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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. PMID:27019491

  2. 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. PMID:26779642

  3. Homogeneous Superpixels from Markov Random Walks

    NASA Astrophysics Data System (ADS)

    Perbet, Frank; Stenger, Björn; Maki, Atsuto

    This paper presents a novel algorithm to generate homogeneous superpixels from Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous, i.e. uniform in size and compact in shape. The original MCL algorithm does not scale well to a graph of an image due to the square computation of the Markov matrix which is necessary for circulating the flow. The proposed pruning scheme has the advantages of faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent techniques, we show that the proposed algorithm achieves state-of-the-art performance.

  4. Defect Detection Using Hidden Markov Random Fields

    NASA Astrophysics Data System (ADS)

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

    2005-04-01

    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.

  5. 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.

  6. 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

  7. Learning Markov Random Walks for robust subspace clustering and estimation.

    PubMed

    Liu, Risheng; Lin, Zhouchen; Su, Zhixun

    2014-11-01

    Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods. PMID:25005156

  8. 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

  9. 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

  10. Monte Carlo Integration Using Spatial Structure of Markov Random Field

    NASA Astrophysics Data System (ADS)

    Yasuda, Muneki

    2015-03-01

    Monte Carlo integration (MCI) techniques are important in various fields. In this study, a new MCI technique for Markov random fields (MRFs) is proposed. MCI consists of two successive parts: the first involves sampling using a technique such as the Markov chain Monte Carlo method, and the second involves an averaging operation using the obtained sample points. In the averaging operation, a simple sample averaging technique is often employed. The method proposed in this paper improves the averaging operation by addressing the spatial structure of the MRF and is mathematically guaranteed to statistically outperform standard MCI using the simple sample averaging operation. Moreover, the proposed method can be improved in a systematic manner and is numerically verified by numerical simulations using planar Ising models. In the latter part of this paper, the proposed method is applied to the inverse Ising problem and we observe that it outperforms the maximum pseudo-likelihood estimation.

  11. 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. PMID:25375468

  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. 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.

  14. 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

  15. Biomedical image analysis using Markov random fields & efficient linear programing.

    PubMed

    Komodakis, Nikos; Besbes, Ahmed; Glocker, Ben; Paragios, Nikos

    2009-01-01

    Computer-aided diagnosis through biomedical image analysis is increasingly considered in health sciences. This is due to the progress made on the acquisition side, as well as on the processing one. In vivo visualization of human tissues where one can determine both anatomical and functional information is now possible. The use of these images with efficient intelligent mathematical and processing tools allows the interpretation of the tissues state and facilitates the task of the physicians. Segmentation and registration are the two most fundamental tools in bioimaging. The first aims to provide automatic tools for organ delineation from images, while the second focuses on establishing correspondences between observations inter and intra subject and modalities. In this paper, we present some recent results towards a common formulation addressing these problems, called the Markov Random Fields. Such an approach is modular with respect to the application context, can be easily extended to deal with various modalities, provides guarantees on the optimality properties of the obtained solution and is computationally efficient. PMID:19963682

  16. 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

  17. The mean field theory in EM procedures for blind Markov random field image restoration.

    PubMed

    Zhang, J

    1993-01-01

    A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing. PMID:18296192

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

    PubMed Central

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

    2014-01-01

    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. PMID:25523357

  19. Colonoscopy video quality assessment using hidden Markov random fields

    NASA Astrophysics Data System (ADS)

    Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby

    2011-03-01

    With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

  20. 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.

  1. A Markov Chain Model for evaluating the effectiveness of randomized surveillance procedures

    SciTech Connect

    Edmunds, T.A.

    1994-01-01

    A Markov Chain Model has been developed to evaluate the effectiveness of randomized surveillance procedures. The model is applicable for surveillance systems that monitor a collection of assets by randomly selecting and inspecting the assets. The model provides an estimate of the detection probability as a function of the amount of time that an adversary would require to steal or sabotage the asset. An interactive computer code has been written to perform the necessary computations.

  2. Fusion of Hidden Markov Random Field models and its Bayesian estimation.

    PubMed

    Destrempes, François; Angers, Jean-François; Mignotte, Max

    2006-10-01

    In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence. PMID:17022259

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

    SciTech Connect

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

    1996-12-31

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

  4. 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

  5. Markov random field restoration of point correspondences for active shape modeling

    NASA Astrophysics Data System (ADS)

    Hilger, Klaus B.; Paulsen, Rasmus R.; Larsen, Rasmus

    2004-05-01

    In this paper it is described how to build a statistical shape model using a training set with a sparse of landmarks. A well defined model mesh is selected and fitted to all shapes in the training set using thin plate spline warping. This is followed by a projection of the points of the warped model mesh to the target shapes. When this is done by a nearest neighbour projection it can result in folds and inhomogeneities in the correspondence vector field. The novelty in this paper is the use and extension of a Markov random field regularisation of the correspondence field. The correspondence field is regarded as a collection of random variables, and using the Hammersley-Clifford theorem it is proved that it can be treated as a Markov Random Field. The problem of finding the optimal correspondence field is cast into a Bayesian framework for Markov Random Field restoration, where the prior distribution is a smoothness term and the observation model is the curvature of the shapes. The Markov Random Field is optimised using a combination of Gibbs sampling and the Metropolis-Hasting algorithm. The parameters of the model are found using a leave-one-out approach. The method leads to a generative model that produces highly homogeneous polygonised shapes with improved reconstruction capabilities of the training data. Furthermore, the method leads to an overall reduction in the total variance of the resulting point distribution model. The method is demonstrated on a set of human ear canals extracted from 3D-laser scans.

  6. Joint clustering of protein interaction networks through Markov random walk

    PubMed Central

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  7. Joint clustering of protein interaction networks through Markov random walk.

    PubMed

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis. PMID:24565376

  8. 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. PMID:26352447

  9. 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

  10. Adaptation of the projection-slice theorem for stock valuation estimation using random Markov fields

    NASA Astrophysics Data System (ADS)

    Riasati, Vahid R.

    2009-04-01

    The Projection-Slice Synthetic Discriminant function filter is utilized with Random Markov Fields, RMF to estimate trends that may be used as prediction for stock valuation through the representation of the market behavior as a hidden Markov Model, HMM. In this work, we utilize a set of progressive and contiguous time segments of a given stock, and treat the set as a two dimensional object that has been represented by its one-d projections. The abstract two-D object is thus an incarnation of N-temporal projections. The HMM is then utilized to generate N+1 projections that maximizes the two-dimensional correlation peak between the data and the HMM-generated stochastic processes. This application of the PSDF provides a method of stock valuation prediction via the market stochastic behavior utilized in the filter.

  11. Binary 3-D Markov Chain Random Fields: Finite-size Scaling Analysis of Percolation Properties

    NASA Astrophysics Data System (ADS)

    Harter, T.

    2004-12-01

    Percolation phenomena in random media have been extensively studied in a wide variety of fields in physics, chemistry, engineering, bio-, earth-, and environmental sciences. Most work has focused on uncorrelated random fields. The critical behavior in media with short-range correlations is thought to be identical to that in uncorrelated systems. However, the percolation threshold, pc, which is 0.3116 in uncorrelated media, has been observed to vary with the correlation scale and also with the random field type. Here, we present percolation properties and finite-size scaling effects in three-dimensional binary cubic lattices represented by correlated Markov-chain random fields and compare them to those in sequential Gaussian and sequential indicator random fields. We find that the computed percolation threshold in correlated random fields is significantly lower than in the uncorrelated lattice and decreases with increasing correlation scale. The rate of decrease rapidly flattens out for correlation lengths larger than 2-3 grid-blocks. At correlation scales of 5-6 grid blocks, pc is found to be 0.126 for the Markov chain random fields and slightly higher for sequential Gaussian and indicator random fields. The universal scaling constants for mean cluster size, backbone fraction, and connectivity are found to be consistent with results on uncorrelated lattices. For numerical studies, it is critical to understand finite-size effects on the percolation and associated phase connectivity properties of lattices. We present detailed statistical results on the percolation properties in finite sized lattice and their dependence on correlation scale. We show that appropriate grid resolution and choice of simulation boundaries is critical to properly simulate correlated natural geologic systems, which may display significant finite-size effects.

  12. Metastates in Mean-Field Models with Random External Fields Generated by Markov Chains

    NASA Astrophysics Data System (ADS)

    Formentin, M.; Külske, C.; Reichenbachs, A.

    2012-01-01

    We extend the construction by Külske and Iacobelli of metastates in finite-state mean-field models in independent disorder to situations where the local disorder terms are a sample of an external ergodic Markov chain in equilibrium. We show that for non-degenerate Markov chains, the structure of the theorems is analogous to the case of i.i.d. variables when the limiting weights in the metastate are expressed with the aid of a CLT for the occupation time measure of the chain. As a new phenomenon we also show in a Potts example that for a degenerate non-reversible chain this CLT approximation is not enough, and that the metastate can have less symmetry than the symmetry of the interaction and a Gaussian approximation of disorder fluctuations would suggest.

  13. Hierarchical Markov random-field modeling for texture classification in chest radiographs

    NASA Astrophysics Data System (ADS)

    Vargas-Voracek, Rene; Floyd, Carey E., Jr.; Nolte, Loren W.; McAdams, Page

    1996-04-01

    A hierarchical Markov random field (MRF) modeling approach is presented for the classification of textures in selected regions of interest (ROIs) of chest radiographs. The procedure integrates possible texture classes and their spatial definition with other components present in an image such as noise and background trend. Classification is performed as a maximum a-posteriori (MAP) estimation of texture class and involves an iterative Gibbs- sampling technique. Two cases are studied: classification of lung parenchyma versus bone and classification of normal lung parenchyma versus miliary tuberculosis (MTB). Accurate classification was obtained for all examined cases showing the potential of the proposed modeling approach for texture analysis of radiographic images.

  14. Classifying Urban Land cover using Spatial Weights: A Comparison of Discriminant Analysis and Markov Random Fields

    NASA Astrophysics Data System (ADS)

    Wentz, E.; Song, Y.

    2011-12-01

    Classifying urban area images is challenging because of the heterogeneous nature of the urban landscape. This means that each pixel represents a mixture of classes with potentially highly variable various spectral values. Land cover classification approaches using ancillary data, such as knowledge based or expert systems, have shown to improve the classification accuracy in urban areas, particularly with medium or low-resolution imagery. This is because information other than the spectral signatures is used to assign pixels to classes. Defining rules is challenging and acquiring appropriate ancillary data may not always be possible. The goal of this study is to compare the results of three approaches to classify urban land cover with medium resolution data with and without ancillary information. We compare discriminant analysis, Markov random fields, and an expert system. Furthermore, we explore whether including spatial weights improves classification accuracy of the discriminant model. Discriminant analysis is a statistical technique used to predict group membership for a pixel based on the linear combination of independent variables. Adding spatial weights to this includes a weighted value for neighboring pixels. Markov random fields represent spatial dependencies through conditional relationships defined using Markov principles. In comparison to using spatial dependencies in neighbouring pixels, strict per pixel statistical analysis, however, does not consider the spatial dependencies among neighbouring pixels. Our study showed that approaches using ancillary data continued to outperform strict spectral classifiers but that using a spatial weight improved the results. Furthermore, results demonstrate that when the discriminant analysis technique works well then the spatially weighted approach works better. However, when the discriminant analysis performs ineffectively, those poor results are magnified. This study suggests that spatial weights improve the

  15. 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.

  16. 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

  17. Mixed Markov models

    PubMed Central

    Fridman, Arthur

    2003-01-01

    Markov random fields can encode complex probabilistic relationships involving multiple variables and admit efficient procedures for probabilistic inference. However, from a knowledge engineering point of view, these models suffer from a serious limitation. The graph of a Markov field must connect all pairs of variables that are conditionally dependent even for a single choice of values of the other variables. This makes it hard to encode interactions that occur only in a certain context and are absent in all others. Furthermore, the requirement that two variables be connected unless always conditionally independent may lead to excessively dense graphs, obscuring the independencies present among the variables and leading to computationally prohibitive inference algorithms. Mumford [Mumford, D. (1996) in ICIAM 95, eds. Kirchgassner, K., Marenholtz, O. & Mennicken, R. (Akademie Verlag, Berlin), pp. 233–256] proposed an alternative modeling framework where the graph need not be rigid and completely determined a priori. Mixed Markov models contain node-valued random variables that, when instantiated, augment the graph by a set of transient edges. A single joint probability distribution relates the values of regular and node-valued variables. In this article, we study the analytical and computational properties of mixed Markov models. In particular, we show that positive mixed models have a local Markov property that is equivalent to their global factorization. We also describe a computationally efficient procedure for answering probabilistic queries in mixed Markov models. PMID:12829802

  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. 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.

  20. 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.

  1. Localization for random and quasiperiodic potentials

    NASA Astrophysics Data System (ADS)

    Spencer, Thomas

    1988-06-01

    A survey is made of some recent mathematical results and techniques for Schrödinger operators with random and quasiperiodic potentials. A new proof of localization for random potentials, established in collaboration with H. von Dreifus, is sketched.

  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. 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.

  4. Face Association for Videos Using Conditional Random Fields and Max-Margin Markov Networks.

    PubMed

    Du, Ming; Chellappa, Rama

    2016-09-01

    We address the video-based face association problem, in which one attempts to extract the face tracks of multiple subjects while maintaining label consistency. Traditional tracking algorithms have difficulty in handling this task, especially when challenging nuisance factors like motion blur, low resolution or significant camera motions are present. We demonstrate that contextual features, in addition to face appearance itself, play an important role in this case. We propose principled methods to combine multiple features using Conditional Random Fields and Max-Margin Markov networks to infer labels for the detected faces. Different from many existing approaches, our algorithms work in online mode and hence have a wider range of applications. We address issues such as parameter learning, inference and handling false positves/negatives that arise in the proposed approach. Finally, we evaluate our approach on several public databases. PMID:26552075

  5. 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.

  6. Object-oriented image coding scheme based on DWT and Markov random field

    NASA Astrophysics Data System (ADS)

    Zheng, Lei; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chan, Andrew K.

    1998-12-01

    In this paper, we introduce an object-oriented image coding algorithm to differentiate regions of interest (ROI) in visual communications. Our scheme is motivated by the fact that in visual communications, image contents (objects) are not equally important. For a given network bandwidth budget, one should give the highest transmission priority to the most interesting object, and serve the remaining ones at lower priorities. We propose a DWT based Multiresolution Markov Random Field technique to segment image objects according to their textures. We show that this technique can effectively distinguish visual objects and assign them different priorities. This scheme can be integrated with our ROI compression coder, the Generalized Self-Similarity Tress codex, for networking applications.

  7. 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.

  8. 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. PMID:21997265

  9. 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.

  10. Prediction coefficient estimation in Markov random fields for iterative x-ray CT reconstruction

    NASA Astrophysics Data System (ADS)

    Wang, Jiao; Sauer, Ken; Thibault, Jean-Baptiste; Yu, Zhou; Bouman, Charles

    2012-02-01

    Bayesian estimation is a statistical approach for incorporating prior information through the choice of an a priori distribution for a random field. A priori image models in Bayesian image estimation are typically low-order Markov random fields (MRFs), effectively penalizing only differences among immediately neighboring voxels. This limits spectral description to a crude low-pass model. For applications where more flexibility in spectral response is desired, potential benefit exists in models which accord higher a priori probability to content in higher frequencies. Our research explores the potential of larger neighborhoods in MRFs to raise the number of degrees of freedom in spectral description. Similarly to classical filter design, the MRF coefficients may be chosen to yield a desired pass-band/stop-band characteristic shape in the a priori model of the images. In this paper, we present an alternative design method, where high-quality sample images are used to estimate the MRF coefficients by fitting them into the spatial correlation of the given ensemble. This method allows us to choose weights that increase the probability of occurrence of strong components at particular spatial frequencies. This allows direct adaptation of the MRFs for different tissue types based on sample images with different frequency content. In this paper, we consider particularly the preservation of detail in bone structure in X-ray CT. Our results show that MRF design can be used to obtain bone emphasis similar to that of conventional filtered back-projection (FBP) with a bone kernel.

  11. 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

  12. A recursive model-reduction method for approximate inference in Gaussian Markov random fields.

    PubMed

    Johnson, Jason K; Willsky, Alan S

    2008-01-01

    This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to subdivide the random field into smaller subfields, constructing cavity models which approximate these subfields. Each cavity model is a concise, yet faithful, model for the surface of one subfield sufficient for near-optimal inference in adjacent subfields. This basic idea leads to a tree-structured algorithm which recursively builds a hierarchy of cavity models during an "upward pass" and then builds a complementary set of blanket models during a reverse "downward pass." The marginal statistics of individual variables can then be approximated using their blanket models. Model thinning plays an important role, allowing us to develop thinned cavity and blanket models thereby providing tractable approximate inference. We develop a maximum-entropy approach that exploits certain tractable representations of Fisher information on thin chordal graphs. Given the resulting set of thinned cavity models, we also develop a fast preconditioner, which provides a simple iterative method to compute optimal estimates. Thus, our overall approach combines recursive inference, variational learning and iterative estimation. We demonstrate the accuracy and scalability of this approach in several challenging, large-scale remote sensing problems. PMID:18229805

  13. 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.

  14. 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

  15. Envelope Synthesis on the Free Surface of a Random Elastic Medium Based on the Markov Approximation

    NASA Astrophysics Data System (ADS)

    Emoto, K.; Sato, H.; Nishimura, T.

    2008-12-01

    Short-period seismograms mostly consist of the waves scattered by random inhomogeneities of the solid earth. For P-waves, the apparent duration is broadened and the transverse amplitude is excited with travel distance increasing. Usually, we observe the seismic waves on the free surface. For the homogeneous medium case, the vertical incident P-wave amplitude is doubled at the free surface; however, for the inhomogeneous medium case it has not been clear how the free surface affects wave amplitudes since the ray directions are widely distributed because of scattering. Here, we develop the synthesis of vector-wave envelopes on the free surface of a random medium. When the wave length is shorter than the correlation distance of 3-D infinite random media, characterized by a Gaussian autocorrelation function, Sato (2006) derives analytical solutions of vector-wave envelopes on the basis of the Markov approximation, which is a stochastic extension of the split stem method to solve the parabolic wave equation. We extend his method for the synthesis of vector-wave envelopes on the free surface of a random medium. In the Markov approximation, Mean square (MS) envelopes are calculated by using the Fourier transform of two-frequency mutual coherence function (TFMCF) with respect to the angular frequency. The TFMCF is statistically defined on the transverse plane, which is perpendicular to the global ray direction. The Fourier transform of the TFMCF with respect to the transverse coordinates gives the angular spectrum that shows the distribution of ray directions. This angular spectrum has a sharp peak in the global ray direction just after the direct wave arrival and gradually increases its width with the lapse time increasing. We can calculate vector-wave envelopes in the infinite space, simply projecting the angular spectrum to each component and integrating it in the wavenumber space. We calculate the vector-wave envelopes on the free surface by multiplying the

  16. Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

    This paper presents a novel spatio-contextual fuzzy clustering algorithm for unsupervised change detection from multispectral and multitemporal remote sensing images. The proposed technique uses fuzzy Gibbs Markov Random Field (GMRF) to model the spatial gray level attributes of the multispectral difference image. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the fuzzy GMRF modeled difference image is found to be exponential in nature. Convergence of conventional fuzzy clustering based search criterion is more likely to lead the clustering solutions to be getting trapped in a local minimum. Hence we adhered to the variable neighborhood searching (VNS) based global convergence criterion for iterative estimation of the fuzzy GMRF parameters. Experiments are carried out on different multispectral and multitemporal remote sensing images. Results confirm the effectiveness of the proposed technique. It is also noticed that the proposed scheme provides better results with less misclassification error as compared to the existing techniques. The computational time taken by the proposed technique is comparable with that of the HTNN scheme.

  17. 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. PMID:24968171

  18. 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. PMID:25420260

  19. A Markov Random Field Model-Based Approach To Image Interpretation

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Modestino, James W.

    1989-11-01

    In this paper, a Markov random field (MRF) model-based approach to automated image interpretation is described and demonstrated as a region-based scheme. In this approach, an image is first segmented into a collection of disjoint regions which form the nodes of an adjacency graph. Image interpretation is then achieved through assigning object labels, or interpretations, to the segmented regions, or nodes, using domain knowledge, extracted feature measurements and spatial relationships between the various regions. The interpretation labels are modeled as a MRF on the corresponding adjacency graph and the image interpretation problem is formulated as a maximum a posteriori (MAP) estimation rule. Simulated annealing is used to find the best realization, or optimal MAP interpretation. Through the MRF model, this approach also provides a systematic method for organizing and representing domain knowledge through the clique functions of the pdf of the underlying MRF. Results of image interpretation experiments performed on synthetic and real-world images using this approach are described and appear promising.

  20. 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. PMID:25586375

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

    NASA Astrophysics Data System (ADS)

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

    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.

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

    PubMed Central

    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. PMID:23161673

  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. 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). PMID:19783509

  5. 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. PMID:21531035

  6. 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.

  7. D Classification of Crossroads from Multiple Aerial Images Using Markov Random Fields

    NASA Astrophysics Data System (ADS)

    Kosov, S.; Rottensteiner, F.; Heipke, C.; Leitloff, J.; Hinz, S.

    2012-08-01

    The precise classification and reconstruction of crossroads from multiple aerial images is a challenging problem in remote sensing. We apply the Markov Random Fields (MRF) approach to this problem, a probabilistic model that can be used to consider context in classification. A simple appearance-based model is combined with a probabilistic model of the co-occurrence of class label at neighbouring image sites to distinguish up to 14 different classes that are relevant for scenes containing crossroads. The parameters of these models are learnt from training data. We use multiple overlap aerial images to derive a digital surface model (DSM) and a true orthophoto without moving cars. From the DSM and the orthophoto we derive feature vectors that are used in the classification. One of the features is a car confidence value that is supposed to support the classification when the road surface is occluded by static cars. Our approach is evaluated on a dataset of airborne photos of an urban area by a comparison of the results to reference data. Whereas the method has problems in distinguishing classes having a similar appearance, it is shown to produce promising results if a reduced set of classes is considered, yielding an overall classification accuracy of 74.8%.

  8. 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.

  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. 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.

  11. Image Enhancement and Speckle Reduction of Full Polarimetric SAR Data by Gaussian Markov Random Field

    NASA Astrophysics Data System (ADS)

    Mahdian, M.; Motagh, M.; Akbari, V.

    2013-09-01

    In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR) data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent imaging systems. Indeed, speckle degrade radiometric resolution of PolSAR images, therefore it is needful to perform speckle filtering on the SAR data type. Markov Random Field (MRF) has proven to be a powerful method for drawing out eliciting contextual information from remotely sensed images. In the present paper, a probability density function (PDF), which is fitted well with the PolSAR data based on the goodness-of-fit test, is first obtained for the pixel-wise analysis. Then the contextual smoothing is achieved with the MRF method. This novel speckle reduction method combines an advanced statistical distribution with spatial contextual information for PolSAR data. These two parts of information are combined based on weighted summation of pixel-wise and contextual models. This approach not only preserves edge information in the images, but also improves signal-to-noise ratio of the results. The method maintains the mean value of original signal in the homogenous areas and preserves the edges of features in the heterogeneous regions. Experiments on real medium resolution ALOS data from Tehran, and also high resolution full polarimetric SAR data over the Oberpfaffenhofen test-site in Germany, demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

  12. Fisher informations and local asymptotic normality for continuous-time quantum Markov processes

    NASA Astrophysics Data System (ADS)

    Catana, Catalin; Bouten, Luc; Guţă, Mădălin

    2015-09-01

    We consider the problem of estimating an arbitrary dynamical parameter of an open quantum system in the input-output formalism. For irreducible Markov processes, we show that in the limit of large times the system-output state can be approximated by a quantum Gaussian state whose mean is proportional to the unknown parameter. This approximation holds locally in a neighbourhood of size {t}-1/2 in the parameter space, and provides an explicit expression of the asymptotic quantum Fisher information in terms of the Markov generator. Furthermore we show that additive statistics of the counting and homodyne measurements also satisfy local asymptotic normality and we compute the corresponding classical Fisher informations. The general theory is illustrated with the examples of a two-level system and the atom maser. Our results contribute towards a better understanding of the statistical and probabilistic properties of the output process, with relevance for quantum control engineering, and the theory of non-equilibrium quantum open systems.

  13. 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

  14. 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.

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

    PubMed Central

    Zheng, Chaozhi; Kuhner, Mary K.

    2014-01-01

    We propose a genealogy sampling algorithm, 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, 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 ten DNA sequences over 50kb. 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., 2000a), a sampler which uses the full model. PMID:24817610

  16. 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

  17. Impact of model order and estimation window for indexing TerraSAR-X images using Gauss Markov random fields

    NASA Astrophysics Data System (ADS)

    Espinoza-Molina, Daniela; Datcu, Mihai

    2010-10-01

    TerraSAR-X is the Synthetic Aperture Radar (SAR) German satellite which provides a high diversity of information due to its high-resolution. TerraSAR-X acquires daily a volume of up to 100 GB of high complexity, multi-mode SAR images, i.e. SpotLight, StripMap, and ScanSAR data, with dual or quad-polarization, and with different look angles. The high and multiple resolutions of the instrument (1m, 3m or 10m) open perspectives for new applications, that were not possible with past lower resolution sensors (20-30m). Mainly the 1m and 3m modes we expect to support a broad range of new applications related to human activities with relevant structures and objects at the 1m scale. Thus, among the most interesting scenes are: urban, industrial, and rural data. In addition, the global coverage and the relatively frequent repeat pass will definitely help to acquire extremely relevant data sets. To analyze the available TerrrSAR-X data we rely on model based methods for feature extraction and despeckling. The image information content is extracted using model-based methods based on Gauss Markov Random Field (GMRF) and Bayesian inference approach. This approach enhances the local adaptation by using a prior model, which learns the image structure and enables to estimate the local description of the structures, acting as primitive feature extraction method. However, the GMRF model-based method uses as input parameters the Model Order (MO) and the size of Estimation Window (EW). The appropriated selection of these parameters allows us to improve the classification and indexing results due to the number of well separated classes could be determined by them. Our belief is that the selection of the MO depends on the kind of information that the image contains, explaining how well the model can recognize complex structures as objects, and according to the size of EW the accuracy of the estimation is determined. In the following, we present an evaluation of the impact of the model

  18. Local volume fraction fluctuations in random media

    SciTech Connect

    Quintanilla, J.; Torquato, S.

    1997-02-01

    Although the volume fraction is a constant for a statistically homogeneous random medium, on a spatially local level it fluctuates. We study the full distribution of volume fraction within an observation window of finite size for models of random media. A formula due to Lu and Torquato for the standard deviation or {open_quotes}coarseness{close_quotes} associated with the {ital local} volume fraction {xi} is extended for the nth moment of {xi} for any n. The distribution function F{sub L} of the local volume fraction of five different model microstructures is evaluated using analytical and computer-simulation methods for a wide range of window sizes and overall volume fractions. On the line, we examine a system of fully penetrable rods and a system of totally impenetrable rods formed by random sequential addition (RSA). In the plane, we study RSA totally impenetrable disks and fully penetrable aligned squares. In three dimensions, we study fully penetrable aligned cubes. In the case of fully penetrable rods, we will also simplify and numerically invert a prior analytical result for the Laplace transform of F{sub L}. In all of these models, we show that, for sufficiently large window sizes, F{sub L} can be reasonably approximated by the normal distribution. {copyright} {ital 1997 American Institute of Physics.}

  19. 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

  20. 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.

  1. 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

  2. Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry.

