonio; Bonamonte, Domenico; Puviani, Mario; Milani, Massimo; Marseglia, GianLuigi

    2014-01-01

    Background A non-steroidal, anti-inflammatory moisturizing cream containing rhamnosoft, ceramides, and L-isoleucine (ILE) (pro-AMP cream) has been recently developed for the specific treatment of atopic eczema (AE) of the face. In this trial, we evaluated the clinical efficacy and tolerability of pro-AMP cream in the treatment of facial AE in children in comparison with an emollient cream. Methods In a randomized, prospective, assessor-blinded, parallel groups (2:1) controlled trial, 107 children (72 allocated to pro-AMP cream and 35 allocated to control group) with mild-to-moderate chronic AE of the face were enrolled. Treatments were applied twice daily for a 6-week period. Facial Eczema Severity Score (ESS) was evaluated at baseline, week 3, and week 6, by an assessor unaware of treatment allocation. Investigator's Global Assessment (IGA) score was assessed at week 3 and at week 6. Tolerability was evaluated at week 3 and at week 6 using a 4-point score (from 0: low tolerability to 3: very good tolerability). Results At baseline ESS, mean (SD) was 6.1 (2.4) in the pro-AMP cream group and 5.3 (3) in the control group. In the pro-AMP group, in comparison with baseline, ESS was significantly reduced to 2.5 (−59%) after 3 wks and to 1.0 (−84%) at week 6 (p = 0.0001). In the control group, ESS was reduced to 3 (−42%) at week 2 and to 2.6 (−50%) at week 6. At week 6, ESS in pro-AMP cream was significantly lower than the control group (1.0 vs. 2.6; p = 0.001). Both products were well tolerated. Conclusion Pro-AMP cream has shown to be effective in the treatment of mild-to-moderate chronic lesion of AE of the face. Clinical efficacy was greater in comparison with an emollient cream. (Clinical trial Registry: NTR4084). PMID:24750568

  8. Efficacy of acupuncture versus local methylprednisolone acetate injection in De Quervain's tenosynovitis: a randomized controlled trial.

    PubMed

    Hadianfard, Mohammadjavad; Ashraf, Alireza; Fakheri, Maryamsadat; Nasiri, Aref

    2014-06-01

    There is no consensus on the management of De Quervain's tenosynovitis, but local corticosteroid injection is considered the mainstay of treatment. However, some patients are reluctant to take steroid injections. This study was performed to compare the efficacy of acupuncture versus corticosteroid injection for the treatment of this disease. Thirty patients were consequently treated in two groups. The acupuncture group received five acupuncture sessions of 30 minutes duration on classic points of LI-5, LU-7, and LU-9 and on ahshi points. The injection group received one methylprednisolone acetate injection in the first dorsal compartment of the wrist. The degree of disability and pain was evaluated by using the Quick Disabilities of the Arm, Shoulder, and Hand (Q-DASH) scale and the Visual Analogue Scale (VAS) at baseline and at 2 weeks and 6 weeks after the start of treatment. The baseline means of the Q-DASH and the VAS scores were 62.8 and 6.9, respectively. At the last follow-up, the mean Q-DASH scores were 9.8 versus 6.2 in the acupuncture and injection groups, respectively, and the mean VAS scores were 2 versus 1.2. We demonstrated short-term improvement of pain and function in both groups. Although the success rate was somewhat higher with corticosteroid injection, acupuncture can be considered as an alternative option for treatment of De Quervain's tenosynovitis. PMID:24929455

  9. Prospective Randomized Comparison of the Effectiveness of Radiation Therapy and Local Steroid Injection for the Treatment of Plantar Fasciitis

    SciTech Connect

    Canyilmaz, Emine; Canyilmaz, Fatih; Aynaci, Ozlem; Colak, Fatma; Serdar, Lasif; Uslu, Gonca Hanedan; Aynaci, Osman; Yoney, Adnan

    2015-07-01

    Purpose: The purpose of this study was to conduct a randomized trial of radiation therapy for plantar fasciitis and to compare radiation therapy with local steroid injections. Methods and Materials: Between March 2013 and April 2014, 128 patients with plantar fasciitis were randomized to receive radiation therapy (total dose of 6.0 Gy applied in 6 fractions of 1.0 Gy three times a week) or local corticosteroid injections a 1 ml injection of 40 mg methylprednisolone and 0.5 ml 1% lidocaine under the guidance of palpation. The results were measured using a visual analog scale, a modified von Pannewitz scale, and a 5-level function score. The fundamental phase of the study was 3 months, with a follow-up period of up to 6 months. Results: The median follow-up period for all patients was 12.5 months (range, 6.5-18.6 months). For the radiation therapy patients, the median follow-up period was 13 months (range, 6.5-18.5 months), whereas in the palpation-guided (PG) steroid injection arm, it was 12.1 months (range, 6.5-18.6 months). After 3 months, results in the radiation therapy arm were significantly superior to those in the PG steroid injection arm (visual analog scale, P<.001; modified von Pannewitz scale, P<.001; 5-level function score, P<.001). Requirements for a second treatment did not significantly differ between the 2 groups, but the time interval for the second treatment was significantly shorter in the PG steroid injection group (P=.045). Conclusion: This study confirms the superior analgesic effect of radiation therapy compared to mean PG steroid injection on plantar fasciitis for at least 6 months after treatment.

  10. Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network.

    PubMed

    Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung

    2016-08-01

    A facial sketch synthesis system is proposed, featuring a 2D direct combined model (2DDCM)-based face-specific Markov network. In contrast to the existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches, which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a data set consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely, a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training data set. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests. PMID:27244737

  11. Building Simple Hidden Markov Models. Classroom Notes

    ERIC Educational Resources Information Center

    Ching, Wai-Ki; Ng, Michael K.

    2004-01-01

    Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

  12. Using Games to Teach Markov Chains

    ERIC Educational Resources Information Center

    Johnson, Roger W.

    2003-01-01

    Games are promoted as examples for classroom discussion of stationary Markov chains. In a game context Markov chain terminology and results are made concrete, interesting, and entertaining. Game length for several-player games such as "Hi Ho! Cherry-O" and "Chutes and Ladders" is investigated and new, simple formulas are given. Slight…

  13. On Markov Earth Mover’s Distance

    PubMed Central

    Wei, Jie

    2015-01-01

    In statistics, pattern recognition and signal processing, it is of utmost importance to have an effective and efficient distance to measure the similarity between two distributions and sequences. In statistics this is referred to as goodness-of-fit problem. Two leading goodness of fit methods are chi-square and Kolmogorov–Smirnov distances. The strictly localized nature of these two measures hinders their practical utilities in patterns and signals where the sample size is usually small. In view of this problem Rubner and colleagues developed the earth mover’s distance (EMD) to allow for cross-bin moves in evaluating the distance between two patterns, which find a broad spectrum of applications. EMD-L1 was later proposed to reduce the time complexity of EMD from super-cubic by one order of magnitude by exploiting the special L1 metric. EMD-hat was developed to turn the global EMD to a localized one by discarding long-distance earth movements. In this work, we introduce a Markov EMD (MEMD) by treating the source and destination nodes absolutely symmetrically. In MEMD, like hat-EMD, the earth is only moved locally as dictated by the degree d of neighborhood system. Nodes that cannot be matched locally is handled by dummy source and destination nodes. By use of this localized network structure, a greedy algorithm that is linear to the degree d and number of nodes is then developed to evaluate the MEMD. Empirical studies on the use of MEMD on deterministic and statistical synthetic sequences and SIFT-based image retrieval suggested encouraging performances. PMID:25983362

  14. Local blanching after epicutaneous application of EMLA cream. A double-blind randomized study among 50 healthy volunteers.

    PubMed

    Villada, G; Zetlaoui, J; Revuz, J

    1990-01-01

    EMLA cream is a topical formulation based upon the eutectic mixture of lidocaine and prilocaine and is used in clinical settings to produce local analgesia after application under occlusive dressing. A blanching reaction has been reported to occur locally after application, but it is not clear whether this reaction is caused by the anesthetic mixture, by the vehicle or the occlusion. We studied this blanching reaction in 50 healthy volunteers in a double-blind randomized assay: EMLA versus placebo, under occlusive dressing for 1 h, each subject being his own control. We found 33 cases (66%) of blanching after application of EMLA cream versus 3 cases (6%) after placebo, this difference being highly significant. Blanching was observed without delay, after removal of the dressing, and was very transient, disappearing in less than 3 h in all cases. We thus conclude that the blanching reaction is (1) frequent but very transient, and (2) determined by the anesthetic mixture included in EMLA cream and not by the vehicle alone, nor by the occlusion, since it is not found with the placebo. The precise mechanism of this reaction is unknown.

  15. Markov processes follow from the principle of maximum caliber

    PubMed Central

    Ge, Hao; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A.

    2012-01-01

    Markov models are widely used to describe stochastic dynamics. Here, we show that Markov models follow directly from the dynamical principle of maximum caliber (Max Cal). Max Cal is a method of deriving dynamical models based on maximizing the path entropy subject to dynamical constraints. We give three different cases. First, we show that if constraints (or data) are given in the form of singlet statistics (average occupation probabilities), then maximizing the caliber predicts a time-independent process that is modeled by identical, independently distributed random variables. Second, we show that if constraints are given in the form of sequential pairwise statistics, then maximizing the caliber dictates that the kinetic process will be Markovian with a uniform initial distribution. Third, if the initial distribution is known and is not uniform we show that the only process that maximizes the path entropy is still the Markov process. We give an example of how Max Cal can be used to discriminate between different dynamical models given data. PMID:22360170

  16. Markov branching in the vertex splitting model

    NASA Astrophysics Data System (ADS)

    Örn Stefánsson, Sigurdur

    2012-04-01

    We study a special case of the vertex splitting model which is a recent model of randomly growing trees. For any finite maximum vertex degree D, we find a one parameter model, with parameter \\alpha \\in [0,1] which has a so-called Markov branching property. When D=\\infty we find a two parameter model with an additional parameter \\gamma \\in [0,1] which also has this feature. In the case D = 3, the model bears resemblance to Ford's α-model of phylogenetic trees and when D=\\infty it is similar to its generalization, the αγ-model. For α = 0, the model reduces to the well known model of preferential attachment. In the case α > 0, we prove convergence of the finite volume probability measures, generated by the growth rules, to a measure on infinite trees which is concentrated on the set of trees with a single spine. We show that the annealed Hausdorff dimension with respect to the infinite volume measure is 1/α. When γ = 0 the model reduces to a model of growing caterpillar graphs in which case we prove that the Hausdorff dimension is almost surely 1/α and that the spectral dimension is almost surely 2/(1 + α). We comment briefly on the distribution of vertex degrees and correlations between degrees of neighbouring vertices.

  17. A randomized controlled trial comparing mandibular local anesthesia techniques in children receiving nitrous oxide-oxygen sedation.

    PubMed Central

    Naidu, Sinuba; Loughlin, Pat; Coldwell, Susan E.; Noonan, Carolyn J.; Milgrom, Peter

    2004-01-01

    The aim of this study was to test the hypothesis that dental pain control using infiltration/intrapapillary injection was less effective than inferior alveolar block/long buccal infiltration anesthesia in children. A total of 101 healthy children, aged 5-8 years, who had no contraindication for local anesthetic and who needed a pulpotomy treatment and stainless steel crown placement in a lower primary molar were studied. A 2-group randomized blinded controlled design was employed comparing the 2 local anesthesia techniques using 2% lidocaine, 1:100,000 epinephrine. All children were given 40% nitrous oxide. Children self-reported pain using the Color Analogue Scale. The study was conducted in a private pediatric dental practice in Mount Vernon, Wash. Overall pain levels reported by the children were low, and there were no differences between conditions at any point in the procedure. Pain reports for clamp placement were block/long buccal 2.8 and infiltration/intrapapillary 1.9 (P = .1). Pain reports for drilling were block/long buccal 2.0 and infiltration/intrapapillary 1.8 (P = .7). Nine percent of children required supplementary local anesthetic: 4 of 52 (7.7%) in the block/long buccal group and 5 of 49 (10.2%) in the infiltration/intrapapillary group (P = .07). The hypothesis that block/long buccal would be more effective than infiltration/intrapapillary was not supported. There was no difference in pain control effectiveness between infiltration/intrapapillary injection and inferior alveolar block/long buccal infiltration using 2% lidocaine with 1:100,000 epinephrine when mandibular primary molars received pulpotomy treatment and stainless steel crowns. PMID:15106686

  18. Impact of hormonal treatment duration in combination with radiotherapy for locally advanced prostate cancer: Meta-analysis of randomized trials

    PubMed Central

    2010-01-01

    Background Hormone therapy plus radiotherapy significantly decreases recurrences and mortality of patients affected by locally advanced prostate cancer. In order to determine if difference exists according to the hormonal treatment duration, a literature-based meta-analysis was performed. Methods Relative risks (RR) were derived through a random-effect model. Differences in primary (biochemical failure, BF; cancer-specific survival, CSS), and secondary outcomes (overall survival, OS; local or distant recurrence, LR/DM) were explored. Absolute differences (AD) and the number needed to treat (NNT) were calculated. Heterogeneity, a meta-regression for clinic-pathological predictors and a correlation test for surrogates were conducted. Results Five trials (3,424 patients) were included. Patient population ranged from 267 to 1,521 patients. The longer hormonal treatment significantly improves BF (with significant heterogeneity) with an absolute benefit of 10.1%, and a non significant trend in CSS. With regard to secondary end-points, the longer hormonal treatment significantly decrease both the LR and the DM with an absolute difference of 11.7% and 11.5%. Any significant difference in OS was observed. None of the three identified clinico-pathological predictors (median PSA, range 9.5-20.35, Gleason score 7-10, 27-55% patients/trial, and T3-4, 13-77% patients/trial), did significantly affect outcomes. At the meta-regression analysis a significant correlation between the overall treatment benefit in BF, CSS, OS, LR and DM, and the length of the treatment was found (p≤0.03). Conclusions Although with significant heterogeneity (reflecting different patient' risk stratifications), a longer hormonal treatment duration significantly decreases biochemical, local and distant recurrences, with a trend for longer cancer specific survival. PMID:21143897

  19. Hidden Markov Models: The Best Models for Forager Movements?

    PubMed Central

    Joo, Rocio; Bertrand, Sophie; Tam, Jorge; Fablet, Ronan

    2013-01-01

    One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (∼1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines) for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%), significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance. PMID:24058400

  20. Topological Charge Evolution in the Markov-Chain of QCD

    SciTech Connect

    Derek Leinweber; Anthony Williams; Jian-bo Zhang; Frank Lee

    2004-04-01

    The topological charge is studied on lattices of large physical volume and fine lattice spacing. We illustrate how a parity transformation on the SU(3) link-variables of lattice gauge configurations reverses the sign of the topological charge and leaves the action invariant. Random applications of the parity transformation are proposed to traverse from one topological charge sign to the other. The transformation provides an improved unbiased estimator of the ensemble average and is essential in improving the ergodicity of the Markov chain process.

  1. Dose-Effect Relationship in Chemoradiotherapy for Locally Advanced Rectal Cancer: A Randomized Trial Comparing Two Radiation Doses

    SciTech Connect

    Jakobsen, Anders; Ploen, John; Vuong, Te; Appelt, Ane; Lindebjerg, Jan; Rafaelsen, Soren R.

    2012-11-15

    Purpose: Locally advanced rectal cancer represents a major therapeutic challenge. Preoperative chemoradiation therapy is considered standard, but little is known about the dose-effect relationship. The present study represents a dose-escalation phase III trial comparing 2 doses of radiation. Methods and Materials: The inclusion criteria were resectable T3 and T4 tumors with a circumferential margin of {<=}5 mm on magnetic resonance imaging. The patients were randomized to receive 50.4 Gy in 28 fractions to the tumor and pelvic lymph nodes (arm A) or the same treatment supplemented with an endorectal boost given as high-dose-rate brachytherapy (10 Gy in 2 fractions; arm B). Concomitant chemotherapy, uftoral 300 mg/m{sup 2} and L-leucovorin 22.5 mg/d, was added to both arms on treatment days. The primary endpoint was complete pathologic remission. The secondary endpoints included tumor response and rate of complete resection (R0). Results: The study included 248 patients. No significant difference was found in toxicity or surgical complications between the 2 groups. Based on intention to treat, no significant difference was found in the complete pathologic remission rate between the 2 arms (18% and 18%). The rate of R0 resection was different in T3 tumors (90% and 99%; P=.03). The same applied to the rate of major response (tumor regression grade, 1+2), 29% and 44%, respectively (P=.04). Conclusions: This first randomized trial comparing 2 radiation doses indicated that the higher dose increased the rate of major response by 50% in T3 tumors. The endorectal boost is feasible, with no significant increase in toxicity or surgical complications.

  2. Hubbard U and Hund exchange J in transition metal oxides: Screening versus localization trends from constrained random phase approximation

    NASA Astrophysics Data System (ADS)

    Vaugier, Loïg; Jiang, Hong; Biermann, Silke

    2012-10-01

    In this work, we address the question of calculating the local effective Coulomb interaction matrix in materials with strong electronic Coulomb interactions from first-principles. To this purpose, we implement the constrained random phase approximation into a density functional code within the linearized augmented plane-wave framework. We apply our approach to the 3d and 4d early transition metal oxides SrMO3 (M= V, Cr, Mn) and (M= Nb, Mo, Tc) in their paramagnetic phases. For these systems, we explicitly assess the differences between two physically motivated low-energy Hamiltonians: The first is the three-orbital model comprising the t2g states only, which is often used for early transition metal oxides. The second choice is a model where both metal d and oxygen p states are retained in the construction of Wannier functions, but the Hubbard interactions are applied to the d states only (“d-dp Hamiltonian”). Interestingly, since (for a given compound) both U and J depend on the choice of the model, so do their trends within a family of these compounds. In the 3d perovskite series SrMO3, the effective Coulomb interactions in the t2g Hamiltonian decrease along the series due to the more efficient screening. The inverse, generally expected, trend, increasing interactions with increasing atomic number, is however recovered within the more localized “d-dp Hamiltonian.” Similar conclusions are established in the layered 4d perovskites series Sr2MO4 (M= Mo, Tc, Ru, Rh). Compared to their isoelectronic and isostructural 3d analogs, the 4d perovskite oxides SrMO3 (M= Nb, Mo, Tc) exhibit weaker screening effects. Interestingly, this leads to an effectively larger U on 4d than on 3d shells when a t2g model is constructed.

  3. Central coordination as an alternative for local coordination in a multicenter randomized controlled trial: the FAITH trial experience

    PubMed Central

    2012-01-01

    Background Surgeons in the Netherlands, Canada and the US participate in the FAITH trial (Fixation using Alternative Implants for the Treatment of Hip fractures). Dutch sites are managed and visited by a financed central trial coordinator, whereas most Canadian and US sites have local study coordinators and receive per patient payment. This study was aimed to assess how these different trial management strategies affected trial performance. Methods Details related to obtaining ethics approval, time to trial start-up, inclusion, and percentage completed follow-ups were collected for each trial site and compared. Pre-trial screening data were compared with actual inclusion rates. Results Median trial start-up ranged from 41 days (P25-P75 10-139) in the Netherlands to 232 days (P25-P75 98-423) in Canada (p = 0.027). The inclusion rate was highest in the Netherlands; median 1.03 patients (P25-P75 0.43-2.21) per site per month, representing 34.4% of the total eligible population. It was lowest in Canada; 0.14 inclusions (P25-P75 0.00-0.28), representing 3.9% of eligible patients (p < 0.001). The percentage completed follow-ups was 83% for Canadian and Dutch sites and 70% for US sites (p = 0.217). Conclusions In this trial, a central financed trial coordinator to manage all trial related tasks in participating sites resulted in better trial progression and a similar follow-up. It is therefore a suitable alternative for appointing these tasks to local research assistants. The central coordinator approach can enable smaller regional hospitals to participate in multicenter randomized controlled trials. Circumstances such as available budget, sample size, and geographical area should however be taken into account when choosing a management strategy. Trial Registration ClinicalTrials.gov: NCT00761813 PMID:22225733

  4. Weekly paclitaxel, gemcitabine, and external irradiation followed by randomized farnesyl transferase inhibitor R115777 for locally advanced pancreatic cancer

    PubMed Central

    Rich, Tyvin A; Winter, Kathryn; Safran, Howard; Hoffman, John P; Erickson, Beth; Anne, Pramila R; Myerson, Robert J; Cline-Burkhardt, Vivian JM; Perez, Kimberly; Willett, Christopher

    2012-01-01

    Purpose The Radiation Therapy Oncology Group (RTOG) multi-institutional Phase II study 98-12, evaluating paclitaxel and concurrent radiation (RT) for locally advanced pancreatic cancer, demonstrated a median survival of 11.3 months and a 1-year survival of 43%. The purpose of the randomized Phase II study by RTOG 0020 was to evaluate the addition of weekly low- dose gemcitabine with concurrent paclitaxel/RT and to evaluate the efficacy and safety of the farnesyl transferase inhibitor R115777 following chemoradiation. Patients and methods Patients with unresectable, nonmetastatic adenocarcinoma of the pancreas were eligible. Patients in Arm 1 received gemcitabine, 75 mg/m2/week, and paclitaxel, 40 mg/m2/week, for 6 weeks, with 50.4 Gy radiation (CXRT). Patients in Arm 2 received an identical chemoradiation regimen but then received maintenance R115777, 300 mg twice a day for 21 days every 28 days (CXRT+R115777), until disease progression or unacceptable toxicity. Results One hundred ninety-five patients were entered into this study, and 184 were analyzable. Grade 4 nonhematologic toxicities occurred in less than 5% of CXRT patients. The most common grade 3/4 toxicity from R115777 was myelosuppression; however, grade 3/4 hepatic, metabolic, musculoskeletal, and neurologic toxicities were also reported. The median survival time was 11.5 months and 8.9 months for the CXRT and CXRT+R115777 arms, respectively. Conclusions The CXRT arm achieved a median survival of almost 1-year, supporting chemoradiation as an important therapeutic modality for locally advanced pancreatic cancer. Maintenance R115777 is not effective and is associated with a broad range of toxicities. These findings provide clinical evidence that inhibition of farnesylation affects many metabolic pathways, underscoring the challenge of developing an effective K-ras inhibitor. PMID:22977306

  5. Higher-Than-Conventional Radiation Doses in Localized Prostate Cancer Treatment: A Meta-analysis of Randomized, Controlled Trials

    SciTech Connect

    Viani, Gustavo Arruda Stefano, Eduardo Jose; Afonso, Sergio Luis

    2009-08-01

    Purpose: To determine in a meta-analysis whether the outcomes in men with localized prostate cancer treated with high-dose radiotherapy (HDRT) are better than those in men treated with conventional-dose radiotherapy (CDRT), by quantifying the effect of the total dose of radiotherapy on biochemical control (BC). Methods and Materials: The MEDLINE, EMBASE, CANCERLIT, and Cochrane Library databases, as well as the proceedings of annual meetings, were systematically searched to identify randomized, controlled studies comparing HDRT with CDRT for localized prostate cancer. To evaluate the dose-response relationship, we conducted a meta-regression analysis of BC ratios by means of weighted linear regression. Results: Seven RCTs with a total patient population of 2812 were identified that met the study criteria. Pooled results from these RCTs showed a significant reduction in the incidence of biochemical failure in those patients with prostate cancer treated with HDRT (p < 0.0001). However, there was no difference in the mortality rate (p = 0.38) and specific prostate cancer mortality rates (p = 0.45) between the groups receiving HDRT and CDRT. However, there were more cases of late Grade >2 gastrointestinal toxicity after HDRT than after CDRT. In the subgroup analysis, patients classified as being at low (p = 0.007), intermediate (p < 0.0001), and high risk (p < 0.0001) of biochemical failure all showed a benefit from HDRT. The meta-regression analysis also detected a linear correlation between the total dose of radiotherapy and biochemical failure (BC = -67.3 + [1.8 x radiotherapy total dose in Gy]; p = 0.04). Conclusions: Our meta-analysis showed that HDRT is superior to CDRT in preventing biochemical failure in low-, intermediate-, and high-risk prostate cancer patients, suggesting that this should be offered as a treatment for all patients, regardless of their risk status.

