Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.
Song, Sanming; Si, Bailu; Herrmann, J Michael; Feng, Xisheng
2016-03-22
A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field (MRF) model. Due to sampling cost, Markov chain Monte Carlo (MCMC) 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 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 pseudo-likelihood (MPL) 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.
Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.
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.
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.
Spectral Design in Markov Random Fields
NASA Astrophysics Data System (ADS)
Wang, Jiao; Thibault, Jean-Baptiste; Yu, Zhou; Sauer, Ken; Bouman, Charles
2011-03-01
Markov random fields (MRFs) have been shown to be a powerful and relatively compact stochastic model for imagery in the context of Bayesian estimation. The simplicity of their conventional embodiment implies local computation in iterative processes and relatively noncommittal statistical descriptions of image ensembles, resulting in stable estimators, particularly under models with strictly convex potential functions. This simplicity may be a liability, however, when the inherent bias of minimum mean-squared error or maximum a posteriori probability (MAP) estimators attenuate all but the lowest spatial frequencies. In this paper we explore generalization of MRFs by considering frequency-domain design of weighting coefficients which describe strengths of interconnections between clique members.
Finite Markov Chains and Random Discrete Structures
1994-07-26
arrays with fixed margins 4. Persi Diaconis and Susan Holmes, Three Examples of Monte- Carlo Markov Chains: at the Interface between Statistical Computing...solutions for a math- ematical model of thermomechanical phase transitions in shape memory materials with Landau- Ginzburg free energy 1168 Angelo Favini
Multiscale Representations of Markov Random Fields
1992-09-08
modeling a wide variety of biological, chelmical, electrical, mechanical and economic phenomena, [10]. Moreover, the Markov structure makes the models...Transactions on Informlation Theory, 18:232-240, March 1972. [65] J. WOODS AND C. RADEWAN, "Kalman Filtering in Two Dimensions," IEEE Trans- actions on
Gaussian Markov Random Field Model without Boundary Conditions
NASA Astrophysics Data System (ADS)
Katakami, Shun; Sakamoto, Hirotaka; Murata, Shin; Okada, Masato
2017-06-01
In this study, we analyzed a Gaussian Markov random field model without periodic boundary conditions. On the basis of a Bayesian inference framework, we showed that image restoration, hyperparameter estimation, and an expectation value of free energy can be conducted analytically. Through numerical simulations, we showed the difference between methods with and without periodic boundary conditions and verified the effectiveness of the proposed method.
Markov Random Fields, Stochastic Quantization and Image Analysis
1990-01-01
Markov random fields based on the lattice Z2 have been extensively used in image analysis in a Bayesian framework as a-priori models for the...of Image Analysis can be given some fundamental justification then there is a remarkable connection between Probabilistic Image Analysis , Statistical Mechanics and Lattice-based Euclidean Quantum Field Theory.
Measuring marine oil spill extent by Markov Random Fields
NASA Astrophysics Data System (ADS)
Moctezuma, Miguel; Parmiggiani, Flavio; Lopez Lopez, Ludwin
2014-10-01
The Deepwater Horizon oil spill of the Gulf of Mexico in the spring of 2010 was the largest accidental marine oil spill in the history of the petroleum industry. An immediate request, after the accident, was to detect the oil slick and to measure its extent: SAR images were the obvious tool to be employed for the task. This paper presents a processing scheme based on Markov Random Fields (MRF) theory. MRF theory describes the global information by probability terms involving local neighborhood representations of the SAR backscatter data. The random degradation introduced by speckle noise is dealt with a pre-processing stage which applies a nonlinear diffusion filter. Spatial context attributes are structured by the Bayes equation derived from a Maximum-A-Posteriori (MAP) estimation. The probability terms define an objective function of a MRF model whose goal is to detect contours and fine structures. The markovian segmentation problem is solved with a numerical optimization method. The scheme was applied to an Envisat/ASAR image over the Gulf of Mexico of May 9, 2010, when the oil spill was already fully developed. The final result was obtained with 51 recursion cycles, where, at each step, the segmentation consists of a 3-class label field (open sea and two oil slick thicknesses). Both the MRF model and the parameters of the stochastic optimization procedure will be provided, together with the area measurement of the two kinds of oil slick.
Learning Markov Random Walks for robust subspace clustering and estimation.
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.
Combinatorial Markov Random Fields and Their Applications to Information Organization
2008-02-01
data clustering —the most important application of unsupervised learning—for which we give some necessary definitions and insights. 2.1 Markov Random...algorithm starts with data instances distributed over k clusters (where k is the desired number of clusters ) and reorga- nizes / updates the clusters ...its original ICM- based version. 4.5 Related work The study of distributional clustering based on co-occurrence data using informa- tion theoretic
Fuzzy Markov random fields versus chains for multispectral image segmentation.
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.
NASA Astrophysics Data System (ADS)
Sivakumar, Krishnamoorthy; Goutsias, John I.
1998-09-01
We study the problem of simulating a class of Gibbs random field models, called morphologically constrained Gibbs random fields, using Markov chain Monte Carlo sampling techniques. Traditional single site updating Markov chain Monte Carlo sampling algorithm, like the Metropolis algorithm, tend to converge extremely slowly when used to simulate these models, particularly at low temperatures and for constraints involving large geometrical shapes. Moreover, the morphologically constrained Gibbs random fields are not, in general, Markov. Hence, a Markov chain Monte Carlo sampling algorithm based on the Gibbs sampler is not possible. We prose a variant of the Metropolis algorithm that, at each iteration, allows multi-site updating and converges substantially faster than the traditional single- site updating algorithm. The set of sites that are updated at a particular iteration is specified in terms of a shape parameter and a size parameter. Computation of the acceptance probability involves a 'test ratio,' which requires computation of the ratio of the probabilities of the current and new realizations. Because of the special structure of our energy function, this computation can be done by means of a simple; local iterative procedure. Therefore lack of Markovianity does not impose any additional computational burden for model simulation. The proposed algorithm has been used to simulate a number of image texture models, both synthetic and natural.
Markov random fields for static foreground classification in surveillance systems
NASA Astrophysics Data System (ADS)
Fitzsimons, Jack K.; Lu, Thomas T.
2014-09-01
We present a novel technique for classifying static foreground in automated airport surveillance systems between abandoned and removed objects by representing the image as a Markov Random Field. The proposed algorithm computes and compares the net probability of the region of interest before and after the event occurs, hence finding which fits more naturally with their respective backgrounds. Having tested on a dataset from the PETS 2006, PETS 2007, AVSS20074, CVSG, VISOR, CANDELA and WCAM datasets, the algorithm has shown capable of matching the results of the state-of-the-art, is highly parallel and has a degree of robustness to noise and illumination changes.
Sub-Markov Random Walk for Image Segmentation.
Dong, Xingping; Shen, Jianbing; Shao, Ling; Van Gool, Luc
2016-02-01
A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.
Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes
Li, Degui; Li, Runze
2016-01-01
In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894
Cover estimation and payload location using Markov random fields
NASA Astrophysics Data System (ADS)
Quach, Tu-Thach
2014-02-01
Payload location is an approach to find the message bits hidden in steganographic images, but not necessarily their logical order. Its success relies primarily on the accuracy of the underlying cover estimators and can be improved if more estimators are used. This paper presents an approach based on Markov random field to estimate the cover image given a stego image. It uses pairwise constraints to capture the natural two-dimensional statistics of cover images and forms a basis for more sophisticated models. Experimental results show that it is competitive against current state-of-the-art estimators and can locate payload embedded by simple LSB steganography and group-parity steganography. Furthermore, when combined with existing estimators, payload location accuracy improves significantly.
Markov random field method for dynamic PET image segmentation
NASA Astrophysics Data System (ADS)
Lin, Kang-Ping; Lou, Shyhliang A.; Yu, Chin-Lung; Chung, Being-Tau; Wu, Liang-Chi; Liu, Ren-Shyan
1998-06-01
In this paper, the Markov random field (MRF) clustering method for highly noisy medical image segmentation is presented. In MRF method, the image to be segmented is analyzed in a probabilistic way that establishes image model by a posteriori probability density function with Bayes' theorem, with relation between pixel positions as well as gray-levels involved. The adaptive threshold parameter is determined in the iterative clustering process to achieve global optimal segmentation. The presented method and other segmentation methods in use are tested on simulation images of different noise levels, and the numerical comparison result is presented. It also is applied on the highly noisy positron emission tomography images, in that the diagnostic hypoxia fraction is automatically calculated. The experimental results are acceptable, and show that the presented method is suitable and robust for noisy image segmentation.
MRFalign: protein homology detection through alignment of Markov random fields.
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.
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Ma, Jianzhu; Wang, Sheng; Wang, Zhiyong; Xu, Jinbo
2014-01-01
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
Entropy of continuous Markov processes in local thermal equilibrium
NASA Astrophysics Data System (ADS)
Hoyuelos, Miguel
2009-05-01
The Boltzmann’s entropy of a continuous Markov process, in local thermal equilibrium, in contact with a reservoir at temperature T , is analyzed. Assuming that the corresponding Fokker-Planck equation has constant coefficients and satisfies detailed balance, an equation for the entropy density is derived, from which it is possible to obtain expressions for the transport coefficients as functions of the diffusion matrix. Expressions for the entropy production terms of the system and of the combination of system plus reservoir are obtained. Known relations among transport coefficients are derived. The multicomponent case is also analyzed and the Prigogine theorem of minimum entropy production is derived in the context of reaction diffusion systems. The derivations presented in this paper are proposed as a framework for a deeper understanding of concepts used in nonequilibrium diffusion systems.
Glaucoma progression detection using nonlocal Markov random field prior
Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A.; Balasubramanian, Madhusudhanan; Weinreb, Robert N.; Zangwill, Linda M.
2014-01-01
Abstract. Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a “non-progressing” or “progressing” glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection. PMID:26158069
Biomedical image analysis using Markov random fields & efficient linear programing.
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.
Markov random field for tumor detection in digital mammography
Li, H.D.; Kallergi, M.; Clarke, L.P.; Clark, R.A.; Jain, V.K.
1995-09-01
A technique is proposed for the detection of tumors in digital mammography. Detection is performed in two steps: segmentation and classification. In segmentation, regions of interest are first extracted from the images by adaptive thresholding. A further reliable segmentation is achieved by a modified Markov random field (MRF) model-based method. In classification, the MRF segmented regions are classified into suspicious and normal by a fuzzy binary decision tree based on a series of radiographic, density-related features. A set of normal (50) and abnormal (45) screen/film mammograms were tested. The latter contained 48 biopsy proven, malignant masses of various types and subtlety. The detection accuracy of the algorithm was evaluated by means of a free response receiver operating characteristic curve which shows the relationship between the detection of true positive masses and the number of false positive alarms per image. The results indicated that a 90% sensitivity can be achieved in the detection of different types of masses at the expense of two falsely detected signals per image. The algorithm was notably successful in the detection of minimal cancers manifested by masses {le} 10 mm in size. For the 16 such cases in their dataset, a 94% sensitivity was observed with 1.5 false alarms per image. An extensive study of the effects of the algorithm`s parameters on its sensitivity and specificity was also performed in order to optimize the method for a clinical, observer performance study.
Glaucoma progression detection using nonlocal Markov random field prior.
Belghith, Akram; Bowd, Christopher; Medeiros, Felipe A; Balasubramanian, Madhusudhanan; Weinreb, Robert N; Zangwill, Linda M
2014-10-01
Glaucoma is neurodegenerative disease characterized by distinctive changes in the optic nerve head and visual field. Without treatment, glaucoma can lead to permanent blindness. Therefore, monitoring glaucoma progression is important to detect uncontrolled disease and the possible need for therapy advancement. In this context, three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT) has been commonly used in the diagnosis and management of glaucoma patients. We present a new framework for detection of glaucoma progression using 3-D SD-OCT images. In contrast to previous works that use the retinal nerve fiber layer thickness measurement provided by commercially available instruments, we consider the whole 3-D volume for change detection. To account for the spatial voxel dependency, we propose the use of the Markov random field (MRF) model as a prior for the change detection map. In order to improve the robustness of the proposed approach, a nonlocal strategy was adopted to define the MRF energy function. To accommodate the presence of false-positive detection, we used a fuzzy logic approach to classify a 3-D SD-OCT image into a "non-progressing" or "progressing" glaucoma class. We compared the diagnostic performance of the proposed framework to the existing methods of progression detection.
Brain tumor segmentation in 3D MRIs using an improved Markov random field model
NASA Astrophysics Data System (ADS)
Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza
2011-10-01
Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.
Comparing quantum versus Markov random walk models of judgements measured by rating scales
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
Spatio-temporal contextual classification based on Markov random field model. [for thematic mapping
NASA Technical Reports Server (NTRS)
Jeon, Byeungwoo; Landgrebe, D. A.
1991-01-01
A contextural classifier based on a Markov random field model, which can utilize both spatial and temporal contexts, is investigated. Spatial and temporal neighbors are defined, and the class assignment of each pixel is assumed to be dependent only on the measurement vectors of itself and those of its spatial and temporal neighbors according to the Markov random field property. Only interpixel class dependency context is used in the classification. The joint prior probability of the classes of each pixel and its spatial and temporal neighbors are modeled by a Gibbs random field. The classification is performed in a recursive manner. Experiments with multi-temporal Thematic Mapper data show promising results.
Spatio-temporal contextual classification based on Markov random field model. [for thematic mapping
NASA Technical Reports Server (NTRS)
Jeon, Byeungwoo; Landgrebe, D. A.
1991-01-01
A contextural classifier based on a Markov random field model, which can utilize both spatial and temporal contexts, is investigated. Spatial and temporal neighbors are defined, and the class assignment of each pixel is assumed to be dependent only on the measurement vectors of itself and those of its spatial and temporal neighbors according to the Markov random field property. Only interpixel class dependency context is used in the classification. The joint prior probability of the classes of each pixel and its spatial and temporal neighbors are modeled by a Gibbs random field. The classification is performed in a recursive manner. Experiments with multi-temporal Thematic Mapper data show promising results.
NASA Astrophysics Data System (ADS)
Senno, Gabriel; Bendersky, Ariel; Figueira, Santiago
2016-07-01
The concepts of randomness and non-locality are intimately intertwined outcomes of randomly chosen measurements over entangled systems exhibiting non-local correlations are, if we preclude instantaneous influence between distant measurement choices and outcomes, random. In this paper, we survey some recent advances in the knowledge of the interplay between these two important notions from a quantum information science perspective.
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.
Entropy, complexity, and Markov diagrams for random walk cancer models
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
Entropy, complexity, and Markov diagrams for random walk cancer models.
Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-19
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
A Markov Chain Model for evaluating the effectiveness of randomized surveillance procedures
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.
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.
Zhang, J
1996-01-01
The Gibbs-Bogoliubov-Feynman (GBF) inequality of statistical mechanics is adopted, with an information-theoretic interpretation, as a general optimization framework for deriving and examining various mean field approximations for Markov random fields (MRF's). The efficacy of this approach is demonstrated through the compound Gauss-Markov (CGM) model, comparisons between different mean field approximations, and experimental results in image restoration.
Tameness for the distribution of sums of Markov random variables
NASA Astrophysics Data System (ADS)
Bisbas, A.; Karanikas, C.; Moran, W.
1997-01-01
This paper studies the spectral properties of a class of probability measures on the circle. The key aim is to describe the local structure of the maximal ideal space on the L-subspaces generated by the measures and hence the spectral properties of these measures. In particular we give a necessary and sufficient condition for such measures to belong to M0 (T).
Joint clustering of protein interaction networks through Markov random walk
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
Ge, Mei; Mainprize, James G.; Mawdsley, Gordon E.; Yaffe, Martin J.
2014-01-01
Abstract. Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semielliptic-shaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either “For Processing” or “For Presentation” image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM “For Processing” images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types. PMID:26158068
Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics
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
NASA Astrophysics Data System (ADS)
Wang, Leiguang; Huang, Xin; Zheng, Chen; Zhang, Yun
2017-06-01
This paper develops a novel Markov Random Field (MRF) model for edge-preserving spatial regularization of classification maps. MRF methods based on the uniform smoothness lead to oversmoothed solutions. In contrast, MRF methods which take care of local spectral or gradient discontinuities, lead to unexpected object particles around boundaries. To solve these key problems, our developed MRF method first establishes a spatial energy function integrating local spectral dissimilarity to smooth the initial classification map while preserving object boundaries. Second, a new anisotropic spatial energy function integrating the class co-occurrence dependency is constructed to regularize pixels around object boundaries. The effectiveness of the method is tested using a series of remote sensing data sets. The obtained results indicate that the method can avoid oversmoothing and significantly improve the classification accuracy with regards to traditional MRF classification models and some other state-of-the-art methods.
Theory of Distribution Estimation of Hyperparameters in Markov Random Field Models
NASA Astrophysics Data System (ADS)
Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato
2016-06-01
We investigated the performance of distribution estimation of hyperparameters in Markov random field models proposed by Nakanishi-Ohno et al., http://doi.org/10.1088/1751-8113/47/4/045001, J. Phys. A 47, 045001 (2014) when used to evaluate the confidence of data. We analytically calculated the configurational average, with respect to data, of the negative logarithm of the posterior distribution, which is called free energy based on an analogy with statistical mechanics. This configurational average of free energy shrinks as the amount of data increases. Our results theoretically confirm the numerical results from that previous study.
2010-01-01
Background Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data. Results In this paper, we present an Abstraction Augmented Markov Model (AAMM) based approach to semi-supervised protein subcellular localization prediction problem. We investigate the effectiveness of AAMMs in exploiting unlabeled data. We compare semi-supervised AAMMs with: (i) Markov models (MMs) (which do not take advantage of unlabeled data); (ii) an expectation maximization (EM); and (iii) a co-training based approaches to semi-supervised training of MMs (that make use of unlabeled data). Conclusions The results of our experiments on three protein subcellular localization data sets show that semi-supervised AAMMs: (i) can effectively exploit unlabeled data; (ii) are more accurate than both the MMs and the EM based semi-supervised MMs; and (iii) are comparable in performance, and in some cases outperform, the co-training based semi-supervised MMs. PMID:21034431
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.
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.
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.
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
Multi-fidelity modelling via recursive co-kriging and Gaussian-Markov random fields.
Perdikaris, P; Venturi, D; Royset, J O; Karniadakis, G E
2015-07-08
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.
Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields
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
NASA Astrophysics Data System (ADS)
Yasuda, Muneki; Kataoka, Shun
2017-08-01
In this paper, we address the inverse problem, or the statistical machine learning problem, in Markov random fields with a non-parametric pair-wise energy function with continuous variables. The inverse problem is formulated by maximum likelihood estimation. The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partition function and (2) it is formulated in the form of functional optimization. We avoid Problem (1) by using Bethe approximation. Bethe approximation is an approximation technique equivalent to the loopy belief propagation. Problem (2) can be solved by using orthonormal function expansion. Orthonormal function expansion can reduce a functional optimization problem to a function optimization problem. Our method can provide an analytic form of the solution of the inverse problem within the framework of Bethe approximation as a result of variational optimization.
Mixture model and Markov random field-based remote sensing image unsupervised clustering method
NASA Astrophysics Data System (ADS)
Hou, Y.; Yang, Y.; Rao, N.; Lun, X.; Lan, J.
2011-03-01
In this paper, a novel method for remote sensing image clustering based on mixture model and Markov random field (MRF) is proposed. A remote sensing image can be considered as Gaussian mixture model. The image clustering result corresponding to the image label field is a MRF. So, the image clustering procedure is transformed to a maximum a posterior (MAP) problem by Bayesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique are introduced into the traditional MRF potential function. The iterative conditional model (ICM) is employed to find the solution of MAP. We use the max entropy criterion to choose the optimal clustering number. In the experiments, the method is compared with the traditional MRF clustering method using ICM and simulated annealing (SA). The results show that this method is better than the traditional MRF model both in noise filtering and miss-classification ratio.
Daniels, Noah M; Gallant, Andrew; Ramsey, Norman; Cowen, Lenore J
2015-01-01
We introduce MRFy, a tool for protein remote homology detection that captures beta-strand dependencies in the Markov random field. Over a set of 11 SCOP beta-structural superfamilies, MRFy shows a 14 percent improvement in mean Area Under the Curve for the motif recognition problem as compared to HMMER, 25 percent improvement as compared to RAPTOR, 14 percent improvement as compared to HHPred, and a 18 percent improvement as compared to CNFPred and RaptorX. MRFy was implemented in the Haskell functional programming language, and parallelizes well on multi-core systems. MRFy is available, as source code as well as an executable, from http://mrfy.cs.tufts.edu/.
Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET
NASA Astrophysics Data System (ADS)
Bousse, Alexandre; Pedemonte, Stefano; Thomas, Benjamin A.; Erlandsson, Kjell; Ourselin, Sébastien; Arridge, Simon; Hutton, Brian F.
2012-10-01
In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image.
3D Mesh Segmentation Based on Markov Random Fields and Graph Cuts
NASA Astrophysics Data System (ADS)
Shi, Zhenfeng; Le, Dan; Yu, Liyang; Niu, Xiamu
3D Mesh segmentation has become an important research field in computer graphics during the past few decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. However, only a few algorithms based on Markov Random Field (MRF) has been presented for 3D object segmentation. In this letter, we present a definition of mesh segmentation according to the labeling problem. Inspired by the capability of MRF combining the geometric information and the topology information of a 3D mesh, we propose a novel 3D mesh segmentation model based on MRF and Graph Cuts. Experimental results show that our MRF-based schema achieves an effective segmentation.
Markov random field model for segmenting large populations of lipid vesicles from micrographs.
Zupanc, Jernej; Drobne, Damjana; Ster, Branko
2011-12-01
Giant unilamellar lipid vesicles, artificial replacements for cell membranes, are a promising tool for in vitro assessment of interactions between products of nanotechnologies and biological membranes. However, the effect of nanoparticles can not be derived from observations on a single specimen, vesicle populations should be observed instead. We propose an adaptation of the Markov random field image segmentation model which allows detection and segmentation of numerous vesicles in micrographs. The reliability of this model with different lighting, blur, and noise characteristics of micrographs is examined and discussed. Moreover, the automatic segmentation is tested on micrographs with thousands of vesicles and the result is compared to that of manual segmentation. The segmentation step presented is part of a methodology we are developing for bio-nano interaction assessment studies on lipid vesicles.
Change point estimation in high dimensional Markov random-field models.
Roy, Sandipan; Atchadé, Yves; Michailidis, George
2017-09-01
This paper investigates a change-point estimation problem in the context of high-dimensional Markov random field models. Change-points represent a key feature in many dynamically evolving network structures. The change-point estimate is obtained by maximizing a profile penalized pseudo-likelihood function under a sparsity assumption. We also derive a tight bound for the estimate, up to a logarithmic factor, even in settings where the number of possible edges in the network far exceeds the sample size. The performance of the proposed estimator is evaluated on synthetic data sets and is also used to explore voting patterns in the US Senate in the 1979-2012 period.
A Markov random field approach for modeling spatio-temporal evolution of microstructures
NASA Astrophysics Data System (ADS)
Acar, Pinar; Sundararaghavan, Veera
2016-10-01
The following problem is addressed: ‘Can one synthesize microstructure evolution over a large area given experimental movies measured over smaller regions?’ Our input is a movie of microstructure evolution over a small sample window. A Markov random field (MRF) algorithm is developed that uses this data to estimate the evolution of microstructure over a larger region. Unlike the standard microstructure reconstruction problem based on stationary images, the present algorithm is also able to reconstruct time-evolving phenomena such as grain growth. Such an algorithm would decrease the cost of full-scale microstructure measurements by coupling mathematical estimation with targeted small-scale spatiotemporal measurements. The grain size, shape and orientation distribution statistics of synthesized polycrystalline microstructures at different times are compared with the original movie to verify the method.
Scene estimation from speckled synthetic aperture radar imagery: Markov-random-field approach.
Lankoande, Ousseini; Hayat, Majeed M; Santhanam, Balu
2006-06-01
A novel Markov-random-field model for speckled synthetic aperture radar (SAR) imagery is derived according to the physical, spatial statistical properties of speckle noise in coherent imaging. A convex Gibbs energy function for speckled images is derived and utilized to perform speckle-compensating image estimation. The image estimation is formed by computing the conditional expectation of the noisy image at each pixel given its neighbors, which is further expressed in terms of the derived Gibbs energy function. The efficacy of the proposed technique, in terms of reducing speckle noise while preserving spatial resolution, is studied by using both real and simulated SAR imagery. Using a number of commonly used metrics, the performance of the proposed technique is shown to surpass that of existing speckle-noise-filtering methods such as the Gamma MAP, the modified Lee, and the enhanced Frost.
Segmentation of angiodysplasia lesions in WCE images using a MAP approach with Markov Random Fields.
Vieira, Pedro M; Goncalves, Bruno; Goncalves, Carla R; Lima, Carlos S
2016-08-01
This paper deals with the segmentation of angiodysplasias in wireless capsule endoscopy images. These lesions are the cause of almost 10% of all gastrointestinal bleeding episodes, and its detection using the available software presents low sensitivity. This work proposes an automatic selection of a ROI using an image segmentation module based on the MAP approach where an accelerated version of the EM algorithm is used to iteratively estimate the model parameters. Spatial context is modeled in the prior probability density function using Markov Random Fields. The color space used was CIELab, specially the a component, which highlighted most these type of lesions. The proposed method is the first regarding this specific type of lesions, but when compared to other state-of-the-art segmentation methods, it almost doubles the results.
NASA Astrophysics Data System (ADS)
Emoto, K.; Sato, H.; Nishimura, T.
2010-12-01
Short-period seismograms provide rich information of small-scale heterogeneities in the earth. However, such seismograms are too complex due to random velocity inhomogeneities to use deterministic methods for the wave form synthesis. We can use stochastic methods for the synthesis of wave envelopes instead. The Markov approximation, which is a stochastic extension of the phase screen method, is a powerful tool for the synthesis of wave envelopes in random media when the wavelength is shorter than the correlation distance of the inhomogeneity. Recently, Saito et al. (2008) synthesized the envelopes in layered random media with a constant background velocity, and Emoto et al. (2010) calculated envelopes on the free surface of random media. Considering more realistic cases, we synthesize vector wave envelopes on the free surface of 2-D layered random media with background velocity discontinuities for the vertical incidence of a plane wavelet. In the Markov approximation, we define the two frequency mutual coherence function (TFMCF) of the potential on the transverse plane which is perpendicular to the global propagation direction. The TFMCF satisfies the parabolic type wave equation when backscattering is negligible. We use the angular spectrum, which is the TFMCF in the wavenumber domain, represents the ray angle distribution of scattered wave’s power. First, we solve the parabolic wave equation in the bottom layer and calculate the angular spectrum at the layer boundary. We multiply the angular spectrum by the transmission or conversion coefficient at the velocity discontinuity, where scattered waves are treated as a superposition of plane waves just beneath the boundary. We note that PS conversion occurs at the velocity boundary. Then, taking the inverse Fourier transform to the space domain (modified TFMCF), we solve the parabolic wave equation in the upper layer where the modified TFMCF calculated before is used as the initial condition. We repeat this
Pereyra, Marcelo; Dobigeon, Nicolas; Batatia, Hadj; Tourneret, Jean-Yves
2013-06-01
This paper addresses the problem of estimating the Potts parameter β jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on β requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method, the estimation of β is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating β jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of β. On the other hand, choosing an incorrect value of β can degrade estimation performance significantly. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images.
Bayesian inference of local trees along chromosomes by the sequential Markov coalescent.
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.
Khan, Mohammad Ibrahim; Kamal, Md Sarwar
2015-03-01
Markov Chain is very effective in prediction basically in long data set. In DNA sequencing it is always very important to find the existence of certain nucleotides based on the previous history of the data set. We imposed the Chapman Kolmogorov equation to accomplish the task of Markov Chain. Chapman Kolmogorov equation is the key to help the address the proper places of the DNA chain and this is very powerful tools in mathematics as well as in any other prediction based research. It incorporates the score of DNA sequences calculated by various techniques. Our research utilize the fundamentals of Warshall Algorithm (WA) and Dynamic Programming (DP) to measures the score of DNA segments. The outcomes of the experiment are that Warshall Algorithm is good for small DNA sequences on the other hand Dynamic Programming are good for long DNA sequences. On the top of above findings, it is very important to measure the risk factors of local sequencing during the matching of local sequence alignments whatever the length.
NASA Astrophysics Data System (ADS)
Wu, Jing-Chun; Qin, Sheng-Gao; Wang, Yang
2009-08-01
The convective dispersion equation with adsorption is derived on the basis of the Chapman-Kolmogroff equation which expresses the statistical properties of the Markov transition probability. The acquired equation has the same expression as the one derived on the basis of the combination of both the mass balance equation and the particles retention kinetics equation. The probability variables that describe the random movement of solute particles have a definite physical significance associated with the parameters in the convective dispersion equation. The derivation confirms the validity of the Markov process to describe the particles movement in the process of convective dispersion.