    PubMed

    Karchin, Rachel; Cline, Melissa; Mandel-Gutfreund, Yael; Karplus, Kevin

    2003-06-01

    An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE. PMID:12784210

  3. Precise estimation of pressure-temperature paths from zoned minerals using Markov random field modeling: theory and synthetic inversion

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

    The chemical zoning profile in metamorphic minerals is often used to deduce the pressure-temperature ( P- T) history of rock. However, it remains difficult to restore detailed paths from zoned minerals because thermobarometric evaluation of metamorphic conditions involves several uncertainties, including measurement errors and geological noise. We propose a new stochastic framework for estimating precise P- T paths from a chemical zoning structure using the Markov random field (MRF) model, which is a type of Bayesian stochastic method that is often applied to image analysis. The continuity of pressure and temperature during mineral growth is incorporated by Gaussian Markov chains as prior probabilities in order to apply the MRF model to the P- T path inversion. The most probable P- T path can be obtained by maximizing the posterior probability of the sequential set of P and T given the observed compositions of zoned minerals. Synthetic P- T inversion tests were conducted in order to investigate the effectiveness and validity of the proposed model from zoned Mg-Fe-Ca garnet in the divariant KNCFMASH system. In the present study, the steepest descent method was implemented in order to maximize the posterior probability using the Markov chain Monte Carlo algorithm. The proposed method successfully reproduced the detailed shape of the synthetic P- T path by eliminating appropriately the statistical compositional noises without operator's subjectivity and prior knowledge. It was also used to simultaneously evaluate the uncertainty of pressure, temperature, and mineral compositions for all measurement points. The MRF method may have potential to deal with several geological uncertainties, which cause cumbersome systematic errors, by its Bayesian approach and flexible formalism, so that it comprises potentially powerful tools for various inverse problems in petrology.

  4. 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

  5. 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

  6. Image segmentation for automatic particle identification in electron micrographs based on hidden Markov random field models and expectation maximization

    PubMed Central

    Singh, Vivek; Marinescu, Dan C.; Baker, Timothy S.

    2014-01-01

    Three-dimensional reconstruction of large macromolecules like viruses at resolutions below 10 ÅA requires a large set of projection images. Several automatic and semi-automatic particle detection algorithms have been developed along the years. Here we present a general technique designed to automatically identify the projection images of particles. The method is based on Markov random field modelling of the projected images and involves a pre-processing of electron micrographs followed by image segmentation and post-processing. The image is modelled as a coupling of two fields—a Markovian and a non-Markovian. The Markovian field represents the segmented image. The micrograph is the non-Markovian field. The image segmentation step involves an estimation of coupling parameters and the maximum áa posteriori estimate of the realization of the Markovian field i.e, segmented image. Unlike most current methods, no bootstrapping with an initial selection of particles is required. PMID:15065680

  7. Object-oriented Markov random model for classification of high resolution satellite imagery based on wavelet transform

    NASA Astrophysics Data System (ADS)

    Hong, Liang; Liu, Cun; Yang, Kun; Deng, Ming

    2013-07-01

    The high resolution satellite imagery (HRSI) have higher spatial resolution and less spectrum number, so there are some "object with different spectra, different objects with same spectrum" phenomena. The objective of this paper is to utilize the extracted features of high resolution satellite imagery (HRSI) obtained by the wavelet transform(WT) for segmentation. WT provides the spatial and spectral characteristics of a pixel along with its neighbors. The object-oriented Markov random Model in the wavelet domain is proposed in order to segment high resolution satellite imagery (HRSI). The proposed method is made up of three blocks: (1) WT-based feature extrcation.the aim of extraction of feature using WT for original spectral bands is to exploit the spatial and frequency information of the pixels; (2) over-segmentation object generation. Mean-Shift algorithm is employed to obtain over-segmentation objects; (3) classification based on Object-oriented Markov Random Model. Firstly the object adjacent graph (OAG) can be constructed on the over-segmentation objects. Secondly MRF model is easily defined on the OAG, in which WT-based feature of pixels are modeled in the feature field model and the neighbor system, potential cliques and energy functions of OAG are exploited in the labeling model. Experiments are conducted on one HRSI dataset-QuickBird images. We evaluate and compare the proposed approach with the well-known commercial software eCognition(object-based analysis approach) and Maximum Likelihood(ML) based pixels. Experimental results show that the proposed the method in this paper obviously outperforms the other methods.

  8. 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

  9. a Comparative Study Between Pair-Point Clique and Multi-Point Clique Markov Random Field Models for Land Cover Classification

    NASA Astrophysics Data System (ADS)

    Hu, B.; Li, P.

    2013-07-01

    Markov random field (MRF) is an effective method for description of local spatial-temporal dependence of image and has been widely used in land cover classification and change detection. However, existing studies only use pair-point clique (PPC) to describe spatial dependence of neighbouring pixels, which may not fully quantify complex spatial relations, particularly in high spatial resolution images. In this study, multi-point clique (MPC) is adopted in MRF model to quantitatively express spatial dependence among pixels. A modified least squares fit (LSF) method based on robust estimation is proposed to calculate potential parameters for MRF models with different types. The proposed MPC-MRF method is evaluated and quantitatively compared with traditional PPCMRF in urban land cover classification using high resolution hyperspectral HYDICE data of Washington DC. The experimental results revealed that the proposed MPC-MRF method outperformed the traditional PPC-MRF method in terms of classification details. The MPC-MRF provides a sophisticated way of describing complex spatial dependence for relevant applications.

  10. 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. PMID:26926866

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.

    PubMed

    Camproux, A C; Tufféry, P

    2005-08-01

    Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence. PMID:16040198

  16. Coloring random graphs and maximizing local diversity.

    PubMed

    Bounkong, S; van Mourik, J; Saad, D

    2006-11-01

    We study a variation of the graph coloring problem on random graphs of finite average connectivity. Given the number of colors, we aim to maximize the number of different colors at neighboring vertices (i.e., one edge distance) of any vertex. Two efficient algorithms, belief propagation and Walksat, are adapted to carry out this task. We present experimental results based on two types of random graphs for different system sizes and identify the critical value of the connectivity for the algorithms to find a perfect solution. The problem and the suggested algorithms have practical relevance since various applications, such as distributed storage, can be mapped onto this problem. PMID:17280022

  17. 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. PMID:24333394

  18. 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

  19. 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

  20. 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.

  1. Tuneabilities of localized electromagnetic modes in random nanostructures for random lasing

    NASA Astrophysics Data System (ADS)

    Takeda, S.; Obara, M.

    2010-02-01

    The modal characteristics of localized electromagnetic waves inside random nanostructures are theoretically presented. It is crucial to know the tuneabilities of the localized modes systematically for demonstrating a specific random lasing application. By use of FDTD (Finite-Difference Time-Domain) method, we investigated the impulse response of two-dimensional random nanostructures consisting of closely packed cylindrical dielectric columns, and precisely analyzed the localized modes. We revealed the tuneability of the frequency of the localized modes by controlling the medium configurations: diameter, spatial density, and refractive index of the cylinders. Furthermore, it is found to be able to tune the Q (quality) factors of the localized modes dramatically by controlling simply the system size of the entire medium. The observed Q factors of approximately 1.6×104 were exhibited in our random disordered structures.

  2. 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

  3. 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

  4. Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system

    PubMed Central

    Menke, Matt; Berger, Bonnie; Cowen, Lenore

    2010-01-01

    The recent explosion in newly sequenced bacterial genomes is outpacing the capacity of researchers to try to assign functional annotation to all the new proteins. Hence, computational methods that can help predict structural motifs provide increasingly important clues in helping to determine how these proteins might function. We introduce a Markov Random Field approach tailored for recognizing proteins that fold into mainly β-structural motifs, and apply it to build recognizers for the β-propeller shapes. As an application, we identify a potential class of hybrid two-component sensor proteins, that we predict contain a double-propeller domain. PMID:20147619

  5. HaplotypeCN: copy number haplotype inference with Hidden Markov Model and localized haplotype clustering.

    PubMed

    Lin, Yen-Jen; Chen, Yu-Tin; Hsu, Shu-Ni; Peng, Chien-Hua; Tang, Chuan-Yi; Yen, Tzu-Chen; Hsieh, Wen-Ping

    2014-01-01

    Copy number variation (CNV) has been reported to be associated with disease and various cancers. Hence, identifying the accurate position and the type of CNV is currently a critical issue. There are many tools targeting on detecting CNV regions, constructing haplotype phases on CNV regions, or estimating the numerical copy numbers. However, none of them can do all of the three tasks at the same time. This paper presents a method based on Hidden Markov Model to detect parent specific copy number change on both chromosomes with signals from SNP arrays. A haplotype tree is constructed with dynamic branch merging to model the transition of the copy number status of the two alleles assessed at each SNP locus. The emission models are constructed for the genotypes formed with the two haplotypes. The proposed method can provide the segmentation points of the CNV regions as well as the haplotype phasing for the allelic status on each chromosome. The estimated copy numbers are provided as fractional numbers, which can accommodate the somatic mutation in cancer specimens that usually consist of heterogeneous cell populations. The algorithm is evaluated on simulated data and the previously published regions of CNV of the 270 HapMap individuals. The results were compared with five popular methods: PennCNV, genoCN, COKGEN, QuantiSNP and cnvHap. The application on oral cancer samples demonstrates how the proposed method can facilitate clinical association studies. The proposed algorithm exhibits comparable sensitivity of the CNV regions to the best algorithm in our genome-wide study and demonstrates the highest detection rate in SNP dense regions. In addition, we provide better haplotype phasing accuracy than similar approaches. The clinical association carried out with our fractional estimate of copy numbers in the cancer samples provides better detection power than that with integer copy number states. PMID:24849202

  6. 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).

  7. A random matrix model with localization and ergodic transitions

    NASA Astrophysics Data System (ADS)

    Kravtsov, V. E.; Khaymovich, I. M.; Cuevas, E.; Amini, M.

    2015-12-01

    Motivated by the problem of many-body localization and the recent numerical results for the level and eigenfunction statistics on the random regular graphs, a generalization of the Rosenzweig-Porter random matrix model is suggested that possesses two transitions. One of them is the Anderson localization transition from the localized to the extended states. The other one is the ergodic transition from the extended non-ergodic (multifractal) states to the extended ergodic states. We confirm the existence of both transitions by computing the two-level spectral correlation function, the spectrum of multifractality f(α ) and the wave function overlap which consistently demonstrate these two transitions.

  8. 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.

  9. 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 +

  10. 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…

  11. Random matrix analysis of localization properties of gene coexpression network

    NASA Astrophysics Data System (ADS)

    Jalan, Sarika; Solymosi, Norbert; Vattay, Gábor; Li, Baowen

    2010-04-01

    We analyze gene coexpression network under the random matrix theory framework. The nearest-neighbor spacing distribution of the adjacency matrix of this network follows Gaussian orthogonal statistics of random matrix theory (RMT). Spectral rigidity test follows random matrix prediction for a certain range and deviates afterwards. Eigenvector analysis of the network using inverse participation ratio suggests that the statistics of bulk of the eigenvalues of network is consistent with those of the real symmetric random matrix, whereas few eigenvalues are localized. Based on these IPR calculations, we can divide eigenvalues in three sets: (a) The nondegenerate part that follows RMT. (b) The nondegenerate part, at both ends and at intermediate eigenvalues, which deviates from RMT and expected to contain information about important nodes in the network. (c) The degenerate part with zero eigenvalue, which fluctuates around RMT-predicted value. We identify nodes corresponding to the dominant modes of the corresponding eigenvectors and analyze their structural properties.

  12. 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.

  13. 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.

  14. 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.

  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. 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.

  17. 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

  18. 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

  19. 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.

  20. Autoregressive Higher-Order Hidden Markov Models: Exploiting Local Chromosomal Dependencies in the Analysis of Tumor Expression Profiles

    PubMed Central

    Seifert, Michael; Abou-El-Ardat, Khalil; Friedrich, Betty; Klink, Barbara; Deutsch, Andreas

    2014-01-01

    Changes in gene expression programs play a central role in cancer. Chromosomal aberrations such as deletions, duplications and translocations of DNA segments can lead to highly significant positive correlations of gene expression levels of neighboring genes. This should be utilized to improve the analysis of tumor expression profiles. Here, we develop a novel model class of autoregressive higher-order Hidden Markov Models (HMMs) that carefully exploit local data-dependent chromosomal dependencies to improve the identification of differentially expressed genes in tumor. Autoregressive higher-order HMMs overcome generally existing limitations of standard first-order HMMs in the modeling of dependencies between genes in close chromosomal proximity by the simultaneous usage of higher-order state-transitions and autoregressive emissions as novel model features. We apply autoregressive higher-order HMMs to the analysis of breast cancer and glioma gene expression data and perform in-depth model evaluation studies. We find that autoregressive higher-order HMMs clearly improve the identification of overexpressed genes with underlying gene copy number duplications in breast cancer in comparison to mixture models, standard first- and higher-order HMMs, and other related methods. The performance benefit is attributed to the simultaneous usage of higher-order state-transitions in combination with autoregressive emissions. This benefit could not be reached by using each of these two features independently. We also find that autoregressive higher-order HMMs are better able to identify differentially expressed genes in tumors independent of the underlying gene copy number status in comparison to the majority of related methods. This is further supported by the identification of well-known and of previously unreported hotspots of differential expression in glioblastomas demonstrating the efficacy of autoregressive higher-order HMMs for the analysis of individual tumor expression

  1. 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.

  2. 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.

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

    NASA Astrophysics Data System (ADS)

    Battefeld, T.; Modi, C.

    2015-03-01

    We generate random functions locally via a novel generalization of Dyson Brownian motion, such that the functions are in a desired differentiability class Ck, 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 (0D~ 10). 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.

  4. 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.

  5. 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

  6. 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.

  7. Nonlinear Markov processes

    NASA Astrophysics Data System (ADS)

    Frank, T. D.

    2008-06-01

    Some elementary properties and examples of Markov processes are reviewed. It is shown that the definition of the Markov property naturally leads to a classification of Markov processes into linear and nonlinear ones.

  8. Incorporating uncertanity into Markov random field classification with the combine use of optical and SAR images and aduptive fuzzy mean vector

    NASA Astrophysics Data System (ADS)

    Welikanna, D. R.; Tamura, M.; Susaki, J.

    2014-09-01

    A Markov Random Field (MRF) model accounting for the classification uncertainty using multisource satellite images and an adaptive fuzzy class mean vector is proposed in this study. The work also highlights the initialization of the class values for an MRF based classification for synthetic aperture radar (SAR) images using optical data. The model uses the contextual information from the optical image pixels and the SAR pixel intensity with corresponding fuzzy grade of memberships respectively, in the classification mechanism. Sub pixel class fractions estimated using Singular Value Decomposition (SVD) from the optical image initializes the class arrangement for the MRF process. Pair-site interactions of the pixels are used to model the prior energy from the initial class arrangement. Fuzzy class mean vector from the SAR intensity pixels is calculated using Fuzzy C-means (FCM) partitioning. Conditional probability for each class was determined by a Gamma distribution for the SAR image. Simulated annealing (SA) to minimize the global energy was executed using a logarithmic and power-law combined annealing schedule. Proposed technique was tested using an Advanced Land Observation Satellite (ALOS) phased array type L-band SAR (PALSAR) and Advanced Visible and Near-Infrared Radiometer-2 (AVNIR-2) data set over a disaster effected urban region in Japan. Proposed method and the conventional MRF results were evaluated with neural network (NN) and support vector machine (SVM) based classifications. The results suggest the possible integration of an adaptive fuzzy class mean vector and multisource data is promising for imprecise class discrimination using a MRF based classification.

  9. 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.

  10. 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.

  11. 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.

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

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

    Chen, Tian; Zhang, Xiangdong

    2016-05-01

    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.

  14. 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

  15. 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

  16. 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.

  17. Quantification of deterministic matched-field source localization error in the face of random model inputs

    NASA Astrophysics Data System (ADS)

    Daly, Peter M.; Hebenstreit, Gerald T.

    2003-04-01

    Deterministic source localization using matched-field processing (MFP) has yielded good results in propagation scenarios where the nonrandom model parameter input assumption is valid. In many shallow water environments, inputs to acoustic propagation models may be better represented using random distributions rather than fixed quantities. One can estimate the negative effect of random source inputs on deterministic MFP by (1) obtaining a realistic statistical representation of a signal model parameter, then (2) using the mean of the parameter as input to the MFP signal model (the so-called ``replica vector''), (3) synthesizing a source signal using multiple realizations of the random parameter, and (4) estimating the source localization error by correlating the synthesized signal vector with the replica vector over a three dimensional space. This approach allows one to quantify deterministic localization error introduced by random model parameters, including sound velocity profile, hydrophone locations, and sediment thickness and speed. [Work supported by DARPA Advanced Technology Office.

  18. High-order local spatial context modeling by spatialized random forest.

    PubMed

    Ni, Bingbing; Yan, Shuicheng; Wang, Meng; Kassim, Ashraf A; Tian, Qi

    2013-02-01

    In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e., co-occurrence relations. Motivated by the recent success of random forest in learning discriminative visual codebook, we present a spatialized random forest (SRF) approach, which can encode an unlimited length of high-order local spatial contexts. By spatially random neighbor selection and random histogram-bin partition during the tree construction, the SRF can explore much more complicated and informative local spatial patterns in a randomized manner. Owing to the discriminative capability test for the random partition in each tree node's split process, a set of informative high-order local spatial patterns are derived, and new images are then encoded by counting the occurrences of such discriminative local spatial patterns. Extensive comparison experiments on face recognition and object/scene classification clearly demonstrate the superiority of the proposed spatial context modeling method over other state-of-the-art approaches for this purpose. PMID:23060330

  19. 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. PMID:25860723

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. Reconstruction of seismic data with missing traces based on local random sampling and curvelet transform

    NASA Astrophysics Data System (ADS)

    Liu, Wei; Cao, Siyuan; Li, Guofa; He, Yuan

    2015-04-01

    It is likely to yield seismic data with missing traces in field data acquisition, however, the subsequent processing requires complete seismic data; therefore, it is more necessary to reconstruct missing traces in seismic data. The reconstruction of seismic data becomes a sparse optimization problem based on sparsity of seismic data in curvelet transform domain and the gradient projection algorithm is employed to solve it. To overcome the limitations of uncontrolled random sampling, the local random sampling is presented in the paper; it can not only control the size of the sampling gaps effectively, but also keep the randomness of the sampling. The numerical modeling shows that the reconstructed result of local random sampling is better than that of traditional random sampling and jitter sampling. In addition, the proposed approach is also applied to pre-stack shot gather and stacked section, the field examples indicate that this method is effective and applicable for the reconstruction of seismic data with missing traces again. Furthermore, this approach can provide satisfactory result for the following processing on seismic data.

  5. Markov information sources

    NASA Technical Reports Server (NTRS)

    Massey, J. L.

    1975-01-01

    A regular Markov source is defined as the output of a deterministic, but noisy, channel driven by the state sequence of a regular finite-state Markov chain. The rate of such a source is the per letter uncertainty of its digits. The well-known result that the rate of a unifilar regular Markov source is easily calculable is demonstrated, where unifilarity means that the present state of the Markov chain and the next output of the deterministic channel uniquely determine the next state. At present, there is no known method to calculate the rate of a nonunifilar source. Two tentative approaches to this unsolved problem are given, namely source identical twins and the master-slave source, which appear to shed some light on the question of rate calculation for a nonunifilar source.

  6. FAST TRACK COMMUNICATION Local randomness in Hardy's correlations: implications from the information causality principle

    NASA Astrophysics Data System (ADS)

    Rajjak Gazi, MD.; Rai, Ashutosh; Kunkri, Samir; Rahaman, Ramij

    2010-11-01

    Study of non-local correlations in terms of Hardy's argument has been quite popular in quantum mechanics. Hardy's non-locality argument depends on some kind of asymmetry, but a two-qubit maximally entangled state, being symmetric, does not exhibit this kind of non-locality. Here we ask the following question: can this feature be explained by some principle outside quantum mechanics? The no-signaling condition does not provide a solution. But, interestingly, the information causality principle (Pawlowski et al 2009 Nature 461 1101) offers an explanation. It shows that any generalized probability theory which gives completely random results for local dichotomic observable, cannot provide Hardy's non-local correlation if it is restricted by a necessary condition for respecting the information causality principle. In fact, the applied necessary condition imposes even more restrictions on the local randomness of measured observable. Still, there are some restrictions imposed by quantum mechanics that are not reproduced from the considered information causality condition.

  7. The use of negative pressure wound therapy for random local flaps at the ankle region.

    PubMed

    Goldstein, Jesse A; Iorio, Matthew L; Brown, Benjamin; Attinger, Christopher E

    2010-01-01

    Local random flaps are seldom used for reconstruction of complex ankle wounds because of concern for flap failure attributable to vascular compromise and tissue edema. Negative pressure wound therapy has been shown to improve perfusion and limit tissue edema. The objective of this study was to demonstrate the utility of negative pressure wound therapy in improving outcomes for local flaps of the ankle. Ten consecutive patients presenting with complex ankle wounds and reconstructed using local flaps were treated with negative pressure wound therapy postoperatively. Type of flap, immediate and long-term outcomes, and complications were assessed. Seventeen local flaps were performed on 10 patients to reconstruct their ankle wounds. Mean follow up was 88 days. All flaps healed without tissue compromise or necrosis. Only one partial dehiscence and no infections were observed. This study demonstrates that negative pressure therapy may contribute to the viability of random local flaps by decreasing venous congestion. Our experience using negative pressure wound therapy on local flaps suggests that it may serve as a useful adjunct to ensure successful closure of high-risk wounds. PMID:20801691

  8. Dual nature of localization in guiding systems with randomly corrugated boundaries: Anderson-type versus entropic

    SciTech Connect

    Tarasov, Yu.V. Shostenko, L.D.

    2015-05-15

    A unified theory for the conductance of an infinitely long multimode quantum wire whose finite segment has randomly rough lateral boundaries is developed. It enables one to rigorously take account of all feasible mechanisms of wave scattering, both related to boundary roughness and to contacts between the wire rough section and the perfect leads within the same technical frameworks. The rough part of the conducting wire is shown to act as a mode-specific randomly modulated effective potential barrier whose height is governed essentially by the asperity slope. The mean height of the barrier, which is proportional to the average slope squared, specifies the number of conducting channels. Under relatively small asperity amplitude this number can take on arbitrary small, up to zero, values if the asperities are sufficiently sharp. The consecutive channel cut-off that arises when the asperity sharpness increases can be regarded as a kind of localization, which is not related to the disorder per se but rather is of entropic or (equivalently) geometric origin. The fluctuating part of the effective barrier results in two fundamentally different types of guided wave scattering, viz., inter- and intramode scattering. The intermode scattering is shown to be for the most part very strong except in the cases of (a) extremely smooth asperities, (b) excessively small length of the corrugated segment, and (c) the asperities sharp enough for only one conducting channel to remain in the wire. Under strong intermode scattering, a new set of conducting channels develops in the corrugated waveguide, which have the form of asymptotically decoupled extended modes subject to individual solely intramode random potentials. In view of this fact, two transport regimes only are realizable in randomly corrugated multimode waveguides, specifically, the ballistic and the localized regime, the latter characteristic of one-dimensional random systems. Two kinds of localization are thus shown to

  9. Large deviations for Markov processes with resetting.

    PubMed

    Meylahn, Janusz M; Sabhapandit, Sanjib; Touchette, Hugo

    2015-12-01

    Markov processes restarted or reset at random times to a fixed state or region in space have been actively studied recently in connection with random searches, foraging, and population dynamics. Here we study the large deviations of time-additive functions or observables of Markov processes with resetting. By deriving a renewal formula linking generating functions with and without resetting, we are able to obtain the rate function of such observables, characterizing the likelihood of their fluctuations in the long-time limit. We consider as an illustration the large deviations of the area of the Ornstein-Uhlenbeck process with resetting. Other applications involving diffusions, random walks, and jump processes with resetting or catastrophes are discussed. PMID:26764673

  10. 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.

  11. 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

  12. 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.

  13. Existence of Anderson localization of classical waves in a random two-component medium

    SciTech Connect

    Soukoulis, C.M.; Economou, E.N.; Grest, G.S.; Cohen, M.H.

    1989-01-30

    An exact mapping of the classical wave problem to that of electronic motion is utilized together with extensive numerical results to examine the question of the existence of genuine localization (i.e., one occurring when both components have real positive dielectric constants) of classical waves in random binary alloys A/sub 1-//sub x/B/sub x/. We find that scalar waves do exhibit localization. We have also developed a coherent potential approximation which for x<0.2 gives results not that much different from the numerical ones. This result can be easily generalized to electromagnetic fields as well.

  14. Diffusive and localization behavior of electromagnetic waves in a two-dimensional random medium

    NASA Astrophysics Data System (ADS)

    Wang, Ken Kang-Hsin; Ye, Zhen

    2003-10-01

    In this paper, we discuss the transport phenomena of electromagnetic waves in a two-dimensional random system which is composed of arrays of electrical dipoles, following the model presented earlier by Erdogan et al. [J. Opt. Soc. Am. B 10, 391 (1993)]. A set of self-consistent equations is presented, accounting for the multiple scattering in the system, and is then solved numerically. A strong localization regime is discovered in the frequency domain. The transport properties within, near the edge of, and nearly outside the localization regime are investigated for different parameters such as filling factor and system size. The results show that within the localization regime, waves are trapped near the transmitting source. Meanwhile, the diffusive waves follow an intuitive but expected picture. That is, they increase with traveling path as more and more random scattering incurs, followed by a saturation, then start to decay exponentially when the travelling path is large enough, signifying the localization effect. For the cases where the frequencies are near the boundary of or outside the localization regime, the results of diffusive waves are compared with the diffusion approximation, showing less encouraging agreement as in other systems [Asatryan et al., Phys. Rev. E 67, 036605 (2003)].

  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. 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.

  17. Distribution of the Height of Local Maxima of Gaussian Random Fields*

    PubMed Central

    Cheng, Dan; Schwartzman, Armin

    2015-01-01

    Let {f(t) : t ∈ T} be a smooth Gaussian random field over a parameter space T, where T may be a subset of Euclidean space or, more generally, a Riemannian manifold. We provide a general formula for the distribution of the height of a local maximum P{f(t0)>u∣t0 is a local maximum of f(t)} when f is non-stationary. Moreover, we establish asymptotic approximations for the overshoot distribution of a local maximum P{f(t0)>u+v∣t0 is a local maximum of f(t) and f(t0) > v} as v → ∞. Assuming further that f is isotropic, we apply techniques from random matrix theory related to the Gaussian orthogonal ensemble to compute such conditional probabilities explicitly when T is Euclidean or a sphere of arbitrary dimension. Such calculations are motivated by the statistical problem of detecting peaks in the presence of smooth Gaussian noise. PMID:26478714

  18. 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

  19. Localization of directed polymers in random media due to a columnar defect

    NASA Astrophysics Data System (ADS)

    Lee, Jong-Dae; Kim, Jin Min; Lee, Jae Hwan

    2015-04-01

    We investigate the localization of directed polymers in random media due to the presence of an attractive columnar defect at the center of a two-dimensional substrate. If the defect's strength is too weak to affect the polymers, the localization length of the polymers exhibits a power-law behavior as a function of the polymer length, as in the case of no defect. When the defect's strength is greater than a critical value, the localization length approaches a finite value, thereby yielding a localization length exponent (or liberation exponent) ν ⊥ = 1.8004(31). The correlation length, which is perpendicular to the localization length, is defined as the distance over which the polymer is localized by the defect. The correlation length exponent ν ‖ = 2.881(5) is estimated from the data collapse via the scaling relation ζ = ν ⊥/ ν ‖, where ζ = 5/8 represents the wandering exponent. In addition, we measure the number of times that the optimal path passes through the defect, because that number increases as the defect's strength increases. This measurement yields a new critical exponent λ = 0.59.

  20. Markov Processes: Linguistics and Zipf's Law

    NASA Astrophysics Data System (ADS)

    Kanter, I.; Kessler, D. A.