  6. Final Results of Local-Regional Control and Late Toxicity of RTOG 9003: A Randomized Trial of Altered Fractionation Radiation for Locally Advanced Head and Neck Cancer

    SciTech Connect

    Beitler, Jonathan J.; Zhang, Qiang; Fu, Karen K.; Trotti, Andy; Spencer, Sharon A.; Jones, Christopher U.; Garden, Adam S.; Shenouda, George; Harris, Jonathan; Ang, Kian K.

    2014-05-01

    Purpose: To test whether altered radiation fractionation schemes (hyperfractionation [HFX], accelerated fractionation, continuous [AFX-C], and accelerated fractionation with split [AFX-S]) improved local-regional control (LRC) rates for patients with squamous cell cancers (SCC) of the head and neck when compared with standard fractionation (SFX) of 70 Gy. Methods and Materials: Patients with stage III or IV (or stage II base of tongue) SCC (n=1076) were randomized to 4 treatment arms: (1) SFX, 70 Gy/35 daily fractions/7 weeks; (2) HFX, 81.6 Gy/68 twice-daily fractions/7 weeks; (3) AFX-S, 67.2 Gy/42 fractions/6 weeks with a 2-week rest after 38.4 Gy; and (4) AFX-C, 72 Gy/42 fractions/6 weeks. The 3 experimental arms were to be compared with SFX. Results: With patients censored for LRC at 5 years, only the comparison of HFX with SFX was significantly different: HFX, hazard ratio (HR) 0.79 (95% confidence interval 0.62-1.00), P=.05; AFX-C, 0.82 (95% confidence interval 0.65-1.05), P=.11. With patients censored at 5 years, HFX improved overall survival (HR 0.81, P=.05). Prevalence of any grade 3, 4, or 5 toxicity at 5 years; any feeding tube use after 180 days; or feeding tube use at 1 year did not differ significantly when the experimental arms were compared with SFX. When 7-week treatments were compared with 6-week treatments, accelerated fractionation appeared to increase grade 3, 4 or 5 toxicity at 5 years (P=.06). When the worst toxicity per patient was considered by treatment only, the AFX-C arm seemed to trend worse than the SFX arm when grade 0-2 was compared with grade 3-5 toxicity (P=.09). Conclusions: At 5 years, only HFX improved LRC and overall survival for patients with locally advanced SCC without increasing late toxicity.

  7. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    PubMed

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency.

  8. [A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].

    PubMed

    Gao, Kun; Liu, Ying; Wang, Li-jing; Zhu, Zhen-yu; Cheng, Hao-bo

    2015-10-01

    With the development of spectral imaging technology, hyperspectral anomaly detection is getting more and more widely used in remote sensing imagery processing. The traditional RX anomaly detection algorithm neglects spatial correlation of images. Besides, it does not validly reduce the data dimension, which costs too much processing time and shows low validity on hyperspectral data. The hyperspectral images follow Gauss-Markov Random Field (GMRF) in space and spectral dimensions. The inverse matrix of covariance matrix is able to be directly calculated by building the Gauss-Markov parameters, which avoids the huge calculation of hyperspectral data. This paper proposes an improved RX anomaly detection algorithm based on three-dimensional GMRF. The hyperspectral imagery data is simulated with GMRF model, and the GMRF parameters are estimated with the Approximated Maximum Likelihood method. The detection operator is constructed with GMRF estimation parameters. The detecting pixel is considered as the centre in a local optimization window, which calls GMRF detecting window. The abnormal degree is calculated with mean vector and covariance inverse matrix, and the mean vector and covariance inverse matrix are calculated within the window. The image is detected pixel by pixel with the moving of GMRF window. The traditional RX detection algorithm, the regional hypothesis detection algorithm based on GMRF and the algorithm proposed in this paper are simulated with AVIRIS hyperspectral data. Simulation results show that the proposed anomaly detection method is able to improve the detection efficiency and reduce false alarm rate. We get the operation time statistics of the three algorithms in the same computer environment. The results show that the proposed algorithm improves the operation time by 45.2%, which shows good computing efficiency. PMID:26904830

  9. Exact significance test for Markov order

    NASA Astrophysics Data System (ADS)

    Pethel, S. D.; Hahs, D. W.

    2014-02-01

    We describe an exact significance test of the null hypothesis that a Markov chain is nth order. The procedure utilizes surrogate data to yield an exact test statistic distribution valid for any sample size. Surrogate data are generated using a novel algorithm that guarantees, per shot, a uniform sampling from the set of sequences that exactly match the nth order properties of the observed data. Using the test, the Markov order of Tel Aviv rainfall data is examined.

  10. Semi-Markov Arnason-Schwarz models.

    PubMed

    King, Ruth; Langrock, Roland

    2016-06-01

    We consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. Traditionally, the Arnason-Schwarz model has been fitted to such data where the state process is modeled as a first-order Markov chain, though second-order models have also been proposed and fitted to data. However, low-order Markov models may not accurately represent the underlying biology. For example, specifying a (time-independent) first-order Markov process involves the assumption that the dwell time in each state (i.e., the duration of a stay in a given state) has a geometric distribution, and hence that the modal dwell time is one. Specifying time-dependent or higher-order processes provides additional flexibility, but at the expense of a potentially significant number of additional model parameters. We extend the Arnason-Schwarz model by specifying a semi-Markov model for the state process, where the dwell-time distribution is specified more generally, using, for example, a shifted Poisson or negative binomial distribution. A state expansion technique is applied in order to represent the resulting semi-Markov Arnason-Schwarz model in terms of a simpler and computationally tractable hidden Markov model. Semi-Markov Arnason-Schwarz models come with only a very modest increase in the number of parameters, yet permit a significantly more flexible state process. Model selection can be performed using standard procedures, and in particular via the use of information criteria. The semi-Markov approach allows for important biological inference to be drawn on the underlying state process, for example, on the times spent in the different states. The feasibility of the approach is demonstrated in a simulation study, before being applied to real data corresponding to house finches where the states correspond to the presence or absence of conjunctivitis. PMID:26584064

  11. Semi-Markov Arnason-Schwarz models.

    PubMed

    King, Ruth; Langrock, Roland

    2016-06-01

    We consider multi-state capture-recapture-recovery data where observed individuals are recorded in a set of possible discrete states. Traditionally, the Arnason-Schwarz model has been fitted to such data where the state process is modeled as a first-order Markov chain, though second-order models have also been proposed and fitted to data. However, low-order Markov models may not accurately represent the underlying biology. For example, specifying a (time-independent) first-order Markov process involves the assumption that the dwell time in each state (i.e., the duration of a stay in a given state) has a geometric distribution, and hence that the modal dwell time is one. Specifying time-dependent or higher-order processes provides additional flexibility, but at the expense of a potentially significant number of additional model parameters. We extend the Arnason-Schwarz model by specifying a semi-Markov model for the state process, where the dwell-time distribution is specified more generally, using, for example, a shifted Poisson or negative binomial distribution. A state expansion technique is applied in order to represent the resulting semi-Markov Arnason-Schwarz model in terms of a simpler and computationally tractable hidden Markov model. Semi-Markov Arnason-Schwarz models come with only a very modest increase in the number of parameters, yet permit a significantly more flexible state process. Model selection can be performed using standard procedures, and in particular via the use of information criteria. The semi-Markov approach allows for important biological inference to be drawn on the underlying state process, for example, on the times spent in the different states. The feasibility of the approach is demonstrated in a simulation study, before being applied to real data corresponding to house finches where the states correspond to the presence or absence of conjunctivitis.

  12. Long-Term Results of a Randomized Trial in Locally Advanced Rectal Cancer: No Benefit From Adding a Brachytherapy Boost

    SciTech Connect

    Appelt, Ane L.; Vogelius, Ivan R.; Pløen, John; Rafaelsen, Søren R.; Lindebjerg, Jan; Havelund, Birgitte M.; Bentzen, Søren M.; Jakobsen, Anders

    2014-09-01

    Purpose/Objective(s): Mature data on tumor control and survival are presented from a randomized trial of the addition of a brachytherapy boost to long-course neoadjuvant chemoradiation therapy (CRT) for locally advanced rectal cancer. Methods and Materials: Between March 2005 and November 2008, 248 patients with T3-4N0-2M0 rectal cancer were prospectively randomized to either long-course preoperative CRT (50.4 Gy in 28 fractions, per oral tegafur-uracil and L-leucovorin) alone or the same CRT schedule plus a brachytherapy boost (10 Gy in 2 fractions). The primary trial endpoint was pathologic complete response (pCR) at the time of surgery; secondary endpoints included overall survival (OS), progression-free survival (PFS), and freedom from locoregional failure. Results: Results for the primary endpoint have previously been reported. This analysis presents survival data for the 224 patients in the Danish part of the trial. In all, 221 patients (111 control arm, 110 brachytherapy boost arm) had data available for analysis, with a median follow-up time of 5.4 years. Despite a significant increase in tumor response at the time of surgery, no differences in 5-year OS (70.6% vs 63.6%, hazard ratio [HR] = 1.24, P=.34) and PFS (63.9% vs 52.0%, HR=1.22, P=.32) were observed. Freedom from locoregional failure at 5 years were 93.9% and 85.7% (HR=2.60, P=.06) in the standard and in the brachytherapy arms, respectively. There was no difference in the prevalence of stoma. Explorative analysis based on stratification for tumor regression grade and resection margin status indicated the presence of response migration. Conclusions: Despite increased pathologic tumor regression at the time of surgery, we observed no benefit on late outcome. Improved tumor regression does not necessarily lead to a relevant clinical benefit when the neoadjuvant treatment is followed by high-quality surgery.

  13. Flow towards diagonalization for many-body-localization models: adaptation of the Toda matrix differential flow to random quantum spin chains

    NASA Astrophysics Data System (ADS)

    Monthus, Cécile

    2016-07-01

    The iterative methods to diagonalize matrices and many-body Hamiltonians can be reformulated as flows of Hamiltonians towards diagonalization driven by unitary transformations that preserve the spectrum. After a comparative overview of the various types of discrete flows (Jacobi, QR-algorithm) and differential flows (Toda, Wegner, White) that have been introduced in the past, we focus on the random XXZ chain with random fields in order to determine the best closed flow within a given subspace of running Hamiltonians. For the special case of the free-fermion random XX chain with random fields, the flow coincides with the Toda differential flow for tridiagonal matrices which is related to the classical integrable Toda chain and which can be seen as the continuous analog of the discrete QR-algorithm. For the random XXZ chain with random fields that displays a many-body-localization transition, the present differential flow should be an interesting alternative to compare with the discrete flow that has been proposed recently to study the many-body-localization properties in a model of interacting fermions (Rademaker and Ortuno 2016 Phys. Rev. Lett. 116, 010404).

  14. A random graph model of density thresholds in swarming cells.

    PubMed

    Jena, Siddhartha G

    2016-03-01

    Swarming behaviour is a type of bacterial motility that has been found to be dependent on reaching a local density threshold of cells. With this in mind, the process through which cell-to-cell interactions develop and how an assembly of cells reaches collective motility becomes increasingly important to understand. Additionally, populations of cells and organisms have been modelled through graphs to draw insightful conclusions about population dynamics on a spatial level. In the present study, we make use of analogous random graph structures to model the formation of large chain subgraphs, representing interactions between multiple cells, as a random graph Markov process. Using numerical simulations and analytical results on how quickly paths of certain lengths are reached in a random graph process, metrics for intercellular interaction dynamics at the swarm layer that may be experimentally evaluated are proposed. PMID:26893102

  15. Markov chain analysis of random walks in disordered media

    NASA Astrophysics Data System (ADS)

    Mukherjee, Sonali; Nakanishi, Hisao; Fuchs, Norman H.

    1994-06-01

    We study the dynamical exponents dw and ds for a particle diffusing in a disordered medium (modeled by a percolation cluster), from the regime of extreme disorder (i.e., when the percolation cluster is a fractal at p=pc) to the Lorentz gas regime when the cluster has weak disorder at p>pc and the leading behavior is standard diffusion. The velocity autocorrelation function and the return to the starting point probability are related to the asymptotic spectral properties of the hopping transition probability matrix of the diffusing particle; the latter is numerically analyzed by the Arnoldi-Saad algorithm We propose and present evidence for a scaling relation for the second largest eigenvalue in terms of the size of the cluster, ||lnλ2||~S-dw/df. This relation provides a very efficient and accurate method of extracting the spectral dimension ds where ds=2df/dw.

  16. Continuous Local Infiltration Analgesia for Pain Control After Total Knee Arthroplasty: A Meta-analysis of Randomized Controlled Trials.

    PubMed

    Sun, Xiao-Lei; Zhao, Zhi-Hu; Ma, Jian-Xiong; Li, Feng-Bo; Li, Yan-Jun; Meng, Xin-Min; Ma, Xin-Long

    2015-11-01

    A total knee arthroplasty (TKA) has always been associated with moderate to severe pain. As more research is conducted on the use of continuous local infiltration analgesia (CLIA) to manage pain after a TKA, it is necessary to reassess the efficacy and safety of the TKA method. The purpose of this systematic review and meta-analysis of randomized controlled trials was to evaluate the efficacy and safety of pain control of CLIA versus placebo after a TKA. In January 2015, a systematic computer-based search was conducted in the Medline, Embase, PubMed, CENTRAL (Cochrane Controlled Trials Register), Web of Science, Google database, and Chinese Wanfang databases. This systematic review and meta-analysis were performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement criteria. The primary endpoint was the visual analog scale score after a TKA with rest or mobilization at 24, 48, and 72 hours, which represents the effect of pain control after TKA. The complications of infection, nausea, and whether it prolonged wound drainage were also compiled to assess the safety of CLIA. RevMan 5.30 software was used for the meta-analysis. After testing for publication bias and heterogeneity across studies, data were aggregated for random-effects modeling when necessary. Ten studies involving 735 patients met the inclusion criteria. The meta-analysis revealed that continuous infusion analgesia provided better pain control with rest at 24 hours (mean difference [MD] -12.54, 95% confidence interval [CI] -16.63 to 8.45), and with mobilization at 24 hours (MD -18.27, 95% CI -27.52 to 9.02) and 48 hours (MD -14.19, 95% CI -21.46 to 6.93). There was no significant difference with respect to the visual analog scale score at 48 hours (MD -6.15, 95% CI -13.51 to 1.22, P = 0.10) and 72 hours (MD -3.63, 95% CI -10.43 to 3.16, P = 0.29) with rest and at 72 hours with mobilization (MD -4.25, 95% CI -16.27 to 7.77, P = 0

  17. Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework

    NASA Astrophysics Data System (ADS)

    Humblot, Fabrice; Mohammad-Djafari, Ali

    2006-12-01

    This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.

  18. A Comparison of Bayesian Monte Carlo Markov Chain and Maximum Likelihood Estimation Methods for the Statistical Analysis of Geodetic Time Series

    NASA Astrophysics Data System (ADS)

    Olivares, G.; Teferle, F. N.

    2013-12-01

    Geodetic time series provide information which helps to constrain theoretical models of geophysical processes. It is well established that such time series, for example from GPS, superconducting gravity or mean sea level (MSL), contain time-correlated noise which is usually assumed to be a combination of a long-term stochastic process (characterized by a power-law spectrum) and random noise. Therefore, when fitting a model to geodetic time series it is essential to also estimate the stochastic parameters beside the deterministic ones. Often the stochastic parameters include the power amplitudes of both time-correlated and random noise, as well as, the spectral index of the power-law process. To date, the most widely used method for obtaining these parameter estimates is based on maximum likelihood estimation (MLE). We present an integration method, the Bayesian Monte Carlo Markov Chain (MCMC) method, which, by using Markov chains, provides a sample of the posteriori distribution of all parameters and, thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. This algorithm automatically optimizes the Markov chain step size and estimates the convergence state by spectral analysis of the chain. We assess the MCMC method through comparison with MLE, using the recently released GPS position time series from JPL and apply it also to the MSL time series from the Revised Local Reference data base of the PSMSL. Although the parameter estimates for both methods are fairly equivalent, they suggest that the MCMC method has some advantages over MLE, for example, without further computations it provides the spectral index uncertainty, is computationally stable and detects multimodality.

  19. Hypnosis and Local Anesthesia for Dental Pain Relief-Alternative or Adjunct Therapy?-A Randomized, Clinical-Experimental Crossover Study.

    PubMed

    Wolf, Thomas Gerhard; Wolf, Dominik; Callaway, Angelika; Below, Dagna; d'Hoedt, Bernd; Willershausen, Brita; Daubländer, Monika

    2016-01-01

    This prospective randomized clinical crossover trial was designed to compare hypnosis and local anesthesia for experimental dental pain relief. Pain thresholds of the dental pulp were determined. A targeted standardized pain stimulus was applied and rated on the Visual Analogue Scale (0-10). The pain threshold was lower under hypnosis (58.3 ± 17.3, p < .001), maximal (80.0) under local anesthesia. The pain stimulus was scored higher under hypnosis (3.9 ± 3.8) than with local anesthesia (0.0, p < .001). Local anesthesia was superior to hypnosis and is a safe and effective method for pain relief in dentistry. Hypnosis seems to produce similar effects observed under sedation. It can be used in addition to local anesthesia and in individual cases as an alternative for pain control in dentistry.

  20. Hypnosis and Local Anesthesia for Dental Pain Relief-Alternative or Adjunct Therapy?-A Randomized, Clinical-Experimental Crossover Study.

    PubMed

    Wolf, Thomas Gerhard; Wolf, Dominik; Callaway, Angelika; Below, Dagna; d'Hoedt, Bernd; Willershausen, Brita; Daubländer, Monika

    2016-01-01

    This prospective randomized clinical crossover trial was designed to compare hypnosis and local anesthesia for experimental dental pain relief. Pain thresholds of the dental pulp were determined. A targeted standardized pain stimulus was applied and rated on the Visual Analogue Scale (0-10). The pain threshold was lower under hypnosis (58.3 ± 17.3, p < .001), maximal (80.0) under local anesthesia. The pain stimulus was scored higher under hypnosis (3.9 ± 3.8) than with local anesthesia (0.0, p < .001). Local anesthesia was superior to hypnosis and is a safe and effective method for pain relief in dentistry. Hypnosis seems to produce similar effects observed under sedation. It can be used in addition to local anesthesia and in individual cases as an alternative for pain control in dentistry. PMID:27585724

  1. Semi-Markov adjunction to the Computer-Aided Markov Evaluator (CAME)

    NASA Technical Reports Server (NTRS)

    Rosch, Gene; Hutchins, Monica A.; Leong, Frank J.; Babcock, Philip S., IV

    1988-01-01

    The rule-based Computer-Aided Markov Evaluator (CAME) program was expanded in its ability to incorporate the effect of fault-handling processes into the construction of a reliability model. The fault-handling processes are modeled as semi-Markov events and CAME constructs and appropriate semi-Markov model. To solve the model, the program outputs it in a form which can be directly solved with the Semi-Markov Unreliability Range Evaluator (SURE) program. As a means of evaluating the alterations made to the CAME program, the program is used to model the reliability of portions of the Integrated Airframe/Propulsion Control System Architecture (IAPSA 2) reference configuration. The reliability predictions are compared with a previous analysis. The results bear out the feasibility of utilizing CAME to generate appropriate semi-Markov models to model fault-handling processes.

  2. Zipf exponent of trajectory distribution in the hidden Markov model

    NASA Astrophysics Data System (ADS)

    Bochkarev, V. V.; Lerner, E. Yu

    2014-03-01

    This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.

  3. Control theory for random systems

    NASA Technical Reports Server (NTRS)

    Bryson, A. E., Jr.

    1972-01-01

    A survey is presented of the current knowledge available for designing and predicting the effectiveness of controllers for dynamic systems which can be modeled by ordinary differential equations. A short discussion of feedback control is followed by a description of deterministic controller design and the concept of system state. The need for more realistic disturbance models led to the use of stochastic process concepts, in particular the Gauss-Markov process. A compensator controlled system, with random forcing functions, random errors in the measurements, and random initial conditions, is treated as constituting a Gauss-Markov random process; hence the mean-square behavior of the controlled system is readily predicted. As an example, a compensator is designed for a helicopter to maintain it in hover in a gusty wind over a point on the ground.

  4. Policy Transfer via Markov Logic Networks

    NASA Astrophysics Data System (ADS)

    Torrey, Lisa; Shavlik, Jude

    We propose using a statistical-relational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effective models for capturing both source-task Q-functions and source-task policies. We apply them via demonstration, which involves using them for decision making in an initial stage of the target task before continuing to learn. Through experiments in the RoboCup simulated-soccer domain, we show that transfer via Markov Logic Networks can significantly improve early performance in complex tasks, and that transferring policies is more effective than transferring Q-functions.