NASA Astrophysics Data System (ADS)
Guta, Madalin; Kiukas, Jukka
2017-05-01
This paper deals with the problem of identifying and estimating dynamical parameters of continuous-time Markovian quantum open systems, in the input-output formalism. First, we characterise the space of identifiable parameters for ergodic dynamics, assuming full access to the output state for arbitrarily long times, and show that the equivalence classes of undistinguishable parameters are orbits of a Lie group acting on the space of dynamical parameters. Second, we define an information geometric structure on this space, including a principal bundle given by the action of the group, as well as a compatible connection, and a Riemannian metric based on the quantum Fisher information of the output. We compute the metric explicitly in terms of the Markov covariance of certain "fluctuation operators" and relate it to the horizontal bundle of the connection. Third, we show that the system-output and reduced output state satisfy local asymptotic normality, i.e., they can be approximated by a Gaussian model consisting of coherent states of a multimode continuous variables system constructed from the Markov covariance "data." We illustrate the result by working out the details of the information geometry of a physically relevant two-level system.
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.
Markov stochasticity coordinates
NASA Astrophysics Data System (ADS)
Eliazar, Iddo
2017-01-01
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method-termed Markov Stochasticity Coordinates-is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
NASA Astrophysics Data System (ADS)
Heinemann, Colleen
Research in material science is increasingly reliant on image-based data from experiments, demanding construction of new analysis tools that help scientists discover information from digital images. Because there is such a wide variety of materials and image modalities, detecting different compounds from imaged materials continues to be a challenging task. A vast collection of algorithms for filtering, image segmentation, and texture description have facilitated and improved accuracy for sample measurements (see Chapter 1 Introduction and Literature Review). Despite this, the community still lacks scalable, general purpose, easily configurable image analysis frameworks that allow pattern detection on different imaging modalities across multiple scales. The need for such a framework was the motivation behind the development of a distributed-memory parallel Markov Random Field based framework. Markov Random Field (MRF) algorithms provide the ability to explore contextual information about a given dataset. Given the complexity of such algorithms, however, they are limited by performance when running serial. Thus, running in some sort of parallel fashion is necessary. The effects are twofold. Not only does running the MRF algorithm in parallel provide the ability to run current datasets faster and more efficiently, it also provides the ability for datasets to continue to grow in size and still be able to be run with such frameworks. The variation of the Markov Random Field algorithm utilized in this study first oversegments the given input image and constructs a graph model based on photometric and geometric distances. Next, the resulting graph model is refactored specifically into the MRF model to target image segmentation. Finally, a distributed approach is used for the optimization process to obtain the best labeling for the graph, which is essentially the goal of using a MRF algorithm. Given the concept of using a distributed memory parallel framework, specifically
Localization for random and quasiperiodic potentials
Spencer, T.
1988-06-01
A survey is made of some recent mathematical results and techniques for Schroedinger operators with random and quasiperiodic potentials. A new proof of localization for random potentials, established in collaboration with H. von Dreifus, is sketched.
NASA Astrophysics Data System (ADS)
Jia, Chen
2017-09-01
Here we develop an effective approach to simplify two-time-scale Markov chains with infinite state spaces by removal of states with fast leaving rates, which improves the simplification method of finite Markov chains. We introduce the concept of fast transition paths and show that the effective transitions of the reduced chain can be represented as the superposition of the direct transitions and the indirect transitions via all the fast transition paths. Furthermore, we apply our simplification approach to the standard Markov model of single-cell stochastic gene expression and provide a mathematical theory of random gene expression bursts. We give the precise mathematical conditions for the bursting kinetics of both mRNAs and proteins. It turns out that random bursts exactly correspond to the fast transition paths of the Markov model. This helps us gain a better understanding of the physics behind the bursting kinetics as an emergent behavior from the fundamental multiscale biochemical reaction kinetics of stochastic gene expression.
A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model
Lu, Yi; Wei, Dongyan; Lai, Qifeng; Li, Wen; Yuan, Hong
2016-01-01
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. PMID:27916922
A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model.
Lu, Yi; Wei, Dongyan; Lai, Qifeng; Li, Wen; Yuan, Hong
2016-11-30
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian's location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian's starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.
NASA Astrophysics Data System (ADS)
Wang, Jun; Yang, Xuezhi; Jia, Lu; Zhou, Fang; Ai, Jiaqiu
2017-04-01
The problem of change detection in bitemporal synthetic aperture radar (SAR) images is studied. Motivated by utilizing nondense neighborhoods around pixels to detect the change level, a pointwise change detection approach is developed by employing a bilaterally weighted graph model and an irregular Markov random field (I-MRF). First, keypoints with local maximum intensity are extracted from one of the bitemporal images to describe the textural information of the images. Then, two bilaterally weighted graphs with the same topology are constructed for the bitemporal images using the keypoints, respectively. They utilize both the spatial structural and intensity information to provide good performance for feature-based change detection. Next, a change measure function is designed to evaluate the similarity between the graphs, and then the nondense difference image (NDI) is generated. Finally, an I-MRF with a generalized neighborhood system is proposed to classify the discrete keypoints on the NDI. Experiments on real SAR images show that the proposed NDI improves separability between changed and unchanged areas, and I-MRF provides high accuracy and strong noise immunity for change detection tasks with noise-contaminated SAR images. On the whole, the proposed approach is a good candidate for SAR image change detection.
Enhancing gene regulatory network inference through data integration with markov random fields
Banf, Michael; Rhee, Seung Y.
2017-02-01
Here, a gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization schememore » to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.« less
NASA Astrophysics Data System (ADS)
Pathak, Sayan D.; Haynor, David R.; Thompson, Carol L.; Lein, Ed; Hawrylycz, Michael
2009-02-01
Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.
Enhancing gene regulatory network inference through data integration with markov random fields
Banf, Michael; Rhee, Seung Y.
2017-01-01
A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE’s potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation. PMID:28145456
NASA Astrophysics Data System (ADS)
Aghighi, Hossein; Trinder, John
2013-10-01
Markov random field (MRF) is currently the most common method to find the optimal solution for the classification of image data incorporating contextual visual information. The labeling for a site in MRF is dependent on smoothing parameters. Therefore, this paper deals with the development of a new robust two-step method to determine the smoothing parameter which balances spatial and spectral energies for the purpose of maximizing the classification accuracy. Multispectral images obtained by WorldView-2 satellite were employed in this research. In the first step, a support vector machine (SVM) was used to provide a vector of multi-class probability and a class label for each pixel. Then, the summation of the maximum probability of each pixel and its 8 neighbors is calculated for a dynamic block and this value is assigned to the central pixels of each block. The blocks of each class are sorted and an equal proportion of blocks of each class with the highest probability are selected. Then, the class codes and spectral information of the selected blocks are extracted from the classified map and multispectral image, respectively. This information is used to calculate class label co-occurrence matrices of the blocks (CLCMB), class label co-occurrence matrix (CLCM) and class separability indices. Finally, different smoothing parameters are calculated and the results show that estimated smoothing parameter can produce a more accurate map.
Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model.
Cordero-Grande, L; Vegas-Sánchez-Ferrero, G; Casaseca-de-la-Higuera, P; San-Román-Calvar, J Alberto; Revilla-Orodea, Ana; Martín-Fernández, M; Alberola-López, C
2011-06-01
A stochastic deformable model is proposed for the segmentation of the myocardium in Magnetic Resonance Imaging. The segmentation is posed as a probabilistic optimization problem in which the optimal time-dependent surface is obtained for the myocardium of the heart in a discrete space of locations built upon simple geometric assumptions. For this purpose, first, the left ventricle is detected by a set of image analysis tools gathered from the literature. Then, the segmentation solution is obtained by the Maximization of the Posterior Marginals for the myocardium location in a Markov Random Field framework which optimally integrates temporal-spatial smoothness with intensity and gradient related features in an unsupervised way by the Maximum Likelihood estimation of the parameters of the field. This scheme provides a flexible and robust segmentation method which has been able to generate results comparable to manually segmented images for some derived cardiac function parameters in a set of 43 patients affected in different degrees by an Acute Myocardial Infarction. Copyright © 2011 Elsevier B.V. All rights reserved.
Zeng, Jia; Liu, Zhi-Qiang
2008-05-01
This paper proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures, we design the neighborhood system that accounts for the most important stroke relationships. We penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials, and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system, we use the single-site likelihood clique potentials to extract many candidate strokes from character images, and use the pairsite clique potentials to determine the best structural match between the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the KAIST character database demonstrate that MRFs can statistically model character structures, and work well in the HCCR system.
Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model
Guo, Yu; Feng, Yuanming; Sun, Jian; Lin, Wang; Sa, Yu; Wang, Ping
2014-01-01
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum. PMID:24987451
Human fixation detection model in video compressed domain based on Markov random field
NASA Astrophysics Data System (ADS)
Li, Yongjun; Li, Yunsong; Liu, Weijia; Hu, Jing; Ge, Chiru
2017-01-01
Recently, research on and applications of human fixation detection in video compressed domain have gained increasing attention. However, prediction accuracy and computational complexity still remain a challenge. This paper addresses the problem of compressed domain fixations detection in the videos based on residual discrete cosine transform coefficients norm (RDCN) and Markov random field (MRF). RDCN feature is directly extracted from the compressed video with partial decoding and is normalized. After spatial-temporal filtering, the normalized map [Smoothed RDCN (SRDCN) map] is taken to the MRF model, and the optimal binary label map is obtained. Based on the label map and the center saliency map, saliency enhancement and nonsaliency inhibition are done for the SRDCN map, and the final SRDCN-MRF salient map is obtained. Compared with the similar models, we enhance the available energy functions and introduce an energy function that indicates the positional information of the saliency. The procedure is advantageous for improving prediction accuracy and reducing computational complexity. The validation and comparison are made by several accuracy metrics on two ground truth datasets. Experimental results show that the proposed saliency detection model achieves superior performances over several state-of-the-art compressed-domain and pixel-domain algorithms on evaluation metrics. Computationally, our algorithm reduces 26% more computational complexity with comparison to similar algorithms.
Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model
Cheng, Hongju; Su, Zhihuang; Lloret, Jaime; Chen, Guolong
2014-01-01
Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime. PMID:25384005
Szeliski, Richard; Zabih, Ramin; Scharstein, Daniel; Veksler, Olga; Kolmogorov, Vladimir; Agarwala, Aseem; Tappen, Marshall; Rother, Carsten
2008-06-01
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. However, the tradeoffs among different energy minimization algorithms are still not well understood. In this paper we describe a set of energy minimization benchmarks and use them to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods graph cuts, LBP, and tree-reweighted message passing in addition to the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. Benchmarks, code, images, and results are available at http://vision.middlebury.edu/MRF/.
Surface roughness extraction based on Markov random field model in wavelet feature domain
NASA Astrophysics Data System (ADS)
Yang, Lei; Lei, Li-qiao
2014-12-01
Based on the computer texture analysis method, a new noncontact surface roughness measurement technique is proposed. The method is inspired by the nonredundant directional selectivity and highly discriminative nature of the wavelet representation and the capability of the Markov random field (MRF) model to capture statistical regularities. Surface roughness information contained in the texture features may be extracted based on an MRF stochastic model of textures in the wavelet feature domain. The model captures significant intrascale and interscale statistical dependencies between wavelet coefficients. To investigate the relationship between the texture features and surface roughness Ra, a simple research setup, which consists of a charge-coupled diode camera without a lens and a diode laser, was established, and the laser speckle texture patterns are acquired from some standard grinding surfaces. The research results have illustrated that surface roughness Ra has a good monotonic relationship with the texture features of the laser speckle pattern. If this measuring system is calibrated with the surface standard samples roughness beforehand, the surface roughness actual value Ra can be deduced in the case of the same material surfaces ground at the same manufacture conditions.
Geiger, D.; Girosi, F.
1989-05-01
In recent years many researchers have investigated the use of Markov random fields (MRFs) for computer vision. They can be applied for example in the output of the visual processes to reconstruct surfaces from sparse and noisy depth data, or to integrate early vision processes to label physical discontinuities. Drawbacks of MRFs models have been the computational complexity of the implementation and the difficulty in estimating the parameters of the model. This paper derives deterministic approximations to MRFs models. One of the considered models is shown to give in a natural way the graduate non convexity (GNC) algorithm. This model can be applied to smooth a field preserving its discontinuities. A new model is then proposed: it allows the gradient of the field to be enhanced at the discontinuities and smoothed elsewhere. All the theoretical results are obtained in the framework of the mean field theory, that is a well known statistical mechanics technique. A fast, parallel, and iterative algorithm to solve the deterministic equations of the two models is presented, together with experiments on synthetic and real images. The algorithm is applied to the problem of surface reconstruction is in the case of sparse data. A fast algorithm is also described that solves the problem of aligning the discontinuities of different visual models with intensity edges via integration.
Enhancing gene regulatory network inference through data integration with markov random fields.
Banf, Michael; Rhee, Seung Y
2017-02-01
A gene regulatory network links transcription factors to their target genes and represents a map of transcriptional regulation. Much progress has been made in deciphering gene regulatory networks computationally. However, gene regulatory network inference for most eukaryotic organisms remain challenging. To improve the accuracy of gene regulatory network inference and facilitate candidate selection for experimentation, we developed an algorithm called GRACE (Gene Regulatory network inference ACcuracy Enhancement). GRACE exploits biological a priori and heterogeneous data integration to generate high- confidence network predictions for eukaryotic organisms using Markov Random Fields in a semi-supervised fashion. GRACE uses a novel optimization scheme to integrate regulatory evidence and biological relevance. It is particularly suited for model learning with sparse regulatory gold standard data. We show GRACE's potential to produce high confidence regulatory networks compared to state of the art approaches using Drosophila melanogaster and Arabidopsis thaliana data. In an A. thaliana developmental gene regulatory network, GRACE recovers cell cycle related regulatory mechanisms and further hypothesizes several novel regulatory links, including a putative control mechanism of vascular structure formation due to modifications in cell proliferation.
Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.
Guo, Yu; Feng, Yuanming; Sun, Jian; Zhang, Ning; Lin, Wang; Sa, Yu; Wang, Ping
2014-01-01
The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
Spatio-temporal fMRI analysis using Markov random fields.
Descombes, X; Kruggel, F; von Cramon, D Y
1998-12-01
Functional magnetic resonance images (fMRI's) provide high-resolution datasets which allow researchers to obtain accurate delineation and sensitive detection of activation areas involved in cognitive processes. To preserve the resolution of this noninvasive technique, refined methods are required in the analysis of the data. In this paper, we first discuss the widely used methods based on a statistical parameter map (SPM) analysis exposing the different shortcomings of this approach when considering high-resolution data. First, the often used Gaussian filtering results in a blurring effect and in delocalization of the activated area. Secondly, the SPM approach only considers false alarms due to noise but not rejections of activated voxels. We propose to embed the fMRI analysis problem into a Bayesian framework consisting of two steps: i) data restoration and ii) data analysis. We, therefore, propose two Markov random fields (MRF's) to solve these two problems. Results on three protocols (visual, motor and word recognition) are shown for two SPM approaches and compared with the proposed MRF approach.
Building Roof Boundary Extraction from LiDAR and Image Data Based on Markov Random Field
NASA Astrophysics Data System (ADS)
Dal Poz, A. P.; Fernandes, V. J. M.
2017-05-01
In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects' polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.
Medina, Rubén; Garreau, Mireille; Toro, Javier; Le Breton, Hervé; Coatrieux, Jean-Louis; Jugo, Diego
2006-01-01
This paper reports on a method for left ventricle three-dimensional reconstruction from two orthogonal ventriculograms. The proposed algorithm is voxel-based and takes into account the conical projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts with an initial ellipsoidal approximation derived from the input ventriculograms. This model is subsequently deformed in such a way as to match the input projections. To this end, the object is modeled as a three-dimensional Markov-Gibbs random field, and an energy function is defined so that it includes one term that models the projections compatibility and another one that includes the space–time regularity constraints. The performance of this reconstruction method is evaluated by considering the reconstruction of mathematically synthesized phantoms and two 3-D binary databases from two orthogonal synthesized projections. The method is also tested using real biplane ventriculograms. In this case, the performance of the reconstruction is expressed in terms of the projection error, which attains values between 9.50% and 11.78 % for two biplane sequences including a total of 55 images. PMID:16895001
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.
Context-aware patch-based image inpainting using Markov random field modeling.
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.
Handwritten Chinese/Japanese text recognition using semi-Markov conditional random fields.
Zhou, Xiang-Dong; Wang, Da-Han; Tian, Feng; Liu, Cheng-Lin; Nakagawa, Masaki
2013-10-01
This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.
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.
Video object tracking in the compressed domain using spatio-temporal Markov random fields.
Khatoonabadi, Sayed Hossein; Bajić, Ivan V
2013-01-01
Despite the recent progress in both pixel-domain and compressed-domain video object tracking, the need for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method for tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (ST-MRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object's motion. Built upon such a model, the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object's motion. The proposed method is tested on a number of standard sequences, and the results demonstrate its advantages over some of the recent state-of-the-art methods.
Impact of Markov Random Field Optimizer on MRI-based Tissue Segmentation in the Aging Brain
Schwarz, Christopher G.; Tsui, Alex; Fletcher, Evan; Singh, Baljeet; DeCarli, Charles; Carmichael, Owen
2013-01-01
Automatically segmenting brain magnetic resonance images into grey matter, white matter, and cerebrospinal fluid compartments is a fundamentally important neuroimaging problem whose difficulty is heightened in the presence of aging and neurodegenerative disease. Current methods overlap greatly in terms of identifiable algorithmic components, and the impact of specific components on performance is generally unclear in important real-world scenarios involving serial scanning, multiple scanners, and neurodegenerative disease. Therefore we evaluated the impact that one such component, the Markov Random Field (MRF) optimizer that encourages spatially-smooth tissue labelings, has on brain tissue segmentation performance. Two challenging elderly sets were used to test segmentation consistency across scanners and biological plausibility of tissue change estimates; and a simulated young brain data set was used to test accuracy against ground truth. Comparisons among Graph Cuts (GC), Belief Propagation (BP), and Iterative Conditional Modes (ICM) suggested that in the elderly brain, BP and GC provide the highest segmentation performance, with a slight advantage to BP, and that performance is often superior to that provided by popular methods SPM and FAST. Conversely, SPM and FAST excelled in the young brain, thus emphasizing the unique challenges involved in imaging the aging brain. PMID:22256150
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.
Statistical bubble localization with random interactions
NASA Astrophysics Data System (ADS)
Li, Xiaopeng; Deng, Dong-Ling; Wu, Yang-Le; Das Sarma, S.
2017-01-01
We study one-dimensional spinless fermions with random interactions, but without any on-site disorder. We find that random interactions generically stabilize a many-body localized phase, in spite of the completely extended single-particle degrees of freedom. In the large randomness limit, we construct "bubble-neck" eigenstates having a universal area-law entanglement entropy on average, with the number of volume-law states being exponentially suppressed. We argue that this statistical localization is beyond the phenomenological local-integrals-of-motion description of many-body localization. With exact diagonalization, we confirm the robustness of the many-body localized phase at finite randomness by investigating eigenstate properties such as level statistics, entanglement/participation entropies, and nonergodic quantum dynamics. At weak random interactions, the system develops a thermalization transition when the single-particle hopping becomes dominant.
Local Random Quantum Circuits are Approximate Polynomial-Designs
NASA Astrophysics Data System (ADS)
Brandão, Fernando G. S. L.; Harrow, Aram W.; Horodecki, Michał
2016-09-01
We prove that local random quantum circuits acting on n qubits composed of O( t 10 n 2) many nearest neighbor two-qubit gates form an approximate unitary t-design. Previously it was unknown whether random quantum circuits were a t-design for any t > 3. The proof is based on an interplay of techniques from quantum many-body theory, representation theory, and the theory of Markov chains. In particular we employ a result of Nachtergaele for lower bounding the spectral gap of frustration-free quantum local Hamiltonians; a quasi-orthogonality property of permutation matrices; a result of Oliveira which extends to the unitary group the path-coupling method for bounding the mixing time of random walks; and a result of Bourgain and Gamburd showing that dense subgroups of the special unitary group, composed of elements with algebraic entries, are ∞-copy tensor-product expanders. We also consider pseudo-randomness properties of local random quantum circuits of small depth and prove that circuits of depth O( t 10 n) constitute a quantum t-copy tensor-product expander. The proof also rests on techniques from quantum many-body theory, in particular on the detectability lemma of Aharonov, Arad, Landau, and Vazirani. We give applications of the results to cryptography, equilibration of closed quantum dynamics, and the generation of topological order. In particular we show the following pseudo-randomness property of generic quantum circuits: Almost every circuit U of size O( n k ) on n qubits cannot be distinguished from a Haar uniform unitary by circuits of size O( n ( k-9)/11) that are given oracle access to U.
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.
Jin, Ick Hoon; Yuan, Ying; Bandyopadhyay, Dipankar
2016-01-01
Research in dental caries generates data with two levels of hierarchy: that of a tooth overall and that of the different surfaces of the tooth. The outcomes often exhibit spatial referencing among neighboring teeth and surfaces, i.e., the disease status of a tooth or surface might be influenced by the status of a set of proximal teeth/surfaces. Assessments of dental caries (tooth decay) at the tooth level yield binary outcomes indicating the presence/absence of teeth, and trinary outcomes at the surface level indicating healthy, decayed, or filled surfaces. The presence of these mixed discrete responses complicates the data analysis under a unified framework. To mitigate complications, we develop a Bayesian two-level hierarchical model under suitable (spatial) Markov random field assumptions that accommodates the natural hierarchy within the mixed responses. At the first level, we utilize an autologistic model to accommodate the spatial dependence for the tooth-level binary outcomes. For the second level and conditioned on a tooth being non-missing, we utilize a Potts model to accommodate the spatial referencing for the surface-level trinary outcomes. The regression models at both levels were controlled for plausible covariates (risk factors) of caries, and remain connected through shared parameters. To tackle the computational challenges in our Bayesian estimation scheme caused due to the doubly-intractable normalizing constant, we employ a double Metropolis-Hastings sampler. We compare and contrast our model performances to the standard non-spatial (naive) model using a small simulation study, and illustrate via an application to a clinical dataset on dental caries. PMID:27807470
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.
Spatial analysis in a Markov random field framework: The case of burning oil wells in Kuwait
NASA Astrophysics Data System (ADS)
Dezzani, Raymond J.; Al-Dousari, Ahmad
This paper discusses a modeling approach for spatial-temporal prediction of environmental phenomena using classified satellite images. This research was prompted by the analysis of change and landscape redistribution of petroleum residues formed from the residue of the burning oil wells in Kuwait (1991). These surface residues have been termed ``tarcrete'' (El-Baz etal. 1994). The tarcrete forms a thick layer over sand and desert pavement covering a significant portion of south-central Kuwait. The purpose of this study is to develop a method that utilizes satellite images from different time steps to examine the rate-of-change of the oil residue deposits and determine where redistribution is are likely to occur. This problem exhibits general characteristics of environmental diffusion and dispersion phenomena so a theoretical framework for a general solution is sought. The use of a lagged-clique, Markov random field framework and entropy measures is deduced to be an effective solution to satisfy the criteria of determination of time-rate-of-change of the surface deposits and to forecast likely locations of redistribution of dispersed, aggraded residues. The method minimally requires image classification, the determination of time stationarity of classes and the measurement of the level of organization of the state-space information derived from the images. Analysis occurs at levels of both the individual pixels and the system to determine specific states and suites of states in space and time. Convergence of the observed landscape disorder with respect to an analytical maximum provide information on the total dispersion of the residual system.
Markov random field-based clustering applied to the segmentation of masses in digital mammograms.
Suliga, M; Deklerck, R; Nyssen, E
2008-09-01
In this paper we propose a new pixel clustering model applied to the analysis of digital mammograms. The clustering represents here the first step in a more general method and aims at the creation of a concise data-set (clusters) for automatic detection and classification of masses, which are typically among the first symptoms analysed in early diagnosis of breast cancer. For the purpose of this work, a set of mammographic images has been employed, that are 12-bit gray level digital scans and as such, are inherently inhomogeneous and affected by the noise resulting from the film scanning. The image pixels are described only by their intensity (gray level), therefore, the available information is limited to one dimension. We propose a Markov random field (MRF)-based technique that is suitable for performing clustering in an environment which is described by poor or limited data. The proposed method is a statistical classification model, that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics, that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the output. Moreover, we define a probabilistic description of the model, that is characterised by a remarkable simplicity, such that its realisation can be easily and efficiently implemented in any high- or low-level programming language, thus allowing it to be run on virtually any kind of platform. Finally, we evaluate the algorithm against the classical K-means clustering routine. We point out similarities between the two methods and, moreover, show the advantages and superiority of the MRF scheme.
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.
Zhu, Yanjie; Tan, Yongqing; Hua, Yanqing; Zhang, Guozhen; Zhang, Jianguo
2012-06-01
Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.
Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field
Tan, Yongqiang; Schwartz, Lawrence H.; Zhao, Binsheng
2013-01-01
Purpose: Lung lesions vary considerably in size, density, and shape, and can attach to surrounding anatomic structures such as chest wall or mediastinum. Automatic segmentation of the lesions poses a challenge. This work communicates a new three-dimensional algorithm for the segmentation of a wide variety of lesions, ranging from tumors found in patients with advanced lung cancer to small nodules detected in lung cancer screening programs. Methods: The authors’ algorithm uniquely combines the image processing techniques of marker-controlled watershed, geometric active contours as well as Markov random field (MRF). The user of the algorithm manually selects a region of interest encompassing the lesion on a single slice and then the watershed method generates an initial surface of the lesion in three dimensions, which is refined by the active geometric contours. MRF improves the segmentation of ground glass opacity portions of part-solid lesions. The algorithm was tested on an anthropomorphic thorax phantom dataset and two publicly accessible clinical lung datasets. These clinical studies included a same-day repeat CT (prewalk and postwalk scans were performed within 15 min) dataset containing 32 lung lesions with one radiologist's delineated contours, and the first release of the Lung Image Database Consortium (LIDC) dataset containing 23 lung nodules with 6 radiologists’ delineated contours. The phantom dataset contained 22 phantom nodules of known volumes that were inserted in a phantom thorax. Results: For the prewalk scans of the same-day repeat CT dataset and the LIDC dataset, the mean overlap ratios of lesion volumes generated by the computer algorithm and the radiologist(s) were 69% and 65%, respectively. For the two repeat CT scans, the intra-class correlation coefficient (ICC) was 0.998, indicating high reliability of the algorithm. The mean relative difference was −3% for the phantom dataset. Conclusions: The performance of this new segmentation
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
NASA Astrophysics Data System (ADS)
Mota, Bernardo; Pereira, Jose; Campagnolo, Manuel; Killick, Rebeca
2013-04-01
Area burned in tropical savannas of Brazil was mapped using MODIS-AQUA daily 250m resolution imagery by adapting one of the European Space Agency fire_CCI project burned area algorithms, based on change point detection and Markov random fields. The study area covers 1,44 Mkm2 and was performed with data from 2005. The daily 1000 m image quality layer was used for cloud and cloud shadow screening. The algorithm addresses each pixel as a time series and detects changes in the statistical properties of NIR reflectance values, to identify potential burning dates. The first step of the algorithm is robust filtering, to exclude outlier observations, followed by application of the Pruned Exact Linear Time (PELT) change point detection technique. Near-infrared (NIR) spectral reflectance changes between time segments, and post change NIR reflectance values are combined into a fire likelihood score. Change points corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurring outside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps and detection date maps are converted to dichotomous (burned-unburned maps) using Markov random fields, which take into account both spatial and temporal relations in the potential burned area maps. A preliminary assessment of our results is performed by comparison with data from the MODIS 1km active fires and the 500m burned area products, taking into account differences in spatial resolution between the two sensors.
NASA Astrophysics Data System (ADS)
Tian, Yu-Kun; Zhou, Hui; Chen, Han-Ming; Zou, Ya-Ming; Guan, Shou-Jun
2013-12-01
Seismic inversion is a highly ill-posed problem, due to many factors such as the limited seismic frequency bandwidth and inappropriate forward modeling. To obtain a unique solution, some smoothing constraints, e.g., the Tikhonov regularization are usually applied. The Tikhonov method can maintain a global smooth solution, but cause a fuzzy structure edge. In this paper we use Huber-Markov random-field edge protection method in the procedure of inverting three parameters, P-velocity, S-velocity and density. The method can avoid blurring the structure edge and resist noise. For the parameter to be inverted, the Huber-Markov random-field constructs a neighborhood system, which further acts as the vertical and lateral constraints. We use a quadratic Huber edge penalty function within the layer to suppress noise and a linear one on the edges to avoid a fuzzy result. The effectiveness of our method is proved by inverting the synthetic data without and with noises. The relationship between the adopted constraints and the inversion results is analyzed as well.