    1995-05-01

    It is shown that a 2-parameter random Markov process constructed with N states and biased random transitions gives rise to a stationary distribution where the probabilities of occurrence of the states, P\\(k\\), k = 1,...,N, exhibit the following three universal behaviors which characterize biological sequences and texts in natural languages: (a) the rank-ordered frequencies of occurrence of words are given by Zipf's law P\\(k\\)~1/kρ, where ρ\\(k\\) is slowly increasing for small k; (b) the frequencies of occurrence of letters are given by P\\(k\\) = A-Dln\\(k\\); and (c) long-range correlations are observed over long but finite intervals, as a result of the quasiergodicity of the Markov process.

  1. Critical Casimir force in the presence of random local adsorption preference.

    PubMed

    Parisen Toldin, Francesco

    2015-03-01

    We study the critical Casimir force for a film geometry in the Ising universality class. We employ a homogeneous adsorption preference on one of the confining surfaces, while the opposing surface exhibits quenched random disorder, leading to a random local adsorption preference. Disorder is characterized by a parameter p, which measures, on average, the portion of the surface that prefers one component, so that p=0,1 correspond to homogeneous adsorption preference. By means of Monte Carlo simulations of an improved Hamiltonian and finite-size scaling analysis, we determine the critical Casimir force. We show that by tuning the disorder parameter p, the system exhibits a crossover between an attractive and a repulsive force. At p=1/2, disorder allows to effectively realize Dirichlet boundary conditions, which are generically not accessible in classical fluids. Our results are relevant for the experimental realizations of the critical Casimir force in binary liquid mixtures. PMID:25871052

  2. Critical Casimir force in the presence of random local adsorption preference

    NASA Astrophysics Data System (ADS)

    Toldin, Francesco Parisen

    2015-03-01

    We study the critical Casimir force for a film geometry in the Ising universality class. We employ a homogeneous adsorption preference on one of the confining surfaces, while the opposing surface exhibits quenched random disorder, leading to a random local adsorption preference. Disorder is characterized by a parameter p , which measures, on average, the portion of the surface that prefers one component, so that p =0 ,1 correspond to homogeneous adsorption preference. By means of Monte Carlo simulations of an improved Hamiltonian and finite-size scaling analysis, we determine the critical Casimir force. We show that by tuning the disorder parameter p , the system exhibits a crossover between an attractive and a repulsive force. At p =1 /2 , disorder allows to effectively realize Dirichlet boundary conditions, which are generically not accessible in classical fluids. Our results are relevant for the experimental realizations of the critical Casimir force in binary liquid mixtures.

  3. 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.

  4. 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

  5. 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

  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. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. Rank-Driven Markov Processes

    NASA Astrophysics Data System (ADS)

    Grinfeld, Michael; Knight, Philip A.; Wade, Andrew R.

    2012-01-01

    We study a class of Markovian systems of N elements taking values in [0,1] that evolve in discrete time t via randomized replacement rules based on the ranks of the elements. These rank-driven processes are inspired by variants of the Bak-Sneppen model of evolution, in which the system represents an evolutionary `fitness landscape' and which is famous as a simple model displaying self-organized criticality. Our main results are concerned with long-time large- N asymptotics for the general model in which, at each time step, K randomly chosen elements are discarded and replaced by independent U[0,1] variables, where the ranks of the elements to be replaced are chosen, independently at each time step, according to a distribution κ N on {1,2,…, N} K . Our main results are that, under appropriate conditions on κ N , the system exhibits threshold behavior at s ∗∈[0,1], where s ∗ is a function of κ N , and the marginal distribution of a randomly selected element converges to U[ s ∗,1] as t→∞ and N→∞. Of this class of models, results in the literature have previously been given for special cases only, namely the `mean-field' or `random neighbor' Bak-Sneppen model. Our proofs avoid the heuristic arguments of some of the previous work and use Foster-Lyapunov ideas. Our results extend existing results and establish their natural, more general context. We derive some more specialized results for the particular case where K=2. One of our technical tools is a result on convergence of stationary distributions for families of uniformly ergodic Markov chains on increasing state-spaces, which may be of independent interest.

  20. Equivalent Markov processes under gauge group

    NASA Astrophysics Data System (ADS)

    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), 10.1103/PhysRevE.90.022125]. 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. 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. PMID:26651671

  2. 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.

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Fuzzy overlapping community detection based on local random walk and multidimensional scaling

    NASA Astrophysics Data System (ADS)

    Wang, Wenjun; Liu, Dong; Liu, Xiao; Pan, Lin

    2013-12-01

    A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging task. In this work, the concept of local random walk and a new distance metric are introduced. Based on the new distance measurement, the dissimilarity index between each node of a network is calculated firstly. Then in order to keep the original node distance as much as possible, the network structure is mapped into low-dimensional space by the multidimensional scaling (MDS). Finally, the fuzzy c-means clustering is employed to find fuzzy communities in a network. The experimental results show that the proposed algorithm is effective and efficient to identify the fuzzy overlapping communities in both artificial networks and real-world networks.

  5. Modeling of nonlinear microscopy of localized field enhancements in random metal nanostructures

    NASA Astrophysics Data System (ADS)

    Beermann, Jonas; Bozhevolnyi, Sergey I.; Coello, Victor

    2006-03-01

    Nonlinear microscopy of localized field enhancements in random metal nanostructures with a tightly focused laser beam scanning over a sample surface is modeled by making use of analytic representations of the Green dyadic in the near- and far-field regions, with the latter being approximated by the part describing the scattering via excitation of surface plasmon polaritons. The developed approach is applied to scanning second-harmonic (SH) microscopy of small gold spheres placed randomly on a gold surface. We calculate self-consistent fundamental harmonic (FH) and SH field distributions at the illuminated sample surface and, thereby, FH and SH images for different polarization configurations of the illuminating and detected fields. The simulated images bear close resemblance to the images obtained experimentally, exhibiting similar sensitivity to the wavelength and polarization, as well as sensitivity to the scattering configuration. We verify directly our conjecture that very bright spots in the SH images occur due to the spatial overlap of properly polarized FH and SH eigenmodes. Applications and further improvements of the developed model are discussed.

  6. Influence of interface and random arrangement of inclusions on local stresses in composite materials

    SciTech Connect

    Al-Ostaz, A.; Jasiuk, I.

    1995-12-31

    The analysis of the local stress fields in composite materials is a very important and complex problem in micromechanics. There are many factors which influence the stress fields: the material properties of constituents, the shape and relative size of reinforcement (inclusions), the boundary conditions at the inclusion-matrix interfaces, and other. A fundamental problem in micromechanics is one involving a single inclusion. Perhaps the most famous result dealing with a single inclusion is due to Eshelby (1957), who found that the stress in an ellipsoidal and perfectly bonded inclusion is uniform, under a constant loading. However, when two or more inclusions are present, the stress fields are more complex and so is their evaluation. The additional complexity is added when the bonding at the inclusion-matrix interface is not perfect. In this paper we study the joint effect of random geometric arrangement and matrix-inclusion interface in composites by using experimental, numerical, and analytical approaches. We conduct the experiments on model composites consisting of copper inclusions and photoelastic epoxy matrix. We use this very idealized composite system because of its easy handling characteristics and isotropic properties of components. Also, the measurements are facilitated due to the larger scale of the specimen. We prepare specimens with several random arrangements of inclusions, several volume fractions, and different interface conditions. We vary the interfaces by using several types of coatings and include perfectly bonded and debonding cases. In this paper we study the local stress fields in the above model composites by using the photoelasticity method. Additionally, we conduct the finite element calculations for the composite systems with the same geometry and material properties. Also, we employ analytical techniques in the studies of simple geometries.

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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. PMID:16564621

  12. Exact electronic spectra and inverse localization lengths in one-dimensional random systems I. Random alloy, liquid metal and liquid alloy

    NASA Astrophysics Data System (ADS)

    Nieuwenhuizen, T. M.

    1983-07-01

    Analytic continuations into the complex energy plane of Dyson-Schmidt type of equations for the calculation of the density of states are constructed for a random alloy model, a liquid metal and for a liquid alloy. In all these models the characteristic function follows from the solution of this equation. Its imaginary part yields the accumulated density of states and its real part is a measure for the inverse of the localization length of the eigenfunctions. The equations have been solved exactly for some distributions of the random variables. In the random alloy case the strengths of the delta-potentials have an exponential distribution. They may also have finite, exponentially distributed values with probability 0 ⪕ p ⪕ 1 and be infinite with probability q = 1 - p. In the liquid metal the liquid particles are assumed to behave like hard rods. This implies an exponential distribution of the distances between the particles. The common electronic potential may be arbitrary, but is assumed to vanish outside the rods. In the one-dimensional liquid alloy there is, apart from positional randomness of the liquid particles, a distribution of the strengths of the electronic delta-potentials. For Cauchy distributions an argument of Lloyd is extended to obtain the characteristic function from the one in the model with equal strengths. For the case of a liquid of point particles a three parameter class of distributions of the strengths is shown to yield a solution in the form of known functions of the equation mentioned above. For several cases numerical calculations of the density of states and the inverse localization length of the eigenfunctions are presented and discussed. New results are found: exponential decay of the density of states near special energies in the random alloy and liquid metal; divergence of the density of states at certain energies with non-classical exponent {1}/{3} in the random alloy if the average of the potential strengths vanishes

  13. 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

  14. 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.

  15. Local Infiltration Analgesia reduces pain and hospital stay after primary TKA: randomized controlled double blind trial.

    PubMed

    Vaishya, Raju; Wani, Ajaz Majeed; Vijay, Vipul

    2015-12-01

    Postoperative analgesia following Total Knee Arthroplasty (TKA) with the use of parenteral opioids or epidural analgesia can be associated with important side effects. Good perioperative analgesia facilitates faster rehabilitation, improves patient satisfaction, and may reduce the hospital stay. We investigated the analgesic effect of a locally injected mixture of drugs, in a double blinded RCT in 80 primary TKA. They were randomized either to receive a periarticular mixture of drugs containing bupivacaine, ketorolac, morphine, and adrenalline or to receive normal saline. Visual analog scores (VAS) for pain (at rest and during activity) and for patient satisfaction and range of motion were recorded postoperatively. The patients who had received the periarticular injection used significantly less the Patient Controlled Analgesia (PCA) after the surgery as compared to the control group. In addition, they had lower VAS for pain during rest and activity and higher visual analog scores for patient satisfaction 72 hours postoperatively. No major complication related to the drugs was observed. Intraoperative periarticular injection with multimodal drugs following TKA can significantly reduce the postoperative pain and hence the requirements for PCA and hospital stay, with no apparent risks. PMID:26790796

  16. 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.

  17. 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.

  18. 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

  19. Effects of Preoperative Local Estrogen in Postmenopausal Women With Prolapse: A Randomized Trial

    PubMed Central

    Good, Meadow M.; Roshanravan, Shayzreen M.; Shi, Haolin; Schaffer, Joseph I.; Singh, Ravinder J.; Word, R. Ann

    2014-01-01

    Context: Pelvic organ prolapse (POP) increases in prevalence with age; recurrence after surgical repair is common. Objective: The objective of the study was to determine the effects of local estrogen treatment on connective tissue synthesis and breakdown in the vaginal wall of postmenopausal women planning surgical repair of POP. Design: This was a randomized trial. Setting: The study was conducted at an academic tertiary medical center. Patients or Other Participants: Postmenopausal women with a uterus and symptomatic anterior and/or apical prolapse at stage 2 or greater participated in the study. Intervention: Estrogen (Premarin) or placebo cream for 6 weeks preoperatively was the intervention. Main Outcome Measures: Full-thickness anterior apical vaginal wall biopsies were obtained at the time of hysterectomy and analyzed for mucosa and muscularis thickness, connective tissue synthesis, and degradation. Serum levels of estrone and 17β-estradiol were analyzed at baseline and the day of surgery using highly sensitive liquid chromatography-tandem mass spectrometry. Results: Fifteen women per group (n = 30 total) were randomized; 13 per group underwent surgery. Among drug-adherent participants (n = 8 estrogen, n = 13 placebo), epithelial and muscularis thickness was increased 1.8- and 2.7-fold (P = .002 and P =.088, respectively) by estrogen. Collagen types 1α1 and 1α2 mRNA increased 6.0- and 1.8-fold in the vaginal muscularis (P < .05 for both); collagen type Ia protein increased 9-fold in the muscularis (P = .012), whereas collagen III was not changed significantly. MMP-12 (human macrophage elastase) mRNA was suppressed in the vaginal mucosa from estrogen-treated participants (P = .011), and matrix metalloprotease-9 activity was decreased 6-fold in the mucosa and 4-fold in the muscularis (P = .02). Consistent with menopausal norms, serum estrone and 17β-estradiol were low and did not differ among the two groups. Conclusions: Vaginal estrogen application for 6

  20. 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.

  1. 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. PMID:18632387

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

    PubMed

    Sung, Chi Wan; Yuen, Shiu Yin

    2011-01-01

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

  3. 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.

  4. Bibliometric Application of Markov Chains.

    ERIC Educational Resources Information Center

    Pao, Miranda Lee; McCreery, Laurie

    1986-01-01

    A rudimentary description of Markov Chains is presented in order to introduce its use to describe and to predict authors' movements among subareas of the discipline of ethnomusicology. Other possible applications are suggested. (Author)

  5. 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.

  6. Tight asymptotic key rate for the Bennett-Brassard 1984 protocol with local randomization and device imprecisions

    NASA Astrophysics Data System (ADS)

    Woodhead, Erik

    2014-08-01

    Local randomization is a preprocessing procedure in which one of the legitimate parties of a quantum key distribution (QKD) scheme adds noise to their version of the key and was found by Kraus et al. [Phys. Rev. Lett. 95, 080501 (2005), 10.1103/PhysRevLett.95.080501] to improve the security of certain QKD protocols. In this article, the improvement yielded by local randomization is derived for an imperfect implementation of the Bennett-Brassard 1984 (BB84) QKD protocol, in which the source emits four given but arbitrary pure states and the detector performs arbitrarily aligned measurements. Specifically, this is achieved by modifying an approach to analyzing the security of imperfect variants of the BB84 protocol against collective attacks, introduced in [Phys. Rev. A 88, 012331 (2013), 10.1103/PhysRevA.88.012331], to include the additional preprocessing step. The previously known improvement to the threshold channel noise, from 11% to 12.41%, is recovered in the special case of an ideal BB84 implementation and becomes more pronounced in the case of a nonideal source. Finally, the bound derived for the asymptotic key rate, both with and without local randomization, is shown to be tight with the particular source characterization used. This is demonstrated by the explicit construction of a family of source states and optimal attacks for which the key-rate bound is attained with equality.

  7. 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.

  8. 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.

  9. 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.

  10. Universality of the local eigenvalue statistics for a class of unitary invariant random matrix ensembles

    SciTech Connect

    Pastur, L. |; Shcherbina, M.

    1997-01-01

    This paper is devoted to the rigorous proof of the universality conjecture of random matrix theory, according to which the limiting eigenvalue statistics of n x n random matrices within spectral intervals of O(n{sup -1}) is determined by the type of matrix (real symmetric, Hermitian, or quaternion real) and by the density of states. We prove this conjecture for a certain class of the Hermitian matrix ensembles that arise in the quantum field theory and have the unitary invariant distribution defined by a certain function (the potential in the quantum field theory) satisfying some regularity conditions.

  11. Localization phenomenon in gaps of the spectrum of random lattice operators

    NASA Astrophysics Data System (ADS)

    Figotin, Alexander; Klein, Abel

    1994-06-01

    We consider a class of random lattice operators including Schrödinger operators of the form H=-Δ+w+gv, where w(x) is a real-valued periodic function, g is a positive constant, and v(x),x∈ℤ d , are independent, identically distributed real random variables. We prove that if the operator -Δ+ w has gaps in the spectrum and g is sufficiently small, then the operator H develops pure point spectrum with exponentially decaying eigenfunctions in a vicinity of the gaps.

  12. Latent Variable Model for Learning in Pairwise Markov Networks

    PubMed Central

    Amizadeh, Saeed; Hauskrecht, Milos

    2011-01-01

    Pairwise Markov Networks (PMN) are an important class of Markov networks which, due to their simplicity, are widely used in many applications such as image analysis, bioinformatics, sensor networks, etc. However, learning of Markov networks from data is a challenging task; there are many possible structures one must consider and each of these structures comes with its own parameters making it easy to overfit the model with limited data. To deal with the problem, recent learning methods build upon the L1 regularization to express the bias towards sparse network structures. In this paper, we propose a new and more flexible framework that let us bias the structure, that can, for example, encode the preference to networks with certain local substructures which as a whole exhibit some special global structure. We experiment with and show the benefit of our framework on two types of problems: learning of modular networks and learning of traffic networks models. PMID:22228193

  13. 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

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

    PubMed Central

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

    2014-01-01

    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, , 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 , a single eigenvalue of the transmission matrix (TM) dominates transmission, and the distribution of the is Gaussian with a variance equal to the average of , as conjectured by SPS. For samples in the cross-over to localization, , we find a one-sided distribution for . 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. PMID:24516156

  15. 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 .

  16. 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.

  17. Non-Amontons-Coulomb local friction law of randomly rough contact interfaces with rubber

    NASA Astrophysics Data System (ADS)

    Nguyen, Danh Toan; Wandersman, Elie; Prevost, Alexis; Le Chenadec, Yohan; Fretigny, Christian; Chateauminois, Antoine

    2013-12-01

    We report on measurements of the local friction law at a multi-contact interface formed between a smooth rubber and statistically rough glass lenses, under steady-state friction. Using contact imaging, surface displacements are measured, and inverted to extract both distributions of frictional shear stress and contact pressure with a spatial resolution of about 10\\ \\mu\\text{m} . For a glass surface whose topography is self-affine with a Gaussian height asperity distribution, the local frictional shear stress is found to vary sub-linearly with the local contact pressure over the whole investigated pressure range. Such sub-linear behavior is also evidenced for a surface with a non-Gaussian height asperity distribution, demonstrating that, for such multi-contact interfaces, Amontons-Coulomb's friction law does not prevail at the local scale.

  18. 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…

  19. 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

  20. Randomized clinical trial of local infiltration plus patient-controlled opiate analgesia vs. epidural analgesia following liver resection surgery

    PubMed Central

    Revie, Erica J; McKeown, Dermot W; Wilson, John A; Garden, O James; Wigmore, Stephen J

    2012-01-01

    Objectives Epidural analgesia is recommended for the provision of analgesia following major abdominal surgery. Continuous local anaesthetic wound infiltration may be an effective alternative. A prospective randomized trial was undertaken to compare these two methods following open liver resection. The primary outcome was length of time required to fulfil criteria for discharge from hospital. Methods Patients undergoing open liver resection were randomized to receive either epidural (EP group) or local anaesthetic wound infiltration plus patient-controlled opiate analgesia (WI group) for the first 2 days postoperatively. All other care followed a standardized enhanced recovery protocol. Time to fulfil discharge criteria, pain scores, physical activity measurements and complications were recorded. Results Between August 2009 and July 2010, 65 patients were randomized to EP (n= 32) or WI (n= 33). The mean time required to fulfil discharge criteria was 4.5 days (range: 2.5–63.5 days) in the WI group and 6.0 days (range: 3.0–42.5 days) in the EP group (P= 0.044). During the first 48 h following surgery, pain scores were significantly lower in the EP group both at rest and on movement. Resting pain scores within both groups were rated as mild (range: 0–3). There was no significant difference between the groups in time to first mobilization or overall complication rate (48.5% in the WI group vs. 58.1% in the EP group; P= 0.443). Conclusions Local anaesthetic wound infiltration combined with patient-controlled opiate analgesia reduces the length of time required to fulfil criteria for discharge from hospital compared with epidural analgesia following open liver resection. Epidural analgesia provides superior analgesia, but does not confer benefits in terms of faster mobilization or recovery. PMID:22882198

  1. 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

  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. Modeling sediment transport as a spatio-temporal Markov process.

    NASA Astrophysics Data System (ADS)

    Heyman, Joris; Ancey, Christophe

    2014-05-01

    Despite a century of research about sediment transport by bedload occuring in rivers, its constitutive laws remain largely unknown. The proof being that our ability to predict mid-to-long term transported volumes within reasonable confidence interval is almost null. The intrinsic fluctuating nature of bedload transport may be one of the most important reasons why classical approaches fail. Microscopic probabilistic framework has the advantage of taking into account these fluctuations at the particle scale, to understand their effect on the macroscopic variables such as sediment flux. In this framework, bedload transport is seen as the random motion of particles (sand, gravel, pebbles...) over a two-dimensional surface (the river bed). The number of particles in motion, as well as their velocities, are random variables. In this talk, we show how a simple birth-death Markov model governing particle motion on a regular lattice accurately reproduces the spatio-temporal correlations observed at the macroscopic level. Entrainment, deposition and transport of particles by the turbulent fluid (air or water) are supposed to be independent and memoryless processes that modify the number of particles in motion. By means of the Poisson representation, we obtained a Fokker-Planck equation that is exactly equivalent to the master equation and thus valid for all cell sizes. The analysis shows that the number of moving particles evolves locally far from thermodynamic equilibrium. Several analytical results are presented and compared to experimental data. The index of dispersion (or variance over mean ratio) is proved to grow from unity at small scales to larger values at larger scales confirming the non Poisonnian behavior of bedload transport. Also, we study the one and two dimensional K-function, which gives the average number of moving particles located in a ball centered at a particle centroid function of the ball's radius.

  6. Sunspots and ENSO relationship using Markov method

    NASA Astrophysics Data System (ADS)

    Hassan, Danish; Iqbal, Asif; Ahmad Hassan, Syed; Abbas, Shaheen; Ansari, Muhammad Rashid Kamal

    2016-01-01

    The various techniques have been used to confer the existence of significant relations between the number of Sunspots and different terrestrial climate parameters such as rainfall, temperature, dewdrops, aerosol and ENSO etc. Improved understanding and modelling of Sunspots variations can explore the information about the related variables. This study uses a Markov chain method to find the relations between monthly Sunspots and ENSO data of two epochs (1996-2009 and 1950-2014). Corresponding transition matrices of both data sets appear similar and it is qualitatively evaluated by high values of 2-dimensional correlation found between transition matrices of ENSO and Sunspots. The associated transition diagrams show that each state communicates with the others. Presence of stronger self-communication (between same states) confirms periodic behaviour among the states. Moreover, closeness found in the expected number of visits from one state to the other show the existence of a possible relation between Sunspots and ENSO data. Moreover, perfect validation of dependency and stationary tests endorses the applicability of the Markov chain analyses on Sunspots and ENSO data. This shows that a significant relation between Sunspots and ENSO data exists. Improved understanding and modelling of Sunspots variations can help to explore the information about the related variables. This study can be useful to explore the influence of ENSO related local climatic variability.

  7. 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

  8. Classification of acoustic emission signals using wavelets and Random Forests : Application to localized corrosion

    NASA Astrophysics Data System (ADS)

    Morizet, N.; Godin, N.; Tang, J.; Maillet, E.; Fregonese, M.; Normand, B.

    2016-03-01

    This paper aims to propose a novel approach to classify acoustic emission (AE) signals deriving from corrosion experiments, even if embedded into a noisy environment. To validate this new methodology, synthetic data are first used throughout an in-depth analysis, comparing Random Forests (RF) to the k-Nearest Neighbor (k-NN) algorithm. Moreover, a new evaluation tool called the alter-class matrix (ACM) is introduced to simulate different degrees of uncertainty on labeled data for supervised classification. Then, tests on real cases involving noise and crevice corrosion are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as input of the RF algorithm. To this end, a software called RF-CAM has been developed. Results show that this approach is very efficient on ground truth data and is also very promising on real data, especially for its reliability, performance and speed, which are serious criteria for the chemical industry.

  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. 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

  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. 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.

  13. Combined radiotherapy and chemotherapy versus radiotherapy alone in locally advanced epidermoid bronchogenic carcinoma. A randomized study

    SciTech Connect

    Trovo, M.G.; Minatel, E.; Veronesi, A.; Roncadin, M.; De Paoli, A.; Franchin, G.; Magri, D.M.; Tirelli, U.; Carbone, A.; Grigoletto, E. )

    1990-02-01

    Between June 1980 and December 1983, 111 patients with inoperable epidermoid bronchogenic carcinoma (limited disease) were entered into a randomized trial comparing radiotherapy alone versus radiotherapy and combination chemotherapy with cyclophosphamide, Adriamycin (doxorubicin), methotrexate, and procarbazine. Thirty-five of 62 (56.4%) patients treated with 4500 rad in 15 fractions in 3 weeks and 19 of 49 (38.8%) patients treated with the same radiation treatment and chemotherapy had an objective response. The difference in response rate was not significant (P = 0.900). Median time to progression was 5.9 and 7.02 months, respectively, for the radiation treatment and the combined treatment. Median survival was 11.74 and 10.03 months, respectively, without statistically significant differences between the two groups of patients. The toxicity was acceptable and no treatment-related death occurred in either treatment schedule. In this study no significant superiority of combined radiotherapy and chemotherapy treatment over radiation therapy alone was evidenced. Whether different chemotherapy regimens may prove more effective in this context should be clarified by further studies.

  14. Semi-automatic medical image segmentation with adaptive local statistics in Conditional Random Fields framework.

    PubMed

    Hu, Yu-Chi J; Grossberg, Michael D; Mageras, Gikas S

    2008-01-01

    Planning radiotherapy and surgical procedures usually require onerous manual segmentation of anatomical structures from medical images. In this paper we present a semi-automatic and accurate segmentation method to dramatically reduce the time and effort required of expert users. This is accomplished by giving a user an intuitive graphical interface to indicate samples of target and non-target tissue by loosely drawing a few brush strokes on the image. We use these brush strokes to provide the statistical input for a Conditional Random Field (CRF) based segmentation. Since we extract purely statistical information from the user input, we eliminate the need of assumptions on boundary contrast previously used by many other methods, A new feature of our method is that the statistics on one image can be reused on related images without registration. To demonstrate this, we show that boundary statistics provided on a few 2D slices of volumetric medical data, can be propagated through the entire 3D stack of images without using the geometric correspondence between images. In addition, the image segmentation from the CRF can be formulated as a minimum s-t graph cut problem which has a solution that is both globally optimal and fast. The combination of a fast segmentation and minimal user input that is reusable, make this a powerful technique for the segmentation of medical images. PMID:19163362

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. On the entropy of a hidden Markov process⋆

    PubMed Central

    Jacquet, Philippe; Seroussi, Gadiel; Szpankowski, Wojciech

    2008-01-01

    We study the entropy rate of a hidden Markov process (HMP) defined by observing the output of a binary symmetric channel whose input is a first-order binary Markov process. Despite the simplicity of the models involved, the characterization of this entropy is a long standing open problem. By presenting the probability of a sequence under the model as a product of random matrices, one can see that the entropy rate sought is equal to a top Lyapunov exponent of the product. This offers an explanation for the elusiveness of explicit expressions for the HMP entropy rate, as Lyapunov exponents are notoriously difficult to compute. Consequently, we focus on asymptotic estimates, and apply the same product of random matrices to derive an explicit expression for a Taylor approximation of the entropy rate with respect to the parameter of the binary symmetric channel. The accuracy of the approximation is validated against empirical simulation results. We also extend our results to higher-order Markov processes and to Rényi entropies of any order. PMID:19169438

  20. Isotropic Markov semigroups on ultra-metric spaces

    NASA Astrophysics Data System (ADS)

    Bendikov, A. D.; Grigor'yan, A. A.; Pittet, Ch; Woess, W.

    2014-08-01

    Let (X,d) be a separable ultra-metric space with compact balls. Given a reference measure μ on X and a distance distribution function σ on \\lbrack 0,∞), a symmetric Markov semigroup \\{Pt\\}t≥slant 0 acting in L2(X,μ ) is constructed. Let \\{Xt\\} be the corresponding Markov process. The authors obtain upper and lower bounds for its transition density and its Green function, give a transience criterion, estimate its moments, and describe the Markov generator L and its spectrum, which is pure point. In the particular case when X={Q}pn, where {Q}p is the field of p-adic numbers, the construction recovers the Taibleson Laplacian (spectral multiplier), and one can also apply the theory to the study of the Vladimirov Laplacian. Even in this well-established setting, several of the results are new. The paper also describes the relation between the processes involved and Kigami's jump processes on the boundary of a tree which are induced by a random walk. In conclusion, examples illustrating the interplay between the fractional derivatives and random walks are provided. Bibliography: 66 titles.