  5. On multitarget pairwise-Markov models

    NASA Astrophysics Data System (ADS)

    Mahler, Ronald

    2015-05-01

    Single- and multi-target tracking are both typically based on strong independence assumptions regarding both the target states and sensor measurements. In particular, both are theoretically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain that is observed by an independent observation process. Since HMC assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as a way to weaken those assumptions. In this paper it is shown that the PMC model can be directly generalized to multitarget problems. Since the resulting tracking filters are computationally intractable, the paper investigates generalizations of the cardinalized probability hypothesis density (CPHD) filter to applications with PMC models.

  6. Markov chains for testing redundant software

    NASA Technical Reports Server (NTRS)

    White, Allan L.; Sjogren, Jon A.

    1988-01-01

    A preliminary design for a validation experiment has been developed that addresses several problems unique to assuring the extremely high quality of multiple-version programs in process-control software. The procedure uses Markov chains to model the error states of the multiple version programs. The programs are observed during simulated process-control testing, and estimates are obtained for the transition probabilities between the states of the Markov chain. The experimental Markov chain model is then expanded into a reliability model that takes into account the inertia of the system being controlled. The reliability of the multiple version software is computed from this reliability model at a given confidence level using confidence intervals obtained for the transition probabilities during the experiment. An example demonstrating the method is provided.

  7. Entropy Computation in Partially Observed Markov Chains

    NASA Astrophysics Data System (ADS)

    Desbouvries, François

    2006-11-01

    Let X = {Xn}n∈N be a hidden process and Y = {Yn}n∈N be an observed process. We assume that (X,Y) is a (pairwise) Markov Chain (PMC). PMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient parameter estimation and Bayesian restoration algorithms. In this paper we propose a fast (i.e., O(N)) algorithm for computing the entropy of {Xn}n=0N given an observation sequence {yn}n=0N.

  8. Effect of Amifostine on Response Rates in Locally Advanced Non-Small-Cell Lung Cancer Patients Treated on Randomized Controlled Trials: A Meta-Analysis

    SciTech Connect

    Mell, Loren K. . E-mail: lmell@radonc.uchicago.edu; Malik, Renuka; Komaki, Ritsuko; Movsas, Benjamin; Swann, R. Suzanne; Langer, Corey; Antonadou, Dosia; Koukourakis, Michael

    2007-05-01

    Purpose: Amifostine can reduce the cytotoxic effects of chemotherapy and radiotherapy in patients with locally advanced non-small-cell lung cancer, but concerns remain regarding its possible tumor-protective effects. Studies with sufficient statistical power to address this question are lacking. Methods and Materials: We performed a meta-analysis of all published clinical trials involving locally advanced non-small-cell lung cancer patients treated with radiotherapy with or without chemotherapy, who had been randomized to treatment with amifostine vs. no amifostine or placebo. Random effects estimates of the relative risk of overall, partial, and complete response were obtained. Results: Seven randomized trials involving 601 patients were identified. Response rate data were available for six studies (552 patients). The pooled relative risk (RR) estimate was 1.07 (95% confidence interval, 0.97-1.18; p = 0.18), 1.21 (95% confidence interval, 0.83-1.78; p = 0.33), and 0.99 (95% confidence interval, 0.78-1.26; p = 0.95) for overall, complete, and partial response, respectively (a RR >1 indicates improvement in response with amifostine compared with the control arm). The results were similar after sensitivity analyses. No evidence was found of treatment effect heterogeneity across the studies. Conclusions: Amifostine has no effect on tumor response in patients with locally advanced non-small-cell lung cancer treated with radiotherapy with or without chemotherapy.

  9. PULSAR STATE SWITCHING FROM MARKOV TRANSITIONS AND STOCHASTIC RESONANCE

    SciTech Connect

    Cordes, J. M.

    2013-09-20

    Markov processes are shown to be consistent with metastable states seen in pulsar phenomena, including intensity nulling, pulse-shape mode changes, subpulse drift rates, spin-down rates, and X-ray emission, based on the typically broad and monotonic distributions of state lifetimes. Markovianity implies a nonlinear magnetospheric system in which state changes occur stochastically, corresponding to transitions between local minima in an effective potential. State durations (though not transition times) are thus largely decoupled from the characteristic timescales of various magnetospheric processes. Dyadic states are common but some objects show at least four states with some transitions forbidden. Another case is the long-term intermittent pulsar B1931+24 that has binary radio-emission and torque states with wide, but non-monotonic duration distributions. It also shows a quasi-period of 38 ± 5 days in a 13 yr time sequence, suggesting stochastic resonance in a Markov system with a forcing function that could be strictly periodic or quasi-periodic. Nonlinear phenomena are associated with time-dependent activity in the acceleration region near each magnetic polar cap. The polar-cap diode is altered by feedback from the outer magnetosphere and by return currents from the equatorial region outside the light cylinder that may also cause the neutron star to episodically charge and discharge. Orbital perturbations of a disk or current sheet provide a natural periodicity for the forcing function in the stochastic-resonance interpretation of B1931+24. Disk dynamics may introduce additional timescales in observed phenomena. Future work can test the Markov interpretation, identify which pulsar types have a propensity for state changes, and clarify the role of selection effects.

  10. Quantification of heart rate variability by discrete nonstationary non-Markov stochastic processes

    NASA Astrophysics Data System (ADS)

    Yulmetyev, Renat; Hänggi, Peter; Gafarov, Fail

    2002-04-01

    We develop the statistical theory of discrete nonstationary non-Markov random processes in complex systems. The objective of this paper is to find the chain of finite-difference non-Markov kinetic equations for time correlation functions (TCF) in terms of nonstationary effects. The developed theory starts from careful analysis of time correlation through nonstationary dynamics of vectors of initial and final states and nonstationary normalized TCF. Using the projection operators technique we find the chain of finite-difference non-Markov kinetic equations for discrete nonstationary TCF and for the set of nonstationary discrete memory functions (MF's). The last one contains supplementary information about nonstationary properties of the complex system on the whole. Another relevant result of our theory is the construction of the set of dynamic parameters of nonstationarity, which contains some information of the nonstationarity effects. The full set of dynamic, spectral and kinetic parameters, and kinetic functions (TCF, short MF's statistical spectra of non-Markovity parameter, and statistical spectra of nonstationarity parameter) has made it possible to acquire the in-depth information about discreteness, non-Markov effects, long-range memory, and nonstationarity of the underlying processes. The developed theory is applied to analyze the long-time (Holter) series of RR intervals of human ECG's. We had two groups of patients: the healthy ones and the patients after myocardial infarction. In both groups we observed effects of fractality, standard and restricted self-organized criticality, and also a certain specific arrangement of spectral lines. The received results demonstrate that the power spectra of all orders (n=1,2,...) MF mn(t) exhibit the neatly expressed fractal features. We have found out that the full sets of non-Markov, discrete and nonstationary parameters can serve as reliable and powerful means of diagnosis of the cardiovascular system states and can

  11. Active Inference for Binary Symmetric Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Allahverdyan, Armen E.; Galstyan, Aram

    2015-10-01

    We consider active maximum a posteriori (MAP) inference problem for hidden Markov models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states. We focus on the binary symmetric HMM, and employ its known mapping to 1d Ising model in random fields. From the statistical physics viewpoint, the active MAP inference problem reduces to analyzing the ground state of the 1d Ising model under modified external fields. We develop an analytical approach and obtain a closed form solution that relates the expected error reduction to model parameters under the specified active inference scheme. We then use this solution to determine most optimal active inference scheme in terms of error reduction, and examine the relation of those schemes to heuristic principles of uncertainty reduction and solution unicity.

  12. Evaluation of in-plane local stress distribution in stacked IC chip using dynamic random access memory cell array for highly reliable three-dimensional IC

    NASA Astrophysics Data System (ADS)

    Tanikawa, Seiya; Kino, Hisashi; Fukushima, Takafumi; Koyanagi, Mitsumasa; Tanaka, Tetsu

    2016-04-01

    As three-dimensional (3D) ICs have many advantages, IC performances can be enhanced without scaling down of transistor size. However, 3D IC has mechanical stresses inside Si substrates owing to its 3D stacking structure, which induces negative effects on transistor performances such as carrier mobility changes. One of the mechanical stresses is local bending stress due to organic adhesive shrinkage among stacked IC chips. In this paper, we have proposed an evaluation method for in-plane local stress distribution in the stacked IC chips using retention time modulation of a dynamic random access memory (DRAM) cell array. We fabricated a test structure composed of a DRAM chip bonded on a Si interposer with dummy Cu/Sn microbumps. As a result, we clarified that the DRAM cell array can precisely evaluate the in-plane local stress distribution in the stacked IC chips.

  13. Analgesic efficacy of ultrasound guided transversus abdominis plane block versus local anesthetic infiltration in adult patients undergoing single incision laparoscopic cholecystectomy: A randomized controlled trial

    PubMed Central

    Bava, Ejas P.; Ramachandran, Rashmi; Rewari, Vimi; Chandralekha; Bansal, Virinder Kumar; Trikha, Anjan

    2016-01-01

    Background: Transversus abdominis plane (TAP) block has been used to provide intra- and post-operative analgesia with single incision laparoscopic (SIL) bariatric and gynecological surgery with mixed results. Its efficacy in providing analgesia for SIL cholecystectomy (SILC) via the same approach remains unexplored. Aims: The primary objective of our study was to compare the efficacy of bilateral TAP block with local anesthetic infiltration for perioperative analgesia in patients undergoing SILC. Settings and Design: This was a prospective, randomized, controlled, double-blinded trial performed in a tertiary care hospital. Materials and Methods: Forty-two patients undergoing SILC were randomized to receive either ultrasound-guided (USG) bilateral mid-axillary TAP blocks with 0.375% ropivacaine or local anesthetic infiltration of the port site. The primary outcome measure was the requirement of morphine in the first 24 h postoperatively. Statistical Analysis: The data were analyzed using t-test, Mann–Whitney test or Chi-square test. Results: The 24 h morphine requirement (mean ± standard deviation) was 34.57 ± 14.64 mg in TAP group and 32.76 ± 14.34 mg in local infiltration group (P = 0.688). The number of patients requiring intraoperative supplemental fentanyl in TAP group was 8 and in local infiltration group was 16 (P = 0.028). The visual analog scale scores at rest and on coughing were significantly higher in the local infiltration group in the immediate postoperative period (P = 0.034 and P = 0.007, respectively). Conclusion: USG bilateral TAP blocks were not effective in decreasing 24 h morphine requirement as compared to local anesthetic infiltration in patients undergoing SILC although it provided some analgesic benefit intraoperatively and in the initial 4 h postoperatively. Hence, the benefits of TAP blocks are not worth the effort and time spent for administering them for this surgery. PMID:27746552

  14. A Bayesian method for construction of Markov models to describe dynamics on various time-scales

    NASA Astrophysics Data System (ADS)

    Rains, Emily K.; Andersen, Hans C.

    2010-10-01

    CMMM for the chosen mesoscopic time step. We applied this method of Markov model construction to several toy systems (random walks in one and two dimensions) as well as the dynamics of alanine dipeptide in water. The resulting Markov state models were indeed successful in capturing the dynamics of our test systems on a variety of mesoscopic time-scales.

  15. Detection of prostate cancer on histopathology using color fractals and Probabilistic Pairwise Markov models.

    PubMed

    Yu, Elaine; Monaco, James P; Tomaszewski, John; Shih, Natalie; Feldman, Michael; Madabhushi, Anant

    2011-01-01

    In this paper we present a system for detecting regions of carcinoma of the prostate (CaP) in H&E stained radical prostatectomy specimens using the color fractal dimension. Color textural information is known to be a valuable characteristic to distinguish CaP from benign tissue. In addition to color information, we know that cancer tends to form contiguous regions. Our system leverages the color staining information of histology as well as spatial dependencies. The color and textural information is first captured using color fractal dimension. To incorporate spatial dependencies, we combine the probability map constructed via color fractal dimension with a novel Markov prior called the Probabilistic Pairwise Markov Model (PPMM). To demonstrate the capability of this CaP detection system, we applied the algorithm to 27 radical prostatectomy specimens from 10 patients. A per pixel evaluation was conducted with ground truth provided by an expert pathologist using only the color fractal feature first, yielding an area under the receiver operator characteristic curve (AUC) curve of 0.790. In conjunction with a Markov prior, the resultant color fractal dimension + Markov random field (MRF) classifier yielded an AUC of 0.831.

  16. [Markov process of vegetation cover change in arid area of northwest China based on FVC index].

    PubMed

    Wang, Zhi; Chang, Shun-li; Shi, Qing-dong; Ma, Ke; Liang, Feng-chao

    2010-05-01

    Based on the fractional vegetation cover (FVC) data of 1982-2000 NOAA/AVHRR (National Oceanic and Atmospheric Administration/ the Advanced Very High Resolution Radiometer) images, the whole arid area of Northwest China was divided into three sub-areas, and then, the vegetation cover in each sub-area was classified by altitude. Furthermore, the Markov process of vegetation cover change was analyzed and tested through calculating the limit probability of any two years and the continuous and interval mean transition matrixes of vegetation cover change with 8 km x 8 km spatial resolution. By this method, the Markov process of vegetation cover change and its indicative significance were approached. The results showed that the vegetation cover change in the study area was controlled by some random processes and affected by long-term stable driving factors, and the transitional change of vegetation cover was a multiple Markov process. Therefore, only using two term image data, no matter they were successive or intervallic, Markov process could not accurately estimate the trend of vegetation cover change. As for the arid area of Northwest China, more than 10 years successive data could basically reflect all the factors affecting regional vegetation cover change, and using long term average transition matrix data could reliably simulate and predict the vegetation cover change. Vegetation cover change was a long term dynamic balance. Once the balance was broken down, it should be a long time process to establish a new balance.

  17. Markov switching of the electricity supply curve and power prices dynamics

    NASA Astrophysics Data System (ADS)

    Mari, Carlo; Cananà, Lucianna

    2012-02-01

    Regime-switching models seem to well capture the main features of power prices behavior in deregulated markets. In a recent paper, we have proposed an equilibrium methodology to derive electricity prices dynamics from the interplay between supply and demand in a stochastic environment. In particular, assuming that the supply function is described by a power law where the exponent is a two-state strictly positive Markov process, we derived a regime switching dynamics of power prices in which regime switches are induced by transitions between Markov states. In this paper, we provide a dynamical model to describe the random behavior of power prices where the only non-Brownian component of the motion is endogenously introduced by Markov transitions in the exponent of the electricity supply curve. In this context, the stochastic process driving the switching mechanism becomes observable, and we will show that the non-Brownian component of the dynamics induced by transitions from Markov states is responsible for jumps and spikes of very high magnitude. The empirical analysis performed on three Australian markets confirms that the proposed approach seems quite flexible and capable of incorporating the main features of power prices time-series, thus reproducing the first four moments of log-returns empirical distributions in a satisfactory way.

  18. Using higher-order Markov models to reveal flow-based communities in networks

    PubMed Central

    Salnikov, Vsevolod; Schaub, Michael T.; Lambiotte, Renaud

    2016-01-01

    Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing paradigm to model such a network-based dynamics, for instance in the form of random walks or other types of diffusions. Despite the success of this modelling perspective for numerous applications, it represents an over-simplification of several real-world systems. Importantly, simple Markov models lack memory in their dynamics, an assumption often not realistic in practice. Here, we explore possibilities to enrich the system description by means of second-order Markov models, exploiting empirical pathway information. We focus on the problem of community detection and show that standard network algorithms can be generalized in order to extract novel temporal information about the system under investigation. We also apply our methodology to temporal networks, where we can uncover communities shaped by the temporal correlations in the system. Finally, we discuss relations of the framework of second order Markov processes and the recently proposed formalism of using non-backtracking matrices for community detection. PMID:27029508

  19. The Effect of Local Injections of Bupivacaine Plus Ketamine, Bupivacaine Alone, and Placebo on Reducing Postoperative Anal Fistula Pain: A Randomized Clinical Trial

    PubMed Central

    Kazemeini, Alireza; Rahimi, Mojgan; Fazeli, Mohammad Sadegh; Mirjafari, Seyedeh Adeleh; Ghaderi, Hamid; Fani, Kamal; Forozeshfard, Mohammad; Matin, Marzieh

    2014-01-01

    Background and Objective. This study aimed to compare the effects of different local anesthetic solutions on postoperative pain of anal surgery in adult patients. Method. In this randomized double-blind prospective clinical trial, 60 adult patients (18 to 60 years old) with physical status class I and class II that had been brought to a university hospital operating room for fistula anal surgery with spinal anesthesia were selected. Patients were randomly divided into 4 equal groups according to table of random numbers (created by Random Allocation Software 1). Group 1 received 3 mL of normal saline, group 2, 1 mL of normal saline plus 2 mL of bupivacaine 0.5%, group 3, 1 mL of ketamine plus 2 mL of bupivacaine 0.5%, and group 4, no infiltration. Intensity of pain in patients was measured using visual analogue scale (VAS) at 0 (transfer to ward), 2, 6, 12, and 24 hours after surgery. Time interval to administration of drugs and overall dose of drugs were measured in 4 groups. Results. Mean level of pain was the lowest in group 3 at all occasions with a significant difference, followed by groups 2, 4, and lastly 1 (P < 0.001). Furthermore, groups 2 and 3 compared to groups 1 and 4 had the least overall dose of analgesics and requested them the latest, with a significant difference (P < 0.05). Conclusion. Local anesthesia (1 mL of ketamine plus 2 mL of bupivacaine 0.5% or 1 mL of normal saline plus 2 mL of bupivacaine 0.5%) combined with spinal anesthesia reduces postoperative pain and leads to greater comfort in recovering patients. PMID:25544955

  20. Scalar wave propagation in random amplifying media: Influence of localization effects on length and time scales and threshold behavior

    SciTech Connect

    Frank, Regine; Lubatsch, Andreas

    2011-07-15

    We present a detailed discussion of scalar wave propagation and light intensity transport in three-dimensional random dielectric media with optical gain. The intrinsic length and time scales of such amplifying systems are studied and comprehensively discussed as well as the threshold characteristics of single- and two-particle propagators. Our semianalytical theory is based on a self-consistent Cooperon resummation, representing the repeated self-interference, and incorporates as well optical gain and absorption, modeled in a semianalytical way by a finite imaginary part of the dielectric function. Energy conservation in terms of a generalized Ward identity is taken into account.

  1. Local drinking water filters reduce diarrheal disease in Cambodia: a randomized, controlled trial of the ceramic water purifier.

    PubMed

    Brown, Joe; Sobsey, Mark D; Loomis, Dana

    2008-09-01

    A randomized, controlled intervention trial of two household-scale drinking water filters was conducted in a rural village in Cambodia. After collecting four weeks of baseline data on household water quality, diarrheal disease, and other data related to water use and handling practices, households were randomly assigned to one of three groups of 60 households: those receiving a ceramic water purifier (CWP), those receiving a second filter employing an iron-rich ceramic (CWP-Fe), and a control group receiving no intervention. Households were followed for 18 weeks post-baseline with biweekly follow-up. Households using either filter reported significantly less diarrheal disease during the study compared with a control group of households without filters as indicated by longitudinal prevalence ratios CWP: 0.51 (95% confidence interval [CI]: 0.41-0.63); CWP-Fe: 0.58 (95% CI: 0.47-0.71), an effect that was observed in all age groups and both sexes after controlling for clustering within households and within individuals over time. PMID:18784232

  2. Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

    NASA Astrophysics Data System (ADS)

    Bui, Lam Thu; Barlow, Michael

    We propose a methodology for employing memetics (local search) within the framework of evolutionary algorithms to optimize parameters of hidden markov models. With this proposal, the rate and frequency of using local search are automatically changed over time either at a population or individual level. At the population level, we allow the rate of using local search to decay over time to zero (at the final generation). At the individual level, each individual is equipped with information of when it will do local search and for how long. This information evolves over time alongside the main elements of the chromosome representing the individual.

  3. The efficacy of 'Radio guided Occult Lesion Localization' (ROLL) versus 'Wire-guided Localization' (WGL) in breast conserving surgery for non-palpable breast cancer: A randomized clinical trial – ROLL study

    PubMed Central

    van Esser, Stijn; Hobbelink, Monique GG; Peeters, Petra HM; Buskens, Erik; van der Ploeg, Iris M; Mali, Willem PTHM; Rinkes, Inne H M Borel; van Hillegersberg, Richard

    2008-01-01

    Background With the increasing number of non palpable breast carcinomas, the need of a good and reliable localization method increases. Currently the wire guided localization (WGL) is the standard of care in most countries. Radio guided occult lesion localization (ROLL) is a new technique that may improve the oncological outcome, cost effectiveness, patient comfort and cosmetic outcome. However, the studies published hitherto are of poor quality providing less than convincing evidence to change the current standard of care. The aim of this study is to compare the ROLL technique with the standard of care (WGL) regarding the percentage of tumour free margins, cost effectiveness, patient comfort and cosmetic outcome. Methods/design The ROLL trial is a multi center randomized clinical trial. Over a period of 2–3 years 316 patients will be randomized between the ROLL and the WGL technique. With this number, the expected 15% difference in tumour free margins can be detected with a power of 80%. Other endpoints include cosmetic outcome, cost effectiveness, patient (dis)comfort, degree of difficulty of the procedures and the success rate of the sentinel node procedure. The rationale, study design and planned analyses are described. Trial Registration (, study protocol number NCT00539474) PMID:18495027

  4. A critical appraisal of Markov state models

    NASA Astrophysics Data System (ADS)

    Schütte, Ch.; Sarich, M.

    2015-09-01

    Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a molecular system in equilibrium has gained a lot of attention recently. The last 10 years have seen an ever increasing publication activity on how to construct Markov State Models (MSMs) for very different molecular systems ranging from peptides to proteins, from RNA to DNA, and via molecular sensors to molecular aggregation. Simultaneously the accompanying theory behind MSM building and approximation quality has been developed well beyond the concepts and ideas used in practical applications. This article reviews the main theoretical results, provides links to crucial new developments, outlines the full power of MSM building today, and discusses the essential limitations still to overcome.

  5. Estimating Neuronal Ageing with Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Wang, Bing; Pham, Tuan D.

    2011-06-01

    Neuronal degeneration is widely observed in normal ageing, meanwhile the neurode-generative disease like Alzheimer's disease effects neuronal degeneration in a faster way which is considered as faster ageing. Early intervention of such disease could benefit subjects with potentials of positive clinical outcome, therefore, early detection of disease related brain structural alteration is required. In this paper, we propose a computational approach for modelling the MRI-based structure alteration with ageing using hidden Markov model. The proposed hidden Markov model based brain structural model encodes intracortical tissue/fluid distribution using discrete wavelet transformation and vector quantization. Further, it captures gray matter volume loss, which is capable of reflecting subtle intracortical changes with ageing. Experiments were carried out on healthy subjects to validate its accuracy and robustness. Results have shown its ability of predicting the brain age with prediction error of 1.98 years without training data, which shows better result than other age predition methods.