Markov Chains for Random Urinalysis III: Daily Model and Drug Kinetics
1994-01-01
III: Daily M and Drug Kinetcs E L ’-- TF " 94-04546 9 4 2 0 9 0 6 2 Ap ,o.vod f r p c, tease cdsibutn Is un a ted NPRDC-TN-94-12 January 1994 Markov...and maintairi- 9 the d~ata needed. a~d corro’eting a-!d rev~ewing the collection of infrmt~ationi Seid conriments regarding this burden estimate or any...PERFORMiNG ORGANIZATION Navy Personnel Research -and Development Center REPORT NUMBER San Diego, CA 92152-7250 NPRDC-TN-94-12 9 . SPONSO R!NGIMO NTO
NASA Astrophysics Data System (ADS)
Raupov, Dmitry S.; Myakinin, Oleg O.; Bratchenko, Ivan A.; Zakharov, Valery P.; Khramov, Alexander G.
2016-10-01
In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, Markov random field method and the complex directional features from different tissues. Described features have been used to detect specific spatial characteristics, which can differentiate healthy tissue from diverse skin cancers in cross-section OCT images (B- and/or C-scans). In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images. The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A box-counting method is performed to evaluate fractal dimension of skin probes. Markov random field have been used for the quality enhancing of the classifying. Additionally, we used the complex directional field calculated by the local gradient methodology to increase of the assessment quality of the diagnosis method. Our results demonstrate that these texture features may present helpful information to discriminate tumor from healthy tissue. The experimental data set contains 488 OCT-images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. All images were acquired from our laboratory SD-OCT setup based on broadband light source, delivering an output power of 20 mW at the central wavelength of 840 nm with a bandwidth of 25 nm. We obtained sensitivity about 97% and specificity about 73% for a task of discrimination between MM and Nevus.
Xu, Jun; Monaco, James P; Madabhushi, Anant
2010-01-01
In this paper we present a Markov random field (MRF) driven region-based active contour model (MaRACel) for medical image segmentation. State-of-the-art region-based active contour (RAC) models assume that every spatial location in the image is statistically independent of the others, thereby ignoring valuable contextual information. To address this shortcoming we incorporate a MRF prior into the AC model, further generalizing Chan & Vese's (CV) and Rousson and Deriche's (RD) AC models. This incorporation requires a Markov prior that is consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analogue to the discrete Potts model. To demonstrate the effectiveness of MaRACel, we compare its performance to those of the CV and RD AC models in the following scenarios: (1) the qualitative segmentation of a cancerous lesion in a breast DCE-MR image and (2) the qualitative and quantitative segmentations of prostatic acini (glands) in 200 histopathology images. Across the 200 prostate needle core biopsy histology images, MaRACel yielded an average sensitivity, specificity, and positive predictive value of 71%, 95%, 74% with respect to the segmented gland boundaries; the CV and RD models have corresponding values of 19%, 81%, 20% and 53%, 88%, 56%, respectively.
NASA Astrophysics Data System (ADS)
Bratsolis, E.; Sigelle, M.; Charou, E.
2016-10-01
Building detection has been a prominent area in the area of image classification. Most of the research effort is adapted to the specific application requirements and available datasets. Our dataset includes aerial orthophotos (with spatial resolution 20cm), a DSM generated from LiDAR (with spatial resolution 1m and elevation resolution 20 cm) and DTM (spatial resolution 2m) from an area of Athens, Greece. Our aim is to classify these data by means of Markov Random Fields (MRFs) in a Bayesian framework for building block extraction and perform a comparative analysis with other supervised classification techniques namely Feed Forward Neural Net (FFNN), Cascade-Correlation Neural Network (CCNN), Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). We evaluated the performance of each method using a subset of the test area. We present the classified images, and statistical measures (confusion matrix, kappa coefficient and overall accuracy). Our results demonstrate that the MRFs and FFNN perform better than the other methods.
Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields
Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; ...
2017-05-10
Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the mainmore » materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).« less
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.
NASA Astrophysics Data System (ADS)
Rocha, G.; Pagano, L.; Górski, K. M.; Huffenberger, K. M.; Lawrence, C. R.; Lange, A. E.
2010-04-01
We introduce a new method to propagate uncertainties in the beam shapes used to measure the cosmic microwave background to cosmological parameters determined from those measurements. The method, called markov chain beam randomization (MCBR), randomly samples from a set of templates or functions that describe the beam uncertainties. The method is much faster than direct numerical integration over systematic “nuisance” parameters, and is not restricted to simple, idealized cases as is analytic marginalization. It does not assume the data are normally distributed, and does not require Gaussian priors on the specific systematic uncertainties. We show that MCBR properly accounts for and provides the marginalized errors of the parameters. The method can be generalized and used to propagate any systematic uncertainties for which a set of templates is available. We apply the method to the Planck satellite, and consider future experiments. Beam measurement errors should have a small effect on cosmological parameters as long as the beam fitting is performed after removal of 1/f noise.
NASA Astrophysics Data System (ADS)
Kolesnik, Alexander D.
2017-01-01
We consider the Markov random flight \\varvec{X}(t), t>0, in the three-dimensional Euclidean space R3 with constant finite speed c>0 and the uniform choice of the initial and each new direction at random time instants that form a homogeneous Poisson flow of rate λ >0. Series representations for the conditional characteristic functions of \\varvec{X}(t) corresponding to two and three changes of direction, are obtained. Based on these results, an asymptotic formula, as t→ 0, for the unconditional characteristic function of \\varvec{X}(t) is derived. By inverting it, we obtain an asymptotic relation for the transition density of the process. We show that the error in this formula has the order o(t^3) and, therefore, it gives a good approximation on small time intervals whose lengths depend on λ . An asymptotic formula, as t→ 0, for the probability of being in a three-dimensional ball of radius r
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.
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.
Blanchet, Juliette; Vignes, Matthieu
2009-03-01
The different measurement techniques that interrogate biological systems provide means for monitoring the behavior of virtually all cell components at different scales and from complementary angles. However, data generated in these experiments are difficult to interpret. A first difficulty arises from high-dimensionality and inherent noise of such data. Organizing them into meaningful groups is then highly desirable to improve our knowledge of biological mechanisms. A more accurate picture can be obtained when accounting for dependencies between components (e.g., genes) under study. A second difficulty arises from the fact that biological experiments often produce missing values. When it is not ignored, the latter issue has been solved by imputing the expression matrix prior to applying traditional analysis methods. Although helpful, this practice can lead to unsound results. We propose in this paper a statistical methodology that integrates individual dependencies in a missing data framework. More explicitly, we present a clustering algorithm dealing with incomplete data in a Hidden Markov Random Field context. This tackles the missing value issue in a probabilistic framework and still allows us to reconstruct missing observations a posteriori without imposing any pre-processing of the data. Experiments on synthetic data validate the gain in using our method, and analysis of real biological data shows its potential to extract biological knowledge.
Lu, Yisu; Jiang, Jun; Chen, Wufan
2014-01-01
Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. PMID:25254064
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
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.
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
Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores
2015-09-16
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.
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.
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
NASA Astrophysics Data System (ADS)
Wang, Hui; Wellmann, Florian; Verweij, Elizabeth; von Hebel, Christian; van der Kruk, Jan
2017-04-01
Lateral and vertical spatial heterogeneity of subsurface properties such as soil texture and structure influences the available water and resource supply for crop growth. High-resolution mapping of subsurface structures using non-invasive geo-referenced geophysical measurements, like electromagnetic induction (EMI), enables a characterization of 3D soil structures, which have shown correlations to remote sensing information of the crop states. The benefit of EMI is that it can return 3D subsurface information, however the spatial dimensions are limited due to the labor intensive measurement procedure. Although active and passive sensors mounted on air- or space-borne platforms return 2D images, they have much larger spatial dimensions. Combining both approaches provides us with a potential pathway to extend the detailed 3D geophysical information to a larger area by using remote sensing information. In this study, we aim at extracting and providing insights into the spatial and statistical correlation of the geophysical and remote sensing observations of the soil/vegetation continuum system. To this end, two key points need to be addressed: 1) how to detect and recognize the geometric patterns (i.e., spatial heterogeneity) from multiple data sets, and 2) how to quantitatively describe the statistical correlation between remote sensing information and geophysical measurements. In the current study, the spatial domain is restricted to shallow depths up to 3 meters, and the geostatistical database contains normalized difference vegetation index (NDVI) derived from RapidEye satellite images and apparent electrical conductivities (ECa) measured from multi-receiver EMI sensors for nine depths of exploration ranging from 0-2.7 m. The integrated data sets are mapped into both the physical space (i.e. the spatial domain) and feature space (i.e. a two-dimensional space framed by the NDVI and the ECa data). Hidden Markov Random Fields (HMRF) are employed to model the
A Markov model for the temporal dynamics of balanced random networks of finite size
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
Mina, Marco; Guzzi, Pietro Hiram
2014-01-01
The analysis of protein behavior at the network level had been applied to elucidate the mechanisms of protein interaction that are similar in different species. Published network alignment algorithms proved to be able to recapitulate known conserved modules and protein complexes, and infer new conserved interactions confirmed by wet lab experiments. In the meantime, however, a plethora of continuously evolving protein-protein interaction (PPI) data sets have been developed, each featuring different levels of completeness and reliability. For instance, algorithms performance may vary significantly when changing the data set used in their assessment. Moreover, existing papers did not deeply investigate the robustness of alignment algorithms. For instance, some algorithms performances vary significantly when changing the data set used in their assessment. In this work, we design an extensive assessment of current algorithms discussing the robustness of the results on the basis of input networks. We also present AlignMCL, a local network alignment algorithm based on an improved model of alignment graph and Markov Clustering. AlignMCL performs better than other state-of-the-art local alignment algorithms over different updated data sets. In addition, AlignMCL features high levels of robustness, producing similar results regardless the selected data set.
NASA Astrophysics Data System (ADS)
Foulkes, Stephen B.; Booth, David M.
1997-07-01
Object segmentation is the process by which a mask is generated which identifies the area of an image which is occupied by an object. Many object recognition techniques depend on the quality of such masks for shape and underlying brightness information, however, segmentation remains notoriously unreliable. This paper considers how the image restoration technique of Geman and Geman can be applied to the improvement of object segmentations generated by a locally adaptive background subtraction technique. Also presented is how an artificial neural network hybrid, consisting of a single layer Kohonen network with each of its nodes connected to a different multi-layer perceptron, can be used to approximate the image restoration process. It is shown that the restoration techniques are very well suited for parallel processing and in particular the artificial neural network hybrid has the potential for near real time image processing. Results are presented for the detection of ships in SPOT panchromatic imagery and the detection of vehicles in infrared linescan images, these being a fair representation of the wider class of problem.
Zayeri, Farid; Mansouri, Anita; Sheidaei, Ali; Rahimzadeh, Shadi; Rezaei, Nazila; Modirian, Mitra; Khademioureh, Sara; Baghestani, Ahmad Reza; Farzadfar, Farshad
2016-01-01
Stomach cancer is the fifth most common cancer and the third leading cause of death among cancers throughout the world. Therefore, stomach cancer outcomes can affect health systems at the national and international levels. Although stomach cancer mortality and incidence rates have decreased in developed countries, these indicators have a raising trend in East Asian developing countries, particularity in Iran. In this study, we aimed to determine the time trend of age-standardized rates of stomach cancer in different districts of Iran from 2000 to 2010. Cases of cancer were registered using a pathology-based system during 2000-2007 and with a population-based system since 2008 in Iran. In this study, we collected information about the incidence of stomach cancer during a 10 year period for 31 provinces and 376 districts, with a total of 49,917 cases. We employed two statistical approaches (a random effects and a random effects Markov model) for modeling the incidence of stomach cancer in different districts of Iran during the studied period. The random effects model showed that the incidence rate of stomach cancer among males and females had an increasing trend and it increased by 2.38 and 0.87 persons every year, respectively. However, after adjusting for previous responses, the random effects Markov model showed an increasing rate of 1.53 and 0.75 for males and females, respectively. This study revealed that there are significant differences between different areas of Iran in terms of age-standardized incidence rates of stomach cancer. Our study suggests that a random effects Markov model can adjust for effects of previous. responses.
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
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling
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
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-08-01
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.
Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling.
Barranca, Victor J; Kovačič, Gregor; Zhou, Douglas; Cai, David
2016-08-24
Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.
Randomized Clinical Trials in Localized Anal Cancer.
Smith, Clayton A; Kachnic, Lisa A
2017-10-01
Management of anal carcinoma began as abdominoperineal resection and has evolved to combined chemotherapy and radiation. Early randomized trials demonstrated superior clinical outcomes of combined modality therapy over radiotherapy alone. Subsequent trials investigated alterations in the standard backbone of radiotherapy concurrent with 5-fluorouracil and mitomycin C with intent to maintain clinical outcomes while reducing treatment-related morbidity. The addition of intensity-modulated radiotherapy to radiation planning and delivery has subsequently reduced acute toxicity and detrimental treatment breaks. Ongoing and future trials are aimed at reducing therapy in favorable patient populations to decrease morbidity while intensifying treatment in patients with negative prognostic factors. Copyright © 2017 Elsevier Inc. All rights reserved.
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
Localized motion in random matrix decomposition of complex financial systems
NASA Astrophysics Data System (ADS)
Jiang, Xiong-Fei; Zheng, Bo; Ren, Fei; Qiu, Tian
2017-04-01
With the random matrix theory, we decompose the multi-dimensional time series of complex financial systems into a set of orthogonal eigenmode functions, which are classified into the market mode, sector mode, and random mode. In particular, the localized motion generated by the business sectors, plays an important role in financial systems. Both the business sectors and their impact on the stock market are identified from the localized motion. We clarify that the localized motion induces different characteristics of the time correlations for the stock-market index and individual stocks. With a variation of a two-factor model, we reproduce the return-volatility correlations of the eigenmodes.
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.
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.
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
Beyond Anderson localization in 1D: anomalous localization of microwaves in random waveguides.
Fernández-Marín, A A; Méndez-Bermúdez, J A; Carbonell, J; Cervera, F; Sánchez-Dehesa, J; Gopar, V A
2014-12-05
Experimental evidence demonstrating that anomalous localization of waves can be induced in a controllable manner is reported. A microwave waveguide with dielectric slabs randomly placed is used to confirm the presence of anomalous localization. If the random spacing between slabs follows a distribution with a power-law tail (Lévy-type distribution), unconventional properties in the microwave-transmission fluctuations take place revealing the presence of anomalous localization. We study both theoretically and experimentally the complete distribution of the transmission through random waveguides characterized by α=1/2 ("Lévy waveguides") and α=3/4, α being the exponent of the power-law tail of the Lévy-type distribution. As we show, the transmission distributions are determined by only two parameters, both of them experimentally accessible. Effects of anomalous localization on the transmission are compared with those from the standard Anderson localization.
Protein localization prediction using random walks on graphs
2013-01-01
Background Understanding the localization of proteins in cells is vital to characterizing their functions and possible interactions. As a result, identifying the (sub)cellular compartment within which a protein is located becomes an important problem in protein classification. This classification issue thus involves predicting labels in a dataset with a limited number of labeled data points available. By utilizing a graph representation of protein data, random walk techniques have performed well in sequence classification and functional prediction; however, this method has not yet been applied to protein localization. Accordingly, we propose a novel classifier in the site prediction of proteins based on random walks on a graph. Results We propose a graph theory model for predicting protein localization using data generated in yeast and gram-negative (Gneg) bacteria. We tested the performance of our classifier on the two datasets, optimizing the model training parameters by varying the laziness values and the number of steps taken during the random walk. Using 10-fold cross-validation, we achieved an accuracy of above 61% for yeast data and about 93% for gram-negative bacteria. Conclusions This study presents a new classifier derived from the random walk technique and applies this classifier to investigate the cellular localization of proteins. The prediction accuracy and additional validation demonstrate an improvement over previous methods, such as support vector machine (SVM)-based classifiers. PMID:23815126
Many-body localization due to random interactions
NASA Astrophysics Data System (ADS)
Sierant, Piotr; Delande, Dominique; Zakrzewski, Jakub
2017-02-01
The possibility of observing many-body localization of ultracold atoms in a one-dimensional optical lattice is discussed for random interactions. In the noninteracting limit, such a system reduces to single-particle physics in the absence of disorder, i.e., to extended states. In effect, the observed localization is inherently due to interactions and is thus a genuine many-body effect. In the system studied, many-body localization manifests itself in a lack of thermalization visible in temporal propagation of a specially prepared initial state, in transport properties, in the logarithmic growth of entanglement entropy, and in statistical properties of energy levels.
Daniels, Noah M; Hosur, Raghavendra; Berger, Bonnie; Cowen, Lenore J
2012-05-01
One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif. We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile-profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions. Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/
Random matrix analysis of localization properties of gene coexpression network.
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.
Localization of disordered bosons and magnets in random fields
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.
Li, Hong-Dong; Xu, Qing-Song; Liang, Yi-Zeng
2012-08-31
The identification of disease-relevant genes represents a challenge in microarray-based disease diagnosis where the sample size is often limited. Among established methods, reversible jump Markov Chain Monte Carlo (RJMCMC) methods have proven to be quite promising for variable selection. However, the design and application of an RJMCMC algorithm requires, for example, special criteria for prior distributions. Also, the simulation from joint posterior distributions of models is computationally extensive, and may even be mathematically intractable. These disadvantages may limit the applications of RJMCMC algorithms. Therefore, the development of algorithms that possess the advantages of RJMCMC methods and are also efficient and easy to follow for selecting disease-associated genes is required. Here we report a RJMCMC-like method, called random frog that possesses the advantages of RJMCMC methods and is much easier to implement. Using the colon and the estrogen gene expression datasets, we show that random frog is effective in identifying discriminating genes. The top 2 ranked genes for colon and estrogen are Z50753, U00968, and Y10871_at, Z22536_at, respectively. (The source codes with GNU General Public License Version 2.0 are freely available to non-commercial users at: http://code.google.com/p/randomfrog/.).
On the entropy of wide Markov chains
NASA Astrophysics Data System (ADS)
Girardin, Valerie
2011-03-01
Burg entropy concepts are here introduced in the field of wide Markov chains. These random sequences are the second-order equivalent of Markov chains: their future evolution, in terms of second order properties, conditional on the past and present, depends only on the present. Either periodically correlated or multivariate stationary, they can be characterized in terms of autoregressive models of order one.
Randomized study of phentolamine mesylate for reversal of local anesthesia.
Laviola, M; McGavin, S K; Freer, G A; Plancich, G; Woodbury, S C; Marinkovich, S; Morrison, R; Reader, A; Rutherford, R B; Yagiela, J A
2008-07-01
Local anesthetic solutions frequently contain vasoconstrictors to increase the depth and/or duration of anesthesia. Generally, the duration of soft-tissue anesthesia exceeds that of pulpal anesthesia. Negative consequences of soft-tissue anesthesia include accidental lip and tongue biting as well as difficulty in eating, drinking, speaking, and smiling. A double-blind, randomized, multicenter, Phase 2 study tested the hypothesis that local injection of the vasodilator phentolamine mesylate would shorten the duration of soft-tissue anesthesia following routine dental procedures. Participants (122) received one or two cartridges of local anesthetic/vasoconstrictor prior to dental treatment. Immediately after treatment, 1.8 mL of study drug (containing 0.4 mg phentolamine mesylate or placebo) was injected per cartridge of local anesthetic used. The phentolamine was well-tolerated and reduced the median duration of soft-tissue anesthesia in the lip from 155 to 70 min (p < 0.0001).
Nielsen, Rasmus
2017-01-01
Admixture—the mixing of genomes from divergent populations—is increasingly appreciated as a central process in evolution. To characterize and quantify patterns of admixture across the genome, a number of methods have been developed for local ancestry inference. However, existing approaches have a number of shortcomings. First, all local ancestry inference methods require some prior assumption about the expected ancestry tract lengths. Second, existing methods generally require genotypes, which is not feasible to obtain for many next-generation sequencing projects. Third, many methods assume samples are diploid, however a wide variety of sequencing applications will fail to meet this assumption. To address these issues, we introduce a novel hidden Markov model for estimating local ancestry that models the read pileup data, rather than genotypes, is generalized to arbitrary ploidy, and can estimate the time since admixture during local ancestry inference. We demonstrate that our method can simultaneously estimate the time since admixture and local ancestry with good accuracy, and that it performs well on samples of high ploidy—i.e. 100 or more chromosomes. As this method is very general, we expect it will be useful for local ancestry inference in a wider variety of populations than what previously has been possible. We then applied our method to pooled sequencing data derived from populations of Drosophila melanogaster on an ancestry cline on the east coast of North America. We find that regions of local recombination rates are negatively correlated with the proportion of African ancestry, suggesting that selection against foreign ancestry is the least efficient in low recombination regions. Finally we show that clinal outlier loci are enriched for genes associated with gene regulatory functions, consistent with a role of regulatory evolution in ecological adaptation of admixed D. melanogaster populations. Our results illustrate the potential of local ancestry
Non-local MRI denoising using random sampling.
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.
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.
RRW: repeated random walks on genome-scale protein networks for local cluster discovery
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
Localized surface plasmon enhanced cellular imaging using random metallic structures
NASA Astrophysics Data System (ADS)
Son, Taehwang; Lee, Wonju; Kim, Donghyun
2017-02-01
We have studied fluorescence cellular imaging with randomly distributed localized near-field induced by silver nano-islands. For the fabrication of nano-islands, a 10-nm silver thin film evaporated on a BK7 glass substrate with an adhesion layer of 2-nm thick chromium. Micrometer sized silver square pattern was defined using e-beam lithography and then the film was annealed at 200°C. Raw images were restored using electric field distribution produced on the surface of random nano-islands. Nano-islands were modeled from SEM images. 488-nm p-polarized light source was set to be incident at 60°. Simulation results show that localized electric fields were created among nano-islands and that their average size was found to be 135 nm. The feasibility was tested using conventional total internal reflection fluorescence microscopy while the angle of incidence was adjusted to maximize field enhancement. Mouse microphage cells were cultured on nano-islands, and actin filaments were selectively stained with FITC-conjugated phalloidin. Acquired images were deconvolved based on linear imaging theory, in which molecular distribution was sampled by randomly distributed localized near-field and blurred by point spread function of far-field optics. The optimum fluorophore distribution was probabilistically estimated by repetitively matching a raw image. The deconvolved images are estimated to have a resolution in the range of 100-150 nm largely determined by the size of localized near-fields. We also discuss and compare the results with images acquired with periodic nano-aperture arrays in various optical configurations to excite localized plasmonic fields and to produce super-resolved molecular images.
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.
Abstraction Augmented Markov Models.
Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina; Honavar, Vasant
2010-12-13
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of overfitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric parameters of k(th) order MMs by successively grouping strings of length k (i.e., k-grams) into abstraction hierarchies. We evaluate AAMMs on three protein subcellular localization prediction tasks. The results of our experiments show that abstraction makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) as compared to MMs. AAMMs are competitive with and, in some cases, significantly outperform MMs. Moreover, the results show that AAMMs often perform significantly better than variable order Markov models, such as decomposed context tree weighting, prediction by partial match, and probabilistic suffix trees.
NASA Astrophysics Data System (ADS)
Segovia, Fermín.; Salas-Gonzalez, Diego; Górriz, Juan M.; Ramírez, Javier; Martínez-Murcia, Francisco J.
2017-03-01
18F-DMFP-PET is a neuroimaging modality that allows us to analyze the striatal dopamine. Thus, it is recently emerging as an effective tool to assist the diagnosis of Parkinsonism and differentiate among parkinsonian syndromes. However the analysis of these data, which require specific preprocessing methods, is still poorly covered. In this work we demonstrate a novel methodology based on Hidden Markov Random Fields (HMRF) and the Gaussian distribution to preprocess 18F-DMFP-PET data. First, we performed a selection of voxels based on the analysis of the histogram in order to remove low-signal regions and regions outside the brain. Specifically, we modeled the histogram of intensities of a neuroimage with a mixture of two Gaussians and then, using a HMRF algorithm the voxels corresponding to the low-intensity Gaussian were discarded. This procedure is similar to the tissue segmentation usually applied to Magnetic Resonance Imaging data. Secondly, the intensity of the selected voxels was scaled so that the Gaussian that models the histogram for each neuroimage has same mean and standard deviation. This step made comparable the data from different patients, without removing the characteristic patterns of each patient's disorder. The proposed approach was evaluated using a computer system based on statistical classification that separated the neuroimages according to the parkinsonian variant they represented. The proposed approach achieved higher accuracy rates than standard approaches for voxel selection (based on atlases) and intensity normalization (based on the global mean).
Ghosh, Ashish; Subudhi, Badri Narayan; Bruzzone, Lorenzo
2013-08-01
In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.
Ashraf, Ahmed B; Gavenonis, Sara; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina
2011-01-01
We present a multichannel extension of Markov random fields (MRFs) for incorporating multiple feature streams in the MRF model. We prove that for making inference queries, any multichannel MRF can be reduced to a single channel MRF provided features in different channels are conditionally independent given the hidden variable, Using this result we incorporate kinetic feature maps derived from breast DCE MRI into the observation model of MRF for tumor segmentation. Our algorithm achieves an ROC AUC of 0.97 for tumor segmentation, We present a comparison against the commonly used approach of fuzzy C-means (FCM) and the more recent method of running FCM on enhancement variance features (FCM-VES). These previous methods give a lower AUC of 0.86 and 0.60 respectively, indicating the superiority of our algorithm. Finally, we investigate the effect of superior segmentation on predicting breast cancer recurrence using kinetic DCE MRI features from the segmented tumor regions. A linear prediction model shows significant prediction improvement when segmenting the tumor using the proposed method, yielding a correlation coefficient r = 0.78 (p < 0.05) to validated cancer recurrence probabilities, compared to 0.63 and 0.45 when using FCM and FCM-VES respectively.
Shi, Xu; Barnes, Robert O.; Chen, Li; Shajahan-Haq, Ayesha N.; Hilakivi-Clarke, Leena; Clarke, Robert; Wang, Yue; Xuan, Jianhua
2015-01-01
Summary: Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein–protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence. Availability and implementation: The BMRF-Net package is available at http://sourceforge.net/projects/bmrfcjava/. The package is tested under Ubuntu 12.04 (64-bit), Java 7, glibc 2.15 and Cytoscape 3.1.0. Contact: xuan@vt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25755273
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.
Localization of random acoustic sources in an inhomogeneous medium
NASA Astrophysics Data System (ADS)
Khazaie, Shahram; Wang, Xun; Sagaut, Pierre
2016-12-01
In this paper, the localization of a random sound source via different source localization methods is considered, the emphasis being put on the robustness and the accuracy of classical methods in the presence of uncertainties. The sound source position is described by a random variable and the sound propagation medium is assumed to have spatially varying parameters with known values. Two approaches are used for the source identification: time reversal and beamforming. The probability density functions of the random source position are estimated using both methods. The focal spot resolutions of the time reversal estimates are also evaluated. In the numerical simulations, two media with different correlation lengths are investigated to account for two different scattering regimes: one has a correlation length relatively larger than the wavelength and the other has a correlation length comparable to the wavelength. The results show that the required sound propagation time and source estimation robustness highly depend on the ratio between the correlation length and the wavelength. It is observed that source identification methods have different robustness in the presence of uncertainties. Advantages and weaknesses of each method are discussed.
Adaptive Local Information Transfer in Random Boolean Networks.
Haruna, Taichi
2017-01-01
Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.
Estimating Independent Locally Shifted Random Utility Models for Ranking Data.
Lam, Kar Yin; Koning, Alex J; Franses, Philip Hans
2011-09-30
We consider the estimation of probabilistic ranking models in the context of conjoint experiments. By using approximate rather than exact ranking probabilities, we avoided the computation of high-dimensional integrals. We extended the approximation technique proposed by Henery (1981) in the context of the Thurstone-Mosteller-Daniels model to any independent locally shifted random utility model. In particular, this allowed us to estimate any independent random utility model with common shape (e.g., normal, logistic) and scale. Moreover, our approach also allows for the analysis of any partial ranking. Partial rankings are essential in practical conjoint analysis to collect data efficiently to relieve respondents' task burden. We applied the approach to the reanalysis of the career preference data set described in Maydeu-Olivares and Böckenholt (2005) , and to a holiday preferences data set.
Localization in Interacting Fermionic Chains with Quasi-Random Disorder
NASA Astrophysics Data System (ADS)
Mastropietro, Vieri
2017-04-01
We consider a system of fermions with a quasi-random almost-Mathieu disorder interacting through a many-body short range potential. We establish exponential decay of the zero temperature correlations, indicating localization of the interacting ground state, for weak hopping and interaction and almost everywhere in the frequency and phase; this extends the analysis in Mastropietro (Commun Math Phys 342(1):217-250, 2016) to chemical potentials outside spectral gaps. The proof is based on Renormalization Group and it is inspired by techniques developed to deal with KAM Lindstedt series.
Local random potentials of high differentiability to model the Landscape
Battefeld, T.; Modi, C. E-mail: modichirag@berkeley.edu
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 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 (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.
Local random potentials of high differentiability to model the Landscape
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.
Localization transition of stiff directed lines in random media.