  1. 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

  2. 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). PMID:26285807

  3. Directed polymer in a random medium of dimension 1+3: multifractal properties at the localization-delocalization transition.

    PubMed

    Monthus, Cécile; Garel, Thomas

    2007-05-01

    We consider the model of the directed polymer in a random medium of dimension 1+3 , and investigate its multifractal properties at the localization-delocalization transition. In close analogy with models of the quantum Anderson localization transition, where the multifractality of critical wavefunctions is well established, we analyze the statistics of the position weights w{L}(r[over]) of the endpoint of the polymer of length L via the moments [equation: see text]. We measure the generalized exponents tau(q) and tau[over](q) governing the decay of the typical values [equation: see text] and disorder-averaged values Y{q}(L)[over] approximately L{-tau[over](q)} , respectively. To understand the difference between these exponents, tau(q) not equal to tau[over](q) above some threshold q>q{c} approximately 2 , we compute the probability distributions of [equation: see text] over the samples: We find that these distributions becomes scale invariant with a power-law tail 1/y{1+x{q}} . These results thus correspond to the Evers-Mirlin scenario [Phys. Rev. Lett. 84, 3690 (2000)] for the statistics of inverse participation ratios at the Anderson localization transitions. Finally, the finite-size scaling analysis in the critical region yields the correlation length exponent nu approximately 2 . PMID:17677037

  4. Multiple pattern matching: a Markov chain approach.

    PubMed

    Lladser, Manuel E; Betterton, M D; Knight, Rob

    2008-01-01

    RNA motifs typically consist of short, modular patterns that include base pairs formed within and between modules. Estimating the abundance of these patterns is of fundamental importance for assessing the statistical significance of matches in genomewide searches, and for predicting whether a given function has evolved many times in different species or arose from a single common ancestor. In this manuscript, we review in an integrated and self-contained manner some basic concepts of automata theory, generating functions and transfer matrix methods that are relevant to pattern analysis in biological sequences. We formalize, in a general framework, the concept of Markov chain embedding to analyze patterns in random strings produced by a memoryless source. This conceptualization, together with the capability of automata to recognize complicated patterns, allows a systematic analysis of problems related to the occurrence and frequency of patterns in random strings. The applications we present focus on the concept of synchronization of automata, as well as automata used to search for a finite number of keywords (including sets of patterns generated according to base pairing rules) in a general text. PMID:17668213

  5. 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.

  6. 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

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

    PubMed

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

    1999-06-01

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

  8. Constructing 1/omegaalpha noise from reversible Markov chains.

    PubMed

    Erland, Sveinung; Greenwood, Priscilla E

    2007-09-01

    This paper gives sufficient conditions for the output of 1/omegaalpha 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/omegaalpha condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/omega noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/omegaalpha noise which also has a long memory. PMID:17930206

  9. Effectiveness of splinting and splinting plus local steroid injection in severe carpal tunnel syndrome: A Randomized control clinical trial

    PubMed Central

    Khosrawi, Saeid; Emadi, Masoud; Mahmoodian, Amir Ebrahim

    2016-01-01

    Background: The Study aimed to compare the effectiveness of two commonly used conservative treatments, splinting and local steroid injection in improving clinical and nerve conduction findings of the patients with severe carpal tunnel syndrome (CTS). Materials and Methods: In this randomized control clinical trial, the patients with severe CTS selected and randomized in two interventional groups. Group A was prescribed to use full time neutral wrist splint and group B was injected with 40 mg Depo-Medrol and prescribed to use the full time neutral wrist splint for 12 weeks. Clinical and nerve conduction findings of the patients was evaluated at baseline, 4 and 12 weeks after interventions. Results: Twenty-two and 21 patients were allocated in group A and B, respectively. Mean of clinical symptoms and functional status scores, nerve conduction variables and patients’ satisfaction score were not significant between group at baseline and 4 and 12 weeks after intervention. Within the group comparison, there was significant improvement in the patients’ satisfaction, clinical and nerve conduction items between the baseline level and 4 weeks after intervention and between the baseline and 12 weeks after intervention (P < 0.01). The difference was significant for functional status score between 4 and 12 weeks after intervention in group B (P = 0.02). Conclusion: considering some findings regarding the superior effect of splinting plus local steroid injection on functional status scale and median nerve distal motor latency, it seems that using combination therapy could be more effective for long-term period specially in the field of functional improvement of CTS. PMID:26962518

  10. 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.

  11. 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.

  12. COCIS: Markov processes in single molecule fluorescence

    PubMed Central

    Talaga, David S.

    2009-01-01

    This article examines the current status of Markov processes in single molecule fluorescence. For molecular dynamics to be described by a Markov process, the Markov process must include all states involved in the dynamics and the FPT distributions out of those states must be describable by a simple exponential law. The observation of non-exponential first-passage time distributions or other evidence of non-Markovian dynamics is common in single molecule studies and offers an opportunity to expand the Markov model to include new dynamics or states that improve understanding of the system. PMID:19543444

  13. A compositional framework for Markov processes

    NASA Astrophysics Data System (ADS)

    Baez, John C.; Fong, Brendan; Pollard, Blake S.

    2016-03-01

    We define the concept of an "open" Markov process, or more precisely, continuous-time Markov chain, which is one where probability can flow in or out of certain states called "inputs" and "outputs." One can build up a Markov process from smaller open pieces. This process is formalized by making open Markov processes into the morphisms of a dagger compact category. We show that the behavior of a detailed balanced open Markov process is determined by a principle of minimum dissipation, closely related to Prigogine's principle of minimum entropy production. Using this fact, we set up a functor mapping open detailed balanced Markov processes to open circuits made of linear resistors. We also describe how to "black box" an open Markov process, obtaining the linear relation between input and output data that holds in any steady state, including nonequilibrium steady states with a nonzero flow of probability through the system. We prove that black boxing gives a symmetric monoidal dagger functor sending open detailed balanced Markov processes to Lagrangian relations between symplectic vector spaces. This allows us to compute the steady state behavior of an open detailed balanced Markov process from the behaviors of smaller pieces from which it is built. We relate this black box functor to a previously constructed black box functor for circuits.

  14. 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

  15. 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

  16. Markov constant and quantum instabilities

    NASA Astrophysics Data System (ADS)

    Pelantová, Edita; Starosta, Štěpán; Znojil, Miloslav

    2016-04-01

    For a qualitative analysis of spectra of certain two-dimensional rectangular-well quantum systems several rigorous methods of number theory are shown productive and useful. These methods (and, in particular, a generalization of the concept of Markov constant known in Diophantine approximation theory) are shown to provide a new mathematical insight in the phenomenologically relevant occurrence of anomalies in the spectra. Our results may inspire methodical innovations ranging from the description of the stability properties of metamaterials and of certain hiddenly unitary quantum evolution models up to the clarification of the mechanisms of occurrence of ghosts in quantum cosmology.

  17. 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.

  18. Robust Dynamics and Control of a Partially Observed Markov Chain

    SciTech Connect

    Elliott, R. J. Malcolm, W. P. Moore, J. P.

    2007-12-15

    In a seminal paper, Martin Clark (Communications Systems and Random Process Theory, Darlington, 1977, pp. 721-734, 1978) showed how the filtered dynamics giving the optimal estimate of a Markov chain observed in Gaussian noise can be expressed using an ordinary differential equation. These results offer substantial benefits in filtering and in control, often simplifying the analysis and an in some settings providing numerical benefits, see, for example Malcolm et al. (J. Appl. Math. Stoch. Anal., 2007, to appear).Clark's method uses a gauge transformation and, in effect, solves the Wonham-Zakai equation using variation of constants. In this article, we consider the optimal control of a partially observed Markov chain. This problem is discussed in Elliott et al. (Hidden Markov Models Estimation and Control, Applications of Mathematics Series, vol. 29, 1995). The innovation in our results is that the robust dynamics of Clark are used to compute forward in time dynamics for a simplified adjoint process. A stochastic minimum principle is established.

  19. 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

  20. 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…

  1. 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.

  2. Generators of quantum Markov semigroups

    NASA Astrophysics Data System (ADS)

    Androulakis, George; Ziemke, Matthew

    2015-08-01

    Quantum Markov Semigroups (QMSs) originally arose in the study of the evolutions of irreversible open quantum systems. Mathematically, they are a generalization of classical Markov semigroups where the underlying function space is replaced by a non-commutative operator algebra. In the case when the QMS is uniformly continuous, theorems due to the works of Lindblad [Commun. Math. Phys. 48, 119-130 (1976)], Stinespring [Proc. Am. Math. Soc. 6, 211-216 (1955)], and Kraus [Ann. Phys. 64, 311-335 (1970)] imply that the generator of the semigroup has the form L ( A ) = ∑ n = 1 ∞ Vn ∗ A V n + G A + A G ∗ , where Vn and G are elements of the underlying operator algebra. In the present paper, we investigate the form of the generators of QMSs which are not necessarily uniformly continuous and act on the bounded operators of a Hilbert space. We prove that the generators of such semigroups have forms that reflect the results of Lindblad and Stinespring. We also make some progress towards forms reflecting Kraus' result. Finally, we look at several examples to clarify our findings and verify that some of the unbounded operators we are using have dense domains.

  3. Scaling random walks on arbitrary sets

    NASA Astrophysics Data System (ADS)

    Harris, Simon C.; Williams, David; Sibson, Robin

    1999-01-01

    Let I be a countably infinite set of points in [open face R] which we can write as I={ui: i[set membership][open face Z]}, with uiMarkov chain Y={Y(t): t[gt-or-equal, slanted]0} with state space I such that:Y is driftless; andY jumps only between nearest neighbours.We remember that the simple symmetric random-walk, when repeatedly rescaled suitably in space and time, looks more and more like a Brownian motion. In this paper we explore the convergence properties of the Markov chain Y on the set I under suitable space-time scalings. Later, we consider some cases when the set I consists of the points of a renewal process and the jump rates assigned to each state in I are perhaps also randomly chosen.This work sprang from a question asked by one of us (Sibson) about ‘driftless nearest-neighbour’ Markov chains on countable subsets I of [open face R]d, work of Sibson [7] and of Christ, Friedberg and Lee [2] having identified examples of such chains in terms of the Dirichlet tessellation associated with I. Amongst methods which can be brought to bear on this d-dimensional problem is the theory of Dirichlet forms. There are potential problems in doing this because we wish I to be random (for example, a realization of a Poisson point process), we do not wish to impose artificial boundedness conditions which would clearly make things work for certain deterministic sets I. In the 1-dimensional case discussed here and in the following paper by Harris, much simpler techniques (where we embed the Markov chain in a Brownian motion using local time) work very effectively; and it is these, rather than the theory of Dirichlet forms, that we use.

  4. Testing the Markov hypothesis in fluid flows

    NASA Astrophysics Data System (ADS)

    Meyer, Daniel W.; Saggini, Frédéric

    2016-05-01

    Stochastic Markov processes are used very frequently to model, for example, processes in turbulence and subsurface flow and transport. Based on the weak Chapman-Kolmogorov equation and the strong Markov condition, we present methods to test the Markov hypothesis that is at the heart of these models. We demonstrate the capabilities of our methodology by testing the Markov hypothesis for fluid and inertial particles in turbulence, and fluid particles in the heterogeneous subsurface. In the context of subsurface macrodispersion, we find that depending on the heterogeneity level, Markov models work well above a certain scale of interest for media with different log-conductivity correlation structures. Moreover, we find surprising similarities in the velocity dynamics of the different media considered.

  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. 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

  7. Effect of addition of magnesium to local anesthetics for peribulbar block: A prospective randomized double-blind study

    PubMed Central

    Sinha, R; Sharma, A; Ray, BR; Chandiran, R; Chandralekha, C; Sinha, R

    2016-01-01

    Background: Magnesium sulphate has been used along with local anesthetics in different regional blocks and found to be effective in decreasing the time of onset of the block and increasing the duration of the block. Objective: To evaluate the effect of addition of magnesium sulfate to standard local anesthetics mixture on the time for onset of the globe and lid akinesia for peribulbar block in ophthalmic surgeries. Materials and Methods: Sixty patients with American Society of Anesthesiologists status I to III undergoing ophthalmic surgery under peribulbar block were included in this study. Patients were randomized into two groups. Both the groups received 4.5 ml of 2% lidocaine, 4.5 ml of 0.5% bupivacaine with150 IU hyaluronidase. Group NS received normal saline 1 ml in the peribulbar block and Group MS, magnesium sulfate 50 mg in 1 ml normal saline. The onset of akinesia, satisfactory block and complications were observed by an independent observer. Results: Demographic data was statistically similar. In the Group NS at 3, 5, 10 and 15 min after the block, complete akinesia was seen in 0, 2, 11 and 28 patients respectively. In the Group MS, at 3, 5, 10 and 15 min after the block, complete akinesia was seen in 13, 23, 27 and 28 patients respectively. Patients received magnesium sulfate showed the statistically significant rapid onset of lid and globe akinesia than the control group till 10 min (P < 0.000). None of the patients needed a supplementary block and had complications during the surgery. Conclusion: Addition of 50 mg of magnesium sulfate to the lidocaine-bupivacaine mixture for peribulbar block decreases the onset of akinesia without any obvious side effect. PMID:26955313

  8. 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.

  9. Deriving non-homogeneous DNA Markov chain models by cluster analysis algorithm minimizing multiple alignment entropy.

    PubMed

    Borodovsky, M; Peresetsky, A

    1994-09-01

    Non-homogeneous Markov chain models can represent biologically important regions of DNA sequences. The statistical pattern that is described by these models is usually weak and was found primarily because of strong biological indications. The general method for extracting similar patterns is presented in the current paper. The algorithm incorporates cluster analysis, multiple alignment and entropy minimization. The method was first tested using the set of DNA sequences produced by Markov chain generators. It was shown that artificial gene sequences, which initially have been randomly set up along the multiple alignment panels, are aligned according to the hidden triplet phase. Then the method was applied to real protein-coding sequences and the resulting alignment clearly indicated the triplet phase and produced the parameters of the optimal 3-periodic non-homogeneous Markov chain model. These Markov models were already employed in the GeneMark gene prediction algorithm, which is used in genome sequencing projects. The algorithm can also handle the case in which the sequences to be aligned reveal different statistical patterns, such as Escherichia coli protein-coding sequences belonging to Class II and Class III. The algorithm accepts a random mix of sequences from different classes, and is able to separate them into two groups (clusters), align each cluster separately, and define a non-homogeneous Markov chain model for each sequence cluster. PMID:7952897

  10. 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.

  11. [Felbinac gel for treatment of localized extra-articular rheumatic diseases--a multicenter, placebo controlled, randomized study].

    PubMed

    Bolten, W

    1991-01-01

    281 patients with extra-articular rheumatic disorders (enthesiopathy, bursitis, tendinosis, fibrositis) and moderate or severe localized pain during rest or movement in shoulder, neck, elbow or knee were randomized into groups and treated for 14 days in a double blind study with either 1 g Felbinac Gel 3% (biphenyl acetic acid) three times daily (N = 142) or with the gel formulation only (N = 139). In 50% of the patients treated with Felbinac Gel compared to 29% of the placebo treated patients (p = 0.001), the investigator assessed the global therapeutic success to be good or very good. The magnitude of complaints judged on the basis of a visual analogous scale by patients and doctor showed a significant improvement in pain reduction during rest or activity after 14 days of treatment in the Felbinac group. The rheumatic complaints diminished equally according to patient judgement in both treatment groups and the concomitant use of paracetamol was low in both groups. No significant side-effects or changes in laboratory parameters were observed during therapy. Felbinac Gel therefore is suitable for a low-risk topical therapy of soft tissue rheumatic disorders. PMID:1872042

  12. Detection and measurement of fetal abdominal contour in ultrasound images via local phase information and iterative randomized Hough transform.

    PubMed

    Wang, Weiming; Qin, Jing; Zhu, Lei; Ni, Dong; Chui, Yim-Pan; Heng, Pheng-Ann

    2014-01-01

    Due to the characteristic artifacts of ultrasound images, e.g., speckle noise, shadows and intensity inhomogeneity, traditional intensity-based methods usually have limited success on the segmentation of fetal abdominal contour. This paper presents a novel approach to detect and measure the abdominal contour from fetal ultrasound images in two steps. First, a local phase-based measure called multiscale feature asymmetry (MSFA) is de ned from the monogenic signal to detect the boundaries of fetal abdomen. The MSFA measure is intensity invariant and provides an absolute measurement for the signi cance of features in the image. Second, in order to detect the ellipse that ts to the abdominal contour, the iterative randomized Hough transform is employed to exclude the interferences of the inner boundaries, after which the detected ellipse gradually converges to the outer boundaries of the abdomen. Experimental results in clinical ultrasound images demonstrate the high agreement between our approach and manual approach on the measurement of abdominal circumference (mean sign difference is 0.42% and correlation coef cient is 0.9973), which indicates that the proposed approach can be used as a reliable and accurate tool for obstetrical care and diagnosis. PMID:24212021

  13. Randomized Double-Blind Clinical Trial of Eutectic Mixture of Local Anesthetic Creams in Reducing Pain During Hysterosalpingography

    PubMed Central

    Kalantari, Mojgan; Zadeh Modares, Shahrzad; Ahmadi, Firoozeh; Hazari, Vajihe; Haghighi, Hadieh; Chehrazi, Mohammad; Razaghi, Melika

    2014-01-01

    Background: Hysterosalpingography (HSG) is considered as a primary test in infertility work up worldwide due to its reliability in evaluating abnormalities related to the uterus and fallopian tubes. Objectives: To assess the efficacy of applying eutectic mixture of local anesthetics (lidocaine-prilocaine cream) (EMLA) on the uterine cervix in reducing pain during HSG. Patients and Methods: Eighty patients undergoing HSG as part of infertility evaluation were randomly allocated to groups receiving either EMLA (N = 40) or placebo cream (N = 40) in a double-blinded prospective study. Fifteen minutes before HSG, 5 grams of 5% cream was applied to the uterine cervix using a cervical applicator. The degree of pain experienced by the patient was evaluated during and after HSG at five predefined steps on a visual analogue scale (VAS). Results: There was no significant difference in the efficacy between EMLA and placebo creams in pain perception during the entire procedure. There was no significant difference in long term pain perception half an hour after the HSG performance. Conclusions: This study does not support the use of EMLA for HSG. PMID:25780541

  14. The role of the local chemical environment of Ag on the resistive switching mechanism of conductive bridging random access memories.

    PubMed

    Souchier, E; D'Acapito, F; Noé, P; Blaise, P; Bernard, M; Jousseaume, V

    2015-10-01

    Conductive bridging random access memories (CBRAMs) are one of the most promising emerging technologies for the next generation of non-volatile memory. However, the lack of understanding of the switching mechanism at the nanoscale level prevents successful transfer to industry. In this paper, Ag/GeSx/W CBRAM devices are analyzed using depth selective X-ray Absorption Spectroscopy before and after switching. The study of the local environment around Ag atoms in such devices reveals that Ag is in two very distinct environments with short Ag-S bonds due to Ag dissolved in the GeSx matrix, and longer Ag-Ag bonds related to an Ag metallic phase. These experiments allow the conclusion that the switching process involves the formation of metallic Ag nano-filaments initiated at the Ag electrode. All these experimental features are well supported by ab initio molecular dynamics simulations showing that Ag favorably bonds to S atoms, and permit the proposal of a model at the microscopic level that can explain the instability of the conductive state in these Ag-GeSx CBRAM devices. Finally, the principle of the nondestructive method described here can be extended to other types of resistive memory concepts. PMID:26312954

  15. 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

  16. 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).

  17. Visual Tracking via Random Walks on Graph Model.

    PubMed

    Li, Xiaoli; Han, Zhifeng; Wang, Lijun; Lu, Huchuan

    2016-09-01

    In this paper, we formulate visual tracking as random walks on graph models with nodes representing superpixels and edges denoting relationships between superpixels. We integrate two novel graphs with the theory of Markov random walks, resulting in two Markov chains. First, an ergodic Markov chain is enforced to globally search for the candidate nodes with similar features to the template nodes. Second, an absorbing Markov chain is utilized to model the temporal coherence between consecutive frames. The final confidence map is generated by a structural model which combines both appearance similarity measurement derived by the random walks and internal spatial layout demonstrated by different target parts. The effectiveness of the proposed Markov chains as well as the structural model is evaluated both qualitatively and quantitatively. Experimental results on challenging sequences show that the proposed tracking algorithm performs favorably against state-of-the-art methods. PMID:26292358

  18. Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue

    PubMed Central

    Alomari, Yazan M.; MdZin, Reena Rahayu

    2015-01-01

    Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved. PMID:25793010

  19. Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue.

    PubMed

    Alomari, Yazan M; Sheikh Abdullah, Siti Norul Huda; MdZin, Reena Rahayu; Omar, Khairuddin

    2015-01-01

    Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved. PMID:25793010

  20. 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.

  1. [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

  2. 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

  3. Prospective randomized study comparing concomitant chemoradiotherapy using weekly cisplatin & paclitaxel versus weekly cisplatin in locally advanced carcinoma cervix

    PubMed Central

    Seam, Rajeev; Gupta, Manoj; Gupta, Manish

    2016-01-01

    Background To evaluate the benefit with the addition of paclitaxel to cisplatin-based concurrent chemoradiotherapy (C-CRT) for the treatment of locally advanced carcinoma of the uterine cervix in terms of local control, disease free survival (DFS) and overall survival (OS). Methods From 1/7/2011 to 31/5/2012, 81 women (median age of 50 years) with newly diagnosed, histopathologically proven carcinoma cervix with FIGO stages IIA to IIIB were randomized to two arms—cisplatin 40 mg/m2/week for 5 weeks was given in single agent cisplatin (control arm), while cisplatin 30 mg/m2/week and paclitaxel 50 mg/m2/week for 5 weeks were given in cisplatin and paclitaxel (study arm). External beam radiotherapy (EBRT) was delivered to a total dose of 50 Gray (Gy) in 25 fractions (#) followed by intracavitary (I/C) brachytherapy or supplement EBRT at 20 Gy/10# with 2 cycles of respective chemotherapy. This prospective trial was registered with clinicaltrials.gov (NCT01593306). Results Patients (n=81) had a maximum follow up of 36 months with a median follow up of 29 months. At first follow up study arm showed complete response in 84% vs. 75.6% in control arm (P=0.4095). An increase in toxicities was observed in the study arm in comparison to the control arm in terms of haematological grade II (35% vs. 12.2%), gastrointestinal (GI) grade III (20% vs. 7.4%) and GI grade IV (12.5% vs. 2.4%) toxicities. At median follow-up, the study arm demonstrated enhanced outcomes over the control arm in terms of DFS (79.5% vs. 64.3%; P=0.07) and OS (87.2% vs. 78.6%; P=0.27). Conclusions Despite the expected increase in manageable toxicities, these early results reveal promise with the inclusion of paclitaxel into the standard cisplatin based chemoradiation regime. Larger multi-institutional studies are justified to confirm a potential for the enhancement of response rates and survival. PMID:26904570

  4. A randomized study of inpatient versus outpatient continuous infusion chemotherapy for patients with locally advanced head and neck cancer.

    PubMed

    Vokes, E E; Schilsky, R L; Choi, K E; Magid, D M; Guarnieri, C M; Whaling, S M; Ratain, M J; Weichselbaum, R R; Panje, W R

    1989-01-01

    This study was designed to evaluate the safety, reliability, and patient acceptance of outpatient continuous intravenous infusion (CVI) chemotherapy. Twenty-two patients with locally advanced head and neck cancer received induction chemotherapy with methotrexate, cisplatin and a 5-day CVI of 5-fluorouracil (5-FU). Patients were randomized to receive the 5-FU portion of cycle 1 either by a standard inpatient CVI chemotherapy delivery device (standard pump) or by the Infusor (Baxter Healthcare Corporation, Deerfield, IL), a portable chemotherapy delivery system that provides a constant flow of drug over a period of 24 hours. For cycle 2, patients crossed over to the alternative drug delivery method. Patients receiving chemotherapy via the Infusor could choose to be either inpatients or outpatients. Daily plasma concentrations of 5-FU were determined during the first two cycles of chemotherapy. There was no significant difference in the mean steady state plasma 5-FU levels achieved with either drug delivery method (329.7 +/- 95.8 ng/ml for infusor cycles vs. 352.8 +/- 114.9 ng/ml for standard pump cycles). Clinical toxicities consisted primarily of mucositis for both methods of drug delivery. Eight patients declined to receive CVI chemotherapy as outpatients citing as reasons fear of malfunction of the device, inconvenience of the frequent clinic visits necessitated by daily monitoring of plasma 5-FU concentrations, and restrictions in daily home activities. Eleven patients underwent CVI chemotherapy via Infusor as outpatients. All reported outpatient CVI chemotherapy as convenient and effective and, when eligible, chose it again in subsequent cycles. A comparison of estimated costs revealed reductions in daily costs of +366.00 (+2,200.00 per cycle) for outpatient chemotherapy. Outpatient CVI chemotherapy is a reliable drug delivery method that was accepted by a majority of patients in this study. These factors may help to establish outpatient CVI chemotherapy as a

  5. Can local application of Tranexamic acid reduce post-coronary bypass surgery blood loss? A randomized controlled trial

    PubMed Central

    Fawzy, Hosam; Elmistekawy, Elsayed; Bonneau, Daniel; Latter, David; Errett, Lee

    2009-01-01

    Background Diffuse microvascular bleeding remains a common problem after cardiac procedures. Systemic use of antifibrinolytic reduces the postoperative blood loss. The purpose of this study was to examine the effectiveness of local application of tranexamic acid to reduce blood loss after coronary artery bypass grafting (CABG). Methods Thirty eight patients scheduled for primary isolated coronary artery bypass grafting were included in this double blind, prospective, randomized, placebo controlled study. Tranexamic acid (TA) group (19 patients) received 1 gram of TA diluted in 100 ml normal saline. Placebo group (19 patients) received 100 ml of normal saline only. The solution was purred in the pericardial and mediastinal cavities. Results Both groups were comparable in their baseline demographic and surgical characteristics. During the first 24 hours post-operatively, cumulative blood loss was significantly less in TA group (median of 626 ml) compared to Placebo group (median of 1040 ml) (P = 0.04). There was no significant difference in the post-op Packed RBCs transfusion between both groups (median of one unit in each) (P = 0.82). Significant less platelets transfusion required in TA group (median zero unit) than in placebo group (median 2 units) (P = 0.03). Apart from re-exploration for excessive surgical bleeding in one patient in TA group, no difference was found in morbidity or mortality between both groups. Conclusion Topical application of tranexamic acid in patients undergoing primary coronary artery bypass grafting led to a significant reduction in postoperative blood loss without adding extra risk to the patient. PMID:19538741

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. Comparison of Dexmedetomidine and Clonidine as Adjuvants to Local Anesthetics for Intrathecal Anesthesia: A Meta-Analysis of Randomized Controlled Trials.

    PubMed

    Zhang, Changsheng; Li, Changtian; Pirrone, Massimiliano; Sun, Li; Mi, Weidong

    2016-07-01

    The authors performed a meta-analysis to compare the characteristics of clonidine and dexmedetomidine as adjuvants to local anesthetic in intravertebral anesthesia. Four investigators independently searched electronic databases for randomized trials comparing the characteristics of clonidine vs dexmedetomidine as adjuvants to local anesthetic on adults. The endpoints were onset of analgesia, sensory and motor block, and duration of analgesia. A random-effects model was used to perform quantitative analysis. Seven studies comprising 354 subjects were included in this meta-analysis. The onset of sensory block was significantly 40 seconds shorter when dexmedetomidine was added as an adjuvant in the intrathecal injection. The duration of stable sensory block, duration of overall sensory block, and the time before the need for analgesic requirements were significantly extended, 10.8 minutes, 22.3 minutes, and 38.6 minutes, respectively, when dexmedetomidine was used as an adjuvant to local anesthetics (bupivacaine or ropivacaine). No significant differences were detected in the motor block characteristics and the time to achieve peak sensory level between dexmedetomidine and clonidine as adjuvants to local anesthetics. Compared to clonidine, the addition of dexmedetomidine as an adjuvant to local anesthetics is associated with earlier, prolonged sensory block characteristics and later need for analgesic requirements. PMID:26510095

  12. 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.