  6. The cutoff phenomenon in finite Markov chains.

    PubMed Central

    Diaconis, P

    1996-01-01

    Natural mixing processes modeled by Markov chains often show a sharp cutoff in their convergence to long-time behavior. This paper presents problems where the cutoff can be proved (card shuffling, the Ehrenfests' urn). It shows that chains with polynomial growth (drunkard's walk) do not show cutoffs. The best general understanding of such cutoffs (high multiplicity of second eigenvalues due to symmetry) is explored. Examples are given where the symmetry is broken but the cutoff phenomenon persists. PMID:11607633

  7. Numerical methods in Markov chain modeling

    NASA Technical Reports Server (NTRS)

    Philippe, Bernard; Saad, Youcef; Stewart, William J.

    1989-01-01

    Several methods for computing stationary probability distributions of Markov chains are described and compared. The main linear algebra problem consists of computing an eigenvector of a sparse, usually nonsymmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous singular linear system. Several methods based on combinations of Krylov subspace techniques are presented. The performance of these methods on some realistic problems are compared.

  8. Comparative Analysis of Local Anesthesia with 2 Different Concentrations of Adrenaline: A Randomized and Single Blind Study

    PubMed Central

    Managutti, Anil; Prakasam, Michael; Puthanakar, Nagraj; Menat, Shailesh; Shah, Disha; Patel, Harsh

    2015-01-01

    Background: Local anesthetic agents are more commonly used in dentistry to have painless procedure during surgical intervention in bone and soft tissue. There are many local anesthetic agents available with the wide selection of vaso-constrictive agents that improve the clinical efficacy and the duration of local anesthesia. Most commonly lignocaine with adrenaline is used in various concentrations. Systemically adrenaline like drugs can cause a number of cardiovascular disturbances while most are short lived, permanent injury or even death may follow in drug induced ventricular fibrillation, myocardial infarction or cerebro-vascular accidents. This study compared the efficacy and cardiovascular effects with the use of 2% lignocaine with two different concentrations. Materials and Methods: Forty patients underwent extractions of mandibular bilateral teeth using 2% lignocaine with two different concentrations - one with 1:80000 and the other with 1:200000. Results: There was no significant difference in the efficacy and duration with the 2% lignocaine with 2 different concentrations. 2% lignocaine with 1:80000 adrenaline concentration has significantly increased the heart rate and blood pressure especially systolic compared with the lignocaine with 1:200000. Conclusion: Though 2% lignocaine with 1:80000 is widely used in India, 1:200000 adrenaline concentrations do not much affect the cardiovascular parameters. So it is recommended to use 2% lignocaine with 1:200000 for cardiac patients. PMID:25878474

  9. Hidden Markov Model Analysis of Multichromophore Photobleaching

    PubMed Central

    Messina, Troy C.; Kim, Hiyun; Giurleo, Jason T.; Talaga, David S.

    2007-01-01

    The interpretation of single-molecule measurements is greatly complicated by the presence of multiple fluorescent labels. However, many molecular systems of interest consist of multiple interacting components. We investigate this issue using multiply labeled dextran polymers that we intentionally photobleach to the background on a single-molecule basis. Hidden Markov models allow for unsupervised analysis of the data to determine the number of fluorescent subunits involved in the fluorescence intermittency of the 6-carboxy-tetramethylrhodamine labels by counting the discrete steps in fluorescence intensity. The Bayes information criterion allows us to distinguish between hidden Markov models that differ by the number of states, that is, the number of fluorescent molecules. We determine information-theoretical limits and show via Monte Carlo simulations that the hidden Markov model analysis approaches these theoretical limits. This technique has resolving power of one fluorescing unit up to as many as 30 fluorescent dyes with the appropriate choice of dye and adequate detection capability. We discuss the general utility of this method for determining aggregation-state distributions as could appear in many biologically important systems and its adaptability to general photometric experiments. PMID:16913765

  10. A randomized phase II study of pomegranate extract for men with rising PSA following initial therapy for localized prostate cancer

    PubMed Central

    Paller, CJ; Ye, X; Wozniak, PJ; Gillespie, BK; Sieber, PR; Greengold, RH; Stockton, BR; Hertzman, BL; Efros, MD; Roper, RP; Liker, HR; Carducci, MA

    2012-01-01

    BACKGROUND Pomegranate juice has been associated with PSA doubling time (PSADT) elongation in a single-arm phase II trial. This study assesses biological activity of two doses of pomegranate extract (POMx) in men with recurrent prostate cancer, using changes in PSADT as the primary outcome. METHODS This randomized, multi-center, double-blind phase II, dose-exploring trial randomized men with a rising PSA and without metastases to receive 1 or 3 g of POMx, stratified by baseline PSADT and Gleason score. Patients (104) were enrolled and treated for up to 18 months. The intent-to-treat (ITT) population was 96% white, with median age 74.5 years and median Gleason score 7. This study was designed to detect a 6-month on-study increase in PSADT from baseline in each arm. RESULTS: Overall, median PSADT in the ITT population lengthened from 11.9 months at baseline to 18.5 months after treatment (P<0.001). PSADT lengthened in the low-dose group from 11.9 to 18.8 months and 12.2 to 17.5 months in the high-dose group, with no significant difference between dose groups (P =0.554). PSADT increases >100% of baseline were observed in 43% of patients. Declining PSA levels were observed in 13 patients (13%). In all, 42% of patients discontinued treatment before meeting the protocol-definition of PSA progression, or 18 months, primarily due to a rising PSA. No significant changes occurred in testosterone. Although no clinically significant toxicities were seen, diarrhea was seen in 1.9% and 13.5% of patients in the 1- and 3-g dose groups, respectively. CONCLUSIONS POMx treatment was associated with ≥6 month increases in PSADT in both treatment arms without adverse effects. The significance of this on-study slowing of PSADT remains unclear, reinforcing the need for placebo-controlled studies in this patient population. PMID:22689129

  11. Efficacy of Benzocaine 20% Topical Anesthetic Compared to Placebo Prior to Administration of Local Anesthesia in the Oral Cavity: A Randomized Controlled Trial

    PubMed Central

    de Freiras, Guilherme Camponogara; Pozzobon, Roselaine Terezinha; Blaya, Diego Segatto; Moreira, Carlos Heitor

    2015-01-01

    The aim of the present study was to compare the effects of a topical anesthetic to a placebo on pain perception during administration of local anesthesia in 2 regions of the oral cavity. A split-mouth, double-blind, randomized clinical trial design was used. Thirty-eight subjects, ages 18–50 years, American Society of Anesthesiologists I and II, received 4 anesthetic injections each in regions corresponding to the posterior superior alveolar nerve (PSA) and greater palatine nerve (GPN), totaling 152 sites analyzed. The side of the mouth where the topical anesthetic (benzocaine 20%) or the placebo was to be applied was chosen by a flip of a coin. The needle used was 27G, and the anesthetic used for administration of local anesthesia was 2% lidocaine with 1:100,000 epinephrine. After receiving the administration of local anesthesia, each patient reported pain perception on a visual analog scale (VAS) of 100-mm length. The results showed that the topical anesthetic and the placebo had similar effects: there was no statistically significant VAS difference between the PSA and the GPN pain ratings. A higher value on the VAS for the anesthesia of the GPN, relative to the PSA, was observed for both groups. Regarding gender, male patients had higher values on the VAS compared with female patients, but these differences were not meaningful. The topical anesthetic and the placebo had similar effects on pain perception for injection of local anesthesia for the PSA and GPN. PMID:26061572

  12. Efficacy of Benzocaine 20% Topical Anesthetic Compared to Placebo Prior to Administration of Local Anesthesia in the Oral Cavity: A Randomized Controlled Trial.

    PubMed

    de Freiras, Guilherme Camponogara; Pozzobon, Roselaine Terezinha; Blaya, Diego Segatto; Moreira, Carlos Heitor

    2015-01-01

    The aim of the present study was to compare the effects of a topical anesthetic to a placebo on pain perception during administration of local anesthesia in 2 regions of the oral cavity. A split-mouth, double-blind, randomized clinical trial design was used. Thirty-eight subjects, ages 18-50 years, American Society of Anesthesiologists I and II, received 4 anesthetic injections each in regions corresponding to the posterior superior alveolar nerve (PSA) and greater palatine nerve (GPN), totaling 152 sites analyzed. The side of the mouth where the topical anesthetic (benzocaine 20%) or the placebo was to be applied was chosen by a flip of a coin. The needle used was 27G, and the anesthetic used for administration of local anesthesia was 2% lidocaine with 1:100,000 epinephrine. After receiving the administration of local anesthesia, each patient reported pain perception on a visual analog scale (VAS) of 100-mm length. The results showed that the topical anesthetic and the placebo had similar effects: there was no statistically significant VAS difference between the PSA and the GPN pain ratings. A higher value on the VAS for the anesthesia of the GPN, relative to the PSA, was observed for both groups. Regarding gender, male patients had higher values on the VAS compared with female patients, but these differences were not meaningful. The topical anesthetic and the placebo had similar effects on pain perception for injection of local anesthesia for the PSA and GPN.

  13. Final Report of the Intergroup Randomized Study of Combined Androgen-Deprivation Therapy Plus Radiotherapy Versus Androgen-Deprivation Therapy Alone in Locally Advanced Prostate Cancer

    PubMed Central

    Mason, Malcolm D.; Parulekar, Wendy R.; Sydes, Matthew R.; Brundage, Michael; Kirkbride, Peter; Gospodarowicz, Mary; Cowan, Richard; Kostashuk, Edmund C.; Anderson, John; Swanson, Gregory; Parmar, Mahesh K.B.; Hayter, Charles; Jovic, Gordana; Hiltz, Andrea; Hetherington, John; Sathya, Jinka; Barber, James B.P.; McKenzie, Michael; El-Sharkawi, Salah; Souhami, Luis; Hardman, P.D. John; Chen, Bingshu E.; Warde, Padraig

    2015-01-01

    Purpose We have previously reported that radiotherapy (RT) added to androgen-deprivation therapy (ADT) improves survival in men with locally advanced prostate cancer. Here, we report the prespecified final analysis of this randomized trial. Patients and Methods NCIC Clinical Trials Group PR.3/Medical Research Council PR07/Intergroup T94-0110 was a randomized controlled trial of patients with locally advanced prostate cancer. Patients with T3-4, N0/Nx, M0 prostate cancer or T1-2 disease with either prostate-specific antigen (PSA) of more than 40 μg/L or PSA of 20 to 40 μg/L plus Gleason score of 8 to 10 were randomly assigned to lifelong ADT alone or to ADT+RT. The RT dose was 64 to 69 Gy in 35 to 39 fractions to the prostate and pelvis or prostate alone. Overall survival was compared using a log-rank test stratified for prespecified variables. Results One thousand two hundred five patients were randomly assigned between 1995 and 2005, 602 to ADT alone and 603 to ADT+RT. At a median follow-up time of 8 years, 465 patients had died, including 199 patients from prostate cancer. Overall survival was significantly improved in the patients allocated to ADT+RT (hazard ratio [HR], 0.70; 95% CI, 0.57 to 0.85; P < .001). Deaths from prostate cancer were significantly reduced by the addition of RT to ADT (HR, 0.46; 95% CI, 0.34 to 0.61; P < .001). Patients on ADT+RT reported a higher frequency of adverse events related to bowel toxicity, but only two of 589 patients had grade 3 or greater diarrhea at 24 months after RT. Conclusion This analysis demonstrates that the previously reported benefit in survival is maintained at a median follow-up of 8 years and firmly establishes the role of RT in the treatment of men with locally advanced prostate cancer. PMID:25691677

  14. Outcomes of an automated procedure for the selection of effective platelets for patients refractory to random donors based on cross-matching locally available platelet products.

    PubMed

    Rebulla, Paolo; Morelati, Fernanda; Revelli, Nicoletta; Villa, Maria Antonietta; Paccapelo, Cinzia; Nocco, Angela; Greppi, Noemi; Marconi, Maurizio; Cortelezzi, Agostino; Fracchiolla, Nicola; Martinelli, Giovanni; Deliliers, Giorgio Lambertenghi

    2004-04-01

    In 1999, we implemented an automated platelet cross-matching (XM) programme to select compatible platelets from the local inventory for patients refractory to random donor platelets. In this study, we evaluated platelet count increments in 40 consecutive refractory patients (8.3% of 480 consecutive platelet recipients) given 569 cross-match-negative platelets between April 1999 and December 2001. XM was performed automatically with a commercially available immunoadherence assay. Pre-, 1- and 24-h post-transfusion platelet counts (mean +/- SD) for the 569 XM-negative platelet transfusions containing 302 +/- 71 x 109 platelets were 7.7 +/- 5.5, 32.0 +/- 21.0 and 16.8 +/- 15.5 x 109/l respectively. Increments were significantly higher (P < 0.05, t-test) than those observed in the same patients given 303 random platelet pools (dose = 318 +/- 52 x 109 platelets) during the month before refractoriness was detected, when pre-, 1- and 24-h post-transfusion counts were 7.0 +/- 8.6, 15.9 +/- 16.1 and 9.6 +/- 12.8 x 109/l respectively. The cost of the platelet XM disposable kit per transfusion to produce 1-h post-transfusion platelet count increments >10 x 109/l was euro 447. This programme enabled the rapid selection of effective platelets for refractory patients, from the local inventory. PMID:15015974

  15. Outcomes of an automated procedure for the selection of effective platelets for patients refractory to random donors based on cross-matching locally available platelet products.

    PubMed

    Rebulla, Paolo; Morelati, Fernanda; Revelli, Nicoletta; Villa, Maria Antonietta; Paccapelo, Cinzia; Nocco, Angela; Greppi, Noemi; Marconi, Maurizio; Cortelezzi, Agostino; Fracchiolla, Nicola; Martinelli, Giovanni; Deliliers, Giorgio Lambertenghi

    2004-04-01

    In 1999, we implemented an automated platelet cross-matching (XM) programme to select compatible platelets from the local inventory for patients refractory to random donor platelets. In this study, we evaluated platelet count increments in 40 consecutive refractory patients (8.3% of 480 consecutive platelet recipients) given 569 cross-match-negative platelets between April 1999 and December 2001. XM was performed automatically with a commercially available immunoadherence assay. Pre-, 1- and 24-h post-transfusion platelet counts (mean +/- SD) for the 569 XM-negative platelet transfusions containing 302 +/- 71 x 109 platelets were 7.7 +/- 5.5, 32.0 +/- 21.0 and 16.8 +/- 15.5 x 109/l respectively. Increments were significantly higher (P < 0.05, t-test) than those observed in the same patients given 303 random platelet pools (dose = 318 +/- 52 x 109 platelets) during the month before refractoriness was detected, when pre-, 1- and 24-h post-transfusion counts were 7.0 +/- 8.6, 15.9 +/- 16.1 and 9.6 +/- 12.8 x 109/l respectively. The cost of the platelet XM disposable kit per transfusion to produce 1-h post-transfusion platelet count increments >10 x 109/l was euro 447. This programme enabled the rapid selection of effective platelets for refractory patients, from the local inventory.

  16. Short-Term Soy Isoflavone Intervention in Patients with Localized Prostate Cancer: A Randomized, Double-Blind, Placebo-Controlled Trial

    PubMed Central

    Hamilton-Reeves, Jill M.; Banerjee, Snigdha; Banerjee, Sushanta K.; Holzbeierlein, Jeffrey M.; Thrasher, J. Brantley; Kambhampati, Suman; Keighley, John; Van Veldhuizen, Peter

    2013-01-01

    Purpose We describe the effects of soy isoflavone consumption on prostate specific antigen (PSA), hormone levels, total cholesterol, and apoptosis in men with localized prostate cancer. Methodology/Principal Findings We conducted a double-blinded, randomized, placebo-controlled trial to examine the effect of soy isoflavone capsules (80 mg/d of total isoflavones, 51 mg/d aglucon units) on serum and tissue biomarkers in patients with localized prostate cancer. Eighty-six men were randomized to treatment with isoflavones (n = 42) or placebo (n = 44) for up to six weeks prior to scheduled prostatectomy. We performed microarray analysis using a targeted cell cycle regulation and apoptosis gene chip (GEArrayTM). Changes in serum total testosterone, free testosterone, total estrogen, estradiol, PSA, and total cholesterol were analyzed at baseline, mid-point, and at the time of radical prostatectomy. In this preliminary analysis, 12 genes involved in cell cycle control and 9 genes involved in apoptosis were down-regulated in the treatment tumor tissues versus the placebo control. Changes in serum total testosterone, free testosterone, total estrogen, estradiol, PSA, and total cholesterol in the isoflavone-treated group compared to men receiving placebo were not statistically significant. Conclusions/Significance These data suggest that short-term intake of soy isoflavones did not affect serum hormone levels, total cholesterol, or PSA. Trial Registration ClinicalTrials.gov NCT00255125 PMID:23874588

  17. Bayesian Gibbs Markov chain: MRF-based Stochastic Joint Inversion of Hydrological and Geophysical Datasets for Improved Characterization of Aquifer Heterogeneities.

    NASA Astrophysics Data System (ADS)

    Oware, E. K.

    2015-12-01

    Modeling aquifer heterogeneities (AH) is a complex, multidimensional problem that mostly requires stochastic imaging strategies for tractability. While the traditional Bayesian Markov chain Monte Carlo (McMC) provides a powerful framework to model AH, the generic McMC is computationally prohibitive and, thus, unappealing for large-scale problems. An innovative variant of the McMC scheme that imposes priori spatial statistical constraints on model parameter updates, for improved characterization in a computationally efficient manner is proposed. The proposed algorithm (PA) is based on Markov random field (MRF) modeling, which is an image processing technique that infers the global behavior of a random field from its local properties, making the MRF approach well suited for imaging AH. MRF-based modeling leverages the equivalence of Gibbs (or Boltzmann) distribution (GD) and MRF to identify the local properties of an MRF in terms of the easily quantifiable Gibbs energy. The PA employs the two-step approach to model the lithological structure of the aquifer and the hydraulic properties within the identified lithologies simultaneously. It performs local Gibbs energy minimizations along a random path, which requires parameters of the GD (spatial statistics) to be specified. A PA that implicitly infers site-specific GD parameters within a Bayesian framework is also presented. The PA is illustrated with a synthetic binary facies aquifer with a lognormal heterogeneity simulated within each facies. GD parameters of 2.6, 1.2, -0.4, and -0.2 were estimated for the horizontal, vertical, NESW, and NWSE directions, respectively. Most of the high hydraulic conductivity zones (facies 2) were fairly resolved (see results below) with facies identification accuracy rate of 81%, 89%, and 90% for the inversions conditioned on concentration (R1), resistivity (R2), and joint (R3), respectively. The incorporation of the conditioning datasets improved on the root mean square error (RMSE

  18. An experimental validation of the Gauss-Markov model for nonuniformity noise in infrared focal plane array sensors

    NASA Astrophysics Data System (ADS)

    Zapata, Octavio; Pedreros, Felipe; Torres, Sergio N.

    2012-06-01

    The aim of this research is to experimentally validate a Gauss-Markov model, previously developed by our group, for the non-uniformity parameters of infrared (IR) focal plane arrays (FPAs). The Gauss-Markov model assumed that both, the gain and the offset parameters at each detector, are random state-variables modeled by a recursive discrete-time process. For simplicity, however, we have regarded here the gain parameter as a constant and assumed that solely the offset parameter follows a Gauss-Markov model. Experiments have been conducted at room temperature and IR data was collected from black-body radiator sources using microbolometer-based IR cameras operating in the 8 to 12 μm. Next, well-known statistical techniques were used to analyze the offset time series and determinate whether the Gauss-Markov model truly fits the temporal dynamics of the offset. The validity of the Gauss-Markov model for the offset parameter was tested at two time scales: seconds and minutes. It is worth mentioning that the statistical analysis conducted in this work is a key in providing mechanisms for capturing the drift in the fixed pattern noise parameters.

  19. Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm.

    PubMed

    Onoma, D P; Ruan, S; Thureau, S; Nkhali, L; Modzelewski, R; Monnehan, G A; Vera, P; Gardin, I

    2014-12-01

    A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

  20. Recursive recovery of Markov transition probabilities from boundary value data

    SciTech Connect

    Patch, S.K.

    1994-04-01

    In an effort to mathematically describe the anisotropic diffusion of infrared radiation in biological tissue Gruenbaum posed an anisotropic diffusion boundary value problem in 1989. In order to accommodate anisotropy, he discretized the temporal as well as the spatial domain. The probabilistic interpretation of the diffusion equation is retained; radiation is assumed to travel according to a random walk (of sorts). In this random walk the probabilities with which photons change direction depend upon their previous as well as present location. The forward problem gives boundary value data as a function of the Markov transition probabilities. The inverse problem requires finding the transition probabilities from boundary value data. Problems in the plane are studied carefully in this thesis. Consistency conditions amongst the data are derived. These conditions have two effects: they prohibit inversion of the forward map but permit smoothing of noisy data. Next, a recursive algorithm which yields a family of solutions to the inverse problem is detailed. This algorithm takes advantage of all independent data and generates a system of highly nonlinear algebraic equations. Pluecker-Grassmann relations are instrumental in simplifying the equations. The algorithm is used to solve the 4 {times} 4 problem. Finally, the smallest nontrivial problem in three dimensions, the 2 {times} 2 {times} 2 problem, is solved.