Boltz, Horst-Holger; Kierfeld, Jan
2012-12-01
We investigate the localization of stiff directed lines with bending energy by a short-range random potential. Using perturbative arguments, Flory arguments, and a replica calculation, we show that a stiff directed line in 1+d dimensions undergoes a localization transition with increasing disorder for d>2/3. We demonstrate that this transition is accessible by numerical transfer matrix calculations in 1+1 dimensions and analyze the properties of the disorder-dominated phase. On the basis of the two-replica problem, we propose a relation between the localization of stiff directed lines in 1+d dimensions and of directed lines under tension in 1+3d dimensions, which is strongly supported by identical free energy distributions. This shows that pair interactions in the replicated Hamiltonian determine the nature of directed line localization transitions with consequences for the critical behavior of the Kardar-Parisi-Zhang (KPZ) equation. Furthermore, we quantify how the persistence length of the stiff directed line is reduced by disorder.
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.
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.
No-signaling, perfect bipartite dichotomic correlations and local randomness
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.
From local concern to randomized trial: the Watcombe Housing Project
Somerville, Margaret; Basham, Meryl; Foy, Chris; Ballinger, Gary; Gay, Trevor; Barton, Andrew G.
2002-01-01
Background A randomized study of the effect on people's health of improving their housing is underway in Torbay. The link between poor health, particularly respiratory health, and poor housing conditions has been recognized for a long time, but there have been few intervention studies to demonstrate that improving housing can improve health. In 1994, South and West Devon Health Authority set up a community development project in a deprived area of Torbay, in response to the concerns of local primary health‐care workers. A community development worker helped local residents survey their homes for dampness and record their respiratory symptoms. The survey reported high levels of condensation/dampness and respiratory illness and the Council agreed to direct the majority of their housing improvement funds to the estate over the next 3 years. The Health Authority, University of Plymouth and Torbay Council were successful in obtaining funding to evaluate the housing improvements from the NHS R & D programme. Participants and methods Of 119 houses eligible for the study, 50 were chosen at random and improved in the first year. The rest were improved the following year. Questionnaires screening for health problems were sent to all 580 residents and baseline surveys of the indoor environment were also carried out. More detailed health surveys were completed by community nurses visiting residents in their homes. All adults were asked to complete SF‐36 and GHQ 12 questionnaires, as well as disease‐specific questionnaires if appropriate. Progress All houses in the study have now been improved, including insulation, double‐glazing, re‐roofing, heating, ventilation and electrical rewiring. Follow‐up surveys are underway. PMID:12031053
Flow Localization in Non-Linear Random Networks
NASA Astrophysics Data System (ADS)
Donev, Aleksandar; Phillip, Duxbury
2001-03-01
Local instabilities occur in the flow-potential characterstics of many complex materials, such as superconductors, dielectrics and porous materials. We describe a study of large networks in which each bond has a flow-potential characteristic having a threshold behavior. The thresholds vary randomly with values drawn from a variety of probability distributions. These large networks also exhibit a threshold-type response. The macroscopic onset occurs through a geometrical localization of the flow in the networks. For superconducting materials the critical current occurs when a surface of saturated arcs occurs transverse to the direction of flow. For porous materials on the other hand, at the macroscopic critical pressure a linear flow path begins across the network. We find these geometrical structures at the critical threshold using combinatorial graph algoritmhs (such as Dijkstra or push-relabel algorithms). The overall macroscopic problem is a convex, separable, minimal-cost network optimization problem. We have developed an efficient parallel algorithm for this problem and describe some preliminary results.
NASA Astrophysics Data System (ADS)
Wang, Huiyuan; Mo, H. J.; Yang, Xiaohu; Jing, Y. P.; Lin, W. P.
2014-10-01
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 -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-1 at high z and to k ~ 3.4 h Mpc-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.
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.
Anderson localization and ergodicity on random regular graphs
NASA Astrophysics Data System (ADS)
Tikhonov, K. Â. S.; Mirlin, A. Â. D.; Skvortsov, M. Â. A.
2016-12-01
A numerical study of Anderson transition on random regular graphs (RRGs) with diagonal disorder is performed. The problem can be described as a tight-binding model on a lattice with N sites that is locally a tree with constant connectivity. In a certain sense, the RRG ensemble can be seen as an infinite-dimensional (d →∞ ) cousin of the Anderson model in d dimensions. We focus on the delocalized side of the transition and stress the importance of finite-size effects. We show that the data can be interpreted in terms of the finite-size crossover from a small (N ≪Nc ) to a large (N ≫Nc ) system, where Nc is the correlation volume diverging exponentially at the transition. A distinct feature of this crossover is a nonmonotonicity of the spectral and wave-function statistics, which is related to properties of the critical phase in the studied model and renders the finite-size analysis highly nontrivial. Our results support an analytical prediction that states in the delocalized phase (and at N ≫Nc ) are ergodic in the sense that their inverse participation ratio scales as 1 /N .
Randomized discrepancy bounded local search for transmission expansion planning
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.
Sumner, J G; Fernández-Sánchez, J; Jarvis, P D
2012-04-07
Recent work has discussed the importance of multiplicative closure for the Markov models used in phylogenetics. For continuous-time Markov chains, a sufficient condition for multiplicative closure of a model class is ensured by demanding that the set of rate-matrices belonging to the model class form a Lie algebra. It is the case that some well-known Markov models do form Lie algebras and we refer to such models as "Lie Markov models". However it is also the case that some other well-known Markov models unequivocally do not form Lie algebras (GTR being the most conspicuous example). In this paper, we will discuss how to generate Lie Markov models by demanding that the models have certain symmetries under nucleotide permutations. We show that the Lie Markov models include, and hence provide a unifying concept for, "group-based" and "equivariant" models. For each of two and four character states, the full list of Lie Markov models with maximal symmetry is presented and shown to include interesting examples that are neither group-based nor equivariant. We also argue that our scheme is pleasing in the context of applied phylogenetics, as, for a given symmetry of nucleotide substitution, it provides a natural hierarchy of models with increasing number of parameters. We also note that our methods are applicable to any application of continuous-time Markov chains beyond the initial motivations we take from phylogenetics. Crown Copyright Â© 2011. Published by Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Takahashi, Tsutomu; Sato, Haruo; Nishimura, Takeshi
2008-05-01
Direct waves of microearthquakes in the high-frequency range (>1 Hz) strongly reflect the random inhomogeneities near their ray paths. This study conducts numerical simulations of envelope broadening of impulsively radiated wavelet assuming spatially non-uniform distribution of random inhomogeneities. We assume plural von Kármán type power spectral density functions (PSDF) for random inhomogeneity to clarify how the non-uniformly distributed random media affect the frequency dependence of envelope broadening. We employ the stochastic ray path method based on the Markov approximation for the mutual coherence function. This method is appropriate to simulate multiple forward scattering during the wave propagation. We mainly examine the travel distance and frequency dependence of the peak delay time in relation to the parameters characterizing the PSDFs. The peak delay time, which is defined as the time lag from the direct-wave onset to the maximum amplitude arrival of its envelope, is the best parameter reflecting the accumulated scattering effect in random media and is quite insensitive to the intrinsic attenuation. According to the numerical simulations in various non-uniform random media, we find some remarkable features in travel distance and frequency dependence, which cannot be found in uniform random media. For example, the frequency dependence in uniform random media is uniquely determined by the spectral gradient of PSDF for arbitrary travel distance; however, that in non-uniform media gradually changes as travel distance increases if the waves have experienced a change of spectral gradient in PSDF. Considering the results of our simulation, we propose a simple recursive formula to calculate the peak delay time in non-uniform random media. This recursive formula can predict the simulation results appropriately and relate the peak delay times to two parameters quantifying the von Kármán type PSDF in short wavelengths. It will become a mathematical base for
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.
Xia, Li C; Ai, Dongmei; Cram, Jacob A; Liang, Xiaoyi; Fuhrman, Jed A; Sun, Fengzhu
2015-09-21
Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies. By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns. The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.
Absence of Anderson localization in certain random lattices
NASA Astrophysics Data System (ADS)
Choi, Wonjun; Yin, Cheng; Hooper, Ian R.; Barnes, William L.; Bertolotti, Jacopo
2017-08-01
We report on the transition between an Anderson localized regime and a conductive regime in a one-dimensional microwave scattering system with correlated disorder. We show experimentally that when long-range correlations are introduced, in the form of a power-law spectral density with power larger than 2, the localization length becomes much bigger than the sample size and the transmission peaks typical of an Anderson localized system merge into a pass band. As other forms of long-range correlations are known to have the opposite effect, i.e., to enhance localization, our results show that care is needed when discussing the effects of correlations, as different kinds of long-range correlations can give rise to very different behavior.
Algorithms for Discovery of Multiple Markov Boundaries
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
Handling target obscuration through Markov chain observations
NASA Astrophysics Data System (ADS)
Kouritzin, Michael A.; Wu, Biao
2008-04-01
Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.
Local dependence in random graph models: characterization, properties and statistical inference.
Schweinberger, Michael; Handcock, Mark S
2015-06-01
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'.
Local dependence in random graph models: characterization, properties and statistical inference
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
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.
Hidden Markov model using Dirichlet process for de-identification.
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.
Localization in random bipartite graphs: Numerical and empirical study
NASA Astrophysics Data System (ADS)
Slanina, František
2017-05-01
We investigate adjacency matrices of bipartite graphs with a power-law degree distribution. Motivation for this study is twofold: first, vibrational states in granular matter and jammed sphere packings; second, graphs encoding social interaction, especially electronic commerce. We establish the position of the mobility edge and show that it strongly depends on the power in the degree distribution and on the ratio of the sizes of the two parts of the bipartite graph. At the jamming threshold, where the two parts have the same size, localization vanishes. We found that the multifractal spectrum is nontrivial in the delocalized phase, but still near the mobility edge. We also study an empirical bipartite graph, namely, the Amazon reviewer-item network. We found that in this specific graph the mobility edge disappears, and we draw a conclusion from this fact regarding earlier empirical studies of the Amazon network.
Local time of Lévy random walks: A path integral approach
NASA Astrophysics Data System (ADS)
Zatloukal, Václav
2017-05-01
The local time of a stochastic process quantifies the amount of time that sample trajectories x (τ ) spend in the vicinity of an arbitrary point x . For a generic Hamiltonian, we employ the phase-space path-integral representation of random walk transition probabilities in order to quantify the properties of the local time. For time-independent systems, the resolvent of the Hamiltonian operator proves to be a central tool for this purpose. In particular, we focus on the local times of Lévy random walks (Lévy flights), which correspond to fractional diffusion equations.
Wavelet-based SAR images despeckling using joint hidden Markov model
NASA Astrophysics Data System (ADS)
Li, Qiaoliang; Wang, Guoyou; Liu, Jianguo; Chen, Shaobo
2007-11-01
In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, we propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). The Expectation Maximization algorithm is used to estimate hyperparameters and specify the mixture model. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.
Bloomquist, Erica V; Ajkay, Nicolas; Patil, Sujata; Collett, Abigail E; Frazier, Thomas G; Barrio, Andrea V
2016-01-01
Radioactive seed localization (RSL) has emerged as an alternative to wire localization (WL) in patients with nonpalpable breast cancer. Few studies have prospectively evaluated patient satisfaction and outcomes with RSL. We report the results of a randomized trial comparing RSL to WL in our community hospital. We prospectively enrolled 135 patients with nonpalpable breast cancer between 2011 and 2014. Patients were randomized to RSL or WL. Patients rated the pain and the convenience of the localization on a 5-point Likert scale. Characteristics and outcomes were compared between groups. Of 135 patients enrolled, 10 were excluded (benign pathology, palpable cancer, mastectomy, and previous ipsilateral cancer) resulting in 125 patients. Seventy patients (56%) were randomized to RSL and 55 (44%) to WL. Fewer patients in the RSL group reported moderate to severe pain during the localization procedure compared to the WL group (12% versus 26%, respectively, p = 0.058). The overall convenience of the procedure was rated as very good to excellent in 85% of RSL patients compared to 44% of WL patients (p < 0.0001). There was no difference between the volume of the main specimen (p = 0.67), volume of the first surgery (p = 0.67), or rate of positive margins (p = 0.53) between groups. RSL resulted in less severe pain and higher convenience compared to WL, with comparable excision volume and positive margin rates. High patient satisfaction with RSL provides another incentive for surgeons to strongly consider RSL as an alternative to WL.
Leake, Pierre-Anthony; Toppin, Patrick J; Reid, Marvin; Plummer, Joseph M; Roberts, Patrick O; Harding-Goldson, Hyacinth; McFarlane, Michael E
2017-02-07
Conscious sedation is regularly used in ambulatory surgery to improve patient outcomes, in particular patient satisfaction. Reports suggest that the addition of conscious sedation to local anesthesia for inguinal hernioplasty is safe and effective in improving patient satisfaction. No previous randomized controlled trial has assessed the benefit of conscious sedation in this regard. To determine whether the addition of conscious sedation to local anesthesia improves patient satisfaction with inguinal hernioplasty. This trial is designed as a single-center, randomized, placebo-controlled, blinded trial of 148 patients. Adult patients diagnosed with a reducible, unilateral inguinal hernia eligible for hernioplasty using local anesthesia will be recruited. The intervention will be the use of intravenous midazolam for conscious sedation. Normal saline will be used as placebo in the control group. The primary outcome will be patient satisfaction, measured using the validated Iowa Satisfaction with Anesthesia Scale. Secondary outcomes will include intra- and postoperative pain, operative time, volumes of sedative agent and local anesthetic used, time to discharge, early and late complications, and postoperative functional status. To date, 171 patients have been recruited. Surgery has been performed on 149 patients, meeting the sample size requirements. Follow-up assessments are still ongoing. Trial completion is expected in August 2017. This randomized controlled trial is the first to assess the effectiveness of conscious sedation in improving patient satisfaction with inguinal hernioplasty using local anesthesia. If the results demonstrate improved patient satisfaction with conscious sedation, this would support routine incorporation of conscious sedation in local inguinal hernioplasty and potentially influence national and international hernia surgery guidelines. Clinicaltrials.gov NCT02444260; https://clinicaltrials.gov/ct2/show/NCT02444260 (Archived by WebCite at
Toppin, Patrick J; Reid, Marvin; Plummer, Joseph M; Roberts, Patrick O; Harding-Goldson, Hyacinth; McFarlane, Michael E
2017-01-01
Background Conscious sedation is regularly used in ambulatory surgery to improve patient outcomes, in particular patient satisfaction. Reports suggest that the addition of conscious sedation to local anesthesia for inguinal hernioplasty is safe and effective in improving patient satisfaction. No previous randomized controlled trial has assessed the benefit of conscious sedation in this regard. Objective To determine whether the addition of conscious sedation to local anesthesia improves patient satisfaction with inguinal hernioplasty. Methods This trial is designed as a single-center, randomized, placebo-controlled, blinded trial of 148 patients. Adult patients diagnosed with a reducible, unilateral inguinal hernia eligible for hernioplasty using local anesthesia will be recruited. The intervention will be the use of intravenous midazolam for conscious sedation. Normal saline will be used as placebo in the control group. The primary outcome will be patient satisfaction, measured using the validated Iowa Satisfaction with Anesthesia Scale. Secondary outcomes will include intra- and postoperative pain, operative time, volumes of sedative agent and local anesthetic used, time to discharge, early and late complications, and postoperative functional status. Results To date, 171 patients have been recruited. Surgery has been performed on 149 patients, meeting the sample size requirements. Follow-up assessments are still ongoing. Trial completion is expected in August 2017. Conclusions This randomized controlled trial is the first to assess the effectiveness of conscious sedation in improving patient satisfaction with inguinal hernioplasty using local anesthesia. If the results demonstrate improved patient satisfaction with conscious sedation, this would support routine incorporation of conscious sedation in local inguinal hernioplasty and potentially influence national and international hernia surgery guidelines. Trial registration Clinicaltrials.gov NCT02444260; https
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.
Localization in band random matrix models with and without increasing diagonal elements.
Wang, Wen-ge
2002-06-01
It is shown that localization of eigenfunctions in the Wigner band random matrix model with increasing diagonal elements can be related to localization in a band random matrix model with random diagonal elements. The relation is obtained by making use of a result of a generalization of Brillouin-Wigner perturbation theory, which shows that reduced Hamiltonian matrices with relatively small dimensions can be introduced for nonperturbative parts of eigenfunctions, and by employing intermediate basis states, which can improve the method of the reduced Hamiltonian matrix. The latter model deviates from the standard band random matrix model mainly in two aspects: (i) the root mean square of diagonal elements is larger than that of off-diagonal elements within the band, and (ii) statistical distributions of the matrix elements are close to the Lévy distribution in their central parts, except in the high top regions.
Entanglement detection on an NMR quantum-information processor using random local measurements
NASA Astrophysics Data System (ADS)
Singh, Amandeep; Arvind, Dorai, Kavita
2016-12-01
Random local measurements have recently been proposed to construct entanglement witnesses and thereby detect the presence of bipartite entanglement. We experimentally demonstrate the efficacy of one such scheme on a two-qubit NMR quantum-information processor. We show that a set of three random local measurements suffices to detect the entanglement of a general two-qubit state. We experimentally generate states with different amounts of entanglement and show that the scheme is able to clearly witness entanglement. We perform complete quantum state tomography for each state and compute state fidelity to validate our results. Further, we extend previous results and perform a simulation using random local measurements to optimally detect bipartite entanglement in a hybrid system of 2 ⊗3 dimensionality.
Probabilistic pairwise Markov models: application to prostate cancer detection
NASA Astrophysics Data System (ADS)
Monaco, James; Tomaszewski, John E.; Feldman, Michael D.; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant
2009-02-01
Markov Random Fields (MRFs) provide a tractable means for incorporating contextual information into a Bayesian framework. This contextual information is modeled using multiple local conditional probability density functions (LCPDFs) which the MRF framework implicitly combines into a single joint probability density function (JPDF) that describes the entire system. However, only LCPDFs of certain functional forms are consistent, meaning they reconstitute a valid JPDF. These forms are specified by the Gibbs-Markov equivalence theorem which indicates that the JPDF, and hence the LCPDFs, should be representable as a product of potential functions (i.e. Gibbs distributions). Unfortunately, potential functions are mathematical abstractions that lack intuition; and consequently, constructing LCPDFs through their selection becomes an ad hoc procedure, usually resulting in generic and/or heuristic models. In this paper we demonstrate that under certain conditions the LCDPFs can be formulated in terms of quantities that are both meaningful and descriptive: probability distributions. Using probability distributions instead of potential functions enables us to construct consistent LCPDFs whose modeling capabilities are both more intuitive and expansive than typical MRF models. As an example, we compare the efficacy of our so-called probabilistic pairwise Markov models (PPMMs) to the prevalent Potts model by incorporating both into a novel computer aided diagnosis (CAD) system for detecting prostate cancer in whole-mount histological sections. Using the Potts model the CAD system is able to detection cancerous glands with a specificity of 0.82 and sensitivity of 0.71; its area under the receiver operator characteristic (AUC) curve is 0.83. If instead the PPMM model is employed the sensitivity (specificity is held fixed) and AUC increase to 0.77 and 0.87.
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.
Many-body localization in Ising models with random long-range interactions
NASA Astrophysics Data System (ADS)
Li, Haoyuan; Wang, Jia; Liu, Xia-Ji; Hu, Hui
2016-12-01
We theoretically investigate the many-body localization phase transition in a one-dimensional Ising spin chain with random long-range spin-spin interactions, Vi j∝|i-j |-α , where the exponent of the interaction range α can be tuned from zero to infinitely large. By using exact diagonalization, we calculate the half-chain entanglement entropy and the energy spectral statistics and use them to characterize the phase transition towards the many-body localization phase at infinite temperature and at sufficiently large disorder strength. We perform finite-size scaling to extract the critical disorder strength and the critical exponent of the divergent localization length. With increasing α , the critical exponent experiences a sharp increase at about αc≃1.2 and then gradually decreases to a value found earlier in a disordered short-ranged interacting spin chain. For α <αc , we find that the system is mostly localized and the increase in the disorder strength may drive a transition between two many-body localized phases. In contrast, for α >αc , the transition is from a thermalized phase to the many-body localization phase. Our predictions could be experimentally tested with an ion-trap quantum emulator with programmable random long-range interactions, or with randomly distributed Rydberg atoms or polar molecules in lattices.
Bloomquist, Erica V.; Ajkay, Nicolas; Patil, Sujata; Collett, Abigail E.; Frazier, Thomas G.; Barrio, Andrea V.
2015-01-01
Background Radioactive seed localization (RSL) has emerged as an alternative to wire localization (WL) in patients with non-palpable breast cancer. Few studies have prospectively evaluated patient satisfaction and outcomes with RSL. We report the results of a randomized trial comparing RSL to WL in our community hospital. Materials and Methods We prospectively enrolled 135 patients with non-palpable breast cancer between 2011 and 2014. Patients were randomized to RSL or WL. Patients rated the pain and the convenience of the localization on a 5-point Likert scale. Characteristics and outcomes were compared between groups. Results Of 135 patients enrolled, 10 were excluded (benign pathology, palpable cancer, mastectomy and previous ipsilateral cancer) resulting in 125 patients. Seventy patients (56%) were randomized to RSL and 55 (44%) to WL. Fewer patients in the RSL group reported moderate to severe pain during the localization procedure compared to the WL group (12% versus 26%, respectively, p=0.058). The overall convenience of the procedure was rated as very good to excellent in 85% of RSL patients compared to 44% of WL patients (p<0.0001). There was no difference between the volume of the main specimen (p=0.67), volume of the first surgery (p=0.67), or rate of positive margins (p=0.53) between groups. Conclusions RSL resulted in less severe pain and higher convenience compared to WL, with comparable excision volume and positive margin rates. High patient satisfaction with RSL provides another incentive for surgeons to strongly consider RSL as an alternative to WL. PMID:26696461
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.
Subensemble decomposition and Markov process analysis of Burgers turbulence.
Zhang, Zhi-Xiong; She, Zhen-Su
2011-08-01
A numerical and statistical study is performed to describe the positive and negative local subgrid energy fluxes in the one-dimensional random-force-driven Burgers turbulence (Burgulence). We use a subensemble method to decompose the field into shock wave and rarefaction wave subensembles by group velocity difference. We observe that the shock wave subensemble shows a strong intermittency which dominates the whole Burgulence field, while the rarefaction wave subensemble satisfies the Kolmogorov 1941 (K41) scaling law. We calculate the two subensemble probabilities and find that in the inertial range they maintain scale invariance, which is the important feature of turbulence self-similarity. We reveal that the interconversion of shock and rarefaction waves during the equation's evolution displays in accordance with a Markov process, which has a stationary transition probability matrix with the elements satisfying universal functions and, when the time interval is much greater than the corresponding characteristic value, exhibits the scale-invariant property.
Díez, Francisco J; Yebra, Mar; Bermejo, Iñigo; Palacios-Alonso, Miguel A; Calleja, Manuel Arias; Luque, Manuel; Pérez-Martín, Jorge
2017-02-01
Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs-including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis-with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.
NASA Astrophysics Data System (ADS)
Monthus, Cécile
2017-07-01
When random quantum spin chains are submitted to some periodic Floquet driving, the eigenstates of the time-evolution operator over one period can be localized in real space. For the case of periodic quenches between two Hamiltonians (or periodic kicks), where the time-evolution operator over one period reduces to the product of two simple transfer matrices, we propose a block-self-dual renormalization procedure to construct the localized eigenstates of the Floquet dynamics. We also discuss the corresponding strong disorder renormalization procedure, that generalizes the RSRG-X procedure to construct the localized eigenstates of time-independent Hamiltonians.
Eigenvalue Outliers of Non-Hermitian Random Matrices with a Local Tree Structure
NASA Astrophysics Data System (ADS)
Neri, Izaak; Metz, Fernando Lucas
2016-11-01
Spectra of sparse non-Hermitian random matrices determine the dynamics of complex processes on graphs. Eigenvalue outliers in the spectrum are of particular interest, since they determine the stationary state and the stability of dynamical processes. We present a general and exact theory for the eigenvalue outliers of random matrices with a local tree structure. For adjacency and Laplacian matrices of oriented random graphs, we derive analytical expressions for the eigenvalue outliers, the first moments of the distribution of eigenvector elements associated with an outlier, the support of the spectral density, and the spectral gap. We show that these spectral observables obey universal expressions, which hold for a broad class of oriented random matrices.
Markov invariants, plethysms, and phylogenetics.
Sumner, J G; Charleston, M A; Jermiin, L S; Jarvis, P D
2008-08-07
We explore model-based techniques of phylogenetic tree inference exercising Markov invariants. Markov invariants are group invariant polynomials and are distinct from what is known in the literature as phylogenetic invariants, although we establish a commonality in some special cases. We show that the simplest Markov invariant forms the foundation of the Log-Det distance measure. We take as our primary tool group representation theory, and show that it provides a general framework for analyzing Markov processes on trees. From this algebraic perspective, the inherent symmetries of these processes become apparent, and focusing on plethysms, we are able to define Markov invariants and give existence proofs. We give an explicit technique for constructing the invariants, valid for any number of character states and taxa. For phylogenetic trees with three and four leaves, we demonstrate that the corresponding Markov invariants can be fruitfully exploited in applied phylogenetic studies.
NASA Astrophysics Data System (ADS)
Monthus, Cécile
2016-09-01
For random Lévy matrices of size N× N , where matrix elements are drawn with some heavy-tailed distribution P≤ft({{H}ij}\\right)\\propto {{N} -1}|{{H}ij}{{|}-1-μ} with 0<μ <2 (infinite variance), there exists an extensive number of finite eigenvalues E = O(1), while the maximal eigenvalue grows as {{E}\\text{max}}∼ {{N}\\frac{1μ}} . Here we study the localization properties of the corresponding eigenvectors via some strong disorder perturbative expansion that remains consistent within the localized phase and that yields their inverse participation ratios (IPR) Y q as a function of the continuous parameter 0. In the region 0<μ <1 , we find that all eigenvectors are localized but display some multifractality: the IPR are finite above some threshold q > q c but diverge in the region 0 < q < q c near the origin. In the region 1<μ <2 , only the sub-extensive fraction {{N}\\frac{3{2+μ}}} of the biggest eigenvalues corresponding to the region |E|≥slant {{N}\\frac{(μ -1)μ (2+μ )}}} remains localized, while the extensive number of other states of smaller energy are delocalized. For the extensive number of finite eigenvalues E = O(1), the localization/delocalization transition thus takes place at the critical value {{μ\\text{c}}=1 corresponding to Cauchy matrices: the IPR Y q of the corresponding critical eigenstates follow the strong-multifractality spectrum characterized by the generalized fractal dimensions {{D}\\text{criti}}(q)=\\frac{1-2q}{1-q}θ ≤ft(0≤slant q≤slant \\frac{1}{2}\\right) , which has been found previously in various other Localization problems in spaces of effective infinite dimensionality.
Stochastic Dynamics through Hierarchically Embedded Markov Chains
NASA Astrophysics Data System (ADS)
Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.
2017-02-01
Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.
Stochastic Dynamics through Hierarchically Embedded Markov Chains.
Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M
2017-02-03
Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.
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
Surface-plasmon mode on a random rough metal surface: Enhanced backscattering and localization
NASA Astrophysics Data System (ADS)
Ogura, H.; Wang, Z. L.
1996-04-01
The scattering of light by a silver film with a random rough surface and the excitation of surface-plasmon modes at the metal surface are studied by means of the stochastic functional approach, assuming that the random surface is a homogeneous Gaussian random field. The stochastic wave fields are represented in terms of the Wiener-Hermite orthogonal functionals, and the approximate solutions are obtained for the Wiener kernels. For the attenuated total reflection configuration considered in the paper, the angular distributions of incoherent scattering into both crystal and air are numerically calculated by using first- and second-order Wiener kernels for various combinations of the parameters. In the angular distributions of incoherent scattering into crystal, strong peaks can be observed corresponding to the excitation of forward- and backward-traveling plasmon modes, which are mainly described by the first-order Wiener kernel, and an enhanced scattering peak appears in the backward direction. In the angular distributions of incoherent scattering into air, an enhanced scattering peak also appears in a certain direction, related to the incident angle on the crystal side. The random wave fields at the resonant scattering on the surface of a random rough grating are also numerically calculated from the higher Wiener kernels with an iterative procedure. Localized modes can be clearly observed in the spatial distribution of the random wave fields. The enhanced scattering comes from the second-order Wiener kernel that describes the ``double-scattering'' processes of the ``dressed'' plasmon modes, and is due to the interference of the two double-scattering processes in the reciprocal directions, where the strongly excited plasmon modes take part in the intermediate scattering processes, while the wave localization is a result of ``multiple'' scattering of strongly excited dressed plasmon waves traveling in the ``random media'' created by the surface roughness.