  13. 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.

  14. 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.

  15. Markov chains at the interface of combinatorics, computing, and statistical physics

    NASA Astrophysics Data System (ADS)

    Streib, Amanda Pascoe

    The fields of statistical physics, discrete probability, combinatorics, and theoretical computer science have converged around efforts to understand random structures and algorithms. Recent activity in the interface of these fields has enabled tremendous breakthroughs in each domain and has supplied a new set of techniques for researchers approaching related problems. This thesis makes progress on several problems in this interface whose solutions all build on insights from multiple disciplinary perspectives. First, we consider a dynamic growth process arising in the context of DNA-based self-assembly. The assembly process can be modeled as a simple Markov chain. We prove that the chain is rapidly mixing for large enough bias in regions of Zd. The proof uses a geometric distance function and a variant of path coupling in order to handle distances that can be exponentially large. We also provide the first results in the case of fluctuating bias, where the bias can vary depending on the location of the tile, which arises in the nanotechnology application. Moreover, we use intuition from statistical physics to construct a choice of the biases for which the Markov chain Mmon requires exponential time to converge. Second, we consider a related problem regarding the convergence rate of biased permutations that arises in the context of self-organizing lists. The Markov chain Mnn in this case is a nearest-neighbor chain that allows adjacent transpositions, and the rate of these exchanges is governed by various input parameters. It was conjectured that the chain is always rapidly mixing when the inversion probabilities are positively biased, i.e., we put nearest neighbor pair x < y in order with bias 1/2 ≤ pxy ≤ 1 and out of order with bias 1 - pxy. The Markov chain Mmon was known to have connections to a simplified version of this biased card-shuffling. We provide new connections between Mnn and Mmon by using simple combinatorial bijections, and we prove that Mnn is

  16. 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.

  17. 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.

  18. 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.

  19. Prospective randomized comparison between ultrasound-guided saphenous nerve block within and distal to the adductor canal with low volume of local anesthetic

    PubMed Central

    Adoni, Areti; Paraskeuopoulos, Tilemachos; Saranteas, Theodosios; Sidiropoulou, Tatiana; Mastrokalos, Dimitrios; Kostopanagiotou, Georgia

    2014-01-01

    Background and Aims: The anatomic site and the volume of local anesthetic needed for an ultrasound-guided saphenous nerve block differ in the literature. The purpose of this study was to examine the effect of two different ultrasound-guided low volume injections of local anesthetic on saphenous and vastus medialis nerves. Materials and Methods: Recruited patients (N = 48) scheduled for orthopedic surgery were randomized in two groups; Group distal adductor canal (DAC): Ultrasound-guided injection (5 ml of local anesthetic) distal to the inferior foramina of the adductor canal. Group adductor canal (AC): Ultrasound-guided injection (5 ml local anesthetic) within the adductor canal. Following the injection of local anesthetic, block progression was monitored in 5 min intervals for 15 min in the sartorial branches of the saphenous nerve and vastus medialis nerve. Results: Twenty two patients in each group completed the study. Complete block of the saphenous nerve was observed in 55% and 59% in Group AC and DAC, respectively (P = 0.88). The proportion of patients with vastus medialis weakness at 15 min in Group AC, 36%, was significantly higher than in Group DAC (0/22), (P = 0.021). Conclusions: Low volume of local anesthetic injected within the adductor canal or distally its inferior foramina leads to moderate success rate of the saphenous nerve block, while only the injection within the adductor canal may result in vastus medialis nerve motor block. PMID:25190947

  20. Nanoscale charge localization induced by random orientations of organic molecules in hybrid perovskite CH3NH3PbI3.

    PubMed

    Ma, Jie; Wang, Lin-Wang

    2015-01-14

    Perovskite-based solar cells have achieved high solar-energy conversion efficiencies and attracted wide attentions nowadays. Despite the rapid progress in solar-cell devices, many fundamental issues of the hybrid perovskites have not been fully understood. Experimentally, it is well-known that in CH3NH3PbI3 the organic molecules CH3NH3 are randomly orientated at the room temperature, but the impact of the random molecular orientation has not been investigated. Because of the dipole moment of the organic molecule, the random orientation creates a novel system with long-range potential fluctuations unlike alloys or other conventional disordered systems. Using linear scaling ab initio methods, we find that the charge densities of the conduction band minimum and the valence band maximum are localized in nanoscales due to the potential fluctuations. The charge localization causes electron-hole separation and reduces carrier recombination rates, which may contribute to the long carrier lifetime observed in experiments. PMID:25493911

  1. 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.

  2. 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.

  3. A mixed model for two-state Markov processes under panel observation.

    PubMed

    Cook, R J

    1999-09-01

    Many chronic medical conditions can be meaningfully characterized in terms of a two-state stochastic process. Here we consider the problem in which subjects make transitions among two such states in continuous time but are only observed at discrete, irregularly spaced time points that are possibly unique to each subject. Data arising from such an observation scheme are called panel data, and methods for related analyses are typically based on Markov assumptions. The purpose of this article is to present a conditionally Markov model that accommodates subject-to-subject variation in the model parameters by the introduction of random effects. We focus on a particular random effects formulation that generates a closed-form expression for the marginal likelihood. The methodology is illustrated by application to a data set from a parasitic field infection survey. PMID:11315028

  4. 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.

  5. Wave propagation modeling with non-Markov phase screens.

    PubMed

    Charnotskii, Mikhail

    2016-04-01

    A recently introduced [J. Opt. Soc. Am. A30, 479 (2013)10.1364/JOSAA.30.000479JOAOD61084-7529] sparse spectrum (SS) model of statistically homogeneous random fields makes it possible to generate 3D samples of refractive-index fluctuations with prescribed spectral density at a very reasonable computational cost. The SS technique can be used in the framework of the split-step Fourier method for numerical simulation of wave propagation in turbulence. It allows generation of the phase screen samples that are free from the limitations of the Markov approximation, which is commonly used for theoretical description and numerical modeling of optical waves propagation through turbulence. We investigate statistics of these phase screens and present a numerical algorithm for their generation. PMID:27140765

  6. Drift and diffusion coefficients of Markov diffusional process in problems of predicting the life of atomic-power-plant equipment

    SciTech Connect

    Emel'yanov, V.S.; Klemin, A.I.; Rabchun, A.V.

    1987-06-01

    The authors construct a statistical model based on the Markov diffusion process and the Fokker-Planck-Kolmogorov equation for forecasting the remaining service life of reactor components. The model is one-dimensional and allows changes in the probabilistic characteristics of random aging processes of individual mechanical systems to be predicted with sufficient accuracy for engineering purposes.

  7. 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.

  8. Evaluation of Usability Utilizing Markov Models

    ERIC Educational Resources Information Center

    Penedo, Janaina Rodrigues; Diniz, Morganna; Ferreira, Simone Bacellar Leal; Silveira, Denis S.; Capra, Eliane

    2012-01-01

    Purpose: The purpose of this paper is to analyze the usability of a remote learning system in its initial development phase, using a quantitative usability evaluation method through Markov models. Design/methodology/approach: The paper opted for an exploratory study. The data of interest of the research correspond to the possible accesses of users…

  9. Document Ranking Based upon Markov Chains.

    ERIC Educational Resources Information Center

    Danilowicz, Czeslaw; Balinski, Jaroslaw

    2001-01-01

    Considers how the order of documents in information retrieval responses are determined and introduces a method that uses a probabilistic model of a document set where documents are regarded as states of a Markov chain and where transition probabilities are directly proportional to similarities between documents. (Author/LRW)

  10. Markov Chain Estimation of Avian Seasonal Fecundity

    EPA Science Inventory

    To explore the consequences of modeling decisions on inference about avian seasonal fecundity we generalize previous Markov chain (MC) models of avian nest success to formulate two different MC models of avian seasonal fecundity that represent two different ways to model renestin...

  11. A randomized trial of internet-based versus traditional sexual counseling for couples after localized prostate cancer treatment

    PubMed Central

    Schover, Leslie R.; Canada, Andrea L.; Yuan, Ying; Sui, Dawen; Neese, Leah; Jenkins, Rosell; Rhodes, Michelle Marion

    2014-01-01

    BACKGROUND After treatment for prostate cancer, multidisciplinary sexual rehabilitation involving couples appears more promising than traditional urologic treatment for erectile dysfunction (ED). We conducted a randomized trial comparing traditional or internet-based sexual counseling with a waitlist control. METHODS Couples were adaptively randomized to a 3-month waitlist (WL), a 3-session face-to-face format (FF), or an internet-based format with email contact with the therapist (WEB1). A second internet-based group (WEB2) was added to further examine the relationship between web site usage and outcomes. At baseline, post-waitlist, post-treatment, and at 3-, 6-, and 12-month follow-ups participants completed the International Index of Erectile Function (IIEF), the Female Sexual Function Index (FSFI), the Brief Symptom Inventory-18 to measure emotional distress, and the abbreviated Dyadic Adjustment Scale. RESULTS Outcomes did not change during the waitlist period. Of 115 couples entering the randomized trial and 71 entering the WEB2 group, 33% dropped out. However, a linear mixed model analysis including all participants confirmed improvements in IIEF scores that remained significant at 1-year follow-up (P<0.001). Women with abnormal FSFI scores initially also improved significantly (P=0.0255). Finding an effective medical treatment for ED and normal female sexual function at baseline, but not treatment format, were associated with better outcomes. In the WEB groups, only men completing more than 75% of the intervention had significant improvements in IIEF scores. CONCLUSIONS An internet-based sexual counseling program for couples is as effective as a brief, traditional sex therapy format in producing enduring improvements in men’s sexual outcomes after prostate cancer. PMID:21953578

  12. 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.

  13. 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

  14. Using higher-order Markov models to reveal flow-based communities in networks

    NASA Astrophysics Data System (ADS)

    Salnikov, Vsevolod; Schaub, Michael T.; Lambiotte, Renaud

    2016-03-01

    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.

  15. 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

  16. 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. PMID:22255076

  17. Using higher-order Markov models to reveal flow-based communities in networks.

    PubMed

    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

  18. 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

  19. Cartridge syringe vs computer controlled local anesthetic delivery system: Pain related behaviour over two sequential visits – a randomized controlled trial

    PubMed Central

    Thoppe-Dhamodhara, Yogesh-Kumar; Asokan, Sharath; John, Baby-John; Pollachi-Ramakrishnan, GeethaPriya; Ramachandran, Punithavathy; Vilvanathan, Praburajan

    2015-01-01

    Background Local anesthetic injection is one of the most anxiety provoking procedure in dentistry. Knowledge about change in pain related behaviour during consecutive visits helps in and scheduling of treatment procedures and management of children in dental clinic. Aim To compare the pain perception, behavioural response and the associated change in physiological parameters while receiving local anesthesia injection with cartridge syringe and computer controlled local anesthetic delivery system (CCLAD) over two consecutive visits. Material and Methods In this randomized controlled cross over trial, 120 children aged 7 – 11 years were randomly divided into group A: receiving injections with CCLAD during first visit; group B: receiving injections with cartridge syringe during first visit. The physiological parameters (heart rate and blood pressure) were recorded before and during injection procedure. Objective evaluation of disruptive behaviour and subjective evaluation of pain perceived were done using Face Legs Activity Cry Consolability (FLACC) scale and modified facial image scale (FIS) respectively. Results No statistical difference in pain response (p= 0.164) and disruptive behaviour (p = 0.120) between cartridge syringe and CCLAD injections were seen during the first visit although the latter showed lesser scores. However, during the second visit there were significant increase in pain response (p = 0.004) and disruptive behaviour (p = 0.006) in cartridge syringe group with an associated increase in heart rate. Conclusions Injections with CCLAD produced lesser pain ratings and disruptive behaviour than cartridge syringe in children irrespective of order of visit. Key words:Behaviour, cartridge syringe, CCLAD, local anesthesia. PMID:26535099

  20. 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

  1. 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

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. Antipersistent Markov behavior in foreign exchange markets

    NASA Astrophysics Data System (ADS)

    Baviera, Roberto; Pasquini, Michele; Serva, Maurizio; Vergni, Davide; Vulpiani, Angelo

    2002-09-01

    A quantitative check of efficiency in US dollar/Deutsche mark exchange rates is developed using high-frequency (tick by tick) data. The antipersistent Markov behavior of log-price fluctuations of given size implies, in principle, the possibility of a statistical forecast. We introduce and measure the available information of the quote sequence, and we show how it can be profitable following a particular trading rule.

  8. Markov Chains For Testing Redundant Software

    NASA Technical Reports Server (NTRS)

    White, Allan L.; Sjogren, Jon A.

    1990-01-01

    Preliminary design developed for validation experiment that addresses problems unique to assuring extremely high quality of multiple-version programs in process-control software. Approach takes into account inertia of controlled system in sense it takes more than one failure of control program to cause controlled system to fail. Verification procedure consists of two steps: experimentation (numerical simulation) and computation, with Markov model for each step.

  9. Programs Help Create And Evaluate Markov Models

    NASA Technical Reports Server (NTRS)

    Butler, Ricky W.; Boerschlein, David P.

    1993-01-01

    Pade Approximation With Scaling (PAWS) and Scaled Taylor Exponential Matrix (STEM) computer programs provide flexible, user-friendly, language-based interface for creation and evaluation of Markov models describing behaviors of fault-tolerant reconfigurable computer systems. Produce exact solution for probabilities of system failures and provide conservative estimates of numbers of significant digits in solutions. Also offer as part of bundled package with SURE and ASSIST, two other reliable analysis programs developed by Systems Validation Methods group at Langley Research Center.

  10. 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.

  11. 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

  12. 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

  13. 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

  14. Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering

    NASA Astrophysics Data System (ADS)

    Bruno, Marcelo G. S.; Dias, Stiven S.

    2014-12-01

    We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.

  15. Duration of Androgen Suppression Before Radiotherapy for Localized Prostate Cancer: Radiation Therapy Oncology Group Randomized Clinical Trial 9910

    PubMed Central

    Pisansky, Thomas M.; Hunt, Daniel; Gomella, Leonard G.; Amin, Mahul B.; Balogh, Alexander G.; Chinn, Daniel M.; Seider, Michael J.; Duclos, Marie; Rosenthal, Seth A.; Bauman, Glenn S.; Gore, Elizabeth M.; Rotman, Marvin Z.; Lukka, Himanshu R.; Shipley, William U.; Dignam, James J.; Sandler, Howard M.

    2015-01-01

    Purpose To determine whether prolonged androgen suppression (AS) duration before radiotherapy improves survival and disease control in prostate cancer. Patients and Methods One thousand five hundred seventy-nine men with intermediate-risk prostate cancer were randomly assigned to 8 weeks of AS followed by radiotherapy with an additional 8 weeks of concurrent AS (16 weeks total) or to 28 weeks of AS followed by radiotherapy with an additional 8 weeks of AS (36 weeks total). The trial sought primarily to detect a 33% reduction in the hazard of prostate cancer death in the 28-week assignment. Time-to-event end points are reported for up to 10 years of follow-up. Results There were no between-group differences in baseline characteristics of 1,489 eligible patients with follow-up. For the 8- and 28-week assignments, 10-year disease-specific survival rates were 95% (95% CI, 93.3% to 97.0%) and 96% (95% CI, 94.6% to 98.0%; hazard ratio [HR], 0.81; P = .45), respectively, and 10-year overall survival rates were 66% (95% CI, 62.0% to 69.9%) and 67% (95% CI, 63.0% to 70.8%; HR, 0.95; P = .62), respectively. For the 8- and 28-week assignments, 10-year cumulative incidences of locoregional progression were 6% (95% CI, 4.3% to 8.0%) and 4% (95% CI, 2.5% to 5.7%; HR, 0.65; P = .07), respectively; 10-year distant metastasis cumulative incidences were 6% (95% CI, 4.0% to 7.7%) and 6% (95% CI, 4.0% to 7.6%; HR, 1.07; P = .80), respectively; and 10-year prostate-specific antigen–based recurrence cumulative incidences were 27% (95% CI, 23.1% to 29.8%) and 27% (95% CI, 23.4% to 30.3%; HR, 0.97; P = .77), respectively. Conclusion Extending AS duration from 8 weeks to 28 weeks before radiotherapy did not improve outcomes. A lower than expected prostate cancer death rate reduced ability to detect a between-group difference in disease-specific survival. The schedule of 8 weeks of AS before radiotherapy plus 8 weeks of AS during radiotherapy remains a standard of care in intermediate

  16. 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.

  17. A non-homogeneous Markov model for phased-mission reliability analysis

    NASA Technical Reports Server (NTRS)

    Smotherman, Mark; Zemoudeh, Kay

    1989-01-01

    Three assumptions of Markov modeling for reliability of phased-mission systems that limit flexibility of representation are identified. The proposed generalization has the ability to represent state-dependent behavior, handle phases of random duration using globally time-dependent distributions of phase change time, and model globally time-dependent failure and repair rates. The approach is based on a single nonhomogeneous Markov model in which the concept of state transition is extended to include globally time-dependent phase changes. Phase change times are specified using nonoverlapping distributions with probability distribution functions that are zero outside assigned time intervals; the time intervals are ordered according to the phases. A comparison between a numerical solution of the model and simulation demonstrates that the numerical solution can be several times faster than simulation.

  18. 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.

  19. 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.

  20. Green tea extract as a local drug therapy on periodontitis patients with diabetes mellitus: A randomized case–control study

    PubMed Central

    Gadagi, Jayaprakash S.; Chava, Vijay K.; Reddy, Venkata Ramesh

    2013-01-01

    Background: The green tea extract is a naturally occurring product having beneficial effects that counteract with the pathobiological features of periodontitis and diabetes mellitus. Hence, the present study was aimed at incorporation of green tea extract into hydroxylpropyl methylcellulose and investigates its efficacy in chronic periodontitis patients associated with and without diabetes mellitus. Materials and Methods: For the in vitro study, formulation of green tea strips and placebo strips, and analysis of drug release pattern from the green tea strips at different time intervals were performed. For the in vivo study, 50 patients (20-65 years), including 25 systemically healthy patients with chronic periodontitis (group 1) and 25 diabetic patients with chronic periodontitis (group 2) were enrolled. In each patient, test and control sites were identified for the placement of green tea and placebo strips, respectively. Gingival Index (GI), Probing Pocket Depth (PPD), and Clinical Attachment Level (CAL) were examined at baseline, first, second, third, and fourth weeks. Microbiological analysis for Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans was performed at baseline and fourth week. Results: The in vitro study showed 10.67% green tea release at 30 min; thereafter, a slow release was noted till 120 min. In vivo study: Both groups showed significant reduction in GI scores at the test sites. Group 1 showed significant (P < 0.001) PPD reduction at different time intervals at the test sites. However, group 2 showed significant reduction from baseline (5.30 ± 0.70) to fourth week (3.5 ± 0.97). Statistically significant gain in CAL at the test sites was observed both in group 1 (1.33 mm) and group 2 (1.43 mm). The prevalence of P. gingivalis in group 1 test sites was significantly reduced from baseline (75%) to fourth week (25%). Conclusions: Local drug delivery using green tea extract could be used as an adjunct in the treatment of chronic

  1. Phase III Randomized Trial of Induction Chemotherapy in Patients With N2 or N3 Locally Advanced Head and Neck Cancer

    PubMed Central

    Cohen, Ezra E.W.; Karrison, Theodore G.; Kocherginsky, Masha; Mueller, Jeffrey; Egan, Robyn; Huang, Chao H.; Brockstein, Bruce E.; Agulnik, Mark B.; Mittal, Bharat B.; Yunus, Furhan; Samant, Sandeep; Raez, Luis E.; Mehra, Ranee; Kumar, Priya; Ondrey, Frank; Marchand, Patrice; Braegas, Bettina; Seiwert, Tanguy Y.; Villaflor, Victoria M.; Haraf, Daniel J.; Vokes, Everett E.

    2014-01-01

    Purpose Induction chemotherapy (IC) before radiotherapy lowers distant failure (DF) rates in locally advanced squamous cell carcinoma of the head and neck (SCCHN). The goal of this phase III trial was to determine whether IC before chemoradiotherapy (CRT) further improves survival compared with CRT alone in patients with N2 or N3 disease. Patients and Methods Treatment-naive patients with nonmetastatic N2 or N3 SCCHN were randomly assigned to CRT alone (CRT arm; docetaxel, fluorouracil, and hydroxyurea plus radiotherapy 0.15 Gy twice per day every other week) versus two 21-day cycles of IC (docetaxel 75 mg/m2 on day 1, cisplatin 75 mg/m2 on day 1, and fluorouracil 750 mg/m2 on days 1 to 5) followed by the same CRT regimen (IC + CRT arm). The primary end point was overall survival (OS). Secondary end points included DF-free survival, failure pattern, and recurrence-free survival (RFS). Results A total of 285 patients were randomly assigned. The most common grade 3 to 4 toxicities during IC were febrile neutropenia (11%) and mucositis (9%); during CRT (both arms combined), they were mucositis (49%), dermatitis (21%), and leukopenia (18%). Serious adverse events were more common in the IC arm (47% v 28%; P = .002). With a minimum follow-up of 30 months, there were no statistically significant differences in OS (hazard ratio, 0.91; 95% CI, 0.59 to 1.41), RFS, or DF-free survival. Conclusion IC did not translate into improved OS compared with CRT alone. However, the study was underpowered because it did not meet the planned accrual target, and OS was higher than predicted in both arms. IC cannot be recommended routinely in patients with N2 or N3 locally advanced SCCHN. PMID:25049329

  2. 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.

  3. Health-Related Quality of Life in SCALOP, a Randomized Phase 2 Trial Comparing Chemoradiation Therapy Regimens in Locally Advanced Pancreatic Cancer

    PubMed Central

    Hurt, Christopher N.; Mukherjee, Somnath; Bridgewater, John; Falk, Stephen; Crosby, Tom; McDonald, Alec; Joseph, George; Staffurth, John; Abrams, Ross A.; Blazeby, Jane M.; Bridges, Sarah; Dutton, Peter; Griffiths, Gareth; Maughan, Tim; Johnson, Colin

    2015-01-01

    Purpose Chemoradiation therapy (CRT) for patients with locally advanced pancreatic cancer (LAPC) provides survival benefits but may result in considerable toxicity. Health-related quality of life (HRQL) measurements during CRT have not been widely reported. This paper reports HRQL data from the Selective Chemoradiation in Advanced Localised Pancreatic Cancer (SCALOP) trial, including validation of the QLQ-PAN26 tool in CRT. Methods and Materials Patients with locally advanced, inoperable, nonmetastatic carcinoma of the pancreas were eligible. Following 12 weeks of induction gemcitabine plus capecitabine (GEMCAP) chemotherapy, patients with stable and responding disease were randomized to a further cycle of GEMCAP followed by capecitabine- or gemcitabine-based CRT. HRQL was assessed with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and the EORTC Pancreatic Cancer module (PAN26). Results A total of 114 patients from 28 UK centers were registered and 74 patients randomized. There was improvement in the majority of HRQL scales during induction chemotherapy. Patients with significant deterioration in fatigue, appetite loss, and gastrointestinal symptoms during CRT recovered within 3 weeks following CRT. Differences in changes in HRQL scores between trial arms rarely reached statistical significance; however, where they did, they favored capecitabine therapy. PAN26 scales had good internal consistency and were able to distinguish between subgroups of patients experiencing toxicity. Conclusions Although there is deterioration in HRQL following CRT, this resolves within 3 weeks. HRQL data support the use of capecitabine- over gemcitabine-based chemoradiation. The QLQ-PAN26 is a reliable and valid tool for use in patients receiving CRT. PMID:26530749

  4. Robust Hidden Markov Models for Geophysical Data Analysis

    NASA Astrophysics Data System (ADS)

    Granat, R. A.

    2002-12-01

    We employed robust hidden Markov models (HMMs) to perform statistical analysis of seismic events and crustal deformation. These models allowed us to classify different kinds of events or modes of deformation, and furthermore gave us a statistical basis for understanding relationships between different classes. A hidden Markov model is a statistical model for ordered data (typically in time). The observed data is assumed to have been generated by an unobservable statistical process of a particular form. This process is such that each observation is coincident with the system being in a particular discrete state. Furthermore, the next state is dependent on the current state; in other words, it is a first order Markov process. The model is completely described by a set of model parameters: the initial state probabilities, the first order Markov chain state-to-state transition probabilities, and the probabilities 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 typically 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, such as speech processing, sufficient a priori information about the system is available such that this problem can be avoided. However, for general scientific analysis, such a priori information is often not available, especially in cases where the HMM is being used as an exploratory tool for scientific understanding. Such was the case for the geophysical data sets used in this work. Our approach involves analytical location of sub-optimal local maxima; once the locations of these maxima have been found

  5. 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.

  6. Comparison of oral and vaginal misoprostol for cervical ripening before manual vacuum aspiration of first trimester pregnancy under local anesthesia: a randomized placebo-controlled study.

    PubMed

    Cakir, Leyla; Dilbaz, Berna; Caliskan, Eray; Dede, F Suat; Dilbaz, Serdar; Haberal, Ali

    2005-05-01

    The objective of this prospective randomized placebo-controlled study was to determine the effectiveness of 400 mug oral and 400 mug vaginal misoprostol administration for cervical priming 3 h prior to manual vacuum aspiration (MVA) under local anesthesia for voluntary termination of pregnancy before 10 weeks of gestation in comparison with placebo oral or placebo vaginal administration (n=40 in each group). Postmedication cervical dilatation was similar in the oral (mean, 6.6+/-1.5) and vaginal (mean, 7.2+/-0.8) misoprostol groups but significantly higher compared with the oral (mean, 3.4+/-0.2) and vaginal (mean, 3.6+/-1.9) placebo groups. Duration of the procedure was also significantly shorter in the misoprostol groups in comparison with their placebo counterparts. Preoperative bleeding and side effects were more common in the misoprostol groups, but none required medical intervention. Intraoperative bleeding was less in the vaginal misoprostol group compared with the placebo groups. There was no significant difference in terms of visual analogue scores during the procedure, patient satisfaction, days of postoperative bleeding and rate of postoperative complications among the groups. Cervical priming with misoprostol administered orally or vaginally 3 h before MVA for termination of pregnancy under local anesthesia facilitates the procedure by decreasing the need for cervical dilatation and by shortening its duration without improving patients' pain perception and satisfaction mainly due to side effects. PMID:15854633

  7. Evaluation of Topical Polyinosinic Acid-Polycytidylic Acid in Treatment of Localized Herpes Zoster in Children with Cancer: a Randomized, Double-Blind Controlled Study

    PubMed Central

    Feldman, Sandor; Hughes, Walter T.; Darlington, R. W.; Kim, H. K.