  1. A cluster randomized controlled trial aimed at implementation of local quality improvement collaboratives to improve prescribing and test ordering performance of general practitioners: Study Protocol

    PubMed Central

    Trietsch, Jasper; van der Weijden, Trudy; Verstappen, Wim; Janknegt, Rob; Muijrers, Paul; Winkens, Ron; van Steenkiste, Ben; Grol, Richard; Metsemakers, Job

    2009-01-01

    Background The use of guidelines in general practice is not optimal. Although evidence-based methods to improve guideline adherence are available, variation in physician adherence to general practice guidelines remains relatively high. The objective for this study is to transfer a quality improvement strategy based on audit, feedback, educational materials, and peer group discussion moderated by local opinion leaders to the field. The research questions are: is the multifaceted strategy implemented on a large scale as planned?; what is the effect on general practitioners' (GPs) test ordering and prescribing behaviour?; and what are the costs of implementing the strategy? Methods In order to evaluate the effects, costs and feasibility of this new strategy we plan a multi-centre cluster randomized controlled trial (RCT) with a balanced incomplete block design. Local GP groups in the south of the Netherlands already taking part in pharmacotherapeutic audit meeting groups, will be recruited by regional health officers. Approximately 50 groups of GPs will be randomly allocated to two arms. These GPs will be offered two different balanced sets of clinical topics. Each GP within a group will receive comparative feedback on test ordering and prescribing performance. The feedback will be discussed in the group and working agreements will be created after discussion of the guidelines and barriers to change. The data for the feedback will be collected from existing and newly formed databases, both at baseline and after one year. Discussion We are not aware of published studies on successes and failures of attempts to transfer to the stakeholders in the field a multifaceted strategy aimed at GPs' test ordering and prescribing behaviour. This pragmatic study will focus on compatibility with existing infrastructure, while permitting a certain degree of adaptation to local needs and routines. Trial registration Nederlands Trial Register ISRCTN40008171 PMID:19222840

  2. Segmentation of brain tumors in 4D MR images using the hidden Markov model.

    PubMed

    Solomon, Jeffrey; Butman, John A; Sood, Arun

    2006-12-01

    Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone. PMID:17050032

  3. A novel SAR fusion image segmentation method based on triplet Markov field

    NASA Astrophysics Data System (ADS)

    Wang, Jiajing; Jiao, Shuhong; Sun, Zhenyu

    2015-03-01

    Markov random field (MRF) has been widely used in SAR image segmentation because of the advantage of directly modeling the posterior distribution and suppresses the speckle on the influence of the segmentation result. However, when the real SAR images are nonstationary images, the unsupervised segmentation results by MRF can be poor. The recent proposed triplet Markov field (TMF) model is well appropriate for nonstationary SAR image processing due to the introduction of an auxiliary field which reflects the nonstationarity. In addition, on account of the texture features of SAR image, a fusion image segmentation method is proposed by fusing the gray level image and texture feature image. The effectiveness of the proposed method in this paper is demonstrated by a synthesis SAR image and the real SAR images segmentation experiments, and it is better than the state-of-art methods.

  4. A path-independent method for barrier option pricing in hidden Markov models

    NASA Astrophysics Data System (ADS)

    Rashidi Ranjbar, Hedieh; Seifi, Abbas

    2015-12-01

    This paper presents a method for barrier option pricing under a Black-Scholes model with Markov switching. We extend the option pricing method of Buffington and Elliott to price continuously monitored barrier options under a Black-Scholes model with regime switching. We use a regime switching random Esscher transform in order to determine an equivalent martingale pricing measure, and then solve the resulting multidimensional integral for pricing barrier options. We have calculated prices for down-and-out call options under a two-state hidden Markov model using two different Monte-Carlo simulation approaches and the proposed method. A comparison of the results shows that our method is faster than Monte-Carlo simulation methods.

  5. Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment

    NASA Technical Reports Server (NTRS)

    English, Thomas

    2005-01-01

    A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.

  6. Interplay between spin-density wave and 3 d local moments with random exchange in a molecular conductor

    NASA Astrophysics Data System (ADS)

    Kawaguchi, Genta; Maesato, Mitsuhiko; Komatsu, Tokutaro; Imakubo, Tatsuro; Kitagawa, Hiroshi

    2016-02-01

    We present the results of high-pressure transport measurements on the anion-mixed molecular conductors (DIETSe)2M Br2Cl2 [DIETSe = diiodo(ethylenedithio)tetraselenafulvalene; M =Fe , Ga]. They undergo a metal-insulator (M-I) transition below 9 K at ambient pressure, which is suppressed by applying pressure, indicating a spin-density-wave (SDW) transition caused by a nesting instability of the quasi-one-dimensional (Q1D) Fermi surface, as observed in the parent compounds (DIETSe)2M Cl4 (M =Fe , Ga). In the metallic state, the existence of the Q1D Fermi surface is confirmed by observing the Lebed resonance. The critical pressures of the SDW, Pc, of the M Br2Cl2 (M =Fe , Ga) salts are significantly lower than those of the the M Cl4 (M = Fe, Ga) salts, suggesting chemical pressure effects. Above Pc, field-induced SDW transitions appear, as evidenced by kink structures in the magnetoresistance (MR) in both salts. The FeBr2Cl2 salt also shows antiferromagnetic (AF) ordering of d spins at 4 K, below which significant spin-charge coupling is observed. A large positive MR change up to 150% appears above the spin-flop field at high pressure. At low pressure, in particular below Pc, a dip or kink structure appears in MR at the spin-flop field, which shows unconventionally large hysteresis at low temperature (T <1 K). The hysteresis region clearly decreases with increasing pressure towards Pc, strongly indicating that the coexisting SDW plays an important role in the enhancement of magnetic hysteresis besides the random exchange interaction.

  7. Preserving spatial linear correlations between neighboring stations in simulating daily precipitation using extended Markov models

    NASA Astrophysics Data System (ADS)

    Ababaei, Behnam; Sohrabi, Teymour; Mirzaei, Farhad

    2014-10-01

    Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.

  8. Children's behavioral pain reactions during local anesthetic injection using cotton-roll vibration method compared with routine topical anesthesia: A randomized controlled trial

    PubMed Central

    Bagherian, Ali; Sheikhfathollahi, Mahmood

    2016-01-01

    Background: Topical anesthesia has been widely advocated as an important component of atraumatic administration of intraoral local anesthesia. The aim of this study was to use direct observation of children's behavioral pain reactions during local anesthetic injection using cotton-roll vibration method compared with routine topical anesthesia. Materials and Methods: Forty-eight children participated in this randomized controlled clinical trial. They received two separate inferior alveolar nerve block or primary maxillary molar infiltration injections on contralateral sides of the jaws by both cotton-roll vibration (a combination of topical anesthesia gel, cotton roll, and vibration for physical distraction) and control (routine topical anesthesia) methods. Behavioral pain reactions of children were measured according to the author-developed face, head, foot, hand, trunk, and cry (FHFHTC) scale, resulting in total scores between 0 and 18. Results: The total scores on the FHFHTC scale ranged between 0-5 and 0-10 in the cotton-roll vibration and control methods, respectively. The mean ± standard deviation values of total scores on FHFHTC scale were lower in the cotton-roll vibration method (1.21 ± 1.38) than in control method (2.44 ± 2.18), and this was statistically significant (P < 0.001). Conclusion: It may be concluded that the cotton-roll vibration method can be more helpful than the routine topical anesthesia in reducing behavioral pain reactions in children during local anesthesia administration. PMID:27274349

  9. Generator estimation of Markov jump processes

    NASA Astrophysics Data System (ADS)

    Metzner, P.; Dittmer, E.; Jahnke, T.; Schütte, Ch.

    2007-11-01

    Estimating the generator of a continuous-time Markov jump process based on incomplete data is a problem which arises in various applications ranging from machine learning to molecular dynamics. Several methods have been devised for this purpose: a quadratic programming approach (cf. [D.T. Crommelin, E. Vanden-Eijnden, Fitting timeseries by continuous-time Markov chains: a quadratic programming approach, J. Comp. Phys. 217 (2006) 782-805]), a resolvent method (cf. [T. Müller, Modellierung von Proteinevolution, PhD thesis, Heidelberg, 2001]), and various implementations of an expectation-maximization algorithm ([S. Asmussen, O. Nerman, M. Olsson, Fitting phase-type distributions via the EM algorithm, Scand. J. Stat. 23 (1996) 419-441; I. Holmes, G.M. Rubin, An expectation maximization algorithm for training hidden substitution models, J. Mol. Biol. 317 (2002) 753-764; U. Nodelman, C.R. Shelton, D. Koller, Expectation maximization and complex duration distributions for continuous time Bayesian networks, in: Proceedings of the twenty-first conference on uncertainty in AI (UAI), 2005, pp. 421-430; M. Bladt, M. Sørensen, Statistical inference for discretely observed Markov jump processes, J.R. Statist. Soc. B 67 (2005) 395-410]). Some of these methods, however, seem to be known only in a particular research community, and have later been reinvented in a different context. The purpose of this paper is to compile a catalogue of existing approaches, to compare the strengths and weaknesses, and to test their performance in a series of numerical examples. These examples include carefully chosen model problems and an application to a time series from molecular dynamics.

  10. Hybrid Discrete-Continuous Markov Decision Processes

    NASA Technical Reports Server (NTRS)

    Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

    2003-01-01

    This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

  11. 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.

  12. Overview of Randomized Controlled Treatment Trials for Clinically Localized Prostate Cancer: Implications for Active Surveillance and the United States Preventative Task Force Report on Screening?

    PubMed Central

    2012-01-01

    Prostate cancer and its management have been intensely debated for years. Recommendations range from ardent support for active screening and immediate treatment to resolute avoidance of screening and active surveillance. There is a growing body of level I evidence establishing a clear survival advantage for treatment of subsets of patients with clinically localized prostate cancer. This chapter presents a review of these randomized controlled trials. We argue that an understanding of this literature is relevant not only to those considering active surveillance but also to those evaluating the merits of screening. In addition, a number of important evidence-based conclusions concerning what should and should not be done can be gleaned from these trials. PMID:23271777

  13. Prospective Preference Assessment of Patients' Willingness to Participate in a Randomized Controlled Trial of Intensity-Modulated Radiotherapy Versus Proton Therapy for Localized Prostate Cancer

    SciTech Connect

    Shah, Anand; Efstathiou, Jason A.; Paly, Jonathan J.; Halpern, Scott D.; Bruner, Deborah W.; Christodouleas, John P.; Coen, John J.; Deville, Curtiland; Vapiwala, Neha; Shipley, William U.; Zietman, Anthony L.; Hahn, Stephen M.; Bekelman, Justin E.

    2012-05-01

    Purpose: To investigate patients' willingness to participate (WTP) in a randomized controlled trial (RCT) comparing intensity-modulated radiotherapy (IMRT) with proton beam therapy (PBT) for prostate cancer (PCa). Methods and Materials: We undertook a qualitative research study in which we prospectively enrolled patients with clinically localized PCa. We used purposive sampling to ensure a diverse sample based on age, race, travel distance, and physician. Patients participated in a semi-structured interview in which they reviewed a description of a hypothetical RCT, were asked open-ended and focused follow-up questions regarding their motivations for and concerns about enrollment, and completed a questionnaire assessing characteristics such as demographics and prior knowledge of IMRT or PBT. Patients' stated WTP was assessed using a 6-point Likert scale. Results: Forty-six eligible patients (33 white, 13 black) were enrolled from the practices of eight physicians. We identified 21 factors that impacted patients' WTP, which largely centered on five major themes: altruism/desire to compare treatments, randomization, deference to physician opinion, financial incentives, and time demands/scheduling. Most patients (27 of 46, 59%) stated they would either 'definitely' or 'probably' participate. Seventeen percent (8 of 46) stated they would 'definitely not' or 'probably not' enroll, most of whom (6 of 8) preferred PBT before their physician visit. Conclusions: A substantial proportion of patients indicated high WTP in a RCT comparing IMRT and PBT for PCa.

  14. Randomized Clinical Trial of Weekly vs. Triweekly Cisplatin-Based Chemotherapy Concurrent With Radiotherapy in the Treatment of Locally Advanced Cervical Cancer

    SciTech Connect

    Ryu, Sang-Young; Lee, Won-Moo; Kim, Kidong; Park, Sang-Il; Kim, Beob-Jong; Kim, Moon-Hong; Choi, Seok-Cheol; Cho, Chul-Koo; Nam, Byung-Ho; Lee, Eui-Don

    2011-11-15

    Purpose: To compare compliance, toxicity, and outcome of weekly and triweekly cisplatin administration concurrent with radiotherapy in locally advanced cervical cancer. Methods and Materials: In this open-label, randomized trial, 104 patients with histologically proven Stage IIB-IVA cervical cancer were randomly assigned by a computer-generated procedure to weekly (weekly cisplatin 40 mg/m{sup 2}, six cycles) and triweekly (cisplatin 75 mg/m{sup 2} every 3 weeks, three cycles) chemotherapy arms during concurrent radiotherapy. The difference of compliance and the toxicity profiles between the two arms were investigated, and the overall survival rate was analyzed after 5 years. Results: All patients tolerated both treatments very well, with a high completion rate of scheduled chemotherapy cycles. There was no statistically significant difference in compliance between the two arms (86.3% in the weekly arm, 92.5% in the triweekly arm, p > 0.05). Grade 3-4 neutropenia was more frequent in the weekly arm (39.2%) than in the triweekly arm (22.6%) (p = 0.03). The overall 5-year survival rate was significantly higher in the triweekly arm (88.7%) than in the weekly arm (66.5%) (hazard ratio 0.375; 95% confidence interval 0.154-0.914; p = 0.03). Conclusions: Triweekly cisplatin 75-mg/m{sup 2} chemotherapy concurrent with radiotherapy is more effective and feasible than the conventional weekly cisplatin 40-mg/m{sup 2} regimen and may be a strong candidate for the optimal cisplatin dose and dosing schedule in the treatment of locally advanced cervical cancer.

  15. Stochastic seismic tomography by interacting Markov chains

    NASA Astrophysics Data System (ADS)

    Bottero, Alexis; Gesret, Alexandrine; Romary, Thomas; Noble, Mark; Maisons, Christophe

    2016-10-01

    Markov chain Monte Carlo sampling methods are widely used for non-linear Bayesian inversion where no analytical expression for the forward relation between data and model parameters is available. Contrary to the linear(ized) approaches, they naturally allow to evaluate the uncertainties on the model found. Nevertheless their use is problematic in high-dimensional model spaces especially when the computational cost of the forward problem is significant and/or the a posteriori distribution is multimodal. In this case, the chain can stay stuck in one of the modes and hence not provide an exhaustive sampling of the distribution of interest. We present here a still relatively unknown algorithm that allows interaction between several Markov chains at different temperatures. These interactions (based on importance resampling) ensure a robust sampling of any posterior distribution and thus provide a way to efficiently tackle complex fully non-linear inverse problems. The algorithm is easy to implement and is well adapted to run on parallel supercomputers. In this paper, the algorithm is first introduced and applied to a synthetic multimodal distribution in order to demonstrate its robustness and efficiency compared to a simulated annealing method. It is then applied in the framework of first arrival traveltime seismic tomography on real data recorded in the context of hydraulic fracturing. To carry out this study a wavelet-based adaptive model parametrization has been used. This allows to integrate the a priori information provided by sonic logs and to reduce optimally the dimension of the problem.

  16. Stochastic seismic tomography by interacting Markov chains

    NASA Astrophysics Data System (ADS)

    Bottero, Alexis; Gesret, Alexandrine; Romary, Thomas; Noble, Mark; Maisons, Christophe

    2016-07-01

    Markov chain Monte Carlo sampling methods are widely used for non-linear Bayesian inversion where no analytical expression for the forward relation between data and model parameters is available. Contrary to the linear(ized) approaches they naturally allow to evaluate the uncertainties on the model found. Nevertheless their use is problematic in high dimensional model spaces especially when the computational cost of the forward problem is significant and/or the a posteriori distribution is multimodal. In this case the chain can stay stuck in one of the modes and hence not provide an exhaustive sampling of the distribution of interest. We present here a still relatively unknown algorithm that allows interaction between several Markov chains at different temperatures. These interactions (based on Importance Resampling) ensure a robust sampling of any posterior distribution and thus provide a way to efficiently tackle complex fully non linear inverse problems. The algorithm is easy to implement and is well adapted to run on parallel supercomputers. In this paper the algorithm is first introduced and applied to a synthetic multimodal distribution in order to demonstrate its robustness and efficiency compared to a Simulated Annealing method. It is then applied in the framework of first arrival traveltime seismic tomography on real data recorded in the context of hydraulic fracturing. To carry out this study a wavelet based adaptive model parametrization has been used. This allows to integrate the a priori information provided by sonic logs and to reduce optimally the dimension of the problem.

  17. Equilibrium Control Policies for Markov Chains

    SciTech Connect

    Malikopoulos, Andreas

    2011-01-01

    The average cost criterion has held great intuitive appeal and has attracted considerable attention. It is widely employed when controlling dynamic systems that evolve stochastically over time by means of formulating an optimization problem to achieve long-term goals efficiently. The average cost criterion is especially appealing when the decision-making process is long compared to other timescales involved, and there is no compelling motivation to select short-term optimization. This paper addresses the problem of controlling a Markov chain so as to minimize the average cost per unit time. Our approach treats the problem as a dual constrained optimization problem. We derive conditions guaranteeing that a saddle point exists for the new dual problem and we show that this saddle point is an equilibrium control policy for each state of the Markov chain. For practical situations with constraints consistent to those we study here, our results imply that recognition of such saddle points may be of value in deriving in real time an optimal control policy.

  18. Markov Chain Monte Carlo and Irreversibility

    NASA Astrophysics Data System (ADS)

    Ottobre, Michela

    2016-06-01

    Markov Chain Monte Carlo (MCMC) methods are statistical methods designed to sample from a given measure π by constructing a Markov chain that has π as invariant measure and that converges to π. Most MCMC algorithms make use of chains that satisfy the detailed balance condition with respect to π; such chains are therefore reversible. On the other hand, recent work [18, 21, 28, 29] has stressed several advantages of using irreversible processes for sampling. Roughly speaking, irreversible diffusions converge to equilibrium faster (and lead to smaller asymptotic variance as well). In this paper we discuss some of the recent progress in the study of nonreversible MCMC methods. In particular: i) we explain some of the difficulties that arise in the analysis of nonreversible processes and we discuss some analytical methods to approach the study of continuous-time irreversible diffusions; ii) most of the rigorous results on irreversible diffusions are available for continuous-time processes; however, for computational purposes one needs to discretize such dynamics. It is well known that the resulting discretized chain will not, in general, retain all the good properties of the process that it is obtained from. In particular, if we want to preserve the invariance of the target measure, the chain might no longer be reversible. Therefore iii) we conclude by presenting an MCMC algorithm, the SOL-HMC algorithm [23], which results from a nonreversible discretization of a nonreversible dynamics.

  19. Monitoring volcano activity through Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.

    2013-12-01

    During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.

  20. A Markov model of the Indus script.

    PubMed

    Rao, Rajesh P N; Yadav, Nisha; Vahia, Mayank N; Joglekar, Hrishikesh; Adhikari, R; Mahadevan, Iravatham

    2009-08-18

    Although no historical information exists about the Indus civilization (flourished ca. 2600-1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional subunits of a possible underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Application of this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus script exhibits rich synactic structure and the ability to represent diverse content. both of which are suggestive of a linguistic writing system rather than a nonlinguistic symbol system. PMID:19666571

  1. A Randomized Trial (Irish Clinical Oncology Research Group 97-01) Comparing Short Versus Protracted Neoadjuvant Hormonal Therapy Before Radiotherapy for Localized Prostate Cancer

    SciTech Connect

    Armstrong, John G.; Gillham, Charles M.; Dunne, Mary T.; Fitzpatrick, David A.; Finn, Marie A.; Cannon, Mairin E.; Taylor, Judy C.; O'Shea, Carmel M.; Buckney, Steven J.; Thirion, Pierre G.

    2011-09-01

    Purpose: To examine the long-term outcomes of a randomized trial comparing short (4 months; Arm 1) and long (8 months; Arm 2) neoadjuvant hormonal therapy before radiotherapy for localized prostate cancer. Methods and Materials: Between 1997 and 2001, 276 patients were enrolled and the data from 261 were analyzed. The stratification risk factors were prostate-specific antigen level >20 ng/mL, Gleason score {>=}7, and Stage T3 or more. The intermediate-risk stratum had one factor and the high-risk stratum had two or more. Staging was done from the bone scan and computed tomography findings. The primary endpoint was biochemical failure-free survival. Results: The median follow-up was 102 months. The overall survival, biochemical failure-free survival. and prostate cancer-specific survival did not differ significantly between the two treatment arms, overall or at 5 years. The cumulative probability of overall survival at 5 years was 90% (range, 87-92%) in Arm 1 and 83% (range, 80-86%) in Arm 2. The biochemical failure-free survival rate at 5 years was 66% (range, 62-71%) in Arm 1 and 63% (range, 58-67%) in Arm 2. Conclusion: No statistically significant difference was found in biochemical failure-free survival between 4 months and 8 months of neoadjuvant hormonal therapy before radiotherapy for localized prostate cancer.

  2. Uncovering and Testing the Fuzzy Clusters Based on Lumped Markov Chain in Complex Network

    PubMed Central

    Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu

    2013-01-01

    Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club. PMID:24391729

  3. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

    NASA Astrophysics Data System (ADS)

    Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

    2014-09-01

    Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

  4. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

    SciTech Connect

    Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G. E-mail: gerhard.hummer@biophys.mpg.de; Hummer, Gerhard E-mail: gerhard.hummer@biophys.mpg.de

    2014-09-21

    Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

  5. A randomized, controlled trial of the effects of resveratrol administration in performance horses with lameness localized to the distal tarsal joints.

    PubMed

    Watts, Ashlee E; Dabareiner, Robin; Marsh, Chad; Carter, G Kent; Cummings, Kevin J

    2016-09-15

    OBJECTIVE To determine the effect of resveratrol administration in performance horses with lameness localized to the distal tarsal joints. DESIGN Randomized, blinded, placebo-controlled clinical trial. ANIMALS 45 client-owned horses with lameness localized to the distal tarsal joints. PROCEDURES All horses received injections of triamcinolone acetonide in the centrodistal and tarsometatarsal joints of both hind limbs. A placebo or a supplement containing resveratrol was fed twice daily by owners for 4 months. Primary outcomes were horse performance as determined by rider opinion (better, worse, or the same) and change in lameness severity from the enrollment examination. RESULTS Complete data were obtained for 21 horses that received resveratrol and 20 that received the placebo. Percentage of riders who reported that the horse's performance was better, compared with worse or the same, was significantly higher for the resveratrol group than for the placebo group after 2 (20/21 [95%] vs 14/20 [70%]) and 4 (18/21 [86%] vs 10/20 [50%]) months. The change in A1:A2 ratio between the enrollment and 4-month recheck examinations was significantly better for horses in the resveratrol versus placebo group. However, subjective lameness scores and degree of asymmetry of pelvis movement did not differ between groups. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that in performance horses with lameness localized to the distal tarsal joints, injection of triamcinolone in the centrodistal and tarsometatarsal joints of both hind limbs followed by oral supplementation with resveratrol for 4 months resulted in reduced lameness, compared with triamcinolone injection and supplementation with a placebo. PMID:27585103

  6. A prospective randomized comparison of radiation therapy plus lonidamine versus radiation therapy plus placebo as initial treatment of clinically localized but nonresectable nonsmall cell lung cancer

    SciTech Connect

    Scarantino, C.W.; McCunniff, A.J.; Evans, G.; Young, C.W.; Paggiarino, D.A.