NASA Astrophysics Data System (ADS)
Sato, Haruo; Emoto, Kentaro
2017-10-01
In high-frequency seismograms of small earthquakes, we clearly see the excitation of long lasting coda waves and the envelope broadening of an S-wavelet with travel distance increasing. We can interpret those phenomena resulting from scattering by random inhomogeneities distributed in the earth medium. Those phenomena have been theoretically studied by stochastic methods, which deal with velocity inhomogeneities as random media. As a simple mathematical model, we study the propagation of a scalar wavelet for the spherical radiation from a point source in 3-D von Kármán-type random media, of which the power spectral density function (PSDF) decreases according to a power-law higher than the corner wavenumber. Our objective is to propose a method to synthesize the wavelet intensity time trace, the mean square amplitude trace, at a given travel distance by using statistical parameters characterizing the PSDF and the centre wavenumber of the wavelet. When the phase shift is small, we can use the Born approximation to calculate the non-isotropic scattering coefficient representing the scattering power per unit volume. Using the scattering coefficient in the radiative transfer equation (RTE), we are able to synthesize the wavelet intensity time trace. When the centre wavenumber increases in the power-law spectral range, however, we often face the situation of a large phase shift, where the Born approximation is inapplicable, but we are able to use the Markov approximation based on the parabolic approximation. It well explains the intensity time traces showing envelope broadening with peak delay due to multiple scattering around the forward direction and the wandering effect caused by travel time fluctuations; however, it fails to explain rich coda waves composed of scattered waves in wide angles. In such a case, here, we newly propose the spectrum division method as follows: at first, taking the centre wavenumber with a tuning parameter as a reference, we divide the
Torrisi, J.R.; Dritschilo, A.; Harter, K.W.; Helfrich, B.; Berg, C.D.; Whitfield, G.; Stablein, D.; Alijani, M. )
1990-05-01
A prospective randomized study investigating the effectiveness of adjuvant local graft irradiation (LGI) following renal transplantation was performed at Georgetown University Hospital from 1983 until 1988. One hundred and thirty-eight patients were enrolled in the study with 117 patients receiving cadaver kidney transplantations and 21 patients receiving living related kidney transplantations. Seventy-one patients were randomized to receive adjuvant local graft irradiation consisting of 600 cGy in four fractions with chemical immunosuppression whereas the remaining 67 patients received chemical immunosuppression only (control group). The two groups were comparable at entry with respect to potentially important prognostic variables. Median follow-up for all patients was 30 months. The 3-year actuarial allograft success rate was 75% and 68% for the local graft irradiation and control groups, respectively. A nonsignificant trend favoring the irradiated group was noted. Subgroup analysis of the 21 recipients of kidneys from living related donors suggested an improvement in allograft survival for the local graft irradiation arm. Cadaver allograft survival was not significantly different between the two treatment arms. There was no apparent benefit in kidney function or time to the first rejection episode in the group receiving local graft irradiation.
Diffusive and localization behavior of electromagnetic waves in a two-dimensional random medium.
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)].
Random phase mask in a filamentation regime: application to the localization of point sources.
de la Barrière, Florence; Druart, Guillaume; Guérineau, Nicolas; Ferrec, Yann; Taboury, Jean; Primot, Jérôme
2011-03-01
We present a optical system with an extended point-spread function (PSF) for the localization of point sources in the visible and IR spectral ranges with a subpixel precision. This compact system involves a random phase mask (RPM) as its unique component. It exhibits original properties, because this RPM is used in a particular regime, called the "filamentation regime," before the speckle region. The localization is performed by calculating the phase correlation between the PSF and the image obtained under off-axis illumination. Numerical simulations are presented to assess the basic optical properties of this RPM in the filamentation regime.
Absence of localization in a model with correlation measure as a random lattice
NASA Astrophysics Data System (ADS)
Kroon, Lars; Riklund, Rolf
2004-03-01
A coherent picture of localization in one-dimensional aperiodically ordered systems is still missing. We show the presence of purely singular continuous spectrum for a discrete system whose modulation sequence has a correlation measure which is absolutely continuous, such as for a random sequence. The system showing these properties is modeled by the Rudin-Shapiro sequence, whose correlation measure even has a uniform density. The absence of localization is also supported by a numerical investigation of the dynamics of electronic wave packets showing weakly anomalous diffusion and an extremely slow algebraic decay of the temporal autocorrelation function.
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.
Metastability in Markov processes
NASA Astrophysics Data System (ADS)
Larralde, H.; Leyvraz, F.; Sanders, D. P.
2006-08-01
We present a formalism for describing slowly decaying systems in the context of finite Markov chains obeying detailed balance. We show that phase space can be partitioned into approximately decoupled regions, in which one may introduce restricted Markov chains which are close to the original process but do not leave these regions. Within this context, we identify the conditions under which the decaying system can be considered to be in a metastable state. Furthermore, we show that such metastable states can be described in thermodynamic terms and define their free energy. This is accomplished, showing that the probability distribution describing the metastable state is indeed proportional to the equilibrium distribution, as is commonly assumed. We test the formalism numerically in the case of the two-dimensional kinetic Ising model, using the Wang-Landau algorithm to show this proportionality explicitly, and confirm that the proportionality constant is as derived in the theory. Finally, we extend the formalism to situations in which a system can have several metastable states.
Localization estimates for a random discrete wave equation at high frequency
Faris, W.G.
1987-02-01
It is shown that at high frequencies matrix elements of the Green's function of a random discrete wave equation decay exponentially at long distances. This is the input to the proof of dense point spectrum with localized eigenfunctions in this frequency range. The proof uses techniques of Froehlich and Spencer. A sequence of renormalization transformations shows that large regions where wave propagation is easily maintained become increasingly sparse as resonance is approached.
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.
NASA Astrophysics Data System (ADS)
Hatano, Naomichi; Feinberg, Joshua
2016-12-01
We study Chebyshev-polynomial expansion of the inverse localization length of Hermitian and non-Hermitian random chains as a function of energy. For Hermitian models, the expansion produces this energy-dependent function numerically in one run of the algorithm. This is in strong contrast to the standard transfer-matrix method, which produces the inverse localization length for a fixed energy in each run. For non-Hermitian models, as in the transfer-matrix method, our algorithm computes the inverse localization length for a fixed (complex) energy. We also find a formula of the Chebyshev-polynomial expansion of the density of states of non-Hermitian models. As explained in detail, our algorithm for non-Hermitian models may be the only available efficient algorithm for finding the density of states of models with interactions.
Simulation study of localization of electromagnetic waves in two-dimensional random dipolar systems.
Wang, Ken Kang-Hsin; Ye, Zhen
2003-12-01
We study the propagation and scattering of electromagnetic waves by random arrays of dipolar cylinders in a uniform medium. A set of self-consistent equations, incorporating all orders of multiple scattering of the electromagnetic waves, is derived from first principles and then solved numerically for electromagnetic fields. For certain ranges of frequencies, spatially localized electromagnetic waves appear in such a simple but realistic disordered system. Dependence of localization on the frequency, radiation damping, and filling factor is shown. The spatial behavior of the total, coherent, and diffusive waves is explored in detail, and found to comply with a physical intuitive picture. A phase diagram characterizing localization is presented, in agreement with previous investigations on other systems.
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.
Li, Zhan-Chao; Lai, Yan-Hua; Chen, Li-Li; Chen, Chao; Xie, Yun; Dai, Zong; Zou, Xiao-Yong
2013-04-05
In the post-genome era, one of the most important and challenging tasks is to identify the subcellular localizations of protein complexes, and further elucidate their functions in human health with applications to understand disease mechanisms, diagnosis and therapy. Although various experimental approaches have been developed and employed to identify the subcellular localizations of protein complexes, the laboratory technologies fall far behind the rapid accumulation of protein complexes. Therefore, it is highly desirable to develop a computational method to rapidly and reliably identify the subcellular localizations of protein complexes. In this study, a novel method is proposed for predicting subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm. Protein complexes are modeled as weighted graphs containing nodes and edges, where nodes represent proteins, edges represent protein-protein interactions and weights are descriptors of protein primary structures. Some topological structure features are proposed and adopted to characterize protein complexes based on graph theory. Random forest is employed to construct a model and predict subcellular localizations of protein complexes. Accuracies on a training set by a 10-fold cross-validation test for predicting plasma membrane/membrane attached, cytoplasm and nucleus are 84.78%, 71.30%, and 82.00%, respectively. And accuracies for the independent test set are 81.31%, 69.95% and 81.00%, respectively. These high prediction accuracies exhibit the state-of-the-art performance of the current method. It is anticipated that the proposed method may become a useful high-throughput tool and plays a complementary role to the existing experimental techniques in identifying subcellular localizations of mammalian protein complexes. The source code of Matlab and the dataset can be obtained freely on request from the authors.
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
Localized buckling of a microtubule surrounded by randomly distributed cross linkers.
Jin, M Z; Ru, C Q
2013-07-01
Microtubules supported by surrounding cross linkers in eukaryotic cells can bear a much higher compressive force than free-standing microtubules. Different from some previous studies, which treated the surroundings as a continuum elastic foundation or elastic medium, the present paper develops a micromechanics numerical model to examine the role of randomly distributed discrete cross linkers in the buckling of compressed microtubules. First, the proposed numerical approach is validated by reproducing the uniform multiwave buckling mode predicted by the existing elastic-foundation model. For more realistic buckling of microtubules surrounded by randomly distributed cross linkers, the present numerical model predicts that the buckling mode is localized at one end in agreement with some known experimental observations. In particular, the critical force for localized buckling, predicted by the present model, is insensitive to microtubule length and can be about 1 order of magnitude lower than those given by the elastic-foundation model, which suggests that the elastic-foundation model may have overestimated the critical force for buckling of microtubules in vivo. In addition, unlike the elastic-foundation model, the present model can capture the effect of end conditions on the critical force and wavelength of localized buckling. Based on the known data of spacing and elastic constants of cross linkers available in literature, the critical force and wavelength of the localized buckling mode, predicted by the present model, are compared to some experimental data with reasonable agreement. Finally, two empirical formulas are proposed for the critical force and wavelength of the localized buckling of microtubules surrounded by cross linkers.
The use of local anaesthesia in haemorrhoidal banding: a randomized controlled trial.
Kwok, H C K; Noblett, S E; Murray, N E A; Merrie, A E H; Hayes, J L; Bissett, I P
2013-04-01
Rubber band ligation is a common office procedure for the treatment of symptomatic haemorrhoids. It can be associated with pain and vasovagal symptoms. The effect of local anaesthetic use during banding was studied. A single-blinded randomized controlled trial was carried out in the colorectal outpatient clinic. Patients presenting with symptomatic haemorrhoids suitable for banding were prospectively recruited and randomized to undergo the procedure with local anaesthetic or without (control). Submucosal bupivacaine was injected immediately after banding just proximal to the site. Vasovagal symptoms were assessed at the time of banding and pain scores (visual analogue scale) were recorded at the conclusion of the procedure, after 15 min, and on leaving the clinic. Seventy-two patients (40 local anaesthetic injection, group 1; 32 no injection, group 2) were recruited. The mean ages were 50 and 54 years respectively, the median duration of symptoms was 12 months in each group and the median number of haemorrhoids banded was three in each group. The mean pain score on leaving the clinic was 2.6 (95% CI 2.1, 3.1) in group 1 and 4.1 (95% CI 3.3, 5.0) (P = 0.04) in group 2. There were no complications related to local anaesthetic use. No significant difference in vasovagal symptoms was found (P = 0.832). Local anaesthetic injection at the time of banding is simple and safe. It may reduce patient discomfort following banding of haemorrhoids. © 2013 The Authors. Colorectal Disease © 2013 The Association of Coloproctology of Great Britain and Ireland.
Localization of nonlinear shallow water waves over a randomly rough seabed
NASA Astrophysics Data System (ADS)
Mei, Chiang; Grataloup, Geraldine; Li, Yile
2003-11-01
Localization or spatial attenuation of sea waves can be caused by bottom friction and by radiation of scattered waves. We describe a theory of shallow-water waves scattered by a long stretch of randomly rough seabed, where the root-mean-square height of the roughness is moderately small. Boussinesq equations are used as the starting point. By using two-scale expansions and Green's functions, multiple scattering by the rough bottom and the nonlinear exchanges of energy between different frequencies are accounted for. For monochromatic incident waves, the evolution equations for all harmonics are shown to be nonlinearly coupled ordinary differential equations with damping, whose coefficients are related to the correlation functions of the roughess. Examples of localization and generation of harmonics are shown by numerical examples. For an incident soliton, the evolution equation is shown to be a KdV-Burgers equation with new diffusion and dispersion terms in integral form, implying memory. Numerical results on soliton deformation, fission and localization will be discussed. Long fetch approximation will be described. This theory differs from several existing ones where random potentials are added to the evolution equations.
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.
NASA Astrophysics Data System (ADS)
Setiawan, F.; Deng, Dong-Ling; Pixley, J. H.
2017-09-01
We theoretically study transport properties in one-dimensional interacting quasiperiodic systems at infinite temperature. We compare and contrast the dynamical transport properties across the many-body localization (MBL) transition in quasiperiodic and random models. Using exact diagonalization we compute the optical conductivity σ (ω ) and the return probability R (τ ) and study their average low-frequency and long-time power-law behavior, respectively. We show that the low-energy transport dynamics is markedly distinct in both the thermal and MBL phases in quasiperiodic and random models and find that the diffusive and MBL regimes of the quasiperiodic model are more robust than those in the random system. Using the distribution of the dc conductivity, we quantify the contribution of sample-to-sample and state-to-state fluctuations of σ (ω ) across the MBL transition. We find that the activated dynamical scaling ansatz works poorly in the quasiperiodic model but holds in the random model with an estimated activation exponent ψ ≈0.9 . We argue that near the MBL transition in quasiperiodic systems, critical eigenstates give rise to a subdiffusive crossover regime on finite-size systems.
Cai, Xianfa; Wei, Jia; Wen, Guihua; Yu, Zhiwen
2014-03-01
Precise cancer classification is essential to the successful diagnosis and treatment of cancers. Although semisupervised dimensionality reduction approaches perform very well on clean datasets, the topology of the neighborhood constructed with most existing approaches is unstable in the presence of high-dimensional data with noise. In order to solve this problem, a novel local and global preserving semisupervised dimensionality reduction based on random subspace algorithm marked as RSLGSSDR, which utilizes random subspace for semisupervised dimensionality reduction, is proposed. The algorithm first designs multiple diverse graphs on different random subspace of datasets and then fuses these graphs into a mixture graph on which dimensionality reduction is performed. As themixture graph is constructed in lower dimensionality, it can ease the issues on graph construction on highdimensional samples such that it can hold complicated geometric distribution of datasets as the diversity of random subspaces. Experimental results on public gene expression datasets demonstrate that the proposed RSLGSSDR not only has superior recognition performance to competitive methods, but also is robust against a wide range of values of input parameters.
Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models
NASA Astrophysics Data System (ADS)
Chan, Lay Guat; Ibrahim, Adriana Irawati Nur Binti
2016-10-01
A hidden Markov model (HMM) is a mixture model which has a Markov chain with finite states as its mixing distribution. HMMs have been applied to a variety of fields, such as speech and face recognitions. The main purpose of this study is to investigate the Bayesian approach to HMMs. Using this approach, we can simulate from the parameters' posterior distribution using some Markov chain Monte Carlo (MCMC) sampling methods. HMMs seem to be useful, but there are some limitations. Therefore, by using the Mixture of Dirichlet processes Hidden Markov Model (MDPHMM) based on Yau et. al (2011), we hope to overcome these limitations. We shall conduct a simulation study using MCMC methods to investigate the performance of this model.
Decoherence in quantum Markov chains
NASA Astrophysics Data System (ADS)
Santos, Raqueline Azevedo Medeiros; Portugal, Renato; Fragoso, Marcelo Dutra
2013-11-01
It is known that under some assumptions, the hitting time in quantum Markov chains is quadratically smaller than the hitting time in classical Markov chains. This work extends this result for decoherent quantum Markov chains. The decoherence is introduced using a percolation-like graph model, which allows us to define a decoherent quantum hitting time and to establish a decoherent-intensity range for which the decoherent quantum hitting time is quadratically smaller than the classical hitting time. The detection problem under decoherence is also solved with quadratic speedup in this range.
Critical Casimir force in the presence of random local adsorption preference.
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.
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,
Güven, Can; Hinczewski, Michael; Berker, A Nihat
2010-11-01
The tensor renormalization-group method, developed by Levin and Nave, brings systematic improvability to the position-space renormalization-group method and yields essentially exact results for phase diagrams and entire thermodynamic functions. The method, previously used on systems with no quenched randomness, is extended in this study to systems with quenched randomness. Local magnetizations and correlation functions as a function of spin separation are calculated as tensor products subject to renormalization-group transformation. Phase diagrams are extracted from the long-distance behavior of the correlation functions. The approach is illustrated with the quenched bond-diluted Ising model on the triangular lattice. An accurate phase diagram is obtained in temperature and bond-dilution probability for the entire temperature range down to the percolation threshold at zero temperature.
Many-body localization transition in random quantum spin chains with long-range interactions
NASA Astrophysics Data System (ADS)
Moure, N.; Haas, S.; Kettemann, S.
2015-07-01
While there are well-established methods to study delocalization transitions of single particles in random systems, it remains a challenging problem how to characterize many-body delocalization transitions. Here, we use a generalized real-space renormalization group technique to study the anisotropic Heisenberg model with long-range interactions, decaying with a power α, which are generated by placing spins at random positions along the chain. This method permits a large-scale finite-size scaling analysis. We examine the full distribution function of the excitation energy gap from the ground state and observe a crossover with decreasing α. At αc the full distribution coincides with a critical function. Thereby, we find strong evidence for the existence of a many-body localization transition in disordered antiferromagnetic spin chains with long-range interactions.
NASA Astrophysics Data System (ADS)
Güven, Can; Hinczewski, Michael; Berker, A. Nihat
2011-03-01
The tensor renormalization-group method, developed by Levin and Nave, brings systematic improvability to the position-space renormalization-group method and yields essentially exact results for phase diagrams and entire thermodynamic functions. The method, previously used on systems with no quenched randomness, is extended in this study to systems with quenched randomness. Local magnetizations and correlation functions as a function of spin separation are calculated as tensor products subject to renormalization-group transformation. Phase diagrams are extracted from the long-distance behavior of the correlation functions. The approach is illustrated with the quenched bond-diluted Ising model on the triangular lattice. An accurate phase diagram is obtained in temperature and bond-dilution probability for the entire temperature range down to the percolation threshold at zero temperature. This research was supported by the Alexander von Humboldt Foundation, the Scientific and Technological Research Council of Turkey (TÜBITAK), and the Academy of Sciences of Turkey.
NASA Astrophysics Data System (ADS)
Güven, Can; Hinczewski, Michael; Berker, A. Nihat
2010-11-01
The tensor renormalization-group method, developed by Levin and Nave, brings systematic improvability to the position-space renormalization-group method and yields essentially exact results for phase diagrams and entire thermodynamic functions. The method, previously used on systems with no quenched randomness, is extended in this study to systems with quenched randomness. Local magnetizations and correlation functions as a function of spin separation are calculated as tensor products subject to renormalization-group transformation. Phase diagrams are extracted from the long-distance behavior of the correlation functions. The approach is illustrated with the quenched bond-diluted Ising model on the triangular lattice. An accurate phase diagram is obtained in temperature and bond-dilution probability for the entire temperature range down to the percolation threshold at zero temperature.
Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening.
Kamimura, H A S; Wang, S; Wu, S-Y; Karakatsani, M E; Acosta, C; Carneiro, A A O; Konofagou, E E
2015-10-07
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.
In vivo MRI based prostate cancer localization with random forests and auto-context model
Qian, Chunjun; Wang, Li; Gao, Yaozong; Yousuf, Ambereen; Yang, Xiaoping; Oto, Aytekin; Shen, Dinggang
2017-01-01
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
In vivo MRI based prostate cancer localization with random forests and auto-context model.
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.
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.
Local random quantum circuits: Ensemble completely positive maps and swap algebras
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.
Local random quantum circuits: Ensemble completely positive maps and swap algebras
NASA Astrophysics Data System (ADS)
Zanardi, Paolo
2014-08-01
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.
NASA Astrophysics Data System (ADS)
Yan, Zhi-Zhong; Zhang, Chuanzeng; Wang, Yue-Sheng
2011-03-01
The band structures of in-plane elastic waves propagating in two-dimensional phononic crystals with one-dimensional random disorder and aperiodicity are analyzed in this paper. The localization of wave propagation is discussed by introducing the concept of the localization factor, which is calculated by the plane-wave-based transfer-matrix method. By treating the random disorder and aperiodicity as the deviation from the periodicity in a special way, three kinds of aperiodic phononic crystals that have normally distributed random disorder, Thue-Morse and Rudin-Shapiro sequence in one direction and translational symmetry in the other direction are considered and the band structures are characterized using localization factors. Besides, as a special case, we analyze the band gap properties of a periodic planar layered composite containing a periodic array of square inclusions. The transmission coefficients based on eigen-mode matching theory are also calculated and the results show the same behaviors as the localization factor does. In the case of random disorders, the localization degree of the normally distributed random disorder is larger than that of the uniformly distributed random disorder although the eigenstates are both localized no matter what types of random disorders, whereas, for the case of Thue-Morse and Rudin-Shapiro structures, the band structures of Thue-Morse sequence exhibit similarities with the quasi-periodic (Fibonacci) sequence not present in the results of the Rudin-Shapiro sequence.
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.
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.
Markov Chains and Chemical Processes
ERIC Educational Resources Information Center
Miller, P. J.
1972-01-01
Views as important the relating of abstract ideas of modern mathematics now being taught in the schools to situations encountered in the sciences. Describes use of matrices and Markov chains to study first-order processes. (Author/DF)
Law, W L; Chu, K W
1999-03-01
Rubber band ligation is a common office procedure for hemorrhoids. Triple rubber band ligation in a single session has been shown to be a safe and economical way of treating hemorrhoids. However, postligation discomfort after triple rubber band ligation is not uncommon. The aim of this study was to evaluate the effectiveness of local anesthetic injection to the banded hemorrhoidal tissue in reducing postligation discomfort. Patients attending an outpatient clinic for symptomatic hemorrhoids suitable for triple rubber band ligation were randomly assigned to two groups. In the treatment group rubber band ligation was performed at three columns of hemorrhoids, and 1 to 2 ml of 2 percent lignocaine was injected into the banded hemorrhoidal tissue. In the control group triple rubber band ligation was performed in a similar manner, but local anesthetic was not given. Patients were followed up by telephone at the second week and in the clinic after six weeks. From April to August 1996, 101 patients entered the trial and were treated with triple rubber band ligation. Sixty-two patients were randomly assigned to the local anesthetic injection group and 39 to the control group. Overall good to excellent results occurred in 89 percent of patients, and there was no difference between the two groups. Postligation pain occurred in 26 and 20 percent of patients in the treatment and control groups, respectively (P > 0.05). Postligation tenesmus occurred in 32 and 41 percent of patients in the treatment and control groups, respectively (P > 0.05). No patients suffered from septic complications or bleeding that required transfusion. Triple rubber band ligation in a single session is a safe, economical, and effective way of treating symptomatic hemorrhoids. Postligation pain and tenesmus occurred in 24 and 37 percent, respectively. Discomfort was usually tolerable. Local anesthetic injection to the banded hemorrhoidal tissue did not help to reduce postligation discomfort.
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.
Honest Importance Sampling with Multiple Markov Chains.
Tan, Aixin; Doss, Hani; Hobert, James P
2015-01-01
Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π1, is used to estimate an expectation with respect to another, π. The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asymptotic variance in the CLT, which makes for routine computation of standard errors. Importance sampling can also be used in the Markov chain Monte Carlo (MCMC) context. Indeed, if the random sample from π1 is replaced by a Harris ergodic Markov chain with invariant density π1, then the resulting estimator remains strongly consistent. There is a price to be paid however, as the computation of standard errors becomes more complicated. First, the two simple moment conditions that guarantee a CLT in the iid case are not enough in the MCMC context. Second, even when a CLT does hold, the asymptotic variance has a complex form and is difficult to estimate consistently. In this paper, we explain how to use regenerative simulation to overcome these problems. Actually, we consider a more general set up, where we assume that Markov chain samples from several probability densities, π1, …, πk , are available. We construct multiple-chain importance sampling estimators for which we obtain a CLT based on regeneration. We show that if the Markov chains converge to their respective target distributions at a geometric rate, then under moment conditions similar to those required in the iid case, the MCMC-based importance sampling estimator obeys a CLT. Furthermore, because the CLT is based on a regenerative process, there is a simple consistent estimator of the asymptotic variance. We illustrate the method with two applications in Bayesian sensitivity analysis. The first concerns one-way random effects models under different priors. The second involves Bayesian variable selection in
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.
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.
Inelastic collapse and near-wall localization of randomly accelerated particles.
Belan, S; Chernykh, A; Lebedev, V; Falkovich, G
2016-05-01
Inelastic collapse of stochastic trajectories of a randomly accelerated particle moving in half-space z>0 has been discovered by McKean [J. Math. Kyoto Univ. 2, 227 (1963)] and then independently rediscovered by Cornell et al. [Phys. Rev. Lett. 81, 1142 (1998)PRLTAO0031-900710.1103/PhysRevLett.81.1142]. The essence of this phenomenon is that the particle arrives at the wall at z=0 with zero velocity after an infinite number of inelastic collisions if the restitution coefficient β of particle velocity is smaller than the critical value β_{c}=exp(-π/sqrt[3]). We demonstrate that inelastic collapse takes place also in a wide class of models with spatially inhomogeneous random forcing and, what is more, that the critical value β_{c} is universal. That class includes an important case of inertial particles in wall-bounded random flows. To establish how inelastic collapse influences the particle distribution, we derive the exact equilibrium probability density function ρ(z,v) for the particle position and velocity. The equilibrium distribution exists only at β<β_{c} and indicates that inelastic collapse does not necessarily imply near-wall localization.
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.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
NASA Astrophysics Data System (ADS)
Sheehan, D. P.; Wright, J. H.
2006-10-01
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
Sheehan, D. P.; Wright, J. H.
2006-10-16
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
NASA Astrophysics Data System (ADS)
Schmessane, Andrea; Laboratory of matter out equilibrium Team
2012-11-01
Wave localization explains how a perturbation is trapped by the randomness present in a propagation medium. As it propagates, the localized wave amplitude decreases strongly by multiple internal reflections with randomly positioned scatterers, effectively trapping the perturbation inside the random region. The characteristic length where a localized wave is propagated before being extinguish by randomness is called localization length. We carried experiments in a quasi-onedimensional channel with random bottom in a shallow water regime for surface gravity water waves, using a Perfilometry Fourier Transform method, which enables us to obtain global surface measurements. We discuss keys aspects of the control of variables, the experimental setup and the implementation of the measurement method. Thus, we can control, measure and evaluate fundamental variables present in the localization phenomenon such as the type of randomness, scattering intensity and sample length, which allows us to characterize wave localization. We use the scattering matrix method to compare the experimental measurements with theoretical and numerical predictions, using the Lyapunov exponent of the scattering matrix, and discuss their agreement. Conicyt
Kumar, Vinod; Tryposkiadis, Konstantinos; Gupta, Janesh Kumar
2016-02-01
To evaluate the efficacy of a hysteroscopic local anesthetic intrauterine cornual block (ICOB) on pain experienced during office endometrial ablation (EA) in addition to a traditional direct local anesthetic cervical block (DCB). Prospective, randomized, double-blind, placebo-controlled trial. University teaching hospital. Women with heavy menstrual bleeding scheduled for an office endometrial ablation. Before office EA, DCB plus hysteroscopic ICOB just medial to each tubal ostium using local anesthetic mixture made up of 1 mL 3% mepivacaine plus 1 mL 0.5% bupivacaine versus control group receiving DBC plus ICOB with 2 mL of placebo (saline). pain reported during procedure via visual analogue scale (VAS) from 0 to 10; secondary outcomes: postoperative pain, rescue analgesic requirement, and duration of hospital stay. Most characteristics were similar across groups. The mean VAS score during the procedure was statistically significantly lower by 1.44 (95% confidence interval, -2.65 to -0.21) in the active group compared with the placebo group. There were no statistically significant differences between the two groups in the postprocedural mean VAS scores, rescue analgesic requirement, or duration of hospital stay. Used in addition to DCB, ICOB reduces the pain experienced during office EA compared with DCB alone. NCT01808898. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.
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.
Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening
Kamimura, HAS; Wang, S; Wu, S-Y; Karakatsani, ME; Acosta, C; Carneiro, AAO; Konofagou, EE
2015-01-01
Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n=15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5-1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were 9.38±5.71 mm3, 8.91±3.91 mm3 and 35.47 ± 5.10 mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were 55.40±28.43 V.s, 63.87±29.97 V.s and 356.52±257.15 V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue. PMID:26394091
Tracking Human Pose Using Max-Margin Markov Models.