    1975-01-01

    Twenty-four children with cancer and localized herpes zoster were randomized to receive either topical polyinosinic acid-polycytidylic acid [poly(I:C)] or a saline placebo. Solutions containing 1.0 or 2.5 mg of drug per ml were applied to the lesions every 3 h, seven times a day for 7 consecutive days. Serial, standardized color photographs were used to evaluate the progression of lesions and maximum percentage of dermatome involvement. In patients receiving poly(I:C), the median days of new lesions after therapy was 2.0 days as opposed to 3.5 days in placebo-treated patients. This difference was not statistically significant. Moreover, the total median days of lesions and percentage of dermatome involvement were similar in both groups, as were complications. Neither the concentration nor the total dose of drug was related to the outcome of infection. We conclude that topical poly(I:C), as used in this study, was of no benefit in treatment of localized herpes zoster. PMID:1101820

  8. Hemodynamic changes following injection of local anesthetics with different concentrations of epinephrine during simple tooth extraction: A prospective randomized clinical trial

    PubMed Central

    Al-Showaikhat, Fatimah; Al-Shubbar, Fatimah; Al-Zawad, Kawther; Al-Zawad, Fatimah

    2015-01-01

    Background Presence of epinephrine in local anesthetic cartridge increases the duration of local anesthesia (LA), decreases the risk of toxicity, and provides hemostasis. However, the unfavorable effects are increasing heart rate (HR) and raising blood pressure (BP). The aim was to evaluate hemodynamic changes in the BP, HR, and oxygen saturation (SpO2) of normal patients undergoing tooth extraction using LA with various epinephrine concentrations. Material and Methods A prospective randomized clinical trial was conducted on 120 patients who were divided randomly into 3 parallel groups according to the LA received. Group 1: lidocaine 2% with epinephrine 1:80,000 (L80). Group 2: articaine 4% with epinephrine 1:100,000 (A100). Group 3: articaine 4% with epinephrine 1:200,000 (A200). Inclusion criteria: normal patients whose BP < 140/90. Exclusion criteria: hypertension, cardiovascular disease, hyperthyroidism, pregnancy, and allergy to LA. BP, HR, and (SpO2) were evaluated in 3 different time points: 3 minutes before LA, 3 minutes after LA, and 3 minutes after extraction. Results Systolic blood pressure (SBP) significantly increased after injection of L80 and continued after extraction to be significant than pre-injection. SBP significantly increased after injection of A100 then decreased after extraction. In the group of A200, SBP insignificantly decreased after injection then increased after extraction. The increasing of SBP between time point 1and 2 was significantly higher in G1 than G3 (p=0.014). Diastolic blood pressure decreased after LA in the 3 groups; however it was significant only with L80, then increased after extraction for all. Conclusions The changings of DBP, HR and SpO2 after anesthesia and extraction showed no significant difference among the three groups. However, A200 had significant lesser effect on SBP than L80 and the least effect on other parameters. Therefore, A200 is considered safer than L80 and A100 and is recommended for LA before teeth

  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. Pain during venous cannulation: Double-blind, randomized clinical trial of analgesic effect between topical amethocaine and eutectic mixture of local anesthetic

    PubMed Central

    Yeoh, CN; Lee, CY

    2012-01-01

    Background: Venous cannulation is often a painful procedure for the patient. Eutectic mixture of local anesthetic (EMLA) is the commonest topical analgesic used but suffers from disadvantages such as slow onset and skin blanching, which may interfere with venous cannulation. Amethocaine is a newer topical analgesic which seems to be devoid of such problems. Materials and Methods: This prospective randomized double-blind study compared the analgesic efficacy of EMLA with amethocaine during venous cannulation in adults. Eighty ASA I-II patients, aged 18–65 years, were recruited. The test drug was applied on the designated site of venous cannulation and covered with an occlusive dressing for at least 60 min prior to the procedure. Data collected included visual analogue score (VAS) during first attempt at venous cannulation, the ease and success rate at cannulation, and cutaneous changes at the application site. Results: Mean and median VAS for the EMLA group were 27.9 ± 9.8 and 30 mm, respectively; while for the Amethocaine group were 19.1 ± 14.1 and 20 mm, respectively. Differences in VAS did not reach statistical significance. No statistically significant differences were observed in the ease and success rate at cannulation. Cutaneous changes in the form of local induration and erythema (three patients in the Amethocaine group) and blanching (eight patients in the EMLA group) were mild, localized, and required no further treatment. No patient developed severe allergic reactions. Conclusion: Topical EMLA and amethocaine were comparable in terms of analgesic efficacy and ease of venous cannulation in adult patients. PMID:22557744

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions.

    PubMed

    Nedialkova, Lilia V; Amat, Miguel A; Kevrekidis, Ioannis G; Hummer, Gerhard

    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. PMID:25240340

  19. 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.

  20. 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.

  1. 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.

  2. A Markov model of the Indus script

    PubMed Central

    Rao, Rajesh P. N.; Yadav, Nisha; Vahia, Mayank N.; Joglekar, Hrishikesh; Adhikari, R.; Mahadevan, Iravatham

    2009-01-01

    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

  3. 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

  4. Approximating Markov Chains: What and why

    SciTech Connect

    Pincus, S.

    1996-06-01

    Much of the current study of dynamical systems is focused on geometry (e.g., chaos and bifurcations) and ergodic theory. Yet dynamical systems were originally motivated by an attempt to {open_quote}{open_quote}solve,{close_quote}{close_quote} or at least understand, a discrete-time analogue of differential equations. As such, numerical, analytical solution techniques for dynamical systems would seem desirable. We discuss an approach that provides such techniques, the approximation of dynamical systems by suitable finite state Markov Chains. Steady state distributions for these Markov Chains, a straightforward calculation, will converge to the true dynamical system steady state distribution, with appropriate limit theorems indicated. Thus (i) approximation by a computable, linear map holds the promise of vastly faster steady state solutions for nonlinear, multidimensional differential equations; (ii) the solution procedure is unaffected by the presence or absence of a probability density function for the {ital attractor}, entirely skirting singularity, fractal/multifractal, and renormalization considerations. The theoretical machinery underpinning this development also implies that under very general conditions, steady state measures are weakly continuous with control parameter evolution. This means that even though a system may change periodicity, or become chaotic in its limiting behavior, such statistical parameters as the mean, standard deviation, and tail probabilities change continuously, not abruptly with system evolution. {copyright} {ital 1996 American Institute of Physics.}

  5. 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.

  6. Sorafenib in locally advanced or metastatic, radioactive iodine-refractory, differentiated thyroid cancer: a randomized, double-blind, phase 3 trial

    PubMed Central

    Brose, Marcia S; Nutting, Christopher M; Jarzab, Barbara; Elisei, Rossella; Siena, Salvatore; Bastholt, Lars; de la Fouchardiere, Christelle; Pacini, Furio; Paschke, Ralf; KeeShong, Young; Sherman, Steven I; Smit, Johannes WA; Chung, John; Kappeler, Christian; Pena, Carol; Molnár, István; Schlumberger, Martin J

    2015-01-01

    Background Patients with radioactive iodine (131I, RAI)-refractory locally advanced or metastatic differentiated thyroid cancer (DTC) have a poor prognosis due to the lack of effective treatment options. Methods This multicentre, randomized (1:1), double-blind, placebo-controlled, phase 3 study (DECISION; NCT00984282) investigated sorafenib (400 mg orally twice-daily) in patients with RAI-refractory locally advanced or metastatic DTC progressing within the past 14 months. The primary endpoint was progression-free survival (PFS) by central independent review. Patients receiving placebo could crossover to open-label sorafenib upon progression. Archival tumour tissue was examined for BRAF and RAS mutations. Serum thyroglobulin was measured at baseline and each visit. Findings A total of 417 patients were randomized to sorafenib (n=207) or placebo (n=210). Sorafenib treatment significantly improved PFS compared with placebo (hazard ratio, 0·59; 95% confidence interval, 0·45–0·76; P<0·0001; median 10·8 vs. 5·8 months, respectively). PFS improvement was seen in all pre-specified clinical and genetic biomarker subgroups irrespective of mutation status. There was no statistically significant difference in overall survival (hazard ratio, 0·80; 95% confidence interval, 0·54–1·19; P=0·14); median overall survival had not been reached and 150 (71%) patients receiving placebo crossed over to sorafenib upon progression. Response rates (all partial responses) were 12·2% (24/196; sorafenib) and 0·5% (1/201; placebo; p<0·0001). Median thyroglobulin levels increased in the placebo group, and decreased, then paralleled treatment responses in the sorafenib group. Most adverse events were grade 1 or 2. The most common treatment-emergent adverse events in the sorafenib arm were hand–foot skin reaction (76·3%), diarrhoea (68·6%), alopecia (67·1%), and rash/desquamation (50·2%). Interpretation Sorafenib significantly improved PFS compared with placebo in patients

  7. 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.

  8. An extensive Markov system for ECG exact coding.

    PubMed

    Tai, S C

    1995-02-01

    In this paper, an extensive Markov process, which considers both the coding redundancy and the intersample redundancy, is presented to measure the entropy value of an ECG signal more accurately. It utilizes the intersample correlations by predicting the incoming n samples based on the previous m samples which constitute an extensive Markov process state. Theories of the extensive Markov process and conventional n repeated applications of m-th order Markov process are studied first in this paper. After that, they are realized for ECG exact coding. Results show that a better performance can be achieved by our system. The average code length for the extensive Markov system on the second difference signals was 2.512 b/sample, while the average Huffman code length for the second difference signals was 3.326 b/sample. PMID:7868151

  9. Benchmarking of a Markov multizone model of contaminant transport.

    PubMed

    Jones, Rachael M; Nicas, Mark

    2014-10-01

    A Markov chain model previously applied to the simulation of advection and diffusion process of gaseous contaminants is extended to three-dimensional transport of particulates in indoor environments. The model framework and assumptions are described. The performance of the Markov model is benchmarked against simple conventional models of contaminant transport. The Markov model is able to replicate elutriation predictions of particle deposition with distance from a point source, and the stirred settling of respirable particles. Comparisons with turbulent eddy diffusion models indicate that the Markov model exhibits numerical diffusion in the first seconds after release, but over time accurately predicts mean lateral dispersion. The Markov model exhibits some instability with grid length aspect when turbulence is incorporated by way of the turbulent diffusion coefficient, and advection is present. However, the magnitude of prediction error may be tolerable for some applications and can be avoided by incorporating turbulence by way of fluctuating velocity (e.g. turbulence intensity). PMID:25143517

  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. 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.

  12. Volatility: a hidden Markov process in financial time series.

    PubMed

    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 sigma proportional, variant V0.55, and (iii) future returns are proportional to the current volatility, which suggests some degree of predictability for the size of future returns. PMID:18233716

  13. Finding and Testing Network Communities by Lumped Markov Chains

    PubMed Central

    Piccardi, Carlo

    2011-01-01

    Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called “persistence probability” is associated to a cluster, which is then defined as an “-community” if such a probability is not smaller than . Consistently, a partition composed of -communities is an “-partition.” These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired -level allows one to immediately select the -partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure. PMID:22073245

  14. 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.

  15. 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

  16. Changes in acral blood flux under local application of ropivacaine and lidocaine with and without an adrenaline additive: a double-blind, randomized, placebo-controlled study.

    PubMed

    Häfner, Hans-Martin; Schmid, Ute; Moehrle, Matthias; Strölin, Anke; Breuninger, Helmut

    2008-01-01

    Vascular effects of local anesthetics are especially important in dermatological surgery. In particular, adequate perfusion must be ensured in order to offset surgical manipulations during surgical interventions at the acra. However, the use of adrenaline additives appears fraught with problems when anesthesia affects the terminal vascular system, particularly during interventions at the fingers, toes, penis, outer ears, and tip of the nose. We studied skin blood flux at the fingerpads via laser Doppler flowmetry over the course of 24 hours in a prospective, double-blind, randomized, placebo-controlled study with 20 vascularly healthy test persons following Oberst's-method anesthetic blocks. In each case, 6 ml ropivacaine (7.5 mg/ml) (A), lidocaine 1% without an additive (B), and lidocaine 1% with an adrenaline additive (1:200,000) (C) was used respectively as a verum. Isotonic saline solution was injected as a placebo (D). Measurements were carried out with the aid of a computer simultaneously at D II and D IV on both hands. Administration of (A) led to increased blood flux (+155.2%); of (B) initially to a decrease of 27%; of (C) to a reduction of 55% which was reversible after 40 minutes and of (D) to no change.(A) resulted in sustained vasodilatation which was still demonstrable after 24 h. (B) had notably less vasodilative effect, although comparison with (D) clearly showed that (B) is indeed vasodilative. (C) resulted in only a passing decrease in perfusion; this was no longer measurable when checked after 6 and 24 h. This transient inadequacy of blood flux also appeared after administration of (D). These tests show that adrenaline additive in local anesthesia does not decrease blood flow more than 55% for a period of 16 min. Following these results an adrenaline additive can be safely used for anesthetic blocks at the acra in healthy persons. PMID:18334782

  17. Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.

    PubMed

    You, Zhu-Hong; Chan, Keith C C; Hu, Pengwei

    2015-01-01

    The study of protein-protein interactions (PPIs) can be very important for the understanding of biological cellular functions. However, detecting PPIs in the laboratories are both time-consuming and expensive. For this reason, there has been much recent effort to develop techniques for computational prediction of PPIs as this can complement laboratory procedures and provide an inexpensive way of predicting the most likely set of interactions at the entire proteome scale. Although much progress has already been achieved in this direction, the problem is still far from being solved. More effective approaches are still required to overcome the limitations of the current ones. In this study, a novel Multi-scale Local Descriptor (MLD) feature representation scheme is proposed to extract features from a protein sequence. This scheme can capture multi-scale local information by varying the length of protein-sequence segments. Based on the MLD, an ensemble learning method, the Random Forest (RF) method, is used as classifier. The MLD feature representation scheme facilitates the mining of interaction information from multi-scale continuous amino acid segments, making it easier to capture multiple overlapping continuous binding patterns within a protein sequence. When the proposed method is tested with the PPI data of Saccharomyces cerevisiae, it achieves a prediction accuracy of 94.72% with 94.34% sensitivity at the precision of 98.91%. Extensive experiments are performed to compare our method with existing sequence-based method. Experimental results show that the performance of our predictor is better than several other state-of-the-art predictors also with the H. pylori dataset. The reason why such good results are achieved can largely be credited to the learning capabilities of the RF model and the novel MLD feature representation scheme. The experiment results show that the proposed approach can be very promising for predicting PPIs and can be a useful tool for future

  18. Long-term results of a randomized phase III trial of TPF induction chemotherapy followed by surgery and radiation in locally advanced oral squamous cell carcinoma.

    PubMed

    Zhong, Lai-ping; Zhang, Chen-ping; Ren, Guo-xin; Guo, Wei; William, William N; Hong, Christopher S; Sun, Jian; Zhu, Han-guang; Tu, Wen-yong; Li, Jiang; Cai, Yi-li; Yin, Qiu-ming; Wang, Li-zhen; Wang, Zhong-he; Hu, Yong-jie; Ji, Tong; Yang, Wen-jun; Ye, Wei-min; Li, Jun; He, Yue; Wang, Yan-an; Xu, Li-qun; Zhuang, Zhengping; Lee, J Jack; Myers, Jeffrey N; Zhang, Zhi-yuan

    2015-07-30

    Previously, we conducted a randomized phase III trial of TPF (docetaxel, cisplatin, and 5-fluorouracil) induction chemotherapy in surgically managed locally advanced oral squamous cell carcinoma (OSCC) and found no improvement in overall survival. This study reports long-term follow-up results from our initial trial. All patients had clinical stage III or IVA locally advanced OSCC. In the experimental group, patients received two cycles of TPF induction chemotherapy (75mg/m2 docetaxel d1, 75mg/m2 cisplatin d1, and 750mg/m2/day 5-fluorouracil d1-5) followed by radical surgery and post-operative radiotherapy; in the control group, patients received upfront radical surgery and post-operative radiotherapy. The primary endpoint was overall survival. Among 256 enrolled patients with a median follow-up of 70 months, estimated 5-year overall survival, disease-free survival, locoregional recurrence-free survival, and distant metastasis-free survival rates were 61.1%, 52.7%, 55.2%, and 60.4%, respectively. There were no significant differences in survival rates between experimental and control groups. However, patients with favorable pathologic responses had improved outcomes compared to those with unfavorable pathologic responses and to those in the control group. Although TPF induction chemotherapy did not improve long-term survival compared to surgery upfront in patients with stage III and IVA OSCC, a favorable pathologic response after induction chemotherapy may be used as a major endpoint and prognosticator in future studies. Furthermore, the negative results observed in this trial may be represent type II error from an underpowered study. Future larger scale phase III trials are warranted to investigate whether a significant benefit exists for TPF induction chemotherapy in surgically managed OSCC. PMID:26124084

  19. 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.

  20. 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.

  1. 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

  2. Non-Markov stochastic processes satisfying equations usually associated with a Markov process

    NASA Astrophysics Data System (ADS)

    McCauley, J. L.

    2012-04-01

    There are non-Markov Ito processes that satisfy the Fokker-Planck, backward time Kolmogorov, and Chapman-Kolmogorov equations. These processes are non-Markov in that they may remember an initial condition formed at the start of the ensemble. Some may even admit 1-point densities that satisfy a nonlinear 1-point diffusion equation. However, these processes are linear, the Fokker-Planck equation for the conditional density (the 2-point density) is linear. The memory may be in the drift coefficient (representing a flow), in the diffusion coefficient, or in both. We illustrate the phenomena via exactly solvable examples. In the last section we show how such memory may appear in cooperative phenomena.

  3. Final Results of Local-Regional Control and Late Toxicity of RTOG 90-03; A Randomized Trial of Altered Fractionation Radiation for Locally Advanced Head And Neck Cancer

    PubMed Central

    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

    2015-01-01

    Purpose To test whether altered radiation fractionation schemes improved local-regional control (LRC) rates for patients with squamous cell cancers (SCC) of the head and neck when compared to standard fractionation (SFX) of 70 Gy. Patients and Methods Patients with stage III or IV (or stage II base of tongue) SCC were randomized to 4 treatment arms: (1) SFX-70 Gy/35 daily fxns/7 week; (2) HFX-81.6 Gy/68 BID fxns/7 weeks; (3) AFX-S 67.2 Gy/42 fxns/6 weeks with a 2 week rest after 38.4 Gy; and (4) AFX-C 72 Gy/42 fxns/6 weeks. n=1,076. The 3 experimental arms were to be compared to SFX. Results With patients censored for LRC at 5 years, only the comparison of HFX with SFX was significantly different (HFX: Hazard Ratio-0.79(0.62–1.00), p=0.05; AFX-C 0.82 (0.65–1.05), p=0.11). Censored at 5 years, HFX improved Overall Survival (OS) (Hazard Ratio 0.81, p=0.05). 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 to SFX. When 7 week treatments were compared to 6 week treatments, accelerated fractionation appeared to increase Grade 3, 4 or 5 toxicity at 5 years (p=0.06). Looking at the worst toxicity per patient by treatment only the AFX-C arm seemed to trend worse than the SFX arm when comparing Grade 0–2 vs. 3–5 toxicity(p=0.09). Conclusions At 5 years, only HFX improved LRC & OS for patients with locally advanced SCC without increasing late toxicity. PMID:24613816

  4. 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.

  5. 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.

  6. 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.

  7. Markov state models of protein misfolding.

    PubMed

    Sirur, Anshul; De Sancho, David; Best, Robert B

    2016-02-21

    Markov state models (MSMs) are an extremely useful tool for understanding the conformational dynamics of macromolecules and for analyzing MD simulations in a quantitative fashion. They have been extensively used for peptide and protein folding, for small molecule binding, and for the study of native ensemble dynamics. Here, we adapt the MSM methodology to gain insight into the dynamics of misfolded states. To overcome possible flaws in root-mean-square deviation (RMSD)-based metrics, we introduce a novel discretization approach, based on coarse-grained contact maps. In addition, we extend the MSM methodology to include "sink" states in order to account for the irreversibility (on simulation time scales) of processes like protein misfolding. We apply this method to analyze the mechanism of misfolding of tandem repeats of titin domains, and how it is influenced by confinement in a chaperonin-like cavity. PMID:26897000

  8. 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.

  9. 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.

  10. 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

  11. Markov state models of protein misfolding

    NASA Astrophysics Data System (ADS)

    Sirur, Anshul; De Sancho, David; Best, Robert B.

    2016-02-01

    Markov state models (MSMs) are an extremely useful tool for understanding the conformational dynamics of macromolecules and for analyzing MD simulations in a quantitative fashion. They have been extensively used for peptide and protein folding, for small molecule binding, and for the study of native ensemble dynamics. Here, we adapt the MSM methodology to gain insight into the dynamics of misfolded states. To overcome possible flaws in root-mean-square deviation (RMSD)-based metrics, we introduce a novel discretization approach, based on coarse-grained contact maps. In addition, we extend the MSM methodology to include "sink" states in order to account for the irreversibility (on simulation time scales) of processes like protein misfolding. We apply this method to analyze the mechanism of misfolding of tandem repeats of titin domains, and how it is influenced by confinement in a chaperonin-like cavity.

  12. Probabilistic Resilience in Hidden Markov Models

    NASA Astrophysics Data System (ADS)

    Panerati, Jacopo; Beltrame, Giovanni; Schwind, Nicolas; Zeltner, Stefan; Inoue, Katsumi

    2016-05-01

    Originally defined in the context of ecological systems and environmental sciences, resilience has grown to be a property of major interest for the design and analysis of many other complex systems: resilient networks and robotics systems other the desirable capability of absorbing disruption and transforming in response to external shocks, while still providing the services they were designed for. Starting from an existing formalization of resilience for constraint-based systems, we develop a probabilistic framework based on hidden Markov models. In doing so, we introduce two new important features: stochastic evolution and partial observability. Using our framework, we formalize a methodology for the evaluation of probabilities associated with generic properties, we describe an efficient algorithm for the computation of its essential inference step, and show that its complexity is comparable to other state-of-the-art inference algorithms.

  13. 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.

  14. 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

  15. 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.

  16. Multivariate Markov chain modeling for stock markets

    NASA Astrophysics Data System (ADS)

    Maskawa, Jun-ichi

    2003-06-01

    We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.

  17. 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.

  18. 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

  19. Role of steroidal anti-inflammatory agent prior to intracorporeal lithotripsy under local anesthesia for ureterovesical junction calculus: A prospective randomized controlled study

    PubMed Central

    Lodh, Bijit; Singh, Kaku Akoijam; Sinam, Rajendra Singh

    2015-01-01

    Objective: The objective of the following study is to assess the effect of steroidal anti-inflammatory agent on the outcome of ureterorenoscopic lithotripsy (URSL) for ureterovesical junction (UVJ) calculus. Settings and Design: This was a prospective randomized controlled study conducted at the Department of Urology, Regional Institute of Medical Sciences, Imphal. Subjects and Methods: One hundred and twenty-six patients requiring ureteroscopic lithotripsy for UVJ calculus were randomly assigned into two groups. The study group received tablet deflazacort 30 mg once a day for 10 days prior to the procedure, whereas the control group did not receive such treatment. Parameters with respect to the outcome of the procedure were recorded for all patients in both groups. Statistical Analysis Used: Fisher's exact and independent t-test was used to compare the outcome between the groups where P < 0.05 was considered to be statistically significant. Results: There was significant statistical difference (P - 0.016) on the endoscopic appearance of the region of ureteric orifice in patients receiving steroidal anti-inflammatory agent compared with control. Severe procedure related pain and mean operative time was less in the study group compared to control (P - 0.020 and 0.031, respectively). Re-treatment rates in the study group were lower than the control group (4.76% vs. 17.46%) and found to be statistically significant (P - 0.044). It is found that computed tomography (CT) appearance (r - 0.399) and stone size (r - 0.410) strongly correlate with the endoscopic findings of the region of UVJ (P - 0.001). Conclusions: Inflamed and or obliterated ureteric orifice is the major constraints for stone clearance at ureterovesical junction. The present study showed the administration of tablet deflazacort (a steroidal anti-inflammatory agent) significantly improves the outcome of URSL under local anesthesia. We strongly recommend its use prior to URSL for UVJ calculus, especially

  20. 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. PMID:26400003

  1. 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.

  2. Performability analysis using semi-Markov reward processes

    NASA Technical Reports Server (NTRS)

    Ciardo, Gianfranco; Marie, Raymond A.; Sericola, Bruno; Trivedi, Kishor S.

    1990-01-01

    Beaudry (1978) proposed a simple method of computing the distribution of performability in a Markov reward process. Two extensions of Beaudry's approach are presented. The method is generalized to a semi-Markov reward process by removing the restriction requiring the association of zero reward to absorbing states only. The algorithm proceeds by replacing zero-reward nonabsorbing states by a probabilistic switch; it is therefore related to the elimination of vanishing states from the reachability graph of a generalized stochastic Petri net and to the elimination of fast transient states in a decomposition approach to stiff Markov chains. The use of the approach is illustrated with three applications.

  3. An Overview of Markov Chain Methods for the Study of Stage-Sequential Developmental Processes

    ERIC Educational Resources Information Center

    Kapland, David

    2008-01-01

    This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model.…

  4. 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.

  5. 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.

  6. Prospective randomized trial comparing once-a-week vs daily radiation therapy for locally-advanced, non-metastatic, lung cancer: a preliminary report

    SciTech Connect

    Salazar, O.M.; Slawson, R.G.; Poussin-Rosillo, H.; Amin, P.P.; Sewchand, W.; Strohl, R.A.

    1986-05-01

    This is the first report of an on-going Phase III protocol for patients with locally-advanced, non-metastatic, measurable lung cancer. The study randomizes two arms: 6000 rad using 500 rad fractions once a week (1 X W) for 12 weeks with spinal cord (SC) protection at 3000 rad; and 6000 rad using 200 rad fractions daily (5 X W) for 6 weeks with SC protection at 4500 rad. Both arms use an initially large loco-regional field that is further reduced when tumor doses reach 3000 rad in (1 X W) arm and 5000 rad in (5 X W) arm. The protocol was activated April 1982; as of August 1984, it had accrued 100 patients of whom 68 were evaluable (29 (1 X W) and 39 (5 X W)). There have been no major differences in tumor responses or failure patterns between the (1 X W) and (5 X W) arms; response rates have been 69 and 64%; CR 31 and 20%; total incidence of local failures 20 and 23%; and overall incidence of distant failures 34 and 43%, respectively. The (1 X W) arm has been far better tolerated with 76% of its patients free of any esophagitis and 97% without weight loss, as compared to only 33 and 67% in the (5 X W), respectively. The (1 X W) arm has not conveyed loss in tumor control effectiveness, in-treatment progression, or higher incidence of distant spread. Subacute and chronic complications have been minimal with either treatment. No fatal or life-threatening toxicities have occurred; the incidence of severe complications has been 7% in the (1 X W) arm and 8% in the (5 X W) arm. Nevertheless, the number of patients alive and at risk greater than or equal to 12 months is still relatively small; definitive statements regarding very late toxic reactions cannot yet be made. Results in the present protocol arms have not been different from what was expected. Once a week RT yields results that appear no different from those achieved with conventional RT in lung cancer.

  7. 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.