    1994-07-30

    The purpose was, by means of a multicenter, prospective randomized, placebo-controlled study, to assess the impact of adding the radiation-enhancing agent lonidamine to standard {open_quotes}curative-intent{close_quotes} radiation therapy upon overall survival, progression-free survival, and local progression-free survival of patients with clinically localized but nonresectable nonsmall cell lung cancer. Lonidamine, or the lonidamine-placebo, was administered at a dose of 265 mg/m{sup 2} in three divided daily doses. Drug therapy began 2 days prior to the initiation of radiation therapy and continued until progression of disease mandated a change in therapy. The radiation therapy dose was 55-60 Gy, at a daily dose of 1.8 Gy and five treatments per week. Patients with clinical Stage II or III nonsmall cell lung cancer were stratified within the treatment center, and within two histologic strata: epidermoid vs. other nonsmall cell cancers. A total of 310 patients were enlisted on study, 152 on the placebo arm and 158 on the lonidamine arm. The median survival durations were 326 and 392 days for the placebo and lonidamine-treated groups respectively, p = 0.41 for a comparison of the survival curves. Median progression-free survival and median local progression-free survival durations were 197 days and 341 days for placebo + radiation therapy vs. 230 days and 300 days for lonidamine + radiation therapy; p-values for the respective curves were 0.75 and 0.42. Although there were proportionately more lonidamine-treated patients than placebo-treated patients demonstrating continued local control in excess of 12 months, the numbers of patients still at risk after 24 months were too small for meaningful statistical analysis. This multicenter Phase III study failed to demonstrate a significant advantage in the lonidamine-treated population in overall patient survival, in progression-free survival, or in the median duration of local control. 25 refs., 3 figs., 3 tabs.

  7. MARKOV CHAIN MONTE CARLO POSTERIOR SAMPLING WITH THE HAMILTONIAN METHOD

    SciTech Connect

    K. HANSON

    2001-02-01

    The Markov Chain Monte Carlo technique provides a means for drawing random samples from a target probability density function (pdf). MCMC allows one to assess the uncertainties in a Bayesian analysis described by a numerically calculated posterior distribution. This paper describes the Hamiltonian MCMC technique in which a momentum variable is introduced for each parameter of the target pdf. In analogy to a physical system, a Hamiltonian H is defined as a kinetic energy involving the momenta plus a potential energy {var_phi}, where {var_phi} is minus the logarithm of the target pdf. Hamiltonian dynamics allows one to move along trajectories of constant H, taking large jumps in the parameter space with relatively few evaluations of {var_phi} and its gradient. The Hamiltonian algorithm alternates between picking a new momentum vector and following such trajectories. The efficiency of the Hamiltonian method for multidimensional isotropic Gaussian pdfs is shown to remain constant at around 7% for up to several hundred dimensions. The Hamiltonian method handles correlations among the variables much better than the standard Metropolis algorithm. A new test, based on the gradient of {var_phi}, is proposed to measure the convergence of the MCMC sequence.

  8. Differential evolution Markov chain with snooker updater and fewer chains

    SciTech Connect

    Vrugt, Jasper A; Ter Braak, Cajo J F

    2008-01-01

    Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50--100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5--26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25--50 dimensional Student T{sub 3} distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.

  9. A Network of SCOP Hidden Markov Models and Its Analysis

    PubMed Central

    2011-01-01

    Background The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before. Results In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components. Conclusions The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs. PMID:21635719

  10. Volatility: A hidden Markov process in financial time series

    NASA Astrophysics Data System (ADS)

    Eisler, Zoltán; Perelló, Josep; Masoliver, Jaume

    2007-11-01

    Volatility characterizes the amplitude of price return fluctuations. It is a central magnitude in finance closely related to the risk of holding a certain asset. Despite its popularity on trading floors, volatility is unobservable and only the price is known. Diffusion theory has many common points with the research on volatility, the key of the analogy being that volatility is a time-dependent diffusion coefficient of the random walk for the price return. We present a formal procedure to extract volatility from price data by assuming that it is described by a hidden Markov process which together with the price forms a two-dimensional diffusion process. We derive a maximum-likelihood estimate of the volatility path valid for a wide class of two-dimensional diffusion processes. The choice of the exponential Ornstein-Uhlenbeck (expOU) stochastic volatility model performs remarkably well in inferring the hidden state of volatility. The formalism is applied to the Dow Jones index. The main results are that (i) the distribution of estimated volatility is lognormal, which is consistent with the expOU model, (ii) the estimated volatility is related to trading volume by a power law of the form σ∝V0.55 , and (iii) future returns are proportional to the current volatility, which suggests some degree of predictability for the size of future returns.

  11. A Markov Chain Monte Carlo method for the groundwater inverse problem.

    SciTech Connect

    Lu, Z.; Higdon, D. M.; Zhang, D.

    2004-01-01

    In this study, we develop a Markov Chain Monte Carlo method (MCMC) to estimate the hydraulic conductivity field conditioned on the direct measurements of hydraulic conductivity and indirect measurements of dependent variables such as hydraulic head for saturated flow in randomly heterogeneous porous media. The log hydraulic conductivity field is represented (parameterized) by the combination of some basis kernels centered at fixed spatial locations. The prior distribution for the vector of coefficients {theta} are taken from a posterior distribution {pi}({theta}/d) that is proportional to the product of the likelihood function of measurements d given parameter vector {theta} and the prior distribution of {theta}. Starting from any initial setting, a partial realization of a Markov chain is generated by updating only one component of {theta} at a time according to Metropolis rules. This ensures that output from this chain has {pi}({theta}/d) as its stationary distribution. The posterior mean of the parameter {theta} (and thus the mean log hydraulic conductivity conditional to measurements on hydraulic conductivity, and hydraulic head) can be estimated from the Markov chain realizations (ignoring some early realizations). The uncertainty associated with the mean filed can also be assessed from these realizations. In addition, the MCMC approach provides an alternative for estimating conditional predictions of hydraulic head and concentration and their associated uncertainties. Numerical examples for flow in a hypothetic random porous medium show that estimated log hydraulic conductivity field from the MCMC approach is closer to the original hypothetical random field than those obtained using kriging or cokriging methods.

  12. Plug-in Estimator of the Entropy Rate of a Pure-Jump Two-State Markov Process

    NASA Astrophysics Data System (ADS)

    Regnault, Philippe

    2009-12-01

    The entropy of a distribution with finite support is widely used in all applications involving random variables. A natural equivalent for random processes is the entropy rate. For ergodic pure-jump finite-state Markov processes, this rate is an explicit function of the stationary distribution and the infinitesimal generator. The case of two-state Markov processes is of particular interest. We estimate the entropy rate of such processes by plug-in, from estimators of the stationary distribution and the infinitesimal generator. Three situations of observation are discussed, several independant trajectories are observed, one long trajectory is observed, or the process is observed at discrete times. The asymptotic behavior of the plug-in estimators is established.

  13. Bayesian seismic tomography by parallel interacting Markov chains

    NASA Astrophysics Data System (ADS)

    Gesret, Alexandrine; Bottero, Alexis; Romary, Thomas; Noble, Mark; Desassis, Nicolas

    2014-05-01

    The velocity field estimated by first arrival traveltime tomography is commonly used as a starting point for further seismological, mineralogical, tectonic or similar analysis. In order to interpret quantitatively the results, the tomography uncertainty values as well as their spatial distribution are required. The estimated velocity model is obtained through inverse modeling by minimizing an objective function that compares observed and computed traveltimes. This step is often performed by gradient-based optimization algorithms. The major drawback of such local optimization schemes, beyond the possibility of being trapped in a local minimum, is that they do not account for the multiple possible solutions of the inverse problem. They are therefore unable to assess the uncertainties linked to the solution. Within a Bayesian (probabilistic) framework, solving the tomography inverse problem aims at estimating the posterior probability density function of velocity model using a global sampling algorithm. Markov chains Monte-Carlo (MCMC) methods are known to produce samples of virtually any distribution. In such a Bayesian inversion, the total number of simulations we can afford is highly related to the computational cost of the forward model. Although fast algorithms have been recently developed for computing first arrival traveltimes of seismic waves, the complete browsing of the posterior distribution of velocity model is hardly performed, especially when it is high dimensional and/or multimodal. In the latter case, the chain may even stay stuck in one of the modes. In order to improve the mixing properties of classical single MCMC, we propose to make interact several Markov chains at different temperatures. This method can make efficient use of large CPU clusters, without increasing the global computational cost with respect to classical MCMC and is therefore particularly suited for Bayesian inversion. The exchanges between the chains allow a precise sampling of the

  14. A Stable Clock Error Model Using Coupled First and Second Order Gauss-Markov Processes

    NASA Technical Reports Server (NTRS)

    Carpenter, Russell; Lee, Taesul

    2008-01-01

    Long data outages may occur in applications of global navigation satellite system technology to orbit determination for missions that spend significant fractions of their orbits above the navigation satellite constellation(s). Current clock error models based on the random walk idealization may not be suitable in these circumstances, since the covariance of the clock errors may become large enough to overflow flight computer arithmetic. A model that is stable, but which approximates the existing models over short time horizons is desirable. A coupled first- and second-order Gauss-Markov process is such a model.

  15. Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features.

    PubMed

    Li, Zhan-Chao; Lai, Yan-Hua; Chen, Li-Li; Zhou, Xuan; Dai, Zong; Zou, Xiao-Yong

    2012-03-01

    In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.

  16. Efficacy of local use of probiotics as an adjunct to scaling and root planing in chronic periodontitis and halitosis: A randomized controlled trial

    PubMed Central

    Penala, Soumya; Kalakonda, Butchibabu; Pathakota, Krishnajaneya Reddy; Jayakumar, Avula; Koppolu, Pradeep; Lakshmi, Bolla Vijaya; Pandey, Ruchi; Mishra, Ashank

    2016-01-01

    Objective: Periodontitis is known to have multifactorial etiology, involving interplay between environmental, host and microbial factors. The current treatment approaches are aimed at reducing the pathogenic microorganisms. Administration of beneficial bacteria (probiotics) has emerged as a promising concept in the prevention and treatment of periodontitis. Thus, the aim of the present study is to evaluate the efficacy of the local use of probiotics as an adjunct to scaling and root planing (SRP) in the treatment of patients with chronic periodontitis and halitosis. Methods: This is a randomized, placebo-controlled, double-blinded trial involving 32 systemically healthy chronic periodontitis patients. After SRP, the subjects were randomly assigned into the test and control groups. Test group (SRP + probiotics) received subgingival delivery of probiotics and probiotic mouthwash, and control group (SRP + placebo) received subgingival delivery of placebo and placebo mouthwash for 15 days. Plaque index (PI), modified gingival index (MGI), and bleeding index (BI) were assessed at baseline, 1 and 3 months thereafter, whereas probing depth (PD) and clinical attachment level were assessed at baseline and after 3 months. Microbial assessment using N-benzoyl-DL-arginine-naphthylamide (BANA) and halitosis assessment using organoleptic scores (ORG) was done at baseline, 1 and 3 months. Findings: All the clinical and microbiological parameters were significantly reduced in both groups at the end of the study. Inter-group comparison of PD reduction (PDR) and clinical attachment gain (CAG) revealed no statistical significance except for PDR in moderate pockets for the test group. Test group has shown statistically significant improvement in PI, MGI, and BI at 3 months compared to control group. Inter-group comparison revealed a significant reduction in BANA in test group at 1 month. ORG were significantly reduced in test group when compared to control group. Conclusion: Within

  17. SHARP ENTRYWISE PERTURBATION BOUNDS FOR MARKOV CHAINS

    PubMed Central

    THIEDE, ERIK; VAN KOTEN, BRIAN; WEARE, JONATHAN

    2015-01-01

    For many Markov chains of practical interest, the invariant distribution is extremely sensitive to perturbations of some entries of the transition matrix, but insensitive to others; we give an example of such a chain, motivated by a problem in computational statistical physics. We have derived perturbation bounds on the relative error of the invariant distribution that reveal these variations in sensitivity. Our bounds are sharp, we do not impose any structural assumptions on the transition matrix or on the perturbation, and computing the bounds has the same complexity as computing the invariant distribution or computing other bounds in the literature. Moreover, our bounds have a simple interpretation in terms of hitting times, which can be used to draw intuitive but rigorous conclusions about the sensitivity of a chain to various types of perturbations. PMID:26491218

  18. Estimation and uncertainty of reversible Markov models.

    PubMed

    Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank

    2015-11-01

    Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0. PMID:26547152

  19. Growth and Dissolution of Macromolecular Markov Chains

    NASA Astrophysics Data System (ADS)

    Gaspard, Pierre

    2016-07-01

    The kinetics and thermodynamics of free living copolymerization are studied for processes with rates depending on k monomeric units of the macromolecular chain behind the unit that is attached or detached. In this case, the sequence of monomeric units in the growing copolymer is a kth-order Markov chain. In the regime of steady growth, the statistical properties of the sequence are determined analytically in terms of the attachment and detachment rates. In this way, the mean growth velocity as well as the thermodynamic entropy production and the sequence disorder can be calculated systematically. These different properties are also investigated in the regime of depolymerization where the macromolecular chain is dissolved by the surrounding solution. In this regime, the entropy production is shown to satisfy Landauer's principle.

  20. Mixture Hidden Markov Models in Finance Research

    NASA Astrophysics Data System (ADS)

    Dias, José G.; Vermunt, Jeroen K.; Ramos, Sofia

    Finite mixture models have proven to be a powerful framework whenever unobserved heterogeneity cannot be ignored. We introduce in finance research the Mixture Hidden Markov Model (MHMM) that takes into account time and space heterogeneity simultaneously. This approach is flexible in the sense that it can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity. This methodology is applied to model simultaneously 12 time series of Asian stock markets indexes. Because we selected a heterogeneous sample of countries including both developed and emerging countries, we expect that heterogeneity in market returns due to country idiosyncrasies will show up in the results. The best fitting model was the one with two clusters at country level with different dynamics between the two regimes.

  1. Forest Pest Occurrence Predictionca-Markov Model

    NASA Astrophysics Data System (ADS)

    Xie, Fangyi; Zhang, Xiaoli; Chen, Xiaoyan

    Since the spatial pattern of forest pest occurrence is determined by biological characteristics and habitat conditions, this paper introduced construction of a cellular automaton model combined with Markov model to predicate the forest pest occurrence. Rules of the model includes the cell states rules, neighborhood rules and transition rules which are defined according to the factors from stand conditions, stand structures, climate and the influence of the factors on the state conversion. Coding for the model is also part of the implementations of the model. The participants were designed including attributes and operations of participants expressed with a UML diagram. Finally, the scale issues on forest pest occurrence prediction, of which the core are the prediction of element size and time interval, are partly discussed in this paper.

  2. Markov state models and molecular alchemy

    NASA Astrophysics Data System (ADS)

    Schütte, Christof; Nielsen, Adam; Weber, Marcus

    2015-01-01

    In recent years, Markov state models (MSMs) have attracted a considerable amount of attention with regard to modelling conformation changes and associated function of biomolecular systems. They have been used successfully, e.g. for peptides including time-resolved spectroscopic experiments, protein function and protein folding , DNA and RNA, and ligand-receptor interaction in drug design and more complicated multivalent scenarios. In this article, a novel reweighting scheme is introduced that allows to construct an MSM for certain molecular system out of an MSM for a similar system. This permits studying how molecular properties on long timescales differ between similar molecular systems without performing full molecular dynamics simulations for each system under consideration. The performance of the reweighting scheme is illustrated for simple test cases, including one where the main wells of the respective energy landscapes are located differently and an alchemical transformation of butane to pentane where the dimension of the state space is changed.

  3. Estimation and uncertainty of reversible Markov models

    NASA Astrophysics Data System (ADS)

    Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank

    2015-11-01

    Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software — http://pyemma.org — as of version 2.0.

  4. Transition-Independent Decentralized Markov Decision Processes

    NASA Technical Reports Server (NTRS)

    Becker, Raphen; Silberstein, Shlomo; Lesser, Victor; Goldman, Claudia V.; Morris, Robert (Technical Monitor)

    2003-01-01

    There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied up by a global reward function that depends on both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.

  5. Markov state models of biomolecular conformational dynamics

    PubMed Central

    Chodera, John D.; Noé, Frank

    2014-01-01

    It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges. PMID:24836551

  6. Multiple alignment using hidden Markov models

    SciTech Connect

    Eddy, S.R.

    1995-12-31

    A simulated annealing method is described for training hidden Markov models and producing multiple sequence alignments from initially unaligned protein or DNA sequences. Simulated annealing in turn uses a dynamic programming algorithm for correctly sampling suboptimal multiple alignments according to their probability and a Boltzmann temperature factor. The quality of simulated annealing alignments is evaluated on structural alignments of ten different protein families, and compared to the performance of other HMM training methods and the ClustalW program. Simulated annealing is better able to find near-global optima in the multiple alignment probability landscape than the other tested HMM training methods. Neither ClustalW nor simulated annealing produce consistently better alignments compared to each other. Examination of the specific cases in which ClustalW outperforms simulated annealing, and vice versa, provides insight into the strengths and weaknesses of current hidden Maxkov model approaches.

  7. Anatomy Ontology Matching Using Markov Logic Networks

    PubMed Central

    Li, Chunhua; Zhao, Pengpeng; Wu, Jian; Cui, Zhiming

    2016-01-01

    The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. We combine several different matching strategies through first-order logic formulas according to the structure of anatomy ontologies. Experiments on the adult mouse anatomy and the human anatomy have demonstrated the effectiveness of proposed approach in terms of the quality of result alignment. PMID:27382498

  8. Markov chain Monte Carlo posterior sampling with the Hamiltonian method.

    SciTech Connect

    Hanson, Kenneth M.

    2001-01-01

    A major advantage of Bayesian data analysis is that provides a characterization of the uncertainty in the model parameters estimated from a given set of measurements in the form of a posterior probability distribution. When the analysis involves a complicated physical phenomenon, the posterior may not be available in analytic form, but only calculable by means of a simulation code. In such cases, the uncertainty in inferred model parameters requires characterization of a calculated functional. An appealing way to explore the posterior, and hence characterize the uncertainty, is to employ the Markov Chain Monte Carlo technique. The goal of MCMC is to generate a sequence random of parameter x samples from a target pdf (probability density function), {pi}(x). In Bayesian analysis, this sequence corresponds to a set of model realizations that follow the posterior distribution. There are two basic MCMC techniques. In Gibbs sampling, typically one parameter is drawn from the conditional pdf at a time, holding all others fixed. In the Metropolis algorithm, all the parameters can be varied at once. The parameter vector is perturbed from the current sequence point by adding a trial step drawn randomly from a symmetric pdf. The trial position is either accepted or rejected on the basis of the probability at the trial position relative to the current one. The Metropolis algorithm is often employed because of its simplicity. The aim of this work is to develop MCMC methods that are useful for large numbers of parameters, n, say hundreds or more. In this regime the Metropolis algorithm can be unsuitable, because its efficiency drops as 0.3/n. The efficiency is defined as the reciprocal of the number of steps in the sequence needed to effectively provide a statistically independent sample from {pi}.

  9. Crossing over...Markov meets Mendel.

    PubMed

    Mneimneh, Saad

    2012-01-01

    Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the

  10. Crossing over...Markov meets Mendel.

    PubMed

    Mneimneh, Saad

    2012-01-01

    Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the

  11. Comparison of the efficacy of saline, local anesthetics, and steroids in epidural and facet joint injections for the management of spinal pain: A systematic review of randomized controlled trials

    PubMed Central

    Manchikanti, Laxmaiah; Nampiaparampil, Devi E.; Manchikanti, Kavita N.; Falco, Frank J.E.; Singh, Vijay; Benyamin, Ramsin M.; Kaye, Alan D.; Sehgal, Nalini; Soin, Amol; Simopoulos, Thomas T.; Bakshi, Sanjay; Gharibo, Christopher G.; Gilligan, Christopher J.; Hirsch, Joshua A.

    2015-01-01

    Background: The efficacy of epidural and facet joint injections has been assessed utilizing multiple solutions including saline, local anesthetic, steroids, and others. The responses to these various solutions have been variable and have not been systematically assessed with long-term follow-ups. Methods: Randomized trials utilizing a true active control design were included. The primary outcome measure was pain relief and the secondary outcome measure was functional improvement. The quality of each individual article was assessed by Cochrane review criteria, as well as the criteria developed by the American Society of Interventional Pain Physicians (ASIPP) for assessing interventional techniques. An evidence analysis was conducted based on the qualitative level of evidence (Level I to IV). Results: A total of 31 trials met the inclusion criteria. There was Level I evidence that local anesthetic with steroids was effective in managing chronic spinal pain based on multiple high-quality randomized controlled trials. The evidence also showed that local anesthetic with steroids and local anesthetic alone were equally effective except in disc herniation, where the superiority of local anesthetic with steroids was demonstrated over local anesthetic alone. Conclusion: This systematic review showed equal efficacy for local anesthetic with steroids and local anesthetic alone in multiple spinal conditions except for disc herniation where the superiority of local anesthetic with steroids was seen over local anesthetic alone. PMID:26005584

  12. Concurrent Chemo-Radiation With or Without Induction Gemcitabine, Carboplatin, and Paclitaxel: A Randomized, Phase 2/3 Trial in Locally Advanced Nasopharyngeal Carcinoma

    SciTech Connect

    Tan, Terence; Lim, Wan-Teck; Fong, Kam-Weng; Cheah, Shie-Lee; Soong, Yoke-Lim; Ang, Mei-Kim; Ng, Quan-Sing; Tan, Daniel; Ong, Whee-Sze; Tan, Sze-Huey; Yip, Connie; Quah, Daniel; Soo, Khee-Chee; Wee, Joseph

    2015-04-01

    Purpose: To compare survival, tumor control, toxicities, and quality of life of patients with locally advanced nasopharyngeal carcinoma (NPC) treated with induction chemotherapy and concurrent chemo-radiation (CCRT), against CCRT alone. Patients and Methods: Patients were stratified by N stage and randomized to induction GCP (3 cycles of gemcitabine 1000 mg/m{sup 2}, carboplatin area under the concentration-time-curve 2.5, and paclitaxel 70 mg/m{sup 2} given days 1 and 8 every 21 days) followed by CCRT (radiation therapy 69.96 Gy with weekly cisplatin 40 mg/m{sup 2}), or CCRT alone. The accrual of 172 was planned to detect a 15% difference in 5-year overall survival (OS) with a 5% significance level and 80% power. Results: Between September 2004 and August 2012, 180 patients were accrued, and 172 (GCP 86, control 86) were analyzed by intention to treat. There was no significant difference in OS (3-year OS 94.3% [GCP] vs 92.3% [control]; hazard ratio 1.05; 1-sided P=.494]), disease-free survival (hazard ratio 0.77, 95% confidence interval 0.44-1.35, P=.362), and distant metastases–free survival (hazard ratio 0.80, 95% confidence interval 0.38-1.67, P=.547) between the 2 arms. Treatment compliance in the induction phase was good, but the relative dose intensity for concurrent cisplatin was significantly lower in the GCP arm. Overall, the GCP arm had higher rates of grades 3 and 4 leukopenia (52% vs 37%) and neutropenia (24% vs 12%), but grade 3 and 4 acute radiation toxicities were not statistically different between the 2 arms. The global quality of life scores were comparable in both arms. Conclusion: Induction chemotherapy with GCP before concurrent chemo-irradiation did not improve survival in locally advanced NPC.