Zhao, Lin; Gao, Xinbo; Tao, Dacheng; Li, Xuelong
2015-12-01
We present a new method for tracking human pose by employing max-margin Markov models. Representing a human body by part-based models, such as pictorial structure, the problem of pose tracking can be modeled by a discrete Markov random field. Considering max-margin Markov networks provide an efficient way to deal with both structured data and strong generalization guarantees, it is thus natural to learn the model parameters using the max-margin technique. Since tracking human pose needs to couple limbs in adjacent frames, the model will introduce loops and will be intractable for learning and inference. Previous work has resorted to pose estimation methods, which discard temporal information by parsing frames individually. Alternatively, approximate inference strategies have been used, which can overfit to statistics of a particular data set. Thus, the performance and generalization of these methods are limited. In this paper, we approximate the full model by introducing an ensemble of two tree-structured sub-models, Markov networks for spatial parsing and Markov chains for temporal parsing. Both models can be trained jointly using the max-margin technique, and an iterative parsing process is proposed to achieve the ensemble inference. We apply our model on three challengeable data sets, which contains highly varied and articulated poses. Comprehensive experimental results demonstrate the superior performance of our method over the state-of-the-art approaches.
Assessing significance in a Markov chain without mixing.
Chikina, Maria; Frieze, Alan; Pegden, Wesley
2017-03-14
We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a [Formula: see text] value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a [Formula: see text] outlier compared with the sampled ranks (its rank is in the bottom [Formula: see text] of sampled ranks), then this observation should correspond to a [Formula: see text] value of [Formula: see text] This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an [Formula: see text]-outlier on the walk is significant at [Formula: see text] under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at [Formula: see text] is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting.
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.
Simulation of Anderson localization in a random fiber using a fast Fresnel diffraction algorithm
NASA Astrophysics Data System (ADS)
Davis, Jeffrey A.; Cottrell, Don M.
2016-06-01
Anderson localization has been previously demonstrated both theoretically and experimentally for transmission of a Gaussian beam through long distances in an optical fiber consisting of a random array of smaller fibers, each having either a higher or lower refractive index. However, the computational times were extremely long. We show how to simulate these results using a fast Fresnel diffraction algorithm. In each iteration of this approach, the light passes through a phase mask, undergoes Fresnel diffraction over a small distance, and then passes through the same phase mask. We also show results where we use a binary amplitude mask at the input that selectively illuminates either the higher or the lower index fibers. Additionally, we examine imaging of various sized objects through these fibers. In all cases, our results are consistent with other computational methods and experimental results, but with a much reduced computational time.
Many-body localization in a long range XXZ model with random-field
NASA Astrophysics Data System (ADS)
Li, Bo
2016-12-01
Many-body localization (MBL) in a long range interaction XXZ model with random field are investigated. Using the exact diagonal method, the MBL phase diagram with different tuning parameters and interaction range is obtained. It is found that the phase diagram of finite size results supplies strong evidence to confirm that the threshold interaction exponent α = 2. The tuning parameter Δ can efficiently change the MBL edge in high energy density stats, thus the system can be controlled to transfer from thermal phase to MBL phase by changing Δ. The energy level statistics data are consistent with result of the MBL phase diagram. However energy level statistics data cannot detect the thermal phase correctly in extreme long range case.
NASA Astrophysics Data System (ADS)
Gui, Ming; Huang, Ming-Qiu; Liang, Lin-Mei
2016-10-01
In practical continuous-variable quantum key distribution (CVQKD) systems, due to environmental disturbance or some intrinsic imperfections of devices, inevitably the local oscillator (LO) employed in a coherent detection always fluctuates arbitrarily over time, which compromises the security and performance of practical CVQKD systems. In this paper, we investigate the performance of practical CVQKD systems with LO fluctuating randomly. By revising the measurement result of balanced homodyne detection and embedding fluctuation parameters into security analysis, we find that in addition to the average LO intensity, the fluctuation variance also severely affects the secret key rate. No secret key can be obtained if fluctuation variance is relatively large. This indicates that in a practical CVQKD, LO intensity should be well monitored and stabilized. Our research can be directly applied to improve the robustness of a practical CVQKD system as well as be used to optimize CVQKD protocols.
The many-body localized phase of the quantum random energy model
NASA Astrophysics Data System (ADS)
Baldwin, C. L.; Laumann, C. R.; Pal, A.; Scardicchio, A.
2016-01-01
The random energy model (REM) provides a solvable mean-field description of the equilibrium spin-glass transition. Its quantum sibling (the QREM), obtained by adding a transverse field to the REM, has similar properties and shows a spin-glass phase for sufficiently small transverse field and temperature. In a recent work, some of us have shown that the QREM further exhibits a many-body localization-delocalization (MBLD) transition when viewed as a closed quantum system, evolving according to the quantum dynamics. This phase encloses the familiar equilibrium spin-glass phase. In this paper, we study in detail the MBLD transition within the forward-scattering approximation and replica techniques. The predictions for the transition line are in good agreement with the exact diagonalization numerics. We also observe that the structure of the eigenstates at the MBLD critical point changes continuously with the energy density, raising the possibility of a family of critical theories for the MBLD transition.
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.
Localization properties of random-mass Dirac fermions from real-space renormalization group.
Mkhitaryan, V V; Raikh, M E
2011-06-24
Localization properties of random-mass Dirac fermions for a realization of mass disorder, commonly referred to as the Cho-Fisher model, are studied on the D-class chiral network. We show that a simple renormalization group (RG) description captures accurately a rich phase diagram: thermal metal and two insulators with quantized σ(xy), as well as transitions (including critical exponents) between them. Our main finding is that, even with small transmission of nodes, the RG block exhibits a sizable portion of perfect resonances. Delocalization occurs by proliferation of these resonances to larger scales. Evolution of the thermal conductance distribution towards a metallic fixed point is synchronized with evolution of signs of transmission coefficients, so that delocalization is accompanied with sign percolation.
Many-body localization in a quantum simulator with programmable random disorder
NASA Astrophysics Data System (ADS)
Smith, J.; Lee, A.; Richerme, P.; Neyenhuis, B.; Hess, P. W.; Hauke, P.; Heyl, M.; Huse, D. A.; Monroe, C.
2016-10-01
When a system thermalizes it loses all memory of its initial conditions. Even within a closed quantum system, subsystems usually thermalize using the rest of the system as a heat bath. Exceptions to quantum thermalization have been observed, but typically require inherent symmetries or noninteracting particles in the presence of static disorder. However, for strong interactions and high excitation energy there are cases, known as many-body localization (MBL), where disordered quantum systems can fail to thermalize. We experimentally generate MBL states by applying an Ising Hamiltonian with long-range interactions and programmable random disorder to ten spins initialized far from equilibrium. Using experimental and numerical methods we observe the essential signatures of MBL: initial-state memory retention, Poissonian distributed energy level spacings, and evidence of long-time entanglement growth. Our platform can be scaled to more spins, where a detailed modelling of MBL becomes impossible.
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
NASA Astrophysics Data System (ADS)
Wang, Jin; Plotkin, Steven S.; Wolynes, Peter G.
1997-03-01
We study the time scale for diffusion on a correlated energy landscape using models based on the generalized random energy model (GREM) studied earlier in the context of spin glasses (Derrida B. and Gardner E., J. Phys. C 19 (1986) 2253) with kinetically local connections. The escape barrier and mean escape time are significantly reduced from the uncorrelated landscape (REM) values. Results for the mean escape time from a kinetic trap are obtained for two models approximating random heteropolymers in different regimes, with linear and bi-linear approximations to the configurational entropy versus similarity q with a given state. In both cases, a correlated landscape results in a shorter escape time from a meta-stable state than in the uncorrelated model (Bryngelson J.D. and Wolynes P.G., J. Phys. Chem 93 (1989) 6902). Results are compared to simulations of the diffusion constant for 27-mers. In general there is a second transition temperature above the thermodynamic glass temperature, at and above which kinetics becomes non-activated. In the special case of an entropy linear in q, there is no escape barrier for a model preserving ultrametricity. However, in real heteropolymers a barrier can result from the breaking of ultrametricity, as seen in our non-ultrametric model. The distribution of escape times for a model preserving microscopic ultrametricity is also obtained, and found to reduce to the uncorrelated landscape in well-defined limits.
Local Treatment of Unresectable Colorectal Liver Metastases: Results of a Randomized Phase II Trial
Van Coevorden, Frits; Punt, Cornelis J. A.; Pierie, Jean-Pierre E. N.; Borel-Rinkes, Inne; Ledermann, Jonathan A.; Poston, Graeme; Bechstein, Wolf; Lentz, Marie-Ange; Mauer, Murielle; Folprecht, Gunnar; Van Cutsem, Eric; Ducreux, Michel; Nordlinger, Bernard
2017-01-01
Background: Tumor ablation is often employed for unresectable colorectal liver metastases. However, no survival benefit has ever been demonstrated in prospective randomized studies. Here, we investigate the long-term benefits of such an aggressive approach. Methods: In this randomized phase II trial, 119 patients with unresectable colorectal liver metastases (n < 10 and no extrahepatic disease) received systemic treatment alone or systemic treatment plus aggressive local treatment by radiofrequency ablation ± resection. Previously, we reported that the primary end point (30-month overall survival [OS] > 38%) was met. We now report on long-term OS results. All statistical tests were two-sided. The analyses were according to intention to treat. Results: At a median follow up of 9.7 years, 92 of 119 (77.3%) patients had died: 39 of 60 (65.0%) in the combined modality arm and 53 of 59 (89.8%) in the systemic treatment arm. Almost all patients died of progressive disease (35 patients in the combined modality arm, 49 patients in the systemic treatment arm). There was a statistically significant difference in OS in favor of the combined modality arm (hazard ratio [HR] = 0.58, 95% confidence interval [CI] = 0.38 to 0.88, P = .01). Three-, five-, and eight-year OS were 56.9% (95% CI = 43.3% to 68.5%), 43.1% (95% CI = 30.3% to 55.3%), 35.9% (95% CI = 23.8% to 48.2%), respectively, in the combined modality arm and 55.2% (95% CI = 41.6% to 66.9%), 30.3% (95% CI = 19.0% to 42.4%), 8.9% (95% CI = 3.3% to 18.1%), respectively, in the systemic treatment arm. Median OS was 45.6 months (95% CI = 30.3 to 67.8 months) in the combined modality arm vs 40.5 months (95% CI = 27.5 to 47.7 months) in the systemic treatment arm. Conclusions: This phase II trial is the first randomized study demonstrating that aggressive local treatment can prolong OS in patients with unresectable colorectal liver metastases. PMID:28376151
Adjuvant chemo- and hormonal therapy in locally advanced breast cancer: a randomized clinical study
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.
Phase Separation in Random Cluster Models I: Uniform Upper Bounds on Local Deviation
NASA Astrophysics Data System (ADS)
Hammond, Alan
2012-03-01
This is the first in a series of three papers that addresses the behaviour of the droplet that results, in the percolating phase, from conditioning the planar Fortuin-Kasteleyn random cluster model on the presence of an open dual circuit Γ0 encircling the origin and enclosing an area of at least (or exactly) n 2. (By the Fortuin-Kasteleyn representation, the model is a close relative of the droplet formed by conditioning the Potts model on an excess of spins of a given type.) We consider local deviation of the droplet boundary, measured in a radial sense by the maximum local roughness, MLR(Γ0), this being the maximum distance from a point in the circuit Γ0 to the boundary ∂ of the circuit's convex hull; and in a longitudinal sense by what we term maximum facet length, MLF(Γ0), namely, the length of the longest line segment of which the polygon ∂ is formed. The principal conclusion of the series of papers is the following uniform control on local deviation: that there are constants 0 < c < C < ∞ such that the conditional probability that the normalized quantity n -1/3(log n )-2/3MLR lies in the interval [ c, C] tends to 1 in the high n-limit; and that the same statement holds for n -2/3 (log n )-1/3 MLF. In this way, we confirm the anticipated n 1/3 scaling of maximum local roughness, and provide a sharp logarithmic power-law correction. This local deviation behaviour occurs by means of locally Gaussian effects constrained globally by curvature, and we believe that it arises in many radially defined stochastic interface models, including growth models belonging to the Kardar-Parisi-Zhang universality class. The present paper is devoted to proving the upper bounds in these assertions. In fact, we derive bounds valid in the moderate deviations' regime. The second paper (Hammond in Ann Probab, arXiv:1001.1528, 142(2):229-276, 2011) provides the lower bounds. Crucial to our approach are surgical techniques that renew the conditioned circuit on the scale at
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.
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…
Hooker, G D; Plewes, E A; Rajgopal, C; Taylor, B M
1999-02-01
The aim of this study was to determine if local injection of bupivacaine after hemorrhoidal banding causes a decrease in pain and in the incidence of associated symptoms. After hemorrhoidal banding, patients were randomly assigned to receive a local injection of bupivacaine with 1:200,000 epinephrine, an injection of normal saline, or no injection, just superior to each band. Pain was graded by the patient and by the study nurse within 30 minutes, and any associated symptoms were recorded. At intervals 6, 24, and 48 hours postbanding, the patient recorded pain, limitation of activities, and analgesic requirements. Associated symptoms while at home were recorded. Of 115 patients studied, 42 received bupivacaine injection, 42 received normal saline injection, and 31 received no injection. In patients receiving bupivacaine compared with no injection, within 30 minutes postbanding there was a significant reduction in pain graded by the patient (P = 0.000002) and by the nurse (P = 0.000005) and a significant reduction in incidence of nausea (P = 0.01) and shaking (P = 0.008). However, in the bupivacaine group compared with the other two groups, at the intervals of 6, 24, and 48 hours postbanding there was no sustained reduction in the severity of pain and no reduction in analgesic requirements or limitation of normal activities. In the week after banding, there was no difference between groups in symptoms of nausea, shaking, lightheadedness, urinary retention, or bleeding. Bupivacaine injection may be useful for reducing pain and associated symptoms long enough to tolerate a trip home from the outpatient department but does not show a sustained effect.
Qvamme, G; Axelsson, C K; Lanng, C; Mortensen, M; Wegeberg, B; Okholm, M; Arpi, M R; Szecsi, P B
2015-09-01
Seroma formation, the most prevalent postoperative complication after mastectomy, is an inflammatory process that is potentially preventable via local steroid administration. This study investigated the effect of local steroid administration on seroma formation. This was a double-blind randomized placebo-controlled intervention study of a single dose of 80 mg methylprednisolone versus saline on seroma formation after mastectomy. Patients were further classified according to the surgical axillary procedure: mastectomy with sentinel lymph node biopsy (M + SLNB) or mastectomy with level I-II axillary lymph node dissection (M + ALND). Treatments were administered into the wound cavity via the drain orifice following removal of the drain on the first day after surgery. The primary endpoint was seroma formation; secondary endpoints included the frequency of side-effects and complications. A total of 212 women scheduled for mastectomy for primary breast cancer were included. After M + SLNB, 32 (46 per cent) of 69 women developed a seroma in the methylprednisolone group, compared with 52 (78 per cent) of 67 in the saline group (P < 0.001). The mean cumulative seroma volume in the intention-to-treat population for the first 10 and 30 days was significantly lower in the methylprednisolone group (24 ml versus 127 ml in the saline group, and 177 versus 328 ml respectively) (P < 0.001). After M + ALND, similar proportions of patients developed a seroma in the methylprednisolone (35 of 37, 95 per cent) and saline (34 of 36, 94 per cent) groups, and methylprednisolone administration had no significant effect on seroma formation. No differences in infection rate were observed. Methylprednisolone administered into the wound cavity on the first day after M + SLNB exerted a highly significant preventive effect against seroma formation during the next 30 days. This effect was not seen in the M + ALND group. Future studies may clarify whether higher or repeated methylprednisolone doses
Zhang, Yu; Li, Yan; Shao, Hao; Zhong, Yaozhao; Zhang, Sai; Zhao, Zongxi
2012-06-01
Band structure and wave localization are investigated for sea surface water waves over large-scale sand wave topography. Sand wave height, sand wave width, water depth, and water width between adjacent sand waves have significant impact on band gaps. Random fluctuations of sand wave height, sand wave width, and water depth induce water wave localization. However, random water width produces a perfect transmission tunnel of water waves at a certain frequency so that localization does not occur no matter how large a disorder level is applied. Together with theoretical results, the field experimental observations in the Taiwan Bank suggest band gap and wave localization as the physical mechanism of sea surface water wave propagating over natural large-scale sand waves.
Markov counting models for correlated binary responses.
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. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Kurita, Rei
2015-12-01
We experimentally study the structural properties of a sediment of size distributed colloids. By determining each particle size using a size estimation algorithm, we are able to investigate the relationship between local environment and local ordering. Our results show that ordered environments of particles tend to generate where the local particle-size distribution is within 5%. In addition, we show that particles whose size is close to the average size have 12 coordinate neighbors, which matches the coordination number of the fcc and hcp crystals. On the other hand, bcc structures are observed around larger particles. Our results represent experiments to show a size dependence of the specific ordering in colloidal systems.
Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO.
Chunming Zhang; Yongchun Xie; Da Liu; Li Wang
2017-03-01
In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target signals is the main part of the navigation image, the traditional entropy-related criterions and Ostu-related criterions will bring inadequate segmentation, while the mere 2D Fisher criterion will causes over-segmentation, all the methods show their shortages in dealing with this kind of case. To guarantee a precise image segmentation, a revised 2D fuzzy Fisher is proposed in the paper to make a trade-off between positioning target regions and retaining target fuzzy boundaries. First, to reduce redundant computations in finding the threshold pair, a 2D fuzzy Fisher criterion-based integral image is established by way of simplifying the corresponding fuzzy domains. Then, to quicken the convergence, a random orthogonal component is added in its quasi-optimum particle to enhance its local searching capacity in each iteration. Experimental results show its competence of quick segmentation.
Directional Histogram Ratio at Random Probes: A Local Thresholding Criterion for Capillary Images
Lu, Na; Silva, Jharon; Gu, Yu; Gerber, Scott; Wu, Hulin; Gelbard, Harris; Dewhurst, Stephen; Miao, Hongyu
2013-01-01
With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods. PMID:23525856
Fawzy El-Sayed, Karim M; Dahaba, Moushira A; Aboul-Ela, Shadw; Darhous, Mona S
2012-08-01
Hyaluronic acid application has been proven to be beneficial in a number of medical disciplines. The aim of the current study was to clinically evaluate the effect of local application of hyaluronan gel in conjunction with periodontal surgery. Fourteen patients with chronic periodontitis having four interproximal intrabony defects (≥3 mm) with probing depth values >5 mm were included in this split-mouth study. Following initial nonsurgical periodontal therapy and re-evaluation, defects were randomly assigned to be treated with modified Widman flap (MWF) surgery in conjunction with either 0.8% hyaluronan gel (test) or placebo gel (control) application. Clinical attachment level (CAL), probing depth (PD), gingival recession (GR), plaque index (PI), and bleeding on probing (BOP) values were taken at baseline and 3 and 6 months. Differences between test and control sites were evaluated using a Wilcoxon signed-rank and a McNemar test. A Friedman and a Cochran test were used to test equal ranks over time. Statistically significant differences were noted for CAL and GR (P < 0.05) in favor of the test sites. No significant differences were found regarding PD, BOP, or PI values (P > 0.05). Hyaluronan gel application in conjunction with periodontal surgery appears to result in significant improvement of CAL and in a reduction in GR. Hyaluronan gel application appears to improve the clinical outcome of MWF surgery.
Invariant Markov processes on compact groups and correlation functions
NASA Astrophysics Data System (ADS)
Zimpel, Zbigniew
1990-04-01
Random processes governing the time evolution of probability distributions of many physical systems can be described by continuous homogeneous Markov processes taking values from compact groups. Assuming the transition probability function of the process to be invariant in the sense that Pt( x, B) = Pt( e, x-1B) with e being the neutral element of the group, the harmonic analysis (Weyl theory) is applied to study the properties of the Markov semi-group. The infinitesimal operator and generating functional are decomposed using the Levy-Khinchin formula. Under some auxiliary assumptions the components of this decomposition are interpreted as generators of a one parameter subgroup, Brownian motion and a jump process. The formalism is illustrated for several models of processes taking values from compact Lie groups. The properties of the correlation functions of time dependent random variables are investigated.
Semi-Markov Models for Degradation-Based Reliability
2010-01-01
standard analysis techniques for Markov processes can be employed (cf. Whitt (1984), Altiok (1985), Perros (1994), and Osogami and Harchol-Balter...We want to approximate X by a PH random variable, sayY, with c.d.f. Ĥ. Marie (1980), Altiok (1985), Johnson (1993), Perros (1994), and Osogami and...provides a minimal representation when matching only two moments. By considering the guidance provided by Marie (1980), Whitt (1984), Altiok (1985), Perros
Inferring Animal Densities from Tracking Data Using Markov Chains
Whitehead, Hal; Jonsen, Ian D.
2013-01-01
The distributions and relative densities of species are keys to ecology. Large amounts of tracking data are being collected on a wide variety of animal species using several methods, especially electronic tags that record location. These tracking data are effectively used for many purposes, but generally provide biased measures of distribution, because the starts of the tracks are not randomly distributed among the locations used by the animals. We introduce a simple Markov-chain method that produces unbiased measures of relative density from tracking data. The density estimates can be over a geographical grid, and/or relative to environmental measures. The method assumes that the tracked animals are a random subset of the population in respect to how they move through the habitat cells, and that the movements of the animals among the habitat cells form a time-homogenous Markov chain. We illustrate the method using simulated data as well as real data on the movements of sperm whales. The simulations illustrate the bias introduced when the initial tracking locations are not randomly distributed, as well as the lack of bias when the Markov method is used. We believe that this method will be important in giving unbiased estimates of density from the growing corpus of animal tracking data. PMID:23630574
Markov chain order estimation with conditional mutual information
NASA Astrophysics Data System (ADS)
Papapetrou, M.; Kugiumtzis, D.
2013-04-01
We introduce the Conditional Mutual Information (CMI) for the estimation of the Markov chain order. For a Markov chain of K symbols, we define CMI of order m, Ic(m), as the mutual information of two variables in the chain being m time steps apart, conditioning on the intermediate variables of the chain. We find approximate analytic significance limits based on the estimation bias of CMI and develop a randomization significance test of Ic(m), where the randomized symbol sequences are formed by random permutation of the components of the original symbol sequence. The significance test is applied for increasing m and the Markov chain order is estimated by the last order for which the null hypothesis is rejected. We present the appropriateness of CMI-testing on Monte Carlo simulations and compare it to the Akaike and Bayesian information criteria, the maximal fluctuation method (Peres-Shields estimator) and a likelihood ratio test for increasing orders using ϕ-divergence. The order criterion of CMI-testing turns out to be superior for orders larger than one, but its effectiveness for large orders depends on data availability. In view of the results from the simulations, we interpret the estimated orders by the CMI-testing and the other criteria on genes and intergenic regions of DNA chains.
Catton, Charles N; Lukka, Himu; Gu, Chu-Shu; Martin, Jarad M; Supiot, Stéphane; Chung, Peter W M; Bauman, Glenn S; Bahary, Jean-Paul; Ahmed, Shahida; Cheung, Patrick; Tai, Keen Hun; Wu, Jackson S; Parliament, Matthew B; Tsakiridis, Theodoros; Corbett, Tom B; Tang, Colin; Dayes, Ian S; Warde, Padraig; Craig, Tim K; Julian, Jim A; Levine, Mark N
2017-03-15
Purpose Men with localized prostate cancer often are treated with external radiotherapy (RT) over 8 to 9 weeks. Hypofractionated RT is given over a shorter time with larger doses per treatment than standard RT. We hypothesized that hypofractionation versus conventional fractionation is similar in efficacy without increased toxicity. Patients and Methods We conducted a multicenter randomized noninferiority trial in intermediate-risk prostate cancer (T1 to 2a, Gleason score ≤ 6, and prostate-specific antigen [PSA] 10.1 to 20 ng/mL; T2b to 2c, Gleason ≤ 6, and PSA ≤ 20 ng/mL; or T1 to 2, Gleason = 7, and PSA ≤ 20 ng/mL). Patients were allocated to conventional RT of 78 Gy in 39 fractions over 8 weeks or to hypofractionated RT of 60 Gy in 20 fractions over 4 weeks. Androgen deprivation was not permitted with therapy. The primary outcome was biochemical-clinical failure (BCF) defined by any of the following: PSA failure (nadir + 2), hormonal intervention, clinical local or distant failure, or death as a result of prostate cancer. The noninferiority margin was 7.5% (hazard ratio, < 1.32). Results Median follow-up was 6.0 years. One hundred nine of 608 patients in the hypofractionated arm versus 117 of 598 in the standard arm experienced BCF. Most of the events were PSA failures. The 5-year BCF disease-free survival was 85% in both arms (hazard ratio [short v standard], 0.96; 90% CI, 0.77 to 1.2). Ten deaths as a result of prostate cancer occurred in the short arm and 12 in the standard arm. No significant differences were detected between arms for grade ≥ 3 late genitourinary and GI toxicity. Conclusion The hypofractionated RT regimen used in this trial was not inferior to conventional RT and was not associated with increased late toxicity. Hypofractionated RT is more convenient for patients and should be considered for intermediate-risk prostate cancer.
Tumor segmentation on FDG-PET: usefulness of locally connected conditional random fields
NASA Astrophysics Data System (ADS)
Nishio, Mizuho; Kono, Atsushi K.; Koyama, Hisanobu; Nishii, Tatsuya; Sugimura, Kazuro
2015-03-01
This study aimed to develop software for tumor segmentation on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). To segment the tumor from the background, we used graph cut, whose segmentation energy was generally divided into two terms: the unary and pairwise terms. Locally connected conditional random fields (LCRF) was proposed for the pairwise term. In LCRF, a three-dimensional cubic window with length L was set for each voxel, and voxels within the window were considered for the pairwise term. To evaluate our method, 64 clinically suspected metastatic bone tumors were tested, which were revealed by FDG-PET. To obtain ground truth, the tumors were manually delineated via consensus of two board-certified radiologists. To compare the LCRF accuracy, other types of segmentation were also applied such as region-growing based on 35%, 40%, and 45% of the tumor maximum standardized uptake value (RG35, RG40, and RG45, respectively), SLIC superpixels (SS), and region-based active contour models (AC). To validate the tumor segmentation accuracy, a dice similarity coefficient (DSC) was calculated between manual segmentation and result of each technique. The DSC difference was tested using the Wilcoxon signed rank test. The mean DSCs of LCRF at L = 3, 5, 7, and 9 were 0.784, 0.801, 0.809, and 0.812, respectively. The mean DSCs of other techniques were RG35, 0.633; RG40, 0.675; RG45, 0.689; SS, 0.709; and AC, 0.758. The DSC differences between LCRF and other techniques were statistically significant (p <0.05). In conclusion, tumor segmentation was more reliably performed with LCRF relative to other techniques.
Mold, James W.; Fox, Chester; Wisniewski, Angela; Lipman, Paula Darby; Krauss, Margot R.; Harris, D. Robert; Aspy, Cheryl; Cohen, Rachel A.; Elward, Kurt; Frame, Paul; Yawn, Barbara P.; Solberg, Leif I.; Gonin, René
2014-01-01
PURPOSE Guideline implementation in primary care has proven difficult. Although external assistance through performance feedback, academic detailing, practice facilitation (PF), and learning collaboratives seems to help, the best combination of interventions has not been determined. METHODS In a cluster randomized trial, we compared the independent and combined effectiveness of PF and local learning collaboratives (LLCs), combined with performance feedback and academic detailing, with performance feedback and academic detailing alone on implementation of the National Heart, Lung and Blood Institute’s Asthma Guidelines. The study was conducted in 3 primary care practice-based research networks. Medical records of patients with asthma seen during pre- and postintervention periods were abstracted to determine adherence to 6 guideline recommendations. McNemar’s test and multivariate modeling were used to evaluate the impact of the interventions. RESULTS Across 43 practices, 1,016 patients met inclusion criteria. Overall, adherence to all 6 recommendations increased (P ≤.002). Examination of improvement by study arm in unadjusted analyses showed that practices in the control arm significantly improved adherence to 2 of 6 recommendations, whereas practices in the PF arm improved in 3, practices in the LLCs improved in 4, and practices in the PF + LLC arm improved in 5 of 6 recommendations. In multivariate modeling, PF practices significantly improved assessment of asthma severity (odds ratio [OR] = 2.5, 95% CI, 1.7–3.8) and assessment of asthma level of control (OR = 2.3, 95% CI, 1.5–3.5) compared with control practices. Practices assigned to LLCs did not improve significantly more than control practices for any recommendation. CONCLUSIONS Addition of PF to performance feedback and academic detailing was helpful to practices attempting to improve adherence to asthma guidelines. PMID:24821894
Markov Chain Ontology Analysis (MCOA)
2012-01-01
Background Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. Results In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. Conclusion A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches
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.