  8. Treatment-Related Death during Concurrent Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Meta-Analysis of Randomized Studies

    PubMed Central

    Zhao, Jing; Xia, Yingfeng; Kaminski, Joseph; Hao, Zhonglin; Mott, Frank; Campbell, Jeff; Sadek, Ramses; Kong, Feng-Ming (Spring)

    2016-01-01

    Treatment related death (TRD) is the worst adverse event in chemotherapy and radiotherapy for patients with cancer, the reports for TRDs were sporadically. We aimed to study TRDs in non-small cell lung cancer (NSCLC) patients treated with concurrent chemoradiotherapy (CCRT), and determine whether high radiation dose and newer chemotherapy regimens were associated with the risk of TRD. Data from randomized clinical trials for locally advanced/unresectable NSCLC patients were analyzed. Eligible studies had to have at least one arm with CCRT. The primary endpoint was TRD. Pooled odds ratios (ORs) for TRDs were calculated. In this study, a total of fifty-three trials (8940 patients) were eligible. The pooled TRD rate (accounting for heterogeneity) was 1.44% for all patients. In 20 trials in which comparison of TRDs between CCRT and non-CCRT was possible, the OR (95% CI) of TRDs was 1.08 (0.70–1.66) (P = 0.71). Patients treated with third-generation chemotherapy and concurrent radiotherapy had an increase of TRDs compared to those with other regimens in CCRT (2.70% vs. 1.37%, OR = 1.50, 95% CI: 1.09–2.07, P = 0.008). No significant difference was found in TRDs between high (≥ 66 Gy) and low (< 66 Gy) radiation dose during CCRT (P = 0.605). Neither consolidation (P = 0.476) nor induction chemotherapy (P = 0.175) had significant effects with increased TRDs in this study. We concluded that CCRT is not significantly associated with the risk of TRD compared to non-CCRT. The third-generation chemotherapy regimens may be a risk factor with higher TRDs in CCRT, while high dose radiation is not significantly associated with more TRDs. This observation deserves further study. PMID:27300551

  9. Influence of local anesthetics with or without epinephrine 1/80000 on blood pressure and heart rate: A randomized double-blind experimental clinical trial

    PubMed Central

    Ketabi, Mohammad; Shamami, Mehrnaz Sadighi; Alaie, Maryam; Shamami, Mehrnoosh Sadighi

    2012-01-01

    Background: Local anesthesia (LA) with epinephrine have an important role in pain and bleeding control. However, most clinicians believe LA + epinephrine may cause rapid raise in blood pressure (BP) and heart rate (HR). The aim of this research was to compare the changes in HR and BP after administration of lidocaine with and without epinephrine 1/80000 in two infiltration (INF) and inferior alveolar nerve block methods (IANB). Materials and Methods: The study was a randomized double-blind experimental clinical trial. Forty subjects were divided into two equal groups and two subgroups. In one group, INF and in the other group, IANB were used and, further, in one subgroup lidocaine and in another subgroup, lidocaine plus epinephrine were used. BP and HR were recorded before and 10 min after. The paired t-test for intragroup differences and independent t-test for intergroup analysis were used at the significant level of P≤0.05. Results: The mean BP and HR values were reduced after injection of lidocaine in both INF and IANB compared with baseline. The differences were statistically significant (P < 0.05), but, on comparing these values between the two injection methods, the differences were not statically significant (P = 0.089 and 0.066, respectively). The mean BP and HR values were increased after injection of lidocaine plus epinephrine in both INF and IANB compared with baseline, and these were statistically significant (P < 0.05) but, on comparing these values between the two methods, the differences were not statically significant (P = 0.071 and 0.092, respectively). Conclusion: The rise in BP and HR following injection of lidocaine plus epinephrine was statistically significant compared with baseline in both INF and IANB, but this was not clinically and numerically considerable. PMID:23162585

  10. Co-Localized or Randomly Distributed? Pair Cross Correlation of In Vivo Grown Subgingival Biofilm Bacteria Quantified by Digital Image Analysis

    PubMed Central

    Lux, Renate; Riep, Birgit; Kikhney, Judith; Friedmann, Anton; Wolinsky, Lawrence E.; Göbel, Ulf B.; Daims, Holger; Moter, Annette

    2012-01-01

    The polymicrobial nature of periodontal diseases is reflected by the diversity of phylotypes detected in subgingival plaque and the finding that consortia of suspected pathogens rather than single species are associated with disease development. A number of these microorganisms have been demonstrated in vitro to interact and enhance biofilm integration, survival or even pathogenic features. To examine the in vivo relevance of these proposed interactions, we extended the spatial arrangement analysis tool of the software daime (digital image analysis in microbial ecology). This modification enabled the quantitative analysis of microbial co-localization in images of subgingival biofilm species, where the biomass was confined to fractions of the whole-image area, a situation common for medical samples. Selected representatives of the disease-associated red and orange complexes that were previously suggested to interact with each other in vitro (Tannerella forsythia with Fusobacterium nucleatum and Porphyromonas gingivalis with Prevotella intermedia) were chosen for analysis and labeled with specific fluorescent probes via fluorescence in situ hybridization. Pair cross-correlation analysis of in vivo grown biofilms revealed tight clustering of F. nucleatum/periodonticum and T. forsythia at short distances (up to 6 µm) with a pronounced peak at 1.5 µm. While these results confirmed previous in vitro observations for F. nucleatum and T. forsythia, random spatial distribution was detected between P. gingivalis and P. intermedia in the in vivo samples. In conclusion, we successfully employed spatial arrangement analysis on the single cell level in clinically relevant medical samples and demonstrated the utility of this approach for the in vivo validation of in vitro observations by analyzing statistically relevant numbers of different patients. More importantly, the culture-independent nature of this approach enables similar quantitative analyses for

  11. Treatment-Related Death during Concurrent Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Meta-Analysis of Randomized Studies.

    PubMed

    Zhao, Jing; Xia, Yingfeng; Kaminski, Joseph; Hao, Zhonglin; Mott, Frank; Campbell, Jeff; Sadek, Ramses; Kong, Feng-Ming Spring

    2016-01-01

    Treatment related death (TRD) is the worst adverse event in chemotherapy and radiotherapy for patients with cancer, the reports for TRDs were sporadically. We aimed to study TRDs in non-small cell lung cancer (NSCLC) patients treated with concurrent chemoradiotherapy (CCRT), and determine whether high radiation dose and newer chemotherapy regimens were associated with the risk of TRD. Data from randomized clinical trials for locally advanced/unresectable NSCLC patients were analyzed. Eligible studies had to have at least one arm with CCRT. The primary endpoint was TRD. Pooled odds ratios (ORs) for TRDs were calculated. In this study, a total of fifty-three trials (8940 patients) were eligible. The pooled TRD rate (accounting for heterogeneity) was 1.44% for all patients. In 20 trials in which comparison of TRDs between CCRT and non-CCRT was possible, the OR (95% CI) of TRDs was 1.08 (0.70-1.66) (P = 0.71). Patients treated with third-generation chemotherapy and concurrent radiotherapy had an increase of TRDs compared to those with other regimens in CCRT (2.70% vs. 1.37%, OR = 1.50, 95% CI: 1.09-2.07, P = 0.008). No significant difference was found in TRDs between high (≥ 66 Gy) and low (< 66 Gy) radiation dose during CCRT (P = 0.605). Neither consolidation (P = 0.476) nor induction chemotherapy (P = 0.175) had significant effects with increased TRDs in this study. We concluded that CCRT is not significantly associated with the risk of TRD compared to non-CCRT. The third-generation chemotherapy regimens may be a risk factor with higher TRDs in CCRT, while high dose radiation is not significantly associated with more TRDs. This observation deserves further study. PMID:27300551

  12. 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

  13. A hidden markov model derived structural alphabet for proteins.

    PubMed

    Camproux, A C; Gautier, R; Tufféry, P

    2004-06-01

    Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction. PMID:15147844

  14. 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

  15. 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.

  16. A semi-Markov model for price returns

    NASA Astrophysics Data System (ADS)

    D'Amico, Guglielmo; Petroni, Filippo

    2012-10-01

    We study the high frequency price dynamics of traded stocks by a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov process and the overnight returns are modeled by a Markov chain. Based on this assumptions we derived the equations for the first passage time distribution and the volatility autocorrelation function. Theoretical results have been compared with empirical findings from real data. In particular we analyzed high frequency data from the Italian stock market from 1 January 2007 until the end of December 2010. The semi-Markov hypothesis is also tested through a nonparametric test of hypothesis.

  17. 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.

  18. 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.

  19. 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

  20. Manpower planning using Markov Chain model

    NASA Astrophysics Data System (ADS)

    Saad, Syafawati Ab; Adnan, Farah Adibah; Ibrahim, Haslinda; Rahim, Rahela

    2014-07-01

    Manpower planning is a planning model which understands the flow of manpower based on the policies changes. For such purpose, numerous attempts have been made by researchers to develop a model to investigate the track of movements of lecturers for various universities. As huge number of lecturers in a university, it is difficult to track the movement of lecturers and also there is no quantitative way used in tracking the movement of lecturers. This research is aimed to determine the appropriate manpower model to understand the flow of lecturers in a university in Malaysia by determine the probability and mean time of lecturers remain in the same status rank. In addition, this research also intended to estimate the number of lecturers in different status rank (lecturer, senior lecturer and associate professor). From the previous studies, there are several methods applied in manpower planning model and appropriate method used in this research is Markov Chain model. Results obtained from this study indicate that the appropriate manpower planning model used is validated by compare to the actual data. The smaller margin of error gives a better result which means that the projection is closer to actual data. These results would give some suggestions for the university to plan the hiring lecturers and budgetary for university in future.

  1. 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.

  2. 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.

  3. 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.

  4. Hidden Markov models for threat prediction fusion

    NASA Astrophysics Data System (ADS)

    Ross, Kenneth N.; Chaney, Ronald D.

    2000-04-01

    This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion engine. The threat prediction fusion engine selectively models important aspects of the battlespace state using probability-based methods and information obtained from lower level fusion engines. Our approach uses hidden Markov models of a hierarchical threat state to find the most likely Course of Action (CoA) for the opposing forces. Decision tress use features derived from the CoA probabilities and other information to estimate the level of threat presented by the opposing forces. This approach provides the user with several measures associated with the level of threat, including: probability that the enemy is following a particular CoA, potential threat presented by the opposing forces, and likely time of the threat. The hierarchical approach used for modeling helps us efficiently represent the battlespace with a structure that permits scaling the models to larger scenarios without adding prohibitive computational costs or sacrificing model fidelity.

  5. Concomitant Cisplatin and Hyperfractionated Radiotherapy in Locally Advanced Head and Neck Cancer: 10-Year Follow-Up of a Randomized Phase III Trial (SAKK 10/94)

    SciTech Connect

    Ghadjar, Pirus; Simcock, Mathew; Studer, Gabriela; Allal, Abdelkarim S.; Ozsahin, Mahmut; Bernier, Jacques; Toepfer, Michael; Zimmermann, Frank; Betz, Michael; Glanzmann, Christoph; Aebersold, Daniel M.

    2012-02-01

    Purpose: To compare the long-term outcome of treatment with concomitant cisplatin and hyperfractionated radiotherapy versus treatment with hyperfractionated radiotherapy alone in patients with locally advanced head and neck cancer. Methods and Materials: From July 1994 to July 2000, a total of 224 patients with squamous cell carcinoma of the head and neck were randomized to receive either hyperfractionated radiotherapy alone (median total dose, 74.4 Gy; 1.2 Gy twice daily; 5 days per week) or the same radiotherapy combined with two cycles of cisplatin (20 mg/m{sup 2} for 5 consecutive days during weeks 1 and 5). The primary endpoint was the time to any treatment failure; secondary endpoints were locoregional failure, metastatic failure, overall survival, and late toxicity assessed according to Radiation Therapy Oncology Group criteria. Results: Median follow-up was 9.5 years (range, 0.1-15.4 years). Median time to any treatment failure was not significantly different between treatment arms (hazard ratio [HR], 1.2 [95% confidence interval {l_brace}CI{r_brace}, 0.9-1.7; p = 0.17]). Rates of locoregional failure-free survival (HR, 1.5 [95% CI, 1.1-2.1; p = 0.02]), distant metastasis-free survival (HR, 1.6 [95% CI, 1.1-2.5; p = 0.02]), and cancer-specific survival (HR, 1.6 [95% CI, 1.0-2.5; p = 0.03]) were significantly improved in the combined-treatment arm, with no difference in major late toxicity between treatment arms. However, overall survival was not significantly different (HR, 1.3 [95% CI, 0.9-1.8; p = 0.11]). Conclusions: After long-term follow-up, combined-treatment with cisplatin and hyperfractionated radiotherapy maintained improved rates of locoregional control, distant metastasis-free survival, and cancer-specific survival compared to that of hyperfractionated radiotherapy alone, with no difference in major late toxicity.

  6. 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

  7. 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.

  8. Random Breakage of a Rod into Unit Lengths

    ERIC Educational Resources Information Center

    Gani, Joe; Swift, Randall

    2011-01-01

    In this article we consider the random breakage of a rod into "L" unit elements and present a Markov chain based method that tracks intermediate breakage configurations. The probability of the time to final breakage for L = 3, 4, 5 is obtained and the method is shown to extend in principle, beyond L = 5.

  9. 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.

  10. Optofluidic random laser

    NASA Astrophysics Data System (ADS)

    Shivakiran Bhaktha, B. N.; Bachelard, Nicolas; Noblin, Xavier; Sebbah, Patrick

    2012-10-01

    Random lasing is reported in a dye-circulated structured polymeric microfluidic channel. The role of disorder, which results from limited accuracy of photolithographic process, is demonstrated by the variation of the emission spectrum with local-pump position and by the extreme sensitivity to a local perturbation of the structure. Thresholds comparable to those of conventional microfluidic lasers are achieved, without the hurdle of state-of-the-art cavity fabrication. Potential applications of optofluidic random lasers for on-chip sensors are discussed. Introduction of random lasers in the field of optofluidics is a promising alternative to on-chip laser integration with light and fluidic functionalities.

  11. Reliability calculation using randomization for Markovian fault-tolerant computing systems

    NASA Technical Reports Server (NTRS)

    Miller, D. R.

    1982-01-01

    The randomization technique for computing transient probabilities of Markov processes is presented. The technique is applied to a Markov process model of a simplified fault tolerant computer system for illustrative purposes. It is applicable to much larger and more complex models. Transient state probabilities are computed, from which reliabilities are derived. An accelerated version of the randomization algorithm is developed which exploits ''stiffness' of the models to gain increased efficiency. A great advantage of the randomization approach is that it easily allows probabilities and reliabilities to be computed to any predetermined accuracy.

  12. 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.

  13. 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

  14. 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.

  15. Control with a random access protocol and packet dropouts

    NASA Astrophysics Data System (ADS)

    Wang, Liyuan; Guo, Ge

    2016-08-01

    This paper investigates networked control systems whose actuators communicate with the controller via a limited number of unreliable channels. The access to the channels is decided by a so-called group random access protocol, which is modelled as a binary Markov sequence. Data packet dropouts in the channels are modelled as independent Bernoulli processes. For such systems, a systematic characterisation for controller synthesis is established and stated in terms of the transition probabilities of the Markov protocol and the packet dropout probabilities. The results are illustrated via a numerical example.

  16. 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.

  17. 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.

  18. 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.

  19. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

    PubMed

    Monaco, James P; Tomaszewski, John E; Feldman, Michael D; Hagemann, Ian; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant

    2010-08-01

    In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately

  20. High-Throughput Detection of Prostate Cancer in Histological Sections Using Probabilistic Pairwise Markov Models

    PubMed Central

    Monaco, James P.; Tomaszewski, John E.; Feldman, Michael D.; Hagemann, Ian; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant

    2010-01-01

    In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80K×70K pixels — far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: 1) detecting cancerous regions and 2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2×1.75 cm2) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8 µm per pixel). This motivates the following algorithm: Step 1) glands are segmented, Step 2) the segmented glands are classified as malignant or benign, and Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately incorporate

  1. 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.

  2. Meeting the NICE requirements: a Markov model approach.

    PubMed

    Mauskopf, J

    2000-01-01

    The National Institute of Clinical Excellence (NICE) was established in the United Kingdom in April 1999 to issue guidance for the National Health Service (NHS) on the use of selective new health care interventions. This article describes the NICE requirements for both incidence-based cost-effectiveness analyses and prevalence-based estimates of the aggregate NHS impact of the new drug. The article demonstrates how both of these requirements can be met using Markov modeling techniques. A Markov model for a hypothetical new treatment for HIV infection is used as an illustration of how to generate the estimates that are required by NICE. The article concludes with a discussion of the difficulties of obtaining data of sufficient quality to include in the Markov model to ensure that the submission meets all the NICE requirements and is credible to the NICE advisory board. PMID:16464193

  3. Hidden Markov Models for Fault Detection in Dynamic Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

    Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.

  4. 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.

  5. Nonparametric identification and maximum likelihood estimation for hidden Markov models

    PubMed Central

    Alexandrovich, G.; Holzmann, H.; Leister, A.

    2016-01-01

    Nonparametric identification and maximum likelihood estimation for finite-state hidden Markov models are investigated. We obtain identification of the parameters as well as the order of the Markov chain if the transition probability matrices have full-rank and are ergodic, and if the state-dependent distributions are all distinct, but not necessarily linearly independent. Based on this identification result, we develop a nonparametric maximum likelihood estimation theory. First, we show that the asymptotic contrast, the Kullback–Leibler divergence of the hidden Markov model, also identifies the true parameter vector nonparametrically. Second, for classes of state-dependent densities which are arbitrary mixtures of a parametric family, we establish the consistency of the nonparametric maximum likelihood estimator. Here, identification of the mixing distributions need not be assumed. Numerical properties of the estimates and of nonparametric goodness of fit tests are investigated in a simulation study.

  6. Random Walks on Random Graphs

    NASA Astrophysics Data System (ADS)

    Cooper, Colin; Frieze, Alan

    The aim of this article is to discuss some of the notions and applications of random walks on finite graphs, especially as they apply to random graphs. In this section we give some basic definitions, in Section 2 we review applications of random walks in computer science, and in Section 3 we focus on walks in random graphs.

  7. 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

  8. A Markov model for NASA's Ground Communications Facility

    NASA Technical Reports Server (NTRS)

    Adeyemi, O.

    1974-01-01

    A 'natural' way of constructing finite-state Markov chains (FSMC) is presented for those noise burst channels that can be modeled by them. In particular, a five-state Markov chain is given as a model of errors occurring at the Ground Communications Facility (GCF). A maximum likelihood procedure applicable to any FSMC is developed for estimating all the model parameters starting from the data of error runs. A few of the statistics important for estimating the performance of error control strategies on the channel are provided.

  9. 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.

  10. Markov bases and toric ideals for some contingency tables

    NASA Astrophysics Data System (ADS)

    Mohammed, N. F.; Rakhimov, I. S.; Shitan, M.

    2016-06-01

    The main objective of this work is to study Markov bases and toric ideals for p/(v -1 )(p -v ) 2 v ×v ×p/v - contingency tables that has fixed two-dimensional marginal when p is a multiple of v and greater than or equal to 2v. Moreover, the connected bipartite graph is also constructed by using elements of Markov basis. This work is an extension on results, that has been found by Hadi and Salman in 2014.

  11. 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

  12. The Prostate Cancer Intervention Versus Observation Trial: VA/NCI/AHRQ Cooperative Studies Program #407 (PIVOT): design and baseline results of a randomized controlled trial comparing radical prostatectomy with watchful waiting for men with clinically localized prostate cancer.

    PubMed

    Wilt, Timothy J

    2012-12-01

    Prostate cancer is the most common noncutaneous malignancy and the second leading cause of cancer death in men. In the United States, 90% of men with prostate cancer are more than age 60 years, diagnosed by early detection with the prostate-specific antigen (PSA) blood test, and have disease believed confined to the prostate gland (clinically localized). Common treatments for clinically localized prostate cancer include watchful waiting (WW), surgery to remove the prostate gland (radical prostatectomy), external-beam radiation therapy and interstitial radiation therapy (brachytherapy), and androgen deprivation. Little is known about the relative effectiveness and harms of treatments because of the paucity of randomized controlled trials. The Department of Veterans Affairs/National Cancer Institute/Agency for Healthcare Research and Quality Cooperative Studies Program Study #407:Prostate Cancer Intervention Versus Observation Trial (PIVOT), initiated in 1994, is a multicenter randomized controlled trial comparing radical prostatectomy with WW in men with clinically localized prostate cancer. We describe the study rationale, design, recruitment methods, and baseline characteristics of PIVOT enrollees. We provide comparisons with eligible men declining enrollment and men participating in another recently reported randomized trial of radical prostatectomy vs WW conducted in Scandinavia. We screened 13 022 men with prostate cancer at 52 US medical centers for potential enrollment. From these, 5023 met initial age, comorbidity, and disease eligibility criteria, and a total of 731 men agreed to participate and were randomized. The mean age of enrollees was 67 years. Nearly one-third were African American. Approximately 85% reported that they were fully active. The median PSA was 7.8ng/mL (mean 10.2ng/mL). In three-fourths of men, the primary reason for biopsy leading to a diagnosis of prostate cancer was a PSA elevation or rise. Using previously developed tumor risk

  13. Induction Chemotherapy and Continuous Hyperfractionated Accelerated Radiotherapy (CHART) for Patients With Locally Advanced Inoperable Non-Small-Cell Lung Cancer: The MRC INCH Randomized Trial

    SciTech Connect

    Hatton, Matthew; Nankivell, Matthew; Lyn, Ethan; Falk, Stephen; Pugh, Cheryl; Navani, Neal; Stephens, Richard; Parmar, Mahesh

    2011-11-01

    Purpose: Recent clinical trials and meta-analyses have shown that both CHART (continuous hyperfractionated accelerated radiation therapy) and induction chemotherapy offer a survival advantage over conventional radical radiotherapy for patients with inoperable non-small cell-lung cancer (NSCLC). This multicenter randomized controlled trial (INCH) was set up to assess the value of giving induction chemotherapy before CHART. Methods and Materials: Patients with histologically confirmed, inoperable, Stage I-III NSCLC were randomized to induction chemotherapy (ICT) (three cycles of cisplatin-based chemotherapy followed by CHART) or CHART alone. Results: Forty-six patients were randomized (23 in each treatment arm) from 9 UK centers. As a result of poor accrual, the trial was closed in December 2007. Twenty-eight patients were male, 28 had squamous cell histology, 34 were Stage IIIA or IIIB, and all baseline characteristics were well balanced between the two treatment arms. Seventeen (74%) of the 23 ICT patients completed the three cycles of chemotherapy. All 42 (22 CHART + 20 ICT) patients who received CHART completed the prescribed treatment. Median survival was 17 months in the CHART arm and 25 months in the ICT arm (hazard ratio of 0.60 [95% CI 0.31-1.16], p = 0.127). Grade 3 or 4 adverse events (mainly fatigue, dysphagia, breathlessness, and anorexia) were reported for 13 (57%) CHART and 13 (65%) ICT patients. Conclusions: This small randomized trial indicates that ICT followed by CHART is feasible and well tolerated. Despite closing early because of poor accrual, and so failing to show clear evidence of a survival benefit for the additional chemotherapy, the results suggest that CHART, and ICT before CHART, remain important options for the treatment of inoperable NSCLC and deserve further study.

  14. Markov chain Mote Carlo solution of BK equation through Newton-Kantorovich method

    NASA Astrophysics Data System (ADS)

    BoŻek, Krzysztof; Kutak, Krzysztof; Placzek, Wieslaw

    2013-07-01

    We propose a new method for Monte Carlo solution of non-linear integral equations by combining the Newton-Kantorovich method for solving non-linear equations with the Markov Chain Monte Carlo (MCMC) method for solving linear equations. The Newton-Kantorovich method allows to express the non-linear equation as a system of the linear equations which then can be treated by the MCMC (random walk) algorithm. We apply this method to the Balitsky-Kovchegov (BK) equation describing evolution of gluon density at low x. Results of numerical computations show that the MCMC method is both precise and efficient. The presented algorithm may be particularly suited for solving more complicated and higher-dimensional non-linear integral equation, for which traditional methods become unfeasible.

  15. Guaranteed cost control of mobile sensor networks with Markov switching topologies.

    PubMed

    Zhao, Yuan; Guo, Ge; Ding, Lei

    2015-09-01

    This paper investigates the consensus seeking problem of mobile sensor networks (MSNs) with random switching topologies. The network communication topologies are composed of a set of directed graphs (or digraph) with a spanning tree. The switching of topologies is governed by a Markov chain. The consensus seeking problem is addressed by introducing a global topology-aware linear quadratic (LQ) cost as the performance measure. By state transformation, the consensus problem is transformed to the stabilization of a Markovian jump system with guaranteed cost. A sufficient condition for global mean-square consensus is derived in the context of stochastic stability analysis of Markovian jump systems. A computational algorithm is given to synchronously calculate both the sub-optimal consensus controller gains and the sub-minimum upper bound of the cost. The effectiveness of the proposed design method is illustrated by three numerical examples. PMID:26096955

  16. Localization in a random XY model with long-range interactions: Intermediate case between single-particle and many-body problems

    NASA Astrophysics Data System (ADS)

    Burin, Alexander L.

    2015-09-01

    Many-body localization in an XY model with a long-range interaction is investigated. We show that in the regime of a high strength of disordering compared to the interaction an off-resonant flip-flop spin-spin interaction (hopping) generates the effective Ising interactions of spins in the third order of perturbation theory in a hopping. The combination of hopping and induced Ising interactions for the power-law distance dependent hopping V (R ) ∝R-α always leads to the localization breakdown in a thermodynamic limit of an infinite system at α <3 d /2 where d is a system dimension. The delocalization takes place due to the induced Ising interactions U (R ) ∝R-2 α of "extended" resonant pairs. This prediction is consistent with the numerical finite size scaling in one-dimensional systems. Many-body localization in an XY model is more stable with respect to the long-range interaction compared to a many-body problem with similar Ising and Heisenberg interactions requiring α ≥2 d which makes the practical implementations of this model more attractive for quantum information applications. The full summary of dimension constraints and localization threshold size dependencies for many-body localization in the case of combined Ising and hopping interactions is obtained using this and previous work and it is the subject for the future experimental verification using cold atomic systems.

  17. Some Interesting Characteristics of Markov Chain Transition Matrices.

    ERIC Educational Resources Information Center

    Egelston, Richard L.

    A Monte Carlo investigation of Markov chain matrices was conducted to create empirical distributions for two statistics created from the transition matrices. Curve fitting techniques developed by Karl Pearson were used to deduce if theoretical equations could be fit to the two sets of distributions. The set of distributions which describe the…

  18. Weighted Markov Chains and Graphic State Nodes for Information Retrieval.

    ERIC Educational Resources Information Center

    Benoit, G.

    2002-01-01

    Discusses users' search behavior and decision making in data mining and information retrieval. Describes iterative information seeking as a Markov process during which users advance through states of nodes; and explains how the information system records the decision as weights, allowing the incorporation of users' decisions into the Markov…

  19. Modelling modal gating of ion channels with hierarchical Markov models

    PubMed Central

    Fackrell, Mark; Crampin, Edmund J.; Taylor, Peter

    2016-01-01

    Many ion channels spontaneously switch between different levels of activity. Although this behaviour known as modal gating has been observed for a long time it is currently not well understood. Despite the fact that appropriately representing activity changes is essential for accurately capturing time course data from ion channels, systematic approaches for modelling modal gating are currently not available. In this paper, we develop a modular approach for building such a model in an iterative process. First, stochastic switching between modes and stochastic opening and closing within modes are represented in separate aggregated Markov models. Second, the continuous-time hierarchical Markov model, a new modelling framework proposed here, then enables us to combine these components so that in the integrated model both mode switching as well as the kinetics within modes are appropriately represented. A mathematical analysis reveals that the behaviour of the hierarchical Markov model naturally depends on the properties of its components. We also demonstrate how a hierarchical Markov model can be parametrized using experimental data and show that it provides a better representation than a previous model of the same dataset. Because evidence is increasing that modal gating reflects underlying molecular properties of the channel protein, it is likely that biophysical processes are better captured by our new approach than in earlier models. PMID:27616917

  20. Inferring parental genomic ancestries using pooled semi-Markov processes

    PubMed Central

    Zou, James Y.; Halperin, Eran; Burchard, Esteban; Sankararaman, Sriram

    2015-01-01

    Motivation: A basic problem of broad public and scientific interest is to use the DNA of an individual to infer the genomic ancestries of the parents. In particular, we are often interested in the fraction of each parent’s genome that comes from specific ancestries (e.g. European, African, Native American, etc). This has many applications ranging from understanding the inheritance of ancestry-related risks and traits to quantifying human assortative mating patterns. Results: We model the problem of parental genomic ancestry inference as a pooled semi-Markov process. We develop a general mathematical framework for pooled semi-Markov processes and construct efficient inference algorithms for these models. Applying our inference algorithm to genotype data from 231 Mexican trios and 258 Puerto Rican trios where we have the true genomic ancestry of each parent, we demonstrate that our method accurately infers parameters of the semi-Markov processes and parents’ genomic ancestries. We additionally validated the method on simulations. Our model of pooled semi-Markov process and inference algorithms may be of independent interest in other settings in genomics and machine learning. Contact: jazo@microsoft.com PMID:26072482

  1. Students' Progress throughout Examination Process as a Markov Chain

    ERIC Educational Resources Information Center

    Hlavatý, Robert; Dömeová, Ludmila

    2014-01-01

    The paper is focused on students of Mathematical methods in economics at the Czech university of life sciences (CULS) in Prague. The idea is to create a model of students' progress throughout the whole course using the Markov chain approach. Each student has to go through various stages of the course requirements where his success depends on the…

  2. Building Higher-Order Markov Chain Models with EXCEL

    ERIC Educational Resources Information Center

    Ching, Wai-Ki; Fung, Eric S.; Ng, Michael K.