  13. Application of Gray Markov SCGM(1,1)c Model to Prediction of Accidents Deaths in Coal Mining

    PubMed Central

    Lan, Jian-yi; Zhou, Ying

    2014-01-01

    The prediction of mine accident is the basis of aviation safety assessment and decision making. Gray prediction is suitable for such kinds of system objects with few data, short time, and little fluctuation, and Markov chain theory is just suitable for forecasting stochastic fluctuating dynamic process. Analyzing the coal mine accident human error cause, combining the advantages of both Gray prediction and Markov theory, an amended Gray Markov SCGM(1,1)c model is proposed. The gray SCGM(1,1)c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along the tendency. Finally, the new model is applied to forecast the mine safety accident deaths from 1990 to 2010 in China, and, 2011–2014 coal accidents deaths were predicted. The results show that the new model not only discovers the trend of the mine human error accident death toll but also overcomes the random fluctuation of data affecting precision. It possesses stronger engineering application. PMID:27419203

  14. Specification test for Markov models with measurement errors*

    PubMed Central

    Kim, Seonjin; Zhao, Zhibiao

    2014-01-01

    Most existing works on specification testing assume that we have direct observations from the model of interest. We study specification testing for Markov models based on contaminated observations. The evolving model dynamics of the unobservable Markov chain is implicitly coded into the conditional distribution of the observed process. To test whether the underlying Markov chain follows a parametric model, we propose measuring the deviation between nonparametric and parametric estimates of conditional regression functions of the observed process. Specifically, we construct a nonparametric simultaneous confidence band for conditional regression functions and check whether the parametric estimate is contained within the band. PMID:25346552

  15. Effect of the local administration of betamethasone on pain, swelling and trismus after impacted lower third molar extraction. A randomized, triple blinded, controlled trial

    PubMed Central

    Marques, José; Pié-Sánchez, Jordi; Valmaseda-Castellón, Eduard; Gay-Escoda, Cosme

    2014-01-01

    Objectives: The aim of this study is to compare the analgesic and anti-inflammatory effects of the local postoperative administration of a single 12-mg dose of betamethasone after the surgical removal of impacted lower third molars. Study Design: A split-mouth, triple-blind, randomized, placebo-controlled clinical trial of 25 patients requiring the surgical removal of symmetrical lower third molars was performed. In the experimental side, a 12-mg dose of betamethasone was administered submucosally after the surgical procedure, while in the control side a placebo (sterile saline solution) was injected in the same area. To assess postoperative pain, visual analogue scales and the consumption of rescue analgesic were used. The facial swelling and trismus were evaluated by measuring facial reference distances and maximum mouth opening. Results: There were no significant differences between the two study groups regarding postoperative pain, facial swelling and trismus. Conclusions: The injection of a single dose of betamethasone does not seem to reduce pain, facial swelling and trismus after impacted lower third molar removal when compared to placebo. Key words:Third molar extraction, corticosteroids, betamethasone. PMID:24121915

  16. The effect of buffering on pain and duration of local anesthetic in the face: A double-blind, randomized controlled trial

    PubMed Central

    Afolabi, Oluwatola; Murphy, Amanda; Chung, Bryan; Lalonde, Donald H

    2013-01-01

    BACKGROUND: The acidity of lidocaine preparations is believed to contribute to the pain of local anesthetic injection. OBJECTIVE: To investigate the effect of buffering lidocaine on the pain of injection and duration of anesthetic effect. METHODS: A double-blind, randomized trial involving 44 healthy volunteers was conducted. The upper lip was injected with a solution of: lidocaine 1% (Xylocaine, AstraZeneca, Canada, Inc) with epinephrine; and lidocaine 1% with epinephrine and 8.4% sodium bicarbonate. Volunteers reported pain of injection and duration of anesthetic effect. RESULTS: Twenty-six participants found the unbuffered solution to be more painful. Fifteen participants found the buffered solution to be more painful; the difference was not statistically significant. Twenty-one volunteers reported duration of anesthetic effect. The buffered solution provided longer anesthetic effect than the unbuffered solution (P=0.004). CONCLUSION: Although buffering increased the duration of lidocaine’s anesthetic effect in this particular model, a decrease in the pain of the injection was not demonstrated, likely due to limitations of the study. PMID:24497759

  17. Markov Task Network: A Framework for Service Composition under Uncertainty in Cyber-Physical Systems

    PubMed Central

    Mohammed, Abdul-Wahid; Xu, Yang; Hu, Haixiao; Agyemang, Brighter

    2016-01-01

    In novel collaborative systems, cooperative entities collaborate services to achieve local and global objectives. With the growing pervasiveness of cyber-physical systems, however, such collaboration is hampered by differences in the operations of the cyber and physical objects, and the need for the dynamic formation of collaborative functionality given high-level system goals has become practical. In this paper, we propose a cross-layer automation and management model for cyber-physical systems. This models the dynamic formation of collaborative services pursuing laid-down system goals as an ontology-oriented hierarchical task network. Ontological intelligence provides the semantic technology of this model, and through semantic reasoning, primitive tasks can be dynamically composed from high-level system goals. In dealing with uncertainty, we further propose a novel bridge between hierarchical task networks and Markov logic networks, called the Markov task network. This leverages the efficient inference algorithms of Markov logic networks to reduce both computational and inferential loads in task decomposition. From the results of our experiments, high-precision service composition under uncertainty can be achieved using this approach. PMID:27657084

  18. Decoding coalescent hidden Markov models in linear time

    PubMed Central

    Harris, Kelley; Sheehan, Sara; Kamm, John A.; Song, Yun S.

    2014-01-01

    In many areas of computational biology, hidden Markov models (HMMs) have been used to model local genomic features. In particular, coalescent HMMs have been used to infer ancient population sizes, migration rates, divergence times, and other parameters such as mutation and recombination rates. As more loci, sequences, and hidden states are added to the model, however, the runtime of coalescent HMMs can quickly become prohibitive. Here we present a new algorithm for reducing the runtime of coalescent HMMs from quadratic in the number of hidden time states to linear, without making any additional approximations. Our algorithm can be incorporated into various coalescent HMMs, including the popular method PSMC for inferring variable effective population sizes. Here we implement this algorithm to speed up our demographic inference method diCal, which is equivalent to PSMC when applied to a sample of two haplotypes. We demonstrate that the linear-time method can reconstruct a population size change history more accurately than the quadratic-time method, given similar computation resources. We also apply the method to data from the 1000 Genomes project, inferring a high-resolution history of size changes in the European population. PMID:25340178

  19. Regularized Deterministic Annealing Hidden Markov Models for Identificationand Analysis of Seismic and Aseismic events.

    NASA Astrophysics Data System (ADS)

    Granat, R. A.; Clayton, R.; Kedar, S.; Kaneko, Y.

    2003-12-01

    We employ a robust hidden Markov model (HMM) based technique to perform statistical pattern analysis of suspected seismic and aseismic events in the poorly explored period band of minutes to hours. The technique allows us to classify known events and provides a statistical basis for finding and cataloging similar events represented elsewhere in the observations. In this work, we focus on data collected by the Southern California TriNet system. The hidden Markov model (HMM) approach assumes that the observed data has been generated by an unobservable dynamical statistical process. The process is of a particular form such that each observation is coincident with the system being in a particular discrete state. The dynamics are the model are constructed so that the next state is directly dependent only on the current state -- it is a first order Markov process. The model is completely described by a set of parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probability distribution of observable outputs associated with each state. Application of the model to data involves optimizing these model parameters with respect to some function of the observations, typically the likelihood of the observations given the model. Our work focused on the fact that this objective function has a number of local maxima that is exponential in the model size (the number of states). This means that not only is it very difficult to discover the global maximum, but also that results can vary widely between applications of the model. For some domains which employ HMMs for such purposes, such as speech processing, sufficient a priori information about the system is available to avoid this problem. However, for seismic data in general such a priori information is not available. Our approach involves analytical location of sub-optimal local maxima; once the locations of these maxima have been found, then we can employ a

  20. Time series segmentation with shifting means hidden markov models

    NASA Astrophysics Data System (ADS)

    Kehagias, Ath.; Fortin, V.

    2006-08-01

    We present a new family of hidden Markov models and apply these to the segmentation of hydrological and environmental time series. The proposed hidden Markov models have a discrete state space and their structure is inspired from the shifting means models introduced by Chernoff and Zacks and by Salas and Boes. An estimation method inspired from the EM algorithm is proposed, and we show that it can accurately identify multiple change-points in a time series. We also show that the solution obtained using this algorithm can serve as a starting point for a Monte-Carlo Markov chain Bayesian estimation method, thus reducing the computing time needed for the Markov chain to converge to a stationary distribution.

  1. Modeling of α/β for late rectal toxicity from a randomized phase II study: conventional versus hypofractionated scheme for localized prostate cancer

    PubMed Central

    Marzi, Simona; Saracino, Biancamaria; Petrongari, Maria G; Arcangeli, Stefano; Gomellini, Sara; Arcangeli, Giorgio; Benassi, Marcello; Landoni, Valeria

    2009-01-01

    Background Recently, the use of hypo-fractionated treatment schemes for the prostate cancer has been encouraged due to the fact that α/β ratio for prostate cancer should be low. However a major concern on the use of hypofractionation is the late rectal toxicity, it is important to be able to predict the risk of toxicity for alternative treatment schemes, with the best accuracy. The main purpose of this study is to evaluate the response of rectum wall to changes in fractionation and to quantify the α/β ratio for late rectal toxicity Methods 162 patients with localized prostate cancer, treated with conformal radiotherapy, were enrolled in a phase II randomized trial. The patients were randomly assigned to 80 Gy in 40 fractions over 8 weeks (arm A) or 62 Gy in 20 fractions over 5 weeks (arm B). The median follow-up was 30 months. The late rectal toxicity was evaluated using the Radiation Therapy Oncology Group (RTOG) scale. It was assumed ≥ Grade 2 (G2) toxicity incidence as primary end point. Fit of toxicity incidence by the Lyman-Burman-Kutcher (LKB) model was performed. Results The crude incidence of late rectal toxicity ≥ G2 was 14% and 12% for the standard arm and the hypofractionated arm, respectively. The crude incidence of late rectal toxicity ≥ G2 was 14.0% and 12.3% for the arm A and B, respectively. For the arm A, volumes receiving ≥ 50 Gy (V50) and 70 Gy (V70) were 38.3 ± 7.5% and 23.4 ± 5.5%; for arm B, V38 and V54 were 40.9 ± 6.8% and 24.5 ± 4.4%. An α/β ratio for late rectal toxicity very close to 3 Gy was found. Conclusion The ≥ G2 late toxicities in both arms were comparable, indicating the feasibility of hypofractionated regimes in prostate cancer. An α/β ratio for late rectal toxicity very close to 3 Gy was found. PMID:19689825

  2. Optimal q-Markov COVER for finite precision implementation

    NASA Technical Reports Server (NTRS)

    Williamson, Darrell; Skelton, Robert E.

    1989-01-01

    The existing q-Markov COVER realization theory does not take into account the problems of arithmetic errors due to both the quantization of states and coefficients of the reduced order model. All q-Markov COVERs allow some freedom in the choice of parameters. Here, researchers exploit this freedom in the existing theory to optimize the models with respect to these finite wordlength effects.

  3. A novel image encryption algorithm based on chaos maps with Markov properties

    NASA Astrophysics Data System (ADS)

    Liu, Quan; Li, Pei-yue; Zhang, Ming-chao; Sui, Yong-xin; Yang, Huai-jiang

    2015-02-01

    In order to construct high complexity, secure and low cost image encryption algorithm, a class of chaos with Markov properties was researched and such algorithm was also proposed. The kind of chaos has higher complexity than the Logistic map and Tent map, which keeps the uniformity and low autocorrelation. An improved couple map lattice based on the chaos with Markov properties is also employed to cover the phase space of the chaos and enlarge the key space, which has better performance than the original one. A novel image encryption algorithm is constructed on the new couple map lattice, which is used as a key stream generator. A true random number is used to disturb the key which can dynamically change the permutation matrix and the key stream. From the experiments, it is known that the key stream can pass SP800-22 test. The novel image encryption can resist CPA and CCA attack and differential attack. The algorithm is sensitive to the initial key and can change the distribution the pixel values of the image. The correlation of the adjacent pixels can also be eliminated. When compared with the algorithm based on Logistic map, it has higher complexity and better uniformity, which is nearer to the true random number. It is also efficient to realize which showed its value in common use.

  4. NonMarkov Ito Processes with 1- state memory

    NASA Astrophysics Data System (ADS)

    McCauley, Joseph L.

    2010-08-01

    A Markov process, by definition, cannot depend on any previous state other than the last observed state. An Ito process implies the Fokker-Planck and Kolmogorov backward time partial differential eqns. for transition densities, which in turn imply the Chapman-Kolmogorov eqn., but without requiring the Markov condition. We present a class of Ito process superficially resembling Markov processes, but with 1-state memory. In finance, such processes would obey the efficient market hypothesis up through the level of pair correlations. These stochastic processes have been mislabeled in recent literature as 'nonlinear Markov processes'. Inspired by Doob and Feller, who pointed out that the ChapmanKolmogorov eqn. is not restricted to Markov processes, we exhibit a Gaussian Ito transition density with 1-state memory in the drift coefficient that satisfies both of Kolmogorov's partial differential eqns. and also the Chapman-Kolmogorov eqn. In addition, we show that three of the examples from McKean's seminal 1966 paper are also nonMarkov Ito processes. Last, we show that the transition density of the generalized Black-Scholes type partial differential eqn. describes a martingale, and satisfies the ChapmanKolmogorov eqn. This leads to the shortest-known proof that the Green function of the Black-Scholes eqn. with variable diffusion coefficient provides the so-called martingale measure of option pricing.

  5. Broadband near-field enhancement in the macro-periodic and micro-random structure with a hybridized excitation of propagating Bloch-plasmonic and localized surface-plasmonic modes.

    PubMed

    Lu, Haifei; Ren, Xingang; Sha, Wei E I; Ho, Ho-Pui; Choy, Wallace C H

    2015-10-28

    We demonstrate that the silver nanoplate-based macroscopically periodic (macro-periodic) and microscopically random (micro-random) structure has a broadband near-field enhancement as compared to conventional silver gratings. The specific field enhancement in a wide spectral range (from UV to near-infrared) originates from the abundance of localized surface-plasmonic (LSP) modes in the microscopically random distributed silver nanoplates and propagating Bloch-plasmonic (PBP) modes from the macroscopically periodic pattern. The characterization of polarization dependent spectral absorption, surface-enhanced Raman spectroscopy (SERS), as well as theoretical simulation was conducted to comprehensively understand the features of the broadband spectrum and highly concentrated near-field. The reported macro-periodic and micro-random structure may offer a new route for the design of plasmonic systems for photonic and optoelectronic applications.

  6. Optimized Markov state models for metastable systems

    NASA Astrophysics Data System (ADS)

    Guarnera, Enrico; Vanden-Eijnden, Eric

    2016-07-01

    A method is proposed to identify target states that optimize a metastability index amongst a set of trial states and use these target states as milestones (or core sets) to build Markov State Models (MSMs). If the optimized metastability index is small, this automatically guarantees the accuracy of the MSM, in the sense that the transitions between the target milestones is indeed approximately Markovian. The method is simple to implement and use, it does not require that the dynamics on the trial milestones be Markovian, and it also offers the possibility to partition the system's state-space by assigning every trial milestone to the target milestones it is most likely to visit next and to identify transition state regions. Here the method is tested on the Gly-Ala-Gly peptide, where it is shown to correctly identify the expected metastable states in the dihedral angle space of the molecule without a priori information about these states. It is also applied to analyze the folding landscape of the Beta3s mini-protein, where it is shown to identify the folded basin as a connecting hub between an helix-rich region, which is entropically stabilized, and a beta-rich region, which is energetically stabilized and acts as a kinetic trap.

  7. Hidden Markov models in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Wrzoskowicz, Adam

    1993-11-01

    This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

  8. Systolic Architectures For Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Hwang, J. N.; Vlontzos, J. A.; Kung, S. Y.

    1988-10-01

    This paper proposes an unidirectional ring systolic architecture for implementing the hidden Markov models (HMMs). This array architecture maximizes the strength of VLSI in terms of intensive and pipelined computing and yet circumvents the limitation on communication. Both the scoring and learning phases of an HMM are formulated as a consecutive matrix-vector multiplication problem, which can be executed in a fully pipelined fashion (100% utilization effi-ciency) by using an unidirectional ring systolic architecture. By appropriately scheduling the algorithm, which combines both the operations of the backward evaluation procedure and reestimation algorithm at the same time, we can use this systolic HMM in a most efficient manner. The systolic HMM can also be easily adapted to the left-to-right HMM by using bidirectional semi-global links with significant time saving. This architecture can also incorporate the scaling scheme with little extra effort in the computations of forward and backward evaluation variables to prevent the frequently encountered mathematical undertow problems. We also discuss a possible implementation of this proposed architecture using Inmos transputer (T-800) as the building block.

  9. Markov source model for printed music decoding

    NASA Astrophysics Data System (ADS)

    Kopec, Gary E.; Chou, Philip A.; Maltz, David A.

    1995-03-01

    This paper describes a Markov source model for a simple subset of printed music notation. The model is based on the Adobe Sonata music symbol set and a message language of our own design. Chord imaging is the most complex part of the model. Much of the complexity follows from a rule of music typography that requires the noteheads for adjacent pitches to be placed on opposite sides of the chord stem. This rule leads to a proliferation of cases for other typographic details such as dot placement. We describe the language of message strings accepted by the model and discuss some of the imaging issues associated with various aspects of the message language. We also point out some aspects of music notation that appear problematic for a finite-state representation. Development of the model was greatly facilitated by the duality between image synthesis and image decoding. Although our ultimate objective was a music image model for use in decoding, most of the development proceeded by using the evolving model for image synthesis, since it is computationally far less costly to image a message than to decode an image.

  10. Spatiotemporal pattern recognition using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Fielding, Kenneth H.; Ruck, Dennis W.; Rogers, Steven K.; Welsh, Byron M.; Oxley, Mark E.

    1993-10-01

    A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared to single frame techniques.

  11. Noiseless compression using non-Markov models

    NASA Technical Reports Server (NTRS)

    Blumer, Anselm

    1989-01-01

    Adaptive data compression techniques can be viewed as consisting of a model specified by a database common to the encoder and decoder, an encoding rule and a rule for updating the model to ensure that the encoder and decoder always agree on the interpretation of the next transmission. The techniques which fit this framework range from run-length coding, to adaptive Huffman and arithmetic coding, to the string-matching techniques of Lempel and Ziv. The compression obtained by arithmetic coding is dependent on the generality of the source model. For many sources, an independent-letter model is clearly insufficient. Unfortunately, a straightforward implementation of a Markov model requires an amount of space exponential in the number of letters remembered. The Directed Acyclic Word Graph (DAWG) can be constructed in time and space proportional to the text encoded, and can be used to estimate the probabilities required for arithmetic coding based on an amount of memory which varies naturally depending on the encoded text. The tail of that portion of the text which was encoded is the longest suffix that has occurred previously. The frequencies of letters following these previous occurrences can be used to estimate the probability distribution of the next letter. Experimental results indicate that compression is often far better than that obtained using independent-letter models, and sometimes also significantly better than other non-independent techniques.

  12. Stochastic motif extraction using hidden Markov model

    SciTech Connect

    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.

  13. Is Biochemical Response More Important Than Duration of Neoadjuvant Hormone Therapy Before Radiotherapy for Clinically Localized Prostate Cancer? An Analysis of the 3- Versus 8-Month Randomized Trial

    SciTech Connect

    Alexander, Abraham; Crook, Juanita; Jones, Stuart; Malone, Shawn; Bowen, Julie; Truong, Pauline; Pai, Howard; Ludgate, Charles

    2010-01-15

    Purpose: To ascertain whether biochemical response to neoadjuvant androgen-deprivation therapy (ADT) before radiotherapy (RT), rather than duration, is the critical determinant of benefit in the multimodal treatment of localized prostate cancer, by comparing outcomes of subjects from the Canadian multicenter 3- vs 8-month trial with a pre-RT, post-hormone PSA (PRPH-PSA) <=0.1 ng/ml vs those >0.1 ng/ml. Methods and Materials: From 1995 to 2001, 378 men with localized prostate cancer were randomized to 3 or 8 months of neoadjuvant ADT before RT. On univariate analysis, survival indices were compared between those with a PRPH-PSA <=0.1 ng/ml vs >0.1 ng/ml, for all patients and subgroups, including treatment arm, risk group, and gleason Score. Multivariate analysis identified independent predictors of outcome. Results: Biochemical disease-free survival (bDFS) was significantly higher for those with a PRPH-PSA <=0.1 ng/ml compared with PRPH-PSA >0.1 ng/ml (55.3% vs 49.4%, p = 0.014). No difference in survival indices was observed between treatment arms. There was no difference in bDFS between patients in the 3- and 8-month arms with a PRPH-PSA <=0.1 ng/ml nor those with PRPH-PSA >0.1 ng/ml. bDFS was significantly higher for high-risk patients with PRPH-PSA <=0.1 ng/ml compared with PRPH-PSA >0.1 ng/ml (57.0% vs 29.4%, p = 0.017). Multivariate analysis identified PRPH-PSA (p = 0.041), Gleason score (p = 0.001), initial PSA (p = 0.025), and T-stage (p = 0.003), not ADT duration, as independent predictors of outcome. Conclusion: Biochemical response to neoadjuvant ADT before RT, not duration, appears to be the critical determinant of benefit in the setting of combined therapy. Individually tailored ADT duration based on PRPH-PSA would maximize therapeutic gain, while minimizing the duration of ADT and its related toxicities.