Karimzadeh, Afshin; Raeissadat, Seyed Ahmad; Erfani Fam, Saleh; Sedighipour, Leyla; Babaei-Ghazani, Arash
2017-03-01
Plantar fasciitis is the most common cause of heel pain. Local injection modalities are among treatment options in patients with resistant pain. The aim of the present study was to evaluate the effect of local autologous whole blood compared with corticosteroid local injection in treatment of plantar fasciitis. In this randomized controlled multicenter study, 36 patients with chronic plantar fasciitis were recruited. Patients were allocated randomly into three treatment groups: local autologous blood, local corticosteroid injection, and control groups receiving no injection. Patients were assessed with visual analog scale (VAS), pressure pain threshold (PPT), and plantar fasciitis pain/disability scale (PFPS) before treatment, as well as 4 and 12 weeks post therapy. Variables of pain and function improved significantly in both corticosteroid and autologous blood groups compared to control group. At 4 weeks following treatment, patients in corticosteroid group had significantly lower levels of pain than patients in autologous blood and control groups (higher PPT level, lower PFPS, and VAS). After 12 weeks of treatment, both corticosteroid and autologous blood groups had lower average levels of pain than control group. The corticosteroid group showed an early sharp and then more gradual improvement in pain scores, but autologous blood group had a steady gradual drop in pain. Autologous whole blood and corticosteroid local injection can both be considered as effective methods in the treatment of chronic plantar fasciitis. These treatments decrease pain and significantly improve function compared to no treatment.
A Second Law for Open Markov Processes
NASA Astrophysics Data System (ADS)
Pollard, Blake S.
2016-03-01
In this paper we define the notion of an open Markov process. An open Markov process is a generalization of an ordinary Markov process in which populations are allowed to flow in and out of the system at certain boundary states. We show that the rate of change of relative entropy in an open Markov process is less than or equal to the flow of relative entropy through its boundary states. This can be viewed as a generalization of the Second Law for open Markov processes. In the case of a Markov process whose equilibrium obeys detailed balance, this inequality puts an upper bound on the rate of change of the free energy for any non-equilibrium distribution.
Markov and semi-Markov processes as a failure rate
NASA Astrophysics Data System (ADS)
Grabski, Franciszek
2016-06-01
In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.
Markov and semi-Markov processes as a failure rate
Grabski, Franciszek
2016-06-08
In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.
A secure arithmetic coding based on Markov model
NASA Astrophysics Data System (ADS)
Duan, Lili; Liao, Xiaofeng; Xiang, Tao
2011-06-01
We propose a modification of the standard arithmetic coding that can be applied to multimedia coding standards at entropy coding stage. In particular, we introduce a randomized arithmetic coding scheme based on order-1 Markov model that achieves encryption by scrambling the symbols' order in the model and choosing the relevant order's probability randomly, which is done with higher compression efficiency and good security. Experimental results and security analyses indicate that the algorithm can not only resist to existing attacks based on arithmetic coding, but also be immune to other cryptanalysis.
Reliability characteristics in semi-Markov models
NASA Astrophysics Data System (ADS)
Grabski, Franciszek
2017-07-01
A semi-Markov (SM) process is defined by a renewal kernel and an initial distribution of states or another equivalent parameters. Those quantities contain full information about the process and they allow us to find many characteristics and parameters of the process. Constructing the semi-Markov reliability model means building the kernel of the process based on some assumptions. Many characteristics and parameters of the SM process have a natural interpretation in the semi-Markov reliability model.
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.
The generalization ability of online SVM classification based on Markov sampling.
Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang
2015-03-01
In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.
Impact of using a local protocol in preoperative testing: blind randomized clinical trial.
Santos, Mônica Loureiro; Iglesias, Antônio Carlos
2017-01-01
to evaluate the impact of the use of a local protocol of preoperative test requests in reducing the number of exams requested and in the occurrence of changes in surgical anesthetic management and perioperative complications. we conducted a randomized, blinded clinical trial at the Gaffrée and Guinle University Hospital with 405 patients candidates for elective surgery randomly divided into two groups, according to the practice of requesting preoperative exams: a group with non-selectively requested exams and a protocol group with exams requested according to the study protocol. Studied exams: complete blood count, coagulogram, glycemia, electrolytes, urea and creatinine, ECG and chest X-ray. Primary outcomes: changes in surgical anesthetic management caused by abnormal exams, reduction of the number of exams requested after the use of the protocol and perioperative complications. there was a significant difference (p<0.001) in the number of exams with altered results between the two groups (14.9% vs. 29.1%) and a reduction of 57.3% in the number of exams requested between the two groups (p<0.001), which was more pronounced in patients of lower age groups, ASA I, without associated diseases and submitted to smaller procedures. There was no significant difference in the frequency of conduct changes motivated by the results of exams or complications between the two groups. In the multivariate analysis, complete blood count and coagulogram were the only exams capable of modifying the anesthetic-surgical management. the proposed protocol was effective in eliminating a significant number of complementary exams without clinical indication, without an increase in perioperative morbidity and mortality. avaliar o impacto do uso de um protocolo local de solicitações de exames pré-operatórios na redução do número de exames solicitados e na ocorrência de alterações na conduta anestésico-cirúrgica e de complicações perioperatórias. ensaio clínico randomizado
NASA Astrophysics Data System (ADS)
Boddeti, Ashwin K.; Kumar, Randhir; Mujumdar, Sushil
2017-08-01
Anderson localization of light is an exotic mesoscopic phenomenon sustained in disordered systems through the self-interference of multiply scattered light. The localized modes are essentially eigenfunctions of the structural disorder, and define the resonances in the system. In this paper, we report on the computed figures-of-merit of Anderson cavities in two-dimensional membrane based structures, in which the disorder is written on a periodic-on-average template. We propose a disorder parameter that better reflects the randomization of the lattice points as compared to the conventionally used percentage disorder strength. Our results investigate the viability of such cavities in applications such as random lasing and cavity quantum electrodynamics.
Stochastic algorithms for Markov models estimation with intermittent missing data.
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.
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
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.
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.
NASA Astrophysics Data System (ADS)
He, Daisi; Bao, Weiying; Long, Li; Zhang, Peng; Jiang, Maohua; Zhang, Dingke
2017-06-01
Random multimode lasers are achieved in 4(dicyanomethy-lene)-2- tert-butyl-6(1,1,7,7-tetramethyljulolidyl-9-enyl)4H-pyran (DCJTB) doped Polystrene (PS) thin films by introducing silver nanoparticles (Ag NPs) as scatterers. Ag NPs were prepared by polymer protection method. By optimizing the concentration of reactant of AgNO3 and the ratio of Ag NPs to DCJTB, the devices emit a resonance multimode peak at a center wavelength of 625 nm and the threshold excitation intensity is as low as 0.0031 mJ/pulse. It can be seen that the microscopic random resonance cavities can be formed by multiple scattering of Ag NPs which supply the localized surface-plasmon resonance (LSPR) coupling with the lasing emission to enhance the lasing efficiency. Our results demonstrate that Ag NPs are promising candidate as alternative sources of coherent light emission to realize low-threshold organic random lasers.
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.
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.
2010-01-01
Background Local corticosteroid injection for carpal tunnel syndrome (CTS) provides greater clinical improvement in symptoms one month after injection compared to placebo but significant symptom relief beyond one month has not been demonstrated and the relapse of symptoms is possible. Neuroprotection and myelin repair actions of the progesterone was demonstrated in vivo and in vitro study. We report the design of a randomized controlled trial for the local injection of cortisone versus progesterone in "mild" idiopathic CTS. Methods Sixty women with age between 18 and 60 years affected by "mild" idiopathic CTS, diagnosed on the basis of clinical and electrodiagnostic tests, will be enrolled in one centre. The clinical, electrophysiological and ultasonographic findings of the patients will be evaluate at baseline, 1, 6 and 12 months after injection. The major outcome of this study is to determine whether locally-injected progesterone may be more beneficial than cortisone in CTS at clinical levels, tested with symptoms severity self-administered Boston Questionnaire and with visual analogue pain scale. Secondary outcome measures are: duration of experimental therapy; improvement of electrodiagnostic and ultrasonographic anomalies at various follow-up; comparison of the beneficial and harmful effects of the cortisone versus progesterone. Conclusion We have designed a randomized controlled study to show the clinical effectiveness of local progesterone in the most frequent human focal peripheral mononeuropathy and to demonstrate the neuroprotective effects of the progesterone at the level of the peripheral nervous system in humans. PMID:20420674
Thoppe-Dhamodhara, Yogesh-Kumar; Asokan, Sharath; John, Baby-John; Pollachi-Ramakrishnan, GeethaPriya; Ramachandran, Punithavathy; Vilvanathan, Praburajan
2015-10-01
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. 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. 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. 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. Injections with CCLAD produced lesser pain ratings and disruptive behaviour than cartridge syringe in children irrespective of order of visit. Behaviour, cartridge syringe, CCLAD, local anesthesia.
Zhang, Yun; Xiong, Zhuyou; Li, Guangzao; Cui, Lei
2014-01-01
Background Although the systemic administration of deferoxamine (DFO) is protective in experimental models of normal ischemic flap and diabetic wound, its effect on diabetic flap ischemia using a local injection remains unknown. Objective To explore the feasibility of local injection of DFO to improve the survival of ischemic random skin flaps in streptozotocin (STZ)-induced diabetic mice. Methods Ischemic random skin flaps were made in 125 mice. Animals were divided into the DFO-treated (n = 20), PBS-treated (n = 16) and untreated (n = 16) groups. Surviving area, vessel density, and expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α) were evaluated on the seventh day after local injection. Results The viability of DFO-treated flap was significantly enhanced, with increased regional blood perfusion and capillary density compared with those in the two control groups. Fluorescence-activated cell sorting (FACS) analysis demonstrated a marked increase in systemic Flk-1+/CD11b− endothelial progenitor cells (EPCs) in DFO-treated mice. Furthermore, the expression of VEGF and HIF-1α was increased not only in diabetic flap tissue, but also in dermal fibroblasts cultured under hyperglycemic and hypoxic conditions. Conclusions Local injection of DFO could exert preventive effects against skin flap necrosis in STZ-induced diabetic mice by elevating the expression of HIF-1α and VEGF, increased EPC mobilization, which all contributed to promote ischemic diabetic flap survival. PMID:24963878
NASA Astrophysics Data System (ADS)
Lee, Kean Loon; Grémaud, Benoît; Miniatura, Christian
2014-10-01
As recently discovered [T. Karpiuk et al., Phys. Rev. Lett. 109, 190601 (2012), 10.1103/PhysRevLett.109.190601], Anderson localization in a bulk disordered system triggers the emergence of a coherent forward scattering (CFS) peak in momentum space, which twins the well-known coherent backscattering (CBS) peak observed in weak localization experiments. Going beyond the perturbative regime, we address here the long-time dynamics of the CFS peak in a one-dimensional random system and we relate this novel interference effect to the statistical properties of the eigenfunctions and eigenspectrum of the corresponding random Hamiltonian. Our numerical results show that the dynamics of the CFS peak is governed by the logarithmic level repulsion between localized states, with a time scale that is, with good accuracy, twice the Heisenberg time. This is in perfect agreement with recent findings based on the nonlinear sigma model. In the stationary regime, the width of the CFS peak in momentum space is inversely proportional to the localization length, reflecting the exponential decay of the eigenfunctions in real space, while its height is exactly twice the background, reflecting the Poisson statistical properties of the eigenfunctions. It would be interesting to extend our results to higher dimensional systems and other symmetry classes.
On Markov Earth Mover’s Distance
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
Multi-Observation Continuous Density Hidden Markov Models for Anomaly Detection in Full Motion Video
2012-06-01
variable of summation • λ = (A, B, π) A Hidden Markov Model • ai j Probability of being in state j at time t + 1 given the process was in i at t • bi PDF for...Angular Deviation . A random variable , the difference in heading (in degrees) from the overall direction of movement over the sequence • S : Speed. A... random variable , the speed of the agent at a given time step xiii MULTI-OBSERVATION CONTINUOUS DENSITY HIDDEN MARKOV MODELS FOR ANOMALY DETECTION IN
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.
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…
Semi-Markov Unreliability-Range Evaluator
NASA Technical Reports Server (NTRS)
Butler, Ricky W.
1988-01-01
Reconfigurable, fault-tolerant systems modeled. Semi-Markov unreliability-range evaluator (SURE) computer program is software tool for analysis of reliability of reconfigurable, fault-tolerant systems. Based on new method for computing death-state probabilities of semi-Markov model. Computes accurate upper and lower bounds on probability of failure of system. Written in PASCAL.
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.
An introduction to hidden Markov models.
Schuster-Böckler, Benjamin; Bateman, Alex
2007-06-01
This unit introduces the concept of hidden Markov models in computational biology. It describes them using simple biological examples, requiring as little mathematical knowledge as possible. The unit also presents a brief history of hidden Markov models and an overview of their current applications before concluding with a discussion of their limitations.
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…
NASA Astrophysics Data System (ADS)
Finkemeier, Frank; von Niessen, Wolfgang
1998-08-01
Three different models for a-Si are studied with respect to the vibrational density of states (VDOS) and phonon localization. The degree of disorder is varied for each model in a large range. For all models structural properties are investigated in connection with the VDOS. Phonon localization is examined via scaling approaches and mobility edges are quantified. Two of the models are continuous random networks (CRN's): the vacancy model and the Wooten-Winer-Weaire (WWW) model both relaxed with the Keating potential. The vacancy model causes the appearance of an artificial high-energy shoulder of the TO peak, which leads to wrong predictions on localization too. This shortcoming of the vacancy model is caused by a second maximum of the bond angle distribution at large angles. The WWW model is here the superior CRN model for a-Si. It allows a good reproduction of the experimental VDOS and possesses only about 1% localized states at the upper edge of the VDOS. In the third model, the WWW model relaxed with the Stillinger-Weber potential, dangling bonds and floating bonds are introduced. Its only shortcoming is an artificial maximum in the radial distribution function below the second diffraction peak. Due to defects extra modes at low energies are found that are highly dependent on the quality of the relaxation. The VDOS is well reproduced. About 2% of the modes at high energies are localized. The modes at lowest energies look localized, when systems below 2000 atoms are studied. It turns out that large systems up to 8000 atoms and many independent realizations are required to interpret the phonon properties correctly. The amount of localization is found to be independent of the degree of disorder present in the model, but an increase in the number of localized states with decreasing density is observed. The present investigation permits statements about the suitability of models for amorphous solids, relaxation procedures, standard potentials, and procedures to
Duarte, Carlos; Bastidas, Faustino; de los Reyes, Amelia; Martínez, María Cristina; Hurtado, Gloria; Gómez, María Constanza; Sánchez, Ricardo; Manrique, Jorge
2016-04-01
The aim of this study was to compare the radioguided occult lesion localization (ROLL) technique with the wire-guided lesion localization (WGLL) technique to assess their efficacy and accuracy in the localization of nonpalpable breast lesions in patients at a unique reference medical center. These patients' reports were negative for malignancy but included highly suspicious imaging findings. A controlled clinical trial was designed to compare the WGLL and ROLL techniques in women presenting with breast lesions diagnosed by mammography or ultrasonography at the Instituto Nacional de Cancerología in Bogotá, Colombia, from March 2006 to June 2011. This study examined 129 patients; 64 (49.6%) patients were treated with ROLL, and 65 (51.4%) were treated with WGLL. The ROLL technique achieved better median lesion centricity (ROLL = 11.7 and WGLL = 15.4; P = .038). No significant differences were found regarding demographic variables, operative specimen characteristics, the need to extend margins, operative complications, the degree of difficulty, or patient or surgeon satisfaction. The ROLL technique is as effective as WGLL for the localization of nonpalpable breast lesions. In our study, ROLL achieved better lesion centricity. Therefore, we propose that this technique could be used as a standard procedure in the detection of nonpalpable breast lesions at experienced centers. Copyright © 2016 Elsevier Inc. All rights reserved.
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
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.
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.
Monte Carlo non-local means: random sampling for large-scale image filtering.
Chan, Stanley H; Zickler, Todd; Lu, Yue M
2014-08-01
We propose a randomized version of the nonlocal means (NLM) algorithm for large-scale image filtering. The new algorithm, called Monte Carlo nonlocal means (MCNLM), speeds up the classical NLM by computing a small subset of image patch distances, which are randomly selected according to a designed sampling pattern. We make two contributions. First, we analyze the performance of the MCNLM algorithm and show that, for large images or large external image databases, the random outcomes of MCNLM are tightly concentrated around the deterministic full NLM result. In particular, our error probability bounds show that, at any given sampling ratio, the probability for MCNLM to have a large deviation from the original NLM solution decays exponentially as the size of the image or database grows. Second, we derive explicit formulas for optimal sampling patterns that minimize the error probability bound by exploiting partial knowledge of the pairwise similarity weights. Numerical experiments show that MCNLM is competitive with other state-of-the-art fast NLM algorithms for single-image denoising. When applied to denoising images using an external database containing ten billion patches, MCNLM returns a randomized solution that is within 0.2 dB of the full NLM solution while reducing the runtime by three orders of magnitude.
A self-consistent theory of localization in nonlinear random media
NASA Astrophysics Data System (ADS)
Cherroret, Nicolas
2017-01-01
The self-consistent theory of localization is generalized to account for a weak quadratic nonlinear potential in the wave equation. For spreading wave packets, the theory predicts the destruction of Anderson localization by the nonlinearity and its replacement by algebraic subdiffusion, while classical diffusion remains unaffected. In 3D, this leads to the emergence of a subdiffusion-diffusion transition in place of the Anderson transition. The accuracy and the limitations of the theory are discussed.
Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network.
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.
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 .
Ballesteros-Peña, Sendoa; Fernández-Aedo, Irrintzi; Vallejo-De la Hoz, Gorka
2017-06-01
To compare the efficacy of an ethyl chloride aerosol spray to a placebo spray applied in the emergency department to the skin to reduce pain from arterial puncture for blood gas analysis. Single-blind, randomized placebo-controlled trial in an emergency department of Hospital de Basurto in Bilbao, Spain. We included 126 patients for whom arterial blood gas analysis had been ordered. They were randomly assigned to receive application of the experimental ethyl chloride spray (n=66) or a placebo aerosol spray of a solution of alcohol in water (n=60). The assigned spray was applied just before arterial puncture. The main outcome variable was pain intensity reported on an 11-point numeric rating scale. The median (interquartile range) pain level was 2 (1-5) in the experimental arm and 2 (1-4.5) in the placebo arm (P=.72). Topical application of an ethyl chloride spray did not reduce pain caused by arterial puncture.
NASA Astrophysics Data System (ADS)
Yang, Yongchao; Sun, Peng; Nagarajaiah, Satish; Bachilo, Sergei M.; Weisman, R. Bruce
2017-07-01
Structural damage is typically a local phenomenon that initiates and propagates within a limited area. As such high spatial resolution measurement and monitoring is often needed for accurate damage detection. This requires either significantly increased costs from denser sensor deployment in the case of global simultaneous/parallel measurements, or increased measurement time and labor in the case of global sequential measurements. This study explores the feasibility of an alternative approach to this problem: a computational solution in which a limited set of randomly positioned, low-resolution global strain measurements are used to reconstruct the full-field, high-spatial-resolution, two-dimensional (2D) strain field and rapidly detect local damage. The proposed approach exploits the implicit low-rank and sparse data structure of the 2D strain field: it is highly correlated without many edges and hence has a low-rank structure, unless damage-manifesting itself as sparse local irregularity-is present and alters such a low-rank structure slightly. Therefore, reconstruction of the full-field, high-spatial-resolution strain field from a limited set of randomly positioned low-resolution global measurements is modeled as a low-rank matrix completion framework and damage detection as a sparse decomposition formulation, enabled by emerging convex optimization techniques. Numerical simulations on a plate structure are conducted for validation. The results are discussed and a practical iterative global/local procedure is recommended. This new computational approach should enable the efficient detection of local damage using limited sets of strain measurements.
Localization of a two-component Bose-Einstein condensate in a one-dimensional random potential
NASA Astrophysics Data System (ADS)
Xi, Kui-Tian; Li, Jinbin; Shi, Da-Ning
2015-02-01
We consider a weakly interacting two-component Bose-Einstein condensate (BEC) in a one-dimensional random speckle potential. The problem is studied with solutions of Gross-Pitaevskii (GP) equations by means of numerical method in Crank-Nicolson scheme. Properties of various cases owing to the competition of disorder and repulsive interactions of a cigar-shaped two-component BEC are discussed in detail. It is shown that in the central region, phase separation of a two-component BEC is not only affected by the intra- and inter-component interactions, but also influenced by the strength of the random speckle potential. Due to the strong disorder of the potential, the criterion of phase separation which is independent of the trap strength in an ordered potential, such as a harmonic potential, is no longer available. The influence of different random numbers generated by distinct processes on localization of BEC in the random potential is also investigated, as well as the configurations of the density profiles in the tail regions.
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
Decomposition of conditional probability for high-order symbolic Markov chains
NASA Astrophysics Data System (ADS)
Melnik, S. S.; Usatenko, O. V.
2017-07-01
The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.
Hidden Markov Models: The Best Models for Forager Movements?
Joo, Rocio; Bertrand, Sophie; Tam, Jorge; Fablet, Ronan
2013-01-01
One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (∼1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines) for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%), significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance. PMID:24058400
Markov chain decision model for urinary incontinence procedures.
Kumar, Sameer; Ghildayal, Nidhi; Ghildayal, Neha
2017-03-13
Purpose Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective. Design/methodology/approach This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis. Findings Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure. Originality/value This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.
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
Artan, Yusuf; Haider, Masoom A; Langer, Deanna L; van der Kwast, Theodorus H; Evans, Andrew J; Yang, Yongyi; Wernick, Miles N; Trachtenberg, John; Yetik, Imam Samil
2010-09-01
Prostate cancer is a leading cause of cancer death for men in the United States. Fortunately, the survival rate for early diagnosed patients is relatively high. Therefore, in vivo imaging plays an important role for the detection and treatment of the disease. Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy, and surgery as well as to monitor disease progression. Magnetic resonance imaging (MRI) performed with an endorectal coil provides higher prostate cancer localization accuracy, when compared to transrectal ultrasound (TRUS). However, in general, a single type of MRI is not sufficient for reliable tumor localization. As an alternative, multispectral MRI, i.e., the use of multiple MRI-derived datasets, has emerged as a promising noninvasive imaging technique for the localization of prostate cancer; however almost all studies are with human readers. There is a significant inter and intraobserver variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems, this study presents an automated localization method using cost-sensitive support vector machines (SVMs) and shows that this method results in improved localization accuracy than classical SVM. Additionally, we develop a new segmentation method by combining conditional random fields (CRF) with a cost-sensitive framework and show that our method further improves cost-sensitive SVM results by incorporating spatial information. We test SVM, cost-sensitive SVM, and the proposed cost-sensitive CRF on multispectral MRI datasets acquired from 21 biopsy-confirmed cancer patients. Our results show that multispectral MRI helps to increase the accuracy of prostate cancer localization when compared to single MR images; and that using advanced methods such as cost-sensitive SVM as well as the proposed cost-sensitive CRF can boost the performance significantly when compared to SVM.
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.
Topological Charge Evolution in the Markov-Chain of QCD
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.
Persson, Lars Åke; Nga, Nguyen T; Målqvist, Mats; Thi Phuong Hoa, Dinh; Eriksson, Leif; Wallin, Lars; Selling, Katarina; Huy, Tran Q; Duc, Duong M; Tiep, Tran V; Thi Thu Thuy, Vu; Ewald, Uwe
2013-01-01
Facilitation of local women's groups may reportedly reduce neonatal mortality. It is not known whether facilitation of groups composed of local health care staff and politicians can improve perinatal outcomes. We hypothesised that facilitation of local stakeholder groups would reduce neonatal mortality (primary outcome) and improve maternal, delivery, and newborn care indicators (secondary outcomes) in Quang Ninh province, Vietnam. In a cluster-randomized design 44 communes were allocated to intervention and 46 to control. Laywomen facilitated monthly meetings during 3 years in groups composed of health care staff and key persons in the communes. A problem-solving approach was employed. Births and neonatal deaths were monitored, and interviews were performed in households of neonatal deaths and of randomly selected surviving infants. A latent period before effect is expected in this type of intervention, but this timeframe was not pre-specified. Neonatal mortality rate (NMR) from July 2008 to June 2011 was 16.5/1,000 (195 deaths per 11,818 live births) in the intervention communes and 18.4/1,000 (194 per 10,559 live births) in control communes (adjusted odds ratio [OR] 0.96 [95% CI 0.73-1.25]). There was a significant downward time trend of NMR in intervention communes (p = 0.003) but not in control communes (p = 0.184). No significant difference in NMR was observed during the first two years (July 2008 to June 2010) while the third year (July 2010 to June 2011) had significantly lower NMR in intervention arm: adjusted OR 0.51 (95% CI 0.30-0.89). Women in intervention communes more frequently attended antenatal care (adjusted OR 2.27 [95% CI 1.07-4.8]). A randomized facilitation intervention with local stakeholder groups composed of primary care staff and local politicians working for three years with a perinatal problem-solving approach resulted in increased attendance to antenatal care and reduced neonatal mortality after a latent period.
Geoacoustic Inversion and Source Localization in a Randomly Fluctuating Shallow Water Environment
2010-06-01
with a standard deviation of 570 m. 2.2 SVV06 experiment data analysis: Sei whale localization Comparatively little is known about sei whale ...large number of sei whale calls were unexpectedly collected during the SW06 experiment, which introduced the first evidence of sei whales in this...shallow water region. Using the normal mode approach developed in this project, we are able to track the remote locations of these whales up to tens of
Local and cluster critical dynamics of the 3d random-site Ising model
NASA Astrophysics Data System (ADS)
Ivaneyko, D.; Ilnytskyi, J.; Berche, B.; Holovatch, Yu.
2006-10-01
We present the results of Monte Carlo simulations for the critical dynamics of the three-dimensional site-diluted quenched Ising model. Three different dynamics are considered, these correspond to the local update Metropolis scheme as well as to the Swendsen-Wang and Wolff cluster algorithms. The lattice sizes of L=10-96 are analysed by a finite-size-scaling technique. The site dilution concentration p=0.85 was chosen to minimize the correction-to-scaling effects. We calculate numerical values of the dynamical critical exponents for the integrated and exponential autocorrelation times for energy and magnetization. As expected, cluster algorithms are characterized by lower values of dynamical critical exponent than the local one: also in the case of dilution critical slowing down is more pronounced for the Metropolis algorithm. However, the striking feature of our estimates is that they suggest that dilution leads to decrease of the dynamical critical exponent for the cluster algorithms. This phenomenon is quite opposite to the local dynamics, where dilution enhances critical slowing down.
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.
Engineer, Reena; Mohandas, K M; Shukla, P J; Shrikhande, S V; Mahantshetty, U; Chopra, S; Goel, M; Mehta, S; Patil, P; Ramadwar, M; Deodhar, K; Arya, S; Shrivastava, Shyam Kishore
2013-07-01
This trial was undertaken to compare the rates of resectability between patients treated with neoadjuvant concurrent chemoradiation vs. boosted radiotherapy alone. Patients with clinically unresectable rectal cancer were randomized to receive external beam radiation therapy (EBRT) to pelvis (45 Gy) with concurrent oral Capecitabine (CRT group; Arm 1) or EBRT to pelvis (45 Gy) alone followed by 20 Gy dose of localized radiotherapy boost to the primary tumor site (RT with boost group, Arm 2). All patients were assessed for resectability after 6 weeks by clinical examination and by CT scan and those deemed resectable underwent surgery. A total of 90 patients were randomized, 46 to Arm 1 and 44 to Arm 2. Eighty seven patients (44 in Arm 1 and 41 in Arm 2) completed the prescribed treatment protocol. Overall resectability rate was low in both the groups; R0 resection was achieved in 20 (43 %) patients in Arm 1 vs. 15 (34 %) in Arm 2. Adverse factors that significantly affected the resectability rate in both the groups were extension of tumor to pelvic bones and signet ring cell pathology. Complete pathological response was seen in 7 and 11 %, respectively. There was greater morbidity such as wound infection and delayed wound healing in Arm 2 (16 vs. 40 %; p = 0.03). Escalated radiation dose without chemotherapy does not achieve higher complete (R0) tumor resectability in locally advanced inoperable rectal cancers, compared to concurrent chemoradiation.
NASA Astrophysics Data System (ADS)
Hartford, Edward John
This position-space renormalization-group study focuses on two systems with quenched disorder: the Ising spin glass and the asymmetric random-field Ising model. We have employed the Migdal-Kadanoff approach to determine local recursion relations and have retained the full correlated probability distribution of interactions and fields at each iteration in a series of histograms. We find an equilibrium spin-glass phase in three dimensions, but not in two. The spin glass is characterized by a distribution of effective interactions that broadens under iteration, signaling both the long-range order of the phase and the importance of competing interactions on all length scales. We have introduced a method to calculate the distribution of local properties by differentiating the free energy with respect to a particular magnetic field or interaction. Within the spin-glass phase, the nearest neighbor correlation < S_ {i}S_{j}> ranges from negative one to one, showing the strong correlations and the local variation within the phase. The spin-glass-to-paramagnet phase transition is second order, with a smooth specific heat indicated by a negative critical exponent alpha. The multicritical point separating the spin-glass, paramagnetic, and ferromagnetic phases lies along the Nishimori line and also has a nondivergent specific heat. When the system undergoes quenched dilution, the resulting critical and multicritical behaviors are identical to those of the undiluted system. Even the addition of an infinitesimal magnetic field destroys the long-range spin-glass order; however, the characteristic broadening of the distribution continues for several iterations for small fields and low temperatures, suggesting the persistence of sizable spin-glass domains. Our study of the asymmetric random-field Ising model is motivated by recent experiments on phase transitions in porous media and mean-field treatments, which suggest that new critical behavior could occur when the distribution of
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
The Autonomous Duck: Exploring the Possibilities of a Markov Chain Model in Animation
NASA Astrophysics Data System (ADS)
Villegas, Javier
This document reports the construction of a framework for the generation of animations based in a Markov chain model of the different poses of some drawn character. The model was implemented and is demonstrated with the animation of a virtual duck in a random walk. Some potential uses of this model in interpolation and generation of in between frames are also explored.