    2004-01-01

    Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a…

  3. Multiensemble Markov models of molecular thermodynamics and kinetics

    PubMed Central

    Wu, Hao; Paul, Fabian; Noé, Frank

    2016-01-01

    We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model. PMID:27226302

  4. Multiensemble Markov models of molecular thermodynamics and kinetics.

    PubMed

    Wu, Hao; Paul, Fabian; Wehmeyer, Christoph; Noé, Frank

    2016-06-01

    We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models-clustering of high-dimensional spaces and modeling of complex many-state systems-with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein-ligand binding model. PMID:27226302

  5. Exploring Mass Perception with Markov Chain Monte Carlo

    ERIC Educational Resources Information Center

    Cohen, Andrew L.; Ross, Michael G.

    2009-01-01

    Several previous studies have examined the ability to judge the relative mass of objects in idealized collisions. With a newly developed technique of psychological Markov chain Monte Carlo sampling (A. N. Sanborn & T. L. Griffiths, 2008), this work explores participants; perceptions of different collision mass ratios. The results reveal…

  6. Analyzing Sequential Categorical Data: Individual Variation in Markov Chains.

    ERIC Educational Resources Information Center

    Gardner, William

    1990-01-01

    This paper provides a method for analyzing data consisting of event sequences and covariate observations associated with Markov chains. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. (TJH)

  7. Decoherence free subspaces of a quantum Markov semigroup

    SciTech Connect

    Agredo, Julián; Fagnola, Franco; Rebolledo, Rolando

    2014-11-15

    We give a full characterisation of decoherence free subspaces of a given quantum Markov semigroup with generator in a generalised Lindbald form which is valid also for infinite-dimensional systems. Our results, extending those available in the literature concerning finite-dimensional systems, are illustrated by some examples.

  8. Operations and support cost modeling using Markov chains

    NASA Technical Reports Server (NTRS)

    Unal, Resit

    1989-01-01

    Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.

  9. Using Markov Chain Analyses in Counselor Education Research

    ERIC Educational Resources Information Center

    Duys, David K.; Headrick, Todd C.

    2004-01-01

    This study examined the efficacy of an infrequently used statistical analysis in counselor education research. A Markov chain analysis was used to examine hypothesized differences between students' use of counseling skills in an introductory course. Thirty graduate students participated in the study. Independent raters identified the microskills…

  10. Discrete Latent Markov Models for Normally Distributed Response Data

    ERIC Educational Resources Information Center

    Schmittmann, Verena D.; Dolan, Conor V.; van der Maas, Han L. J.; Neale, Michael C.

    2005-01-01

    Van de Pol and Langeheine (1990) presented a general framework for Markov modeling of repeatedly measured discrete data. We discuss analogical single indicator models for normally distributed responses. In contrast to discrete models, which have been studied extensively, analogical continuous response models have hardly been considered. These…

  11. A Markov chain analysis of fish movements to determine entrainment zones

    SciTech Connect

    Johnson, Gary E.; Hedgepeth, J.; Skalski, John R.; Giorgi, Albert E.

    2004-06-01

    The extent of the biological zone of influence (BZI) of a water withdrawal port, such as a cooling water intake or a smolt bypass, directly reflects its local effect on fish. This study produced a new technique to determine the BZI, defined as the region immediately upstream of a portal where the probability of fish movement toward the portal is greater than 90%. We developed and applied the technique at The Dalles Dam on the Columbia River, where the ice/trash sluiceway functions as a surface flow smolt bypass. To map the BZI, we applied a Markov-Chain analysis to smolt movement data collected with an active fish tracking sonar system. Probabilities of fish movement from cell to cell in the sample volume, calculated from tracked fish data, formed a Markov transition matrix. Multiplying this matrix by itself many times with absorption at the boundaries produced estimates of probability of passage out each side of the sample volume from the cells within. The BZI of a sluiceway entrance at The Dalles Dam was approximately 5 m across and extended 6-8 m out from the face of the dam in the surface layer 2-3 m deep. BZI mapping is applicable to many bioengineering efforts to protect fish populations.

  12. Statistical identification with hidden Markov models of large order splitting strategies in an equity market

    NASA Astrophysics Data System (ADS)

    Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N.

    2010-07-01

    Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders, we fit hidden Markov models to the time series of the sign of the tick-by-tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a significant majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transaction size distributions of these patches are fat tailed. Long patches are characterized by a large fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly dependent on the local market trend. We also compare the hidden Markov model patches with those obtained with the segmentation method used in Vaglica et al (2008 Phys. Rev. E 77 036110), and we conclude that the former ones can be interpreted as a partition of the latter ones.

  13. Detecting targets hidden in random forests

    NASA Astrophysics Data System (ADS)

    Kouritzin, Michael A.; Luo, Dandan; Newton, Fraser; Wu, Biao

    2009-05-01

    Military tanks, cargo or troop carriers, missile carriers or rocket launchers often hide themselves from detection in the forests. This plagues the detection problem of locating these hidden targets. An electro-optic camera mounted on a surveillance aircraft or unmanned aerial vehicle is used to capture the images of the forests with possible hidden targets, e.g., rocket launchers. We consider random forests of longitudinal and latitudinal correlations. Specifically, foliage coverage is encoded with a binary representation (i.e., foliage or no foliage), and is correlated in adjacent regions. We address the detection problem of camouflaged targets hidden in random forests by building memory into the observations. In particular, we propose an efficient algorithm to generate random forests, ground, and camouflage of hidden targets with two dimensional correlations. The observations are a sequence of snapshots consisting of foliage-obscured ground or target. Theoretically, detection is possible because there are subtle differences in the correlations of the ground and camouflage of the rocket launcher. However, these differences are well beyond human perception. To detect the presence of hidden targets automatically, we develop a Markov representation for these sequences and modify the classical filtering equations to allow the Markov chain observation. Particle filters are used to estimate the position of the targets in combination with a novel random weighting technique. Furthermore, we give positive proof-of-concept simulations.

  14. Spike Correlations in a Songbird Agree with a Simple Markov Population Model

    PubMed Central

    Weber, Andrea P; Hahnloser, Richard H. R

    2007-01-01

    The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups. PMID:18159941

  15. Indexed semi-Markov process for wind speed modeling.

    NASA Astrophysics Data System (ADS)

    Petroni, F.; D'Amico, G.; Prattico, F.

    2012-04-01

    The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first

  16. Predicting community structure of ground-foraging ant assemblages with Markov models of behavioral dominance.

    PubMed

    Wittman, Sarah E; Gotelli, Nicholas J

    2011-05-01

    Although interference competition is a conspicuous component of many animal communities, it is still uncertain whether the competitive ability of a species determines its relative abundance and patterns of association with other species. We used replicated arena tests to quantify behavioral dominance of eight common species of co-occurring ground-foraging ants in the Siskiyou Mountains of southern Oregon. We found that behavior recorded in laboratory assays was an accurate representation of a colony's ability to monopolize resources in the field. We used interaction frequencies from the behavioral tests to estimate transition probabilities in a simple Markov chain model to predict patterns of relative abundance in a metacommunity that is dominated by behavioral interactions. We also tested whether behavioral interactions between each pair of species could be used to predict patterns of species co-occurrence. We found that the Markov model did not accurately predict patterns of observed relative abundance on either the local or the regional scale. However, we did detect a significant negative correlation at the local scale in which behaviorally dominant species occupied relatively few baits. Pairwise behavioral data also did not predict species co-occurrence in any site. Although interference competition is a conspicuous process in ant communities, our results suggest that it may not contribute much to patterns of relative abundance and species co-occurrence in the system studied here. However, the negative correlation between behavioral dominance and bait occupancy at the local scale suggests that competition-colonization trade-offs may be important in resource acquisition and persistence of behaviorally subordinate species. PMID:20978797

  17. Detecting structure of haplotypes and local ancestry

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We present a two-layer hidden Markov model to detect the structure of haplotypes for unrelated individuals. This allows us to model two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), thereby taking advantage of rich haplotype information to infer local an...

  18. Markov chains and entropy tests in genetic-based lithofacies analysis of deep-water clastic depositional systems

    NASA Astrophysics Data System (ADS)

    Borka, Szabolcs

    2016-01-01

    The aim of this study was to examine the relationship between structural elements and the so-called genetic lithofacies in a clastic deep-water depositional system. Process-sedimentology has recently been gaining importance in the characterization of these systems. This way the recognized facies attributes can be associated with the depositional processes establishing the genetic lithofacies. In this paper this approach was presented through a case study of a Tertiary deep-water sequence of the Pannonian-basin. Of course it was necessary to interpret the stratigraphy of the sequences in terms of "general" sedimentology, focusing on the structural elements. For this purpose, well-logs and standard deep-water models were applied. The cyclicity of sedimentary sequences can be easily revealed by using Markov chains. Though Markov chain analysis has broad application in mainly fluvial depositional environments, its utilization is uncommon in deep-water systems. In this context genetic lithofacies was determined and analysed by embedded Markov chains. The randomness in the presence of a lithofacies within a cycle was estimated by entropy tests (entropy after depositional, before depositional, for the whole system). Subsequently the relationships between lithofacies were revealed and a depositional model (i.e. modal cycle) was produced with 90% confidence level of stationarity. The non-randomness of the latter was tested by chi-square test. The consequences coming from the comparison of "general" sequences (composed of architectural elements), the genetic-based sequences (showing the distributions of the genetic lithofacies) and the lithofacies relationships were discussed in details. This way main depositional channel has the best, channelized lobes have good potential hydrocarbon reservoir attributes, with symmetric alternation of persistent fine-grained sandstone (Facies D) and muddy fine-grained sandstone with traction structures (Facies F)

  19. Development of a Compound Distribution Markov Chain Model for Stochastic Generation of Rainfall with Long Term Persistence

    NASA Astrophysics Data System (ADS)

    Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George

    2015-04-01

    One of the overriding issues in the rainfall simulation is the underestimation of observed rainfall variability in longer timescales (e.g. monthly, annual and multi-year), which usually results into under-estimation of reservoir reliability in urban water planning. This study has developed a Compound Distribution Markov Chain (CDMC) model for stochastic generation of daily rainfall. We used two parameters of Markov Chain process (transition probabilities of wet-to-wet and dry-to-dry days) for simulating rainfall occurrence and two parameters of gamma distribution (calculated from mean and standard deviation of wet-day rainfall) for simulating wet-day rainfall amounts. While two models with deterministic parameters underestimated long term variability, our investigation found that the long term variability of rainfall in the model is predominantly governed by the long term variability of gamma parameters, rather than the variability of Markov Chain parameters. Therefore, in the third approach, we developed the CDMC model with deterministic parameters of Markov Chain process, but stochastic parameters of gamma distribution by sampling the mean and standard deviation of wet-day rainfall from their log-normal and bivariate-normal distribution. We have found that the CDMC is able to replicate both short term and long term rainfall variability, when we calibrated the model at two sites in east coast of Australia using three types of daily rainfall data - (1) dynamically downscaled, 10 km resolution gridded data produced by NSW/ACT Regional Climate Modelling project, (2) 5 km resolution gridded data by Australian Water Availability Project and (3) point scale raingauge stations data by Bureau of Meteorology, Australia. We also examined the spatial variability of parameters and their link with local orography at our field site. The suitability of the model in runoff generation and urban reservoir-water simulation will be discussed.

  20. Fundamental Vibration Frequency and Damping Estimation: A Comparison Using the Random Decrement Method, the Empirical Mode Decomposition, and the HV Spectral Ratio Method for Local Site Characterization

    NASA Astrophysics Data System (ADS)

    Huerta-Lopez, C. I.; Upegui Botero, F. M.; Pulliam, J.; Willemann, R. J.; Pasyanos, M.; Schmitz, M.; Rojas Mercedes, N.; Louie, J. N.; Moschetti, M. P.; Martinez-Cruzado, J. A.; Suárez, L.; Huerfano Moreno, V.; Polanco, E.

    2013-12-01

    Site characterization in civil engineering demands to know at least two of the dynamic properties of soil systems, which are: (i) dominant vibration frequency, and (ii) damping. As part of an effort to develop understanding of the principles of earthquake hazard analysis, particularly site characterization techniques using non invasive/non destructive seismic methods, a workshop (Pan-American Advanced Studies Institute: New Frontiers in Geophysical Research: Bringing New Tools and Techniques to Bear on Earthquake Hazard Analysis and Mitigation) was conducted during july 15-25, 2013 in Santo Domingo, Dominican Republic by the alliance of Pan-American Advanced Studies Institute (PASI) and Incorporated Research Institutions for Seismology (IRIS), jointly supported by Department of Energy (DOE) and National Science Foundation (NSF). Preliminary results of the site characterization in terms of fundamental vibration frequency and damping are here presented from data collected during the workshop. Three different methods were used in such estimations and later compared in order to identify the stability of estimations as well as the advantage or disadvantage among these methodologies. The used methods were the: (i) Random Decrement Method (RDM), to estimate fundamental vibration frequency and damping simultaneously; (ii) Empirical Mode Decomposition (EMD), to estimate the vibration modes, and (iii) Horizontal to Vertical Spectra ratio (HVSR), to estimate the fundamental vibration frequency. In all cases ambient vibration and induced vibration were used.

  1. Combined postoperative radiotherapy and weekly cisplatin infusion for locally advanced squamous cell carcinoma of the head and neck: Preliminary report of a randomized trial

    SciTech Connect

    Bachaud, J.M.; David, J.M.; Boussin, G.; Daly, N. )

    1991-02-01

    A prospective clinical trial was designed to evaluate efficacy, toxicity, and patient compliance of concomitant postoperative radiotherapy and Cisplatin infusion in patients with Stage III or IV S.C.C. of the head and neck and histological evidence of extra-capsular spread of tumor in lymph node metastase(s). Cisplatin 50 mg IV with forced hydration was given or not every week (i.e., 7 to 9 cycles) concurrently with radiotherapy. Between 1984 and 1988, 83 patients were randomized: 44 were treated by irradiation without chemotherapy (RT group) and 39 by the combined modality (CM group). There was no significant difference between the two groups in terms of patient characteristics, primary sites, tumor differentiation, T.N. stages, or postoperative prognostic factors. All patients completed the planned radiotherapy. There were seven severe toxicities (greater than grade 3) in the RT group. In the CM group, 30 severe toxicities occurred in 16/39 (41%) patients but none was life-threatening. Seven of 39 (18%) patients received less than two-thirds of the scheduled Cisplatin courses because of intolerance, mainly nausea and vomiting. Preliminary results show a better disease-free survival for the CM group (65% at 24 months) than for the RT group (41% at 24 months). This significant difference is largely due to increased loco-regional control in the CM group (79% vs 59%), the actuarial distant metastasis rates in patients controlled above the clavicles not being statistically different in the two groups.

  2. Fisher information and asymptotic normality in system identification for quantum Markov chains

    SciTech Connect

    Guta, Madalin

    2011-06-15

    This paper deals with the problem of estimating the coupling constant {theta} of a mixing quantum Markov chain. For a repeated measurement on the chain's output we show that the outcomes' time average has an asymptotically normal (Gaussian) distribution, and we give the explicit expressions of its mean and variance. In particular, we obtain a simple estimator of {theta} whose classical Fisher information can be optimized over different choices of measured observables. We then show that the quantum state of the output together with the system is itself asymptotically Gaussian and compute its quantum Fisher information, which sets an absolute bound to the estimation error. The classical and quantum Fisher information are compared in a simple example. In the vicinity of {theta}=0 we find that the quantum Fisher information has a quadratic rather than linear scaling in output size, and asymptotically the Fisher information is localized in the system, while the output is independent of the parameter.

  3. Bayesian Smoothing Algorithms in Partially Observed Markov Chains

    NASA Astrophysics Data System (ADS)

    Ait-el-Fquih, Boujemaa; Desbouvries, François

    2006-11-01

    Let x = {xn}n∈N be a hidden process, y = {yn}n∈N an observed process and r = {rn}n∈N some auxiliary process. We assume that t = {tn}n∈N with tn = (xn, rn, yn-1) is a (Triplet) Markov Chain (TMC). TMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient restoration and parameter estimation algorithms. This paper is devoted to Bayesian smoothing algorithms for TMC. We first propose twelve algorithms for general TMC. In the Gaussian case, these smoothers reduce to a set of algorithms which include, among other solutions, extensions to TMC of classical Kalman-like smoothing algorithms (originally designed for HMC) such as the RTS algorithms, the Two-Filter algorithms or the Bryson and Frazier algorithm.

  4. On Factor Maps that Send Markov Measures to Gibbs Measures

    NASA Astrophysics Data System (ADS)

    Yoo, Jisang

    2010-12-01

    Let X and Y be mixing shifts of finite type. Let π be a factor map from X to Y that is fiber-mixing, i.e., given x,bar{x}in X with π(x)=π(bar{x})=yin Y, there is z∈ π -1( y) that is left asymptotic to x and right asymptotic to bar{x}. We show that any Markov measure on X projects to a Gibbs measure on Y under π (for a Hölder continuous potential). In other words, all hidden Markov chains (i.e. sofic measures) realized by π are Gibbs measures. In 2003, Chazottes and Ugalde gave a sufficient condition for a sofic measure to be a Gibbs measure. Our sufficient condition generalizes their condition and is invariant under conjugacy and time reversal. We provide examples demonstrating our result.

  5. Predictive Rate-Distortion for Infinite-Order Markov Processes

    NASA Astrophysics Data System (ADS)

    Marzen, Sarah E.; Crutchfield, James P.

    2016-06-01

    Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

  6. Investigation of Commuting Hamiltonian in Quantum Markov Network

    NASA Astrophysics Data System (ADS)

    Jouneghani, Farzad Ghafari; Babazadeh, Mohammad; Bayramzadeh, Rogayeh; Movla, Hossein

    2014-08-01

    Graphical Models have various applications in science and engineering which include physics, bioinformatics, telecommunication and etc. Usage of graphical models needs complex computations in order to evaluation of marginal functions, so there are some powerful methods including mean field approximation, belief propagation algorithm and etc. Quantum graphical models have been recently developed in context of quantum information and computation, and quantum statistical physics, which is possible by generalization of classical probability theory to quantum theory. The main goal of this paper is preparing a primary generalization of Markov network, as a type of graphical models, to quantum case and applying in quantum statistical physics. We have investigated the Markov network and the role of commuting Hamiltonian terms in conditional independence with simple examples of quantum statistical physics.

  7. Recursive utility in a Markov environment with stochastic growth.

    PubMed

    Hansen, Lars Peter; Scheinkman, José A

    2012-07-24

    Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron-Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility. PMID:22778428

  8. Markov reliability models for digital flight control systems

    NASA Technical Reports Server (NTRS)

    Mcgough, John; Reibman, Andrew; Trivedi, Kishor

    1989-01-01

    The reliability of digital flight control systems can often be accurately predicted using Markov chain models. The cost of numerical solution depends on a model's size and stiffness. Acyclic Markov models, a useful special case, are particularly amenable to efficient numerical solution. Even in the general case, instantaneous coverage approximation allows the reduction of some cyclic models to more readily solvable acyclic models. After considering the solution of single-phase models, the discussion is extended to phased-mission models. Phased-mission reliability models are classified based on the state restoration behavior that occurs between mission phases. As an economical approach for the solution of such models, the mean failure rate solution method is introduced. A numerical example is used to show the influence of fault-model parameters and interphase behavior on system unreliability.

  9. Statistical significance test for transition matrices of atmospheric Markov chains

    NASA Technical Reports Server (NTRS)

    Vautard, Robert; Mo, Kingtse C.; Ghil, Michael

    1990-01-01

    Low-frequency variability of large-scale atmospheric dynamics can be represented schematically by a Markov chain of multiple flow regimes. This Markov chain contains useful information for the long-range forecaster, provided that the statistical significance of the associated transition matrix can be reliably tested. Monte Carlo simulation yields a very reliable significance test for the elements of this matrix. The results of this test agree with previously used empirical formulae when each cluster of maps identified as a distinct flow regime is sufficiently large and when they all contain a comparable number of maps. Monte Carlo simulation provides a more reliable way to test the statistical significance of transitions to and from small clusters. It can determine the most likely transitions, as well as the most unlikely ones, with a prescribed level of statistical significance.

  10. Predictive Rate-Distortion for Infinite-Order Markov Processes

    NASA Astrophysics Data System (ADS)

    Marzen, Sarah E.; Crutchfield, James P.

    2016-05-01

    Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

  11. Timing of High-Dose Rate Brachytherapy With External Beam Radiotherapy in Intermediate and High-Risk Localized Prostate CAncer (THEPCA) Patients and Its Effects on Toxicity and Quality of Life: Protocol of a Randomized Feasibility Trial

    PubMed Central

    Palvai, Sreekanth; Harrison, Michael; Shibu Thomas, Sharon; Hayden, Karen; Green, James; Anderson, Oliver; Romero, Lavinia; Lodge, Richard; Burns, Patricia

    2015-01-01

    Background Prostate cancer is the most common cancer in males in the UK and affects around 105 men for every 100,000. The role of radiotherapy in the management of prostate cancer significantly changed over the last few decades with developments in brachytherapy, external beam radiotherapy (EBRT), intensity-modulated radiotherapy (IMRT), and image-guided radiotherapy (IGRT). One of the challenging factors of radiotherapy treatment of localized prostate cancer is the development of acute and late genitourinary and gastrointestinal toxicities. The recent European guidelines suggest that there is no consensus regarding the timing of high-dose rate (HDR) brachytherapy and EBRT. The schedules vary in different institutions where an HDR boost can be given either before or after EBRT. Few centers deliver HDR in between the fractions of EBRT. Objective Assessment of acute genitourinary and gastrointestinal toxicities at various time points to better understand if the order in which treatment modality is delivered (ie, HDR brachytherapy or EBRT first) has an effect on the toxicity profile. Methods Timing of HDR brachytherapy with EBRT in Prostate CAncer (THEPCA) is a single-center, open, randomized controlled feasibility trial in patients with intermediate and high-risk localized prostate cancer. A group of 50 patients aged 18 years old and over with histological diagnosis of prostate cancer (stages T1b-T3BNOMO), will be randomized to one of two treatment arms (ratio 1:1), following explanation of the study and informed consent. Patients in both arms of the study will be treated with HDR brachytherapy and EBRT, however, the order in which they receive the treatments will vary. In Arm A, patients will receive HDR brachytherapy before EBRT. In Arm B (control arm), patients will receive EBRT before HDR brachytherapy. Study outcomes will look at prospective assessment of genitourinary and gastrointestinal toxicities. The primary endpoint will be grade 3 genitourinary toxicity

  12. Symbolic Heuristic Search for Factored Markov Decision Processes

    NASA Technical Reports Server (NTRS)

    Morris, Robert (Technical Monitor); Feng, Zheng-Zhu; Hansen, Eric A.

    2003-01-01

    We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.

  13. Regenerative Markov Chain Monte Carlo for any distribution.

    SciTech Connect

    Minh, D.

    2012-01-01

    While Markov chain Monte Carlo (MCMC) methods are frequently used for difficult calculations in a wide range of scientific disciplines, they suffer from a serious limitation: their samples are not independent and identically distributed. Consequently, estimates of expectations are biased if the initial value of the chain is not drawn from the target distribution. Regenerative simulation provides an elegant solution to this problem. In this article, we propose a simple regenerative MCMC algorithm to generate variates for any distribution

  14. Markov chain evaluation of acute postoperative pain transition states.

    PubMed

    Tighe, Patrick J; Bzdega, Matthew; Fillingim, Roger B; Rashidi, Parisa; Aytug, Haldun

    2016-03-01

    Previous investigations on acute postoperative pain dynamicity have focused on daily pain assessments, and so were unable to examine intraday variations in acute pain intensity. We analyzed 476,108 postoperative acute pain intensity ratings, which were clinically documented on postoperative days 1 to 7 from 8346 surgical patients using Markov chain modeling to describe how patients are likely to transition from one pain state to another in a probabilistic fashion. The Markov chain was found to be irreducible and positive recurrent, with no absorbing states. Transition probabilities ranged from 0.0031, for the transition from state 10 to state 1, to 0.69 for the transition from state 0 to state 0. The greatest density of transitions was noted in the diagonal region of the transition matrix, suggesting that patients were generally most likely to transition to the same pain state as their current state. There were also slightly increased probability densities in transitioning to a state of asleep or 0 from the current state. An examination of the number of steps required to traverse from a particular first pain score to a target state suggested that overall, fewer steps were required to reach a state of 0 (range 6.1-8.8 steps) or asleep (range 9.1-11) than were required to reach a mild pain intensity state. Our results suggest that using Markov chains is a feasible method for describing probabilistic postoperative pain trajectories, pointing toward the possibility of using Markov decision processes to model sequential interactions between pain intensity ratings, and postoperative analgesic interventions. PMID:26588689

  15. The semi-Markov unreliability range evaluator program

    NASA Technical Reports Server (NTRS)

    Butler, R. W.

    1984-01-01

    The SURE program is a design/validation tool for ultrareliable computer system architectures. The system uses simple algebraic formulas to compute accurate upper and lower bounds for the death state probabilities of a large class of semi-Markov models. The mathematical formulas used in the program were derived from a mathematical theorem proven by Allan White under contract to NASA Langley Research Center. This mathematical theorem is discussed along with the user interface to the SURE program.

  16. An abstract specification language for Markov reliability models

    NASA Technical Reports Server (NTRS)

    Butler, R. W.

    1985-01-01

    Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

  17. Probabilistic Independence Networks for Hidden Markov Probability Models

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic; Heckerman, Cavid; Jordan, Michael I

    1996-01-01

    In this paper we explore hidden Markov models(HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general enference algorithms for arbitrary PINs.

  18. The sharp constant in Markov's inequality for the Laguerre weight

    SciTech Connect

    Sklyarov, Vyacheslav P

    2009-06-30

    We prove that the polynomial of degree n that deviates least from zero in the uniformly weighted metric with Laguerre weight is the extremal polynomial in Markov's inequality for the norm of the kth derivative. Moreover, the corresponding sharp constant does not exceed (8{sup k} n {exclamation_point} k {exclamation_point})/((n-k){exclamation_point} (2k){exclamation_point}). For the derivative of a fixed order this bound is asymptotically sharp as n{yields}{infinity}. Bibliography: 20 items.

  19. Space system operations and support cost analysis using Markov chains

    NASA Technical Reports Server (NTRS)

    Unal, Resit; Dean, Edwin B.; Moore, Arlene A.; Fairbairn, Robert E.

    1990-01-01

    This paper evaluates the use of Markov chain process in probabilistic life cycle cost analysis and suggests further uses of the process as a design aid tool. A methodology is developed for estimating operations and support cost and expected life for reusable space transportation systems. Application of the methodology is demonstrated for the case of a hypothetical space transportation vehicle. A sensitivity analysis is carried out to explore the effects of uncertainty in key model inputs.

  20. Parallel algorithms for simulating continuous time Markov chains

    NASA Technical Reports Server (NTRS)

    Nicol, David M.; Heidelberger, Philip

    1992-01-01

    We have previously shown that the mathematical technique of uniformization can serve as the basis of synchronization for the parallel simulation of continuous-time Markov chains. This paper reviews the basic method and compares five different methods based on uniformization, evaluating their strengths and weaknesses as a function of problem characteristics. The methods vary in their use of optimism, logical aggregation, communication management, and adaptivity. Performance evaluation is conducted on the Intel Touchstone Delta multiprocessor, using up to 256 processors.