  14. Optical energy storage and reemission based weak localization of light and accompanying random lasing action in disordered Nd{sup 3+} doped (Pb, La)(Zr, Ti)O{sub 3} ceramics

    SciTech Connect

    Xu, Long; Zhao, Hua; Xu, Caixia; Zhang, Siqi; Zhang, Jingwen

    2014-08-14

    Multi-mode random lasing action and weak localization of light were evidenced and studied in normally transparent but disordered Nd{sup 3+} doped (Pb,La)(Zr,Ti)O{sub 3} ceramics. Noticeable localized zone and multi-photon process were observed under strong pumping power. A tentative phenomenological physical picture was proposed by taking account of diffusive process, photo-induced scattering, and optical energy storage process as dominant factors in elucidating the weak localization of light observed. Both the decreased transmittance (increased reflectivity) of light and the observed long lasting fading-off phenomenon supported the physical picture proposed by us.

  15. Comparison of nonlinear local Lyapunov vectors with bred vectors, random perturbations and ensemble transform Kalman filter strategies in a barotropic model

    NASA Astrophysics Data System (ADS)

    Feng, Jie; Ding, Ruiqiang; Li, Jianping; Liu, Deqiang

    2016-09-01

    The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors (BVs) are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector (NLLV) scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more components in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random perturbation (RP) technique, and the BV method, as well as its improved version—the ensemble transform Kalman filter (ETKF) method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

  16. Markov Chain Monte-Carlo Orbit Computation for Binary Asteroids

    NASA Astrophysics Data System (ADS)

    Oszkiewicz, D.; Hestroffer, D.; Pedro, David C.

    2013-11-01

    We present a novel method of orbit computation for resolved binary asteroids. The method combines the Thiele, Innes, van den Bos method with a Markov chain Monte Carlo technique (MCMC). The classical Thiele-van den Bos method has been commonly used in multiple applications before, including orbits of binary stars and asteroids; conversely this novel method can be used for the analysis of binary stars, and of other gravitationally bound binaries. The method requires a minimum of three observations (observing times and relative positions - Cartesian or polar) made at the same tangent plane - or close enough for enabling a first approximation. Further, the use of the MCMC technique for statistical inversion yields the whole bundle of possible orbits, including the one that is most probable. In this new method, we make use of the Metropolis-Hastings algorithm to sample the parameters of the Thiele-van den Bos method, that is the orbital period (or equivalently the double areal constant) together with three randomly selected observations from the same tangent plane. The observations are sampled within their observational errors (with an assumed distribution) and the orbital period is the only parameter that has to be tuned during the sampling procedure. We run multiple chains to ensure that the parameter phase space is well sampled and that the solutions have converged. After the sampling is completed we perform convergence diagnostics. The main advantage of the novel approach is that the orbital period does not need to be known in advance and the entire region of possible orbital solutions is sampled resulting in a maximum likelihood solution and the confidence regions. We have tested the new method on several known binary asteroids and conclude a good agreement with the results obtained with other methods. The new method has been implemented into the Gaia DPAC data reduction pipeline and can be used to confirm the binary nature of a suspected system, and for deriving

  17. Is random access memory random?

    NASA Technical Reports Server (NTRS)

    Denning, P. J.

    1986-01-01

    Most software is contructed on the assumption that the programs and data are stored in random access memory (RAM). Physical limitations on the relative speeds of processor and memory elements lead to a variety of memory organizations that match processor addressing rate with memory service rate. These include interleaved and cached memory. A very high fraction of a processor's address requests can be satified from the cache without reference to the main memory. The cache requests information from main memory in blocks that can be transferred at the full memory speed. Programmers who organize algorithms for locality can realize the highest performance from these computers.

  18. Fraunhofer diffraction by a random screen.

    PubMed

    Malinka, Aleksey V

    2011-08-01

    The stochastic approach is applied to the problem of Fraunhofer diffraction by a random screen. The diffraction pattern is expressed through the random chord distribution. Two cases are considered: the sparse ensemble, where the interference between different obstacles can be neglected, and the densely packed ensemble, where this interference is to be taken into account. The solution is found for the general case and the analytical formulas are obtained for the Switzer model of a random screen, i.e., for the case of Markov statistics.

  19. An open Markov chain scheme model for a credit consumption portfolio fed by ARIMA and SARMA processes

    NASA Astrophysics Data System (ADS)

    Esquível, Manuel L.; Fernandes, José Moniz; Guerreiro, Gracinda R.

    2016-06-01

    We introduce a schematic formalism for the time evolution of a random population entering some set of classes and such that each member of the population evolves among these classes according to a scheme based on a Markov chain model. We consider that the flow of incoming members is modeled by a time series and we detail the time series structure of the elements in each of the classes. We present a practical application to data from a credit portfolio of a Cape Verdian bank; after modeling the entering population in two different ways - namely as an ARIMA process and as a deterministic sigmoid type trend plus a SARMA process for the residues - we simulate the behavior of the population and compare the results. We get that the second method is more accurate in describing the behavior of the populations when compared to the observed values in a direct simulation of the Markov chain.

  20. Facies Reconstruction by hidden Markov models

    NASA Astrophysics Data System (ADS)

    Panzeri, M.; Della Rossa, E.; Dovera, L.; Riva, M.; Guadagnini, A.

    2012-04-01

    The inherent heterogeneity of natural aquifer complex systems can be properly described by a doubly stochastic composite medium approach, where distributions of geomaterials (facies) and attributes, e.g., hydraulic conductivity and porosity, can be uncertain. We focus on the reconstruction of the spatial distribution of facies within a porous medium. The key contribution of our work is to provide a methodology for evaluating the unknown facies distribution while maintaining the spatial correlation between the geological bodies. The latter is considered to be known a priori. The geostatistical model for the spatial distribution of facies is defined in the framework of multiple-point geostatistics, relying on transition probabilities (Stien and Kolbjornsen, 2011). Specifically, we model the facies distribution over the domain by employing the notion of Hidden Markov Model. The hidden states of the system are provided by the value of the indicator function at each cell of the grid, while the the petrophysical properties of the soil (e.g., the permeability) are considered as known. In this context, the key issue is the assessment of the spatial architecture of the geological bodies within the domain of interest upon maximizing the probability associated with a given permeability distribution. This objective is achieved through the Viterbi algorithm. This algorithm was initially introduced for signal denoising problems (e.g., Rabiner, 1989) and has been extended here to a two-dimensional system, following the approach proposed by Li et al. (2000) according to the following steps: (1) the parameters of the transitional probabilities of the facies distribution are estimated from a given training image; (2) the facies distribution maximizing the probability of occurrence considering the probability of (i) facies distribution, (ii) conductivity distribution and (iii) their joint conditional probability is then reconstructed. We demonstrate the reliability and advantage of

  1. Early Clinical Outcomes and Toxicity of Intensity Modulated Versus Conventional Pelvic Radiation Therapy for Locally Advanced Cervix Carcinoma: A Prospective Randomized Study

    SciTech Connect

    Gandhi, Ajeet Kumar; Sharma, Daya Nand; Rath, Goura Kisor; Julka, Pramod Kumar; Subramani, Vellaiyan; Sharma, Seema; Manigandan, Durai; Laviraj, M.A.; Kumar, Sunesh; Thulkar, Sanjay

    2013-11-01

    Purpose: To evaluate the toxicity and clinical outcome in patients with locally advanced cervical cancer (LACC) treated with whole pelvic conventional radiation therapy (WP-CRT) versus intensity modulated radiation therapy (WP-IMRT). Methods and Materials: Between January 2010 and January 2012, 44 patients with International Federation of Gynecology and Obstetrics (FIGO 2009) stage IIB-IIIB squamous cell carcinoma of the cervix were randomized to receive 50.4 Gy in 28 fractions delivered via either WP-CRT or WP-IMRT with concurrent weekly cisplatin 40 mg/m{sup 2}. Acute toxicity was graded according to the Common Terminology Criteria for Adverse Events, version 3.0, and late toxicity was graded according to the Radiation Therapy Oncology Group system. The primary and secondary endpoints were acute gastrointestinal toxicity and disease-free survival, respectively. Results: Of 44 patients, 22 patients received WP-CRT and 22 received WP-IMRT. In the WP-CRT arm, 13 patients had stage IIB disease and 9 had stage IIIB disease; in the IMRT arm, 12 patients had stage IIB disease and 10 had stage IIIB disease. The median follow-up time in the WP-CRT arm was 21.7 months (range, 10.7-37.4 months), and in the WP-IMRT arm it was 21.6 months (range, 7.7-34.4 months). At 27 months, disease-free survival was 79.4% in the WP-CRT group versus 60% in the WP-IMRT group (P=.651), and overall survival was 76% in the WP-CRT group versus 85.7% in the WP-IMRT group (P=.645). Patients in the WP-IMRT arm experienced significantly fewer grade ≥2 acute gastrointestinal toxicities (31.8% vs 63.6%, P=.034) and grade ≥3 gastrointestinal toxicities (4.5% vs 27.3%, P=.047) than did patients receiving WP-CRT and had less chronic gastrointestinal toxicity (13.6% vs 50%, P=.011). Conclusion: WP-IMRT is associated with significantly less toxicity compared with WP-CRT and has a comparable clinical outcome. Further studies with larger sample sizes and longer follow-up times are warranted to justify

  2. Unsupervised SAR images change detection with hidden Markov chains on a sliding window

    NASA Astrophysics Data System (ADS)

    Bouyahia, Zied; Benyoussef, Lamia; Derrode, Stéphane

    2007-10-01

    This work deals with unsupervised change detection in bi-date Synthetic Aperture Radar (SAR) images. Whatever the indicator of change used, e.g. log-ratio or Kullback-Leibler divergence, we have observed poor quality change maps for some events when using the Hidden Markov Chain (HMC) model we focus on in this work. The main reason comes from the stationary assumption involved in this model - and in most Markovian models such as Hidden Markov Random Fields-, which can not be justified in most observed scenes: changed areas are not necessarily stationary in the image. Besides the few non stationary Markov models proposed in the literature, the aim of this paper is to describe a pragmatic solution to tackle stationarity by using a sliding window strategy. In this algorithm, the criterion image is scanned pixel by pixel, and a classical HMC model is applied only on neighboring pixels. By moving the window through the image, the process is able to produce a change map which can better exhibit non stationary changes than the classical HMC applied directly on the whole criterion image. Special care is devoted to the estimation of the number of classes in each window, which can vary from one (no change) to three (positive change, negative change and no change) by using the corrected Akaike Information Criterion (AICc) suited to small samples. The quality assessment of the proposed approach is achieved with speckle-simulated images in which simulated changes is introduced. The windowed strategy is also evaluated with a pair of RADARSAT images bracketing the Nyiragongo volcano eruption event in January 2002. The available ground truth confirms the effectiveness of the proposed approach compared to a classical HMC-based strategy.

  3. Unsupervised Segmentation of Hidden Semi-Markov Non Stationary Chains

    NASA Astrophysics Data System (ADS)

    Lapuyade-Lahorgue, Jérôme; Pieczynski, Wojciech

    2006-11-01

    In the classical hidden Markov chain (HMC) model we have a hidden chain X, which is a Markov one and an observed chain Y. HMC are widely used; however, in some situations they have to be replaced by the more general "hidden semi-Markov chains" (HSMC) which are particular "triplet Markov chains" (TMC) T = (X, U, Y), where the auxiliary chain U models the semi-Markovianity of X. Otherwise, non stationary classical HMC can also be modeled by a triplet Markov stationary chain with, as a consequence, the possibility of parameters' estimation. The aim of this paper is to use simultaneously both properties. We consider a non stationary HSMC and model it as a TMC T = (X, U1, U2, Y), where U1 models the semi-Markovianity and U2 models the non stationarity. The TMC T being itself stationary, all parameters can be estimated by the general "Iterative Conditional Estimation" (ICE) method, which leads to unsupervised segmentation. We present some experiments showing the interest of the new model and related processing in image segmentation area.

  4. MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes

    USGS Publications Warehouse

    Williams, B.K.

    1988-01-01

    Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2012-01-01

    A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model. PMID:22558094

  7. General Limit Distributions for Sums of Random Variables with a Matrix Product Representation

    NASA Astrophysics Data System (ADS)

    Angeletti, Florian; Bertin, Eric; Abry, Patrice

    2014-12-01

    The general limit distributions of the sum of random variables described by a finite matrix product ansatz are characterized. Using a mapping to a Hidden Markov Chain formalism, non-standard limit distributions are obtained, and related to a form of ergodicity breaking in the underlying non-homogeneous Hidden Markov Chain. The link between ergodicity and limit distributions is detailed and used to provide a full algorithmic characterization of the general limit distributions.

  8. Influence of credit scoring on the dynamics of Markov chain

    NASA Astrophysics Data System (ADS)

    Galina, Timofeeva

    2015-11-01

    Markov processes are widely used to model the dynamics of a credit portfolio and forecast the portfolio risk and profitability. In the Markov chain model the loan portfolio is divided into several groups with different quality, which determined by presence of indebtedness and its terms. It is proposed that dynamics of portfolio shares is described by a multistage controlled system. The article outlines mathematical formalization of controls which reflect the actions of the bank's management in order to improve the loan portfolio quality. The most important control is the organization of approval procedure of loan applications. The credit scoring is studied as a control affecting to the dynamic system. Different formalizations of "good" and "bad" consumers are proposed in connection with the Markov chain model.

  9. Markov sequential pattern recognition : dependency and the unknown class.

    SciTech Connect

    Malone, Kevin Thomas; Haschke, Greg Benjamin; Koch, Mark William

    2004-10-01

    The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.

  10. Brief Communication: Earthquake sequencing: analysis of time series constructed from the Markov chain model

    NASA Astrophysics Data System (ADS)

    Cavers, M. S.; Vasudevan, K.

    2015-10-01

    Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, derived from the time series using the EEMD, to a detailed analysis to draw information content of the time series. Also, we investigate the influence of random noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behaviour. Here, we extend the Fano factor and Allan factor analysis to the time series of state-to-state transition frequencies of a Markov chain. Our results support not only the usefulness of the intrinsic mode functions in understanding the time series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

  11. Improved Hidden Markov Models for Molecular Motors, Part 2: Extensions and Application to Experimental Data

    PubMed Central

    Syed, Sheyum; Müllner, Fiona E.; Selvin, Paul R.; Sigworth, Fred J.

    2010-01-01

    Unbiased interpretation of noisy single molecular motor recordings remains a challenging task. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. It improves on currently available Markov-model based techniques by allowing for arbitrary distributions of step sizes, and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise. In this article, we extend the VS-HMM framework for better performance with experimental data. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and accounts for random baseline drifts. Further, as an extension, maximum a posteriori estimation is provided. When used as a blind step detector, the VSI-HMM outperforms conventional step detectors. The fidelity of the VSI-HMM is tested with simulations and is applied to in vitro myosin V data where a small 10 nm population of steps is identified. It is also applied to an in vivo recording of melanosome motion, where strong evidence is found for repeated, bidirectional steps smaller than 8 nm in size, implying that multiple motors simultaneously carry the cargo. PMID:21112294

  12. Sampling graphs with a prescribed joint degree distribution using Markov Chains.

    SciTech Connect

    Pinar, Ali; Stanton, Isabelle

    2010-10-01

    One of the most influential results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest that the joint degree distribution of graphs is an interesting avenue of study for further research into network structure. We provide a simple greedy algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via endpoint switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge.

  13. On a Markov chain roulette-type game

    NASA Astrophysics Data System (ADS)

    El-Shehawey, M. A.; El-Shreef, Gh A.

    2009-05-01

    A Markov chain on non-negative integers which arises in a roulette-type game is discussed. The transition probabilities are p01 = ρ, pNj = δNj, pi,i+W = q, pi,i-1 = p = 1 - q, 1 <= W < N, 0 <= ρ <= 1, N - W < j <= N and i = 1, 2, ..., N - W. Using formulae for the determinant of a partitioned matrix, a closed form expression for the solution of the Markov chain roulette-type game is deduced. The present analysis is supported by two mathematical models from tumor growth and war with bargaining.

  14. Efficient maximum likelihood parameterization of continuous-time Markov processes

    PubMed Central

    McGibbon, Robert T.; Pande, Vijay S.

    2015-01-01

    Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce a maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is dramatically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance. We demonstrate and discuss the advantages of these models over existing discrete-time Markov models for the analysis of molecular dynamics simulations. PMID:26203016

  15. Time operator of Markov chains and mixing times. Applications to financial data

    NASA Astrophysics Data System (ADS)

    Gialampoukidis, I.; Gustafson, K.; Antoniou, I.

    2014-12-01

    We extend the notion of Time Operator from Kolmogorov Dynamical Systems and Bernoulli processes to Markov processes. The general methodology is presented and illustrated in the simple case of binary processes. We present a method to compute the eigenfunctions of the Time Operator. Internal Ages are related to other characteristic times of Markov chains, namely the Kemeny time, the convergence rate and Goodman’s intrinsic time. We clarified the concept of mixing time by providing analytic formulas for two-state Markov chains. Explicit formulas for mixing times are presented for any two-state regular Markov chain. The mixing time of a Markov chain is determined also by the Time Operator of the Markov chain, within its Age computation. We illustrate these results in terms of two realistic examples: A Markov chain from US GNP data and a Markov chain from Dow Jones closing prices. We propose moreover a representation for the Kemeny constant, in terms of internal Ages.

  16. Phase Transitions in Sampling Algorithms and the Underlying Random Structures

    NASA Astrophysics Data System (ADS)

    Randall, Dana

    Sampling algorithms based on Markov chains arise in many areas of computing, engineering and science. The idea is to perform a random walk among the elements of a large state space so that samples chosen from the stationary distribution are useful for the application. In order to get reliable results, we require the chain to be rapidly mixing, or quickly converging to equilibrium. For example, to sample independent sets in a given graph G, the so-called hard-core lattice gas model, we can start at any independent set and repeatedly add or remove a single vertex (if allowed). By defining the transition probabilities of these moves appropriately, we can ensure that the chain will converge to a use- ful distribution over the state space Ω. For instance, the Gibbs (or Boltzmann) distribution, parameterized by Λ> 0, is defined so that p(Λ) = π(I) = Λ|I| /Z, where Z = sum_{J in Ω} Λ^{|J|} is the normalizing constant known as the partition function. An interesting phenomenon occurs as Λ is varied. For small values of Λ, local Markov chains converge quickly to stationarity, while for large values, they are prohibitively slow. To see why, imagine the underlying graph G is a region of the Cartesian lattice. Large independent sets will dominate the stationary distribution π when Λ is sufficiently large, and yet it will take a very long time to move from an independent set lying mostly on the odd sublattice to one that is mostly even. This phenomenon is well known in the statistical physics community, and characterizes by a phase transition in the underlying model.

  17. Resilient model approximation for Markov jump time-delay systems via reduced model with hierarchical Markov chains

    NASA Astrophysics Data System (ADS)

    Zhu, Yanzheng; Zhang, Lixian; Sreeram, Victor; Shammakh, Wafa; Ahmad, Bashir

    2016-10-01

    In this paper, the resilient model approximation problem for a class of discrete-time Markov jump time-delay systems with input sector-bounded nonlinearities is investigated. A linearised reduced-order model is determined with mode changes subject to domination by a hierarchical Markov chain containing two different nonhomogeneous Markov chains. Hence, the reduced-order model obtained not only reflects the dependence of the original systems but also model external influence that is related to the mode changes of the original system. Sufficient conditions formulated in terms of bilinear matrix inequalities for the existence of such models are established, such that the resulting error system is stochastically stable and has a guaranteed l2-l∞ error performance. A linear matrix inequalities optimisation coupled with line search is exploited to solve for the corresponding reduced-order systems. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.

  18. Automatically inferred Markov network models for classification of chromosomal band pattern structures.

    PubMed

    Granum, E; Thomason, M G

    1990-01-01

    A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard

  19. A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates.

    PubMed

    Bartolucci, Francesco; Farcomeni, Alessio

    2015-03-01

    Mixed latent Markov (MLM) models represent an important tool of analysis of longitudinal data when response variables are affected by time-fixed and time-varying unobserved heterogeneity, in which the latter is accounted for by a hidden Markov chain. In order to avoid bias when using a model of this type in the presence of informative drop-out, we propose an event-history (EH) extension of the latent Markov approach that may be used with multivariate longitudinal data, in which one or more outcomes of a different nature are observed at each time occasion. The EH component of the resulting model is referred to the interval-censored drop-out, and bias in MLM modeling is avoided by correlated random effects, included in the different model components, which follow common latent distributions. In order to perform maximum likelihood estimation of the proposed model by the expectation-maximization algorithm, we extend the usual forward-backward recursions of Baum and Welch. The algorithm has the same complexity as the one adopted in cases of non-informative drop-out. We illustrate the proposed approach through simulations and an application based on data coming from a medical study about primary biliary cirrhosis in which there are two outcomes of interest, one continuous and the other binary. PMID:25227970

  20. Monte Carlo Markov chain DEM reconstruction of isothermal plasmas

    NASA Astrophysics Data System (ADS)

    Landi, E.; Reale, F.; Testa, P.

    2012-02-01

    Context. Recent studies carried out with SOHO and Hinode high-resolution spectrometers have shown that the plasma in the off-disk solar corona is close to isothermal. If confirmed, these findings may have significant consequences for theoretical models of coronal heating. However, these studies have been carried out with diagnostic techniques whose ability to reconstruct the plasma distribution with temperature has not been thoroughly tested. Aims: In this paper, we carry out tests on the Monte Carlo Markov chain (MCMC) technique with the aim of determining: 1) its ability to retrieve isothermal plasmas from a set of spectral line intensities, with and without random noise; 2) to what extent can it discriminate between an isothermal solution and a narrow multithermal distribution; and 3) how well it can detect multiple isothermal components along the line of sight. We also test the effects of 4) atomic data uncertainties on the results, and 5) the number of ions whose lines are available for the DEM reconstruction. Methods: We first use the CHIANTI database to calculate synthetic spectra from different thermal distributions: single isothermal plasmas, multithermal plasmas made of multiple isothermal components, and multithermal plasmas with a Gaussian DEM distribution with variable width. We then apply the MCMC technique on each of these synthetic spectra, so that the ability of the MCMC technique at reconstructing the original thermal distribution can be evaluated. Next, we add a random noise to the synthetic spectra, and repeat the exercise, in order to determine the effects of random errors on the results. We also we repeat the exercise using a different set of atomic data from those used to calculate synthetic line intensities, to understand the robustness of the results against atomic physics uncertainties. The size of the temperature bin of the MCMC reconstruction is varied in all cases, in order to determine the optimal width. Results: We find that the MCMC