Exact Solution of the Markov Propagator for the Voter Model on the Complete Graph
2014-07-01
the generating function form of the Markov prop- agator of the random walk. This can be easily generalized to other models simply by specifying the...detailed information about the prop- agator than the bound on consensus. VI. CONCLUSIONS We have successfully derived exact solutions to the voter
Observation uncertainty in reversible Markov chains.
Metzner, Philipp; Weber, Marcus; Schütte, Christof
2010-09-01
In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .
Application of Markov Graphs in Marketing
NASA Astrophysics Data System (ADS)
Bešić, C.; Sajfert, Z.; Đorđević, D.; Sajfert, V.
2007-04-01
The applications of Markov's processes theory in marketing are discussed. It was turned out that Markov's processes have wide field of applications. The advancement of marketing by the use of convolution of stationary Markov's distributions is analysed. It turned out that convolution distribution gives average net profit that is two times higher than the one obtained by usual Markov's distribution. It can be achieved if one selling chain is divided onto two parts with different ratios of output and input frequencies. The stability of marketing system was examined by the use of conforming coefficients. It was shown, by means of Jensen inequality that system remains stable if initial capital is higher than averaged losses.
Liguori, Gregory A; Zayas, Victor M; YaDeau, Jacques T; Kahn, Richard L; Paroli, Leonardo; Buschiazzo, Valeria; Wu, Anita
2006-09-01
Postoperative neurologic symptoms (PONS) are relatively common after upper extremity orthopedic surgery performed under peripheral neural blockade. In this study, we prospectively compared the incidence of PONS after shoulder surgery under interscalene (IS) block using the electrical stimulation (ES) or mechanical paresthesia (MP) techniques of nerve localization. For patients randomized to the MP group, a 1-in, 23-g long-beveled needle was placed into the IS groove to elicit a paresthesia to the shoulder, arm, elbow, wrist, or hand. For patients randomized to the ES group, a 5-cm, 22-g short-beveled insulated needle was placed into the IS groove to elicit a motor response including flexion or extension of the elbow, wrist, or fingers or deltoid muscle stimulation at a current between 0.2 and 0.5 mA. Each IS block was performed with 50-60 mL of 1.5% mepivacaine containing 1:300,000 epinephrine and 0.1meq/L sodium bicarbonate. Two-hundred-eighteen patients were randomized between the two groups. One patient was lost to follow-up. Twenty-five patients (23%) in the ES group experienced paresthesia during needle insertion. The incidence of PONS using the ES technique was 10.1% (11/109), whereas the incidence with the MP technique was 9.3% (10/108) (not significant). The PONS lasted a median duration of 2 mo, and symptoms in all patients resolved within 12 mo. The success rate, onset time, and patient satisfaction were also comparable between groups. We conclude that the choice of nerve localization technique can be made based on the patient's and anesthesiologist's comfort and preferences and not on concern for the development of PONS.
Bugada, Dario; De Gregori, Manuela; Compagnone, Christian; Muscoli, Carolina; Raimondi, Ferdinando; Bettinelli, Silvia; Avanzini, Maria Antonietta; Cobianchi, Lorenzo; Peloso, Andrea; Baciarello, Marco; Dagostino, Concetta; Giancotti, Luigino A; Ilari, Sara; Lauro, Filomena; Grimaldi, Stefania; Tasciotti, Ennio; Fini, Massimo; Saccani Jotti, Gloria M R; Meschi, Tiziana; Fanelli, Guido; Allegri, Massimo
2015-08-14
Inflammatory response is one of the key components of pain perception. Continuous infusion (CWI) of local anesthetics has been shown to be effective in controlling pain and reducing postoperative morphine consumption, but the effect of adding a potent anti-inflammatory drug (such as a steroid) has never been addressed. In our study, we want to investigate the effect of CWI with local anesthetic + methylprednisolone on acute and persistent pain, correlating clinical data with biomarkers of inflammation and genetic background. After approval by their institutional review board, three hospitals will enroll 120 patients undergoing major abdominal surgery in a randomized, double-blind, phase III study. After a 24-h CWI of ropivacaine 0.2 % + methylprednisolone 1 mg/kg, patients will be randomly assigned to receive either ropivacaine + steroid or placebo for the next 24 h. Then, patient-controlled CWI with only ropivacaine 0.2 % or placebo (according to the group of randomization) is planned after 48 h up to 7 days (bolus 10 ml, lock-out 1 h, maximum dose of 40 ml in 4 h). Morphine equivalent consumption up to 7 days will be analyzed, together with any catheter- or drug-related side effect. Persistent post-surgical pain (PPSP) incidence will also be investigated. Our primary endpoint is analgesic consumption in the first 7 days after surgery; we will evaluate, as secondary endpoints, any catheter- or drug-related side effect, genotype/phenotype correlations between some polymorphisms and postoperative outcome in terms of morphine consumption, development of the inflammatory response, and incidence of PPSP. Finally, we will collect, in a subgroup of patients, wound exudate samples by micro-dialysis, blood samples, and urine samples up to 72 h to investigate local and systemic inflammation and oxidative stress. This is a phase III trial to evaluate the safety and efficacy of wound infusion with steroid and local anesthetic. The study is aimed also to evaluate how long this
Complex networks: when random walk dynamics equals synchronization
NASA Astrophysics Data System (ADS)
Kriener, Birgit; Anand, Lishma; Timme, Marc
2012-09-01
Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits.
[A Hyperspectral Imagery Anomaly Detection Algorithm Based on Gauss-Markov Model].
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.
Exact significance test for Markov order
NASA Astrophysics Data System (ADS)
Pethel, S. D.; Hahs, D. W.
2014-02-01
We describe an exact significance test of the null hypothesis that a Markov chain is nth order. The procedure utilizes surrogate data to yield an exact test statistic distribution valid for any sample size. Surrogate data are generated using a novel algorithm that guarantees, per shot, a uniform sampling from the set of sequences that exactly match the nth order properties of the observed data. Using the test, the Markov order of Tel Aviv rainfall data is examined.
Semi-Markov Unreliability Range Evaluator
NASA Technical Reports Server (NTRS)
Butler, Ricky W.; Boerschlein, David P.
1993-01-01
Semi-Markov Unreliability Range Evaluator, SURE, computer program is software tool for analysis of reconfigurable, fault-tolerant systems. Traditional reliability analyses based on aggregates of fault-handling and fault-occurrence models. SURE provides efficient means for calculating accurate upper and lower bounds for probabilities of death states for large class of semi-Markov mathematical models, and not merely those reduced to critical-pair architectures.
Semi-Markov Arnason-Schwarz models.
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. © 2015, The International Biometric Society.
Fakheran Esfahani, Omid; Pouraboutaleb, Mohammad Fazel; Khorami, Behnam
2015-01-01
Prolonged numbness following routine dental treatments can cause difficulties in speaking and swallowing and may result in inadvertent biting of soft tissues. Local injection of vasodilator agents may represent a solution to this problem. The aim of this study was to evaluate the effect of submucosal injection of hydralazine hydrochloride (HCl) on the duration of oral soft tissue anesthesia after routine dental treatment. This randomized, single-blinded, controlled clinical trial included 50 patients who received inferior alveolar nerve block (2% lidocaine with 1:100,000 epinephrine) for simple restorative treatment. Upon completion of the dental treatment, patients randomly received a hydralazine HCl or sham injection in the same site as the local anesthetic injection. The reversal time to normal sensation of soft tissues (lips, tongue, and perioral skin) was evaluated and reported every 5 minutes by the patients, who followed an assessment protocol that they were taught in advance of treatment. Median recovery times in the hydralazine group and the sham group were 81.4 (SD, 3.6) and 221.8 (SD, 6.3) minutes, respectively. Based on Kaplan-Meier survival analysis, the duration of soft tissue anesthesia in the 2 groups was significantly different (P < 0.0001). By 1 hour after the reversal injection, 76% of subjects receiving hydralazine HCl had returned to normal intraoral and perioral sensation, but none of the subjects in the sham group reported normal sensation. Based on these results, submucosal injection of hydralazine HCl can be considered a safe and effective method to reduce the duration of local anesthetic-induced soft tissue numbness and the related functional problems.
Lumbroso-Le Rouic, L; Aerts, I; Hajage, D; Lévy-Gabriel, C; Savignoni, A; Algret, N; Cassoux, N; Bertozzi, A-I; Esteve, M; Doz, F; Desjardins, L
2016-01-01
Purpose Intraocular retinoblastoma treatments often combine chemotherapy and focal treatments. A first prospective protocol of conservative treatments in our institution showed the efficacy of the use of two courses of chemoreduction with etoposide and carboplatin, followed by chemothermotherapy using carboplatin as a single agent and diode laser. In order to decrease the possible long-term toxicity of chemotherapy due to etoposide, a randomized neoadjuvant phase II protocol was conducted using vincristine–carboplatin vs etoposide–carboplatin. Patients and methods The study was proposed when initial tumor characteristics did not allow front-line local treatments. Patients included in this phase II noncomparative randomized study of neoadjuvant chemotherapy received vincristin–carboplatin (new arm) vs etoposide–carboplatin (our reference arm). They were subsequently treated by local treatments and chemothermotherapy. Primary end point was the need for secondary enucleation or external beam radiotherapy (EBRT) not exceeding 40% at 2 years. Results A total of 65 eyes in 55 children were included in the study (May 2004 to August 2009). Of these, 32 eyes (27 children) were treated in the arm etoposide–carboplatin and 33 eyes (28 children) in the arm vincristin–carboplatin. At 2 years after treatment, 23/33 (69.7%) eyes were treated and salvaged without EBRT or enucleation in the arm vincristin–carboplatin and 26/32 (81.2%) in the arm etoposide–carboplatin. Conclusion Even if the two treatment arms could be considered as sufficiently active according to the study decision rules, neoadjuvant chemotherapy by two cycles of vincristine–carboplatin followed by chemothermotherapy appear to offer less optimal local control than the etoposide–carboplatin combination. PMID:26427984
Statistics and generation of non-Markov phase screens
NASA Astrophysics Data System (ADS)
Charnotskii, Mikhail; Baker, Gary
2016-09-01
Statistics of the random phase screens used for the modeling of beam propagation and imaging through the turbulent atmosphere is currently based on the Markov Approximation (MA) for wave propagation. This includes the phase structure functions of individual screens and the use of the statistically-independent screens for the multi-screen splitstep simulation of wave propagation. As the propagation modeling progresses to address the deep turbulence conditions, the increased number of phase screens is required to accurately describe the multiple scattering. This makes the MA a critical limitation, both because phase statistic of the thin turbulent layer does not follow MA, and because the closely space screens cannot be considered as statistically and functionally independent. A recently introduced Sparse-Spectrum (SS) model of statistically homogeneous random fields makes it possible to generate 3-D samples of refractive-index fluctuations with prescribed spectral density at a very reasonable computational cost. This leads to generation of samples of the phase screen sets that are free from the limitations of the MA. We investigated statistics of the individual phase screens and cross-correlations between the pairs of phase screens and found that the thickness Δz of the turbulent layer replaced by the phase screen is a new parameter defining the phase statistics in the non-Markov case. SS-based numerical algorithms for generation of the 3-D samples of the turbulent refractive index, and for the phase screen sets are presented. We also compare the split-step simulation results for the traditional MA and non-Markov screens.
Searching for convergence in phylogenetic Markov chain Monte Carlo.
Beiko, Robert G; Keith, Jonathan M; Harlow, Timothy J; Ragan, Mark A
2006-08-01
Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropolis-coupled MCMC is being used, the number of heated chains and their temperatures. Although some parameter settings have been examined in detail in the literature, others are frequently chosen with more regard to computational time or personal experience with other data sets. Such choices may lead to inadequate sampling of tree space or an inefficient use of computational resources. We performed a detailed study of convergence and mixing for 70 randomly selected, putatively orthologous protein sets with different sizes and taxonomic compositions. Replicated runs from multiple random starting points permit a more rigorous assessment of convergence, and we developed two novel statistics, delta and epsilon, for this purpose. Although likelihood values invariably stabilized quickly, adequate sampling of the posterior distribution of tree topologies took considerably longer. Our results suggest that multimodality is common for data sets with 30 or more taxa and that this results in slow convergence and mixing. However, we also found that the pragmatic approach of combining data from several short, replicated runs into a "metachain" to estimate bipartition posterior probabilities provided good approximations, and that such estimates were no worse in approximating a reference posterior distribution than those obtained using a single long run of the same length as the metachain. Precision appears to be best when heated Markov chains have low temperatures, whereas chains with high temperatures appear to sample trees with high posterior probabilities only rarely.
Experiments with central-limit properties of spatial samples from locally covariant random fields
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.
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). © International & American Associations for Dental Research 2015.
El-Gammal, Mona Y; Salem, Ahmed S; Anees, Mohamed M; Tawfik, Mohamed A
2016-04-01
Immediate loading of dental implants in situations where low bone density exist, such as the posterior maxillary region, became possible recently after the introduction of biomimetic agents. This 1-year preliminary clinical trial was carried out to clinically and radiographically evaluate immediate-loaded 1-piece implants with local application of melatonin in the osteotomy site as a biomimetic material. 14 patients with missing maxillary premolars were randomized to receive 14 implants of 1-piece type that were subjected to immediate loading after 2 weeks of initial placement. Group I included 7 implants with acid-etched surface while group II included 7 implants with acid-etched surface combined with local application of melatonin gel at the osteotomy site. Patients were recalled for follow up at 1, 3, 6, and 12 months after loading. All implants were considered successful after 12 months of follow-up. Significant difference (P < 0.05) was found between both groups at 1 month of implant loading when considering the implant stability. At 1 and 3 months there were significant differences in the marginal bone level between the 2 groups. These results suggest that the local application of melatonin at the osteotomy site is associated with good stability and minimal bone resorption. However, more studies for longer follow-up periods are required to confirm the effect of melatonin hormone on osseointegration of dental implants.
Hadianfard, Mohammadjavad; Ashraf, Alireza; Fakheri, Maryamsadat; Nasiri, Aref
2014-06-01
There is no consensus on the management of De Quervain's tenosynovitis, but local corticosteroid injection is considered the mainstay of treatment. However, some patients are reluctant to take steroid injections. This study was performed to compare the efficacy of acupuncture versus corticosteroid injection for the treatment of this disease. Thirty patients were consequently treated in two groups. The acupuncture group received five acupuncture sessions of 30 minutes duration on classic points of LI-5, LU-7, and LU-9 and on ahshi points. The injection group received one methylprednisolone acetate injection in the first dorsal compartment of the wrist. The degree of disability and pain was evaluated by using the Quick Disabilities of the Arm, Shoulder, and Hand (Q-DASH) scale and the Visual Analogue Scale (VAS) at baseline and at 2 weeks and 6 weeks after the start of treatment. The baseline means of the Q-DASH and the VAS scores were 62.8 and 6.9, respectively. At the last follow-up, the mean Q-DASH scores were 9.8 versus 6.2 in the acupuncture and injection groups, respectively, and the mean VAS scores were 2 versus 1.2. We demonstrated short-term improvement of pain and function in both groups. Although the success rate was somewhat higher with corticosteroid injection, acupuncture can be considered as an alternative option for treatment of De Quervain's tenosynovitis. Copyright © 2013. Published by Elsevier B.V.
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
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. 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. 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). At 5 years, only HFX improved LRC and overall survival for patients with locally advanced SCC without increasing late toxicity. Published by Elsevier Inc.
Beevi, K Sabeena; Nair, Madhu S; Bindu, G R
2016-08-01
The exact measure of mitotic nuclei is a crucial parameter in breast cancer grading and prognosis. This can be achieved by improving the mitotic detection accuracy by careful design of segmentation and classification techniques. In this paper, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage, in order to handle diffused intensities present along object boundaries. Further, the application of a new optimal machine learning algorithm capable of classifying strong non-linear data such as Random Kitchen Sink (RKS), shows improved classification performance. The proposed method has been tested on Mitosis detection in breast cancer histological images (MITOS) dataset provided for MITOS-ATYPIA CONTEST 2014. The proposed framework achieved 95% recall, 98% precision and 96% F-score.
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
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.
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.
Monthus, Cécile; Garel, Thomas
2007-08-01
Disordered systems present multifractal properties at criticality. In particular, as discovered by Ludwig [A.W.W. Ludwig, Nucl. Phys. B 330, 639 (1990)] in the case of a diluted two-dimensional Potts model, the moments rho(q) (r) of the local order parameter rho(r) scale with a set x(q) of nontrivial exponents x(q) not = qx(1). We reexamine these ideas to incorporate more recent findings: (i) whenever a multifractal measure w(r) normalized over space sum(r) w(r) = 1 occurs in a random system, it is crucial to distinguish between the typical values and the disorder-averaged values of the generalized moments Y(q) = sum(r) w(q) (r), since they may scale with different generalized dimensions D(q) and D(q), and (ii), as discovered by Wiseman and Domany [S. Wiseman and E. Domany, Phys. Rev. E 52, 3469 (1995)], the presence of an infinite correlation length induces a lack of self-averaging at critical points for thermodynamic observables, in particular for the order parameter. After this general discussion, valid for any random critical point, we apply these ideas to random polymer models that can be studied numerically for large sizes and good statistics over the samples. We study the bidimensional wetting or the Poland-Scheraga DNA model with loop exponents c = 1.5 (marginal disorder) and c = 1.75 (relevant disorder). Finally, we argue that the presence of finite Griffiths-ordered clusters at criticality determines the asymptotic value x(q-->infinity) = d and the minimal value alpha(min) = D(q-->infinity) = d - x(1) of the typical multifractal spectrum f(alpha).
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
Autocatalytic genetic networks modeled by piecewise-deterministic Markov processes.
Zeiser, Stefan; Franz, Uwe; Liebscher, Volkmar
2010-02-01
In the present work we propose an alternative approach to model autocatalytic networks, called piecewise-deterministic Markov processes. These were originally introduced by Davis in 1984. Such a model allows for random transitions between the active and inactive state of a gene, whereas subsequent transcription and translation processes are modeled in a deterministic manner. We consider three types of autoregulated networks, each based on a positive feedback loop. It is shown that if the densities of the stationary distributions exist, they are the solutions of a system of equations for a one-dimensional correlated random walk. These stationary distributions are determined analytically. Further, the distributions are analyzed for different simulation periods and different initial concentration values by numerical means. We show that, depending on the network structure, beside a binary response also a graded response is observable.
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.
Botto, Henry; Rouprêt, Morgan; Mathieu, François; Richard, François
2007-04-01
To report the results of a trial comparing the efficacy of triptorelin and surgical castration in the treatment of locally advanced or metastatic prostate cancer. 80 patients with previously untreated locally advanced or metastatic prostate cancer prostate cancer were included in a one-year multicentre, randomized, prospective, open-label therapeutic trial. Patients either received a monthly injection of triptorelin (group 1; n = 40), or were treated by pulpectomy (group 2; n = 40). Patients were reviewed every 3 months, then every 6 months. The mean age of the patients was 71.22 +/- 8.25 years. At 1 month, 38 patients were castrated (plasma testosterone < 0.5 mg/ml) in the pulpectomy group versus 35 in the triptorelin group. The mean follow-up was 38.8 +/- 26 months in the triptorelin group and 36.3 +/- 25 months in the pulpectomy group. On multivariate analysis, age, impaired performance status and PAP level (> 3.2 ng/ml) were predictive factors of a poor outcome. The median survival was 37.5 +/- 9 months in the triptorelin group and 33 +/- 3 months in the pulpectomy group. At 3 years, no significant difference in specific survival was observed between the 2 groups. At 8 years of follow-up, 63 patients had died. This study demonstrates an equivalent specific survival between patients treated by triptorelin or surgical castration. Castration is rapidly obtained with triptorelin (< 2 months) and is maintained over time throughout the duration of treatment.
Bayat, M.; Garajei, A.; Afshari Pour, E.; Hasheminasab, M.; Ghorbani, Y.; Kalantar Motamedi, M. H.; Bahrami, N.
2017-01-01
Background: Although bone grafts are commonly used in reconstructive surgeries, they are sensitive to local perfusion and are thus prone to severe resorption. Biphosphonates can inactivate osteoclasts and can be used to control the undesirable bone resorption. Objective: To assess the effect of administration of biphosphonates on bone resorption. Methods: 20 patients with bony defects who were candidates for free autogenous grafts were randomized into “pamidronate” and “control” groups. Bone segments were soaked in either pamidronate solution or normal saline and were inserted into the area of the surgery. Bone densities were measured post-surgery and in 6-month follow-up. Data were obtained via Digora software and analyzed. Results: The mean±SD bone density in pamidronate group changed from 93.4±14.6 to 93.6±17.5 (p<0.05); in the control group the density decreased from 89.7±13.2 to 78.9±11.4 (p<0.05). The mean difference of bone density in anterior areas of the jaws showed higher DXA in comparison to posterior regions (p=0.002). Conclusion: Locally administered pamidronate affects reduction in bone resorption. PMID:28299027
[Decision analysis in radiology using Markov models].
Golder, W
2000-01-01
Markov models (Multistate transition models) are mathematical tools to simulate a cohort of individuals followed over time to assess the prognosis resulting from different strategies. They are applied on the assumption that persons are in one of a finite number of states of health (Markov states). Each condition is given a transition probability as well as an incremental value. Probabilities may be chosen constant or varying over time due to predefined rules. Time horizon is divided into equal increments (Markov cycles). The model calculates quality-adjusted life expectancy employing real-life units and values and summing up the length of time spent in each health state adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common decision trees. Markov models can be evaluated by matrix algebra, probabilistic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic and therapeutic procedures in radiology. More interventions should be submitted to Markov analyses in order to elucidate their cost-effectiveness.
Kuldeep, CM; Singhal, Himanshu; Khare, Ashok Kumar; Mittal, Asit; Gupta, Lalit K; Garg, Anubhav
2011-01-01
Background: Alopecia areata (AA) is a common, non-scarring, patchy loss of hair at scalp and elsewhere. Its pathogenesis is uncertain; however, auto-immunity has been exemplified in various studies. Familial incidence of AA is 10-42%, but in monozygotic twins is 50%. Local steroids (topical / intra-lesional) are very effective in treatment of localized AA. Aim: To compare hair regrowth and side effects of topical betamethasone valerate foam, intralesional triamcinolone acetonide and tacrolimus ointment in management of localized AA. Materials and Methods: 105 patients of localized AA were initially registered but 27 were drop out. So, 78 patients allocated at random in group A (28), B (25) and C (25) were prescribed topical betamethasone valerate foam (0.1%) twice daily, intralesional triamcinolone acetonide (10mg/ml) every 3 weeks and tacrolimus ointment (0.1%) twice daily, respectively, for 12 weeks. They were followed for next12 weeks. Hair re-growth was calculated using “HRG Scale”; scale I- (0-25%), S II-(26-50%), S III - (51-75%) and S IV- (75-100%). Results: Hair re-growth started by 3 weeks in group B (Scale I: P<0.03), turned satisfactory at 6 weeks in group A and B (Scale I: P<0.005, Scale IV: P<0.001)), good at 9 weeks (Scale I: P<0.0005, Scale IV: P<0.00015), and better by 12 weeks of treatment (Scale I: P<0.000021, Scale IV: P<0.000009) in both A and B groups. At the end of 12 weeks follow-up hair re-growth (>75%, HRG IV) was the best in group B (15 of 25, 60%), followed by A (15 of 28, 53.6%) and lastly group-C (Nil of 25, 0%) patients. Few patients reported mild pain and atrophy at injection sites, pruritus and burning with betamethasone valerate foam and tacrolimus. Conclusion: Intralesional triamcinolone acetonide is the best, betamethasone valerate foam is better than tacrolimus in management of localized AA. PMID:21769231
Naidu, Sinuba; Loughlin, Pat; Coldwell, Susan E.; Noonan, Carolyn J.; Milgrom, Peter
2004-01-01
The aim of this study was to test the hypothesis that dental pain control using infiltration/intrapapillary injection was less effective than inferior alveolar block/long buccal infiltration anesthesia in children. A total of 101 healthy children, aged 5-8 years, who had no contraindication for local anesthetic and who needed a pulpotomy treatment and stainless steel crown placement in a lower primary molar were studied. A 2-group randomized blinded controlled design was employed comparing the 2 local anesthesia techniques using 2% lidocaine, 1:100,000 epinephrine. All children were given 40% nitrous oxide. Children self-reported pain using the Color Analogue Scale. The study was conducted in a private pediatric dental practice in Mount Vernon, Wash. Overall pain levels reported by the children were low, and there were no differences between conditions at any point in the procedure. Pain reports for clamp placement were block/long buccal 2.8 and infiltration/intrapapillary 1.9 (P = .1). Pain reports for drilling were block/long buccal 2.0 and infiltration/intrapapillary 1.8 (P = .7). Nine percent of children required supplementary local anesthetic: 4 of 52 (7.7%) in the block/long buccal group and 5 of 49 (10.2%) in the infiltration/intrapapillary group (P = .07). The hypothesis that block/long buccal would be more effective than infiltration/intrapapillary was not supported. There was no difference in pain control effectiveness between infiltration/intrapapillary injection and inferior alveolar block/long buccal infiltration using 2% lidocaine with 1:100,000 epinephrine when mandibular primary molars received pulpotomy treatment and stainless steel crowns. PMID:15106686
Generalized semi-Markov quantum evolution
NASA Astrophysics Data System (ADS)
Chruściński, Dariusz; Kossakowski, Andrzej
2017-04-01
We provide a large class of quantum evolutions governed by the memory kernel master equation. This class defines a quantum analog of so-called semi-Markov classical stochastic dynamics. In this paper we provide a precise definition of quantum semi-Markov evolution, and using the appropriate gauge freedom we propose a suitable generalization which contains a majority of examples considered so far in the literature. The key concepts are quantum counterparts of classical waiting time distribution and survival probability—a quantum waiting time operator and a quantum survival operator, respectively. In particular collision models and their generalizations considered recently are special examples of generalized semi-Markov evolution. This approach allows for an interesting generalization of the trajectory description of the quantum dynamics in terms of positive operator-valued measure densities.
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.
Policy Transfer via Markov Logic Networks
NASA Astrophysics Data System (ADS)
Torrey, Lisa; Shavlik, Jude
We propose using a statistical-relational model, the Markov Logic Network, for knowledge transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We show that Markov Logic Networks are effective models for capturing both source-task Q-functions and source-task policies. We apply them via demonstration, which involves using them for decision making in an initial stage of the target task before continuing to learn. Through experiments in the RoboCup simulated-soccer domain, we show that transfer via Markov Logic Networks can significantly improve early performance in complex tasks, and that transferring policies is more effective than transferring Q-functions.
Li, Hui-Jia; Wang, Yong; Wu, Ling-Yun; Zhang, Junhua; Zhang, Xiang-Sun
2012-07-01
The Potts model is a powerful tool to uncover community structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the community structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we show that the local uniform behavior of spin values across multiple timescales in the representation of the Markov variables could naturally reveal the network's hierarchical community structure. In addition, critical topological information regarding multivariate spin configuration could also be inferred from the spectral signatures of the Markov process. Finally an algorithm is developed to determine fuzzy communities based on the optimal number of communities and the stability across multiple timescales. The effectiveness and efficiency of our algorithm are theoretically analyzed as well as experimentally validated.
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.
Parallel Markov chain Monte Carlo simulations
NASA Astrophysics Data System (ADS)
Ren, Ruichao; Orkoulas, G.
2007-06-01
With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.
Parallel Markov chain Monte Carlo simulations.
Ren, Ruichao; Orkoulas, G
2007-06-07
With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.
Entropy production fluctuations of finite Markov chains
NASA Astrophysics Data System (ADS)
Jiang, Da-Quan; Qian, Min; Zhang, Fu-Xi
2003-09-01
For almost every trajectory segment over a finite time span of a finite Markov chain with any given initial distribution, the logarithm of the ratio of its probability to that of its time-reversal converges exponentially to the entropy production rate of the Markov chain. The large deviation rate function has a symmetry of Gallavotti-Cohen type, which is called the fluctuation theorem. Moreover, similar symmetries also hold for the rate functions of the joint distributions of general observables and the logarithmic probability ratio.
Protein family classification using sparse Markov transducers.
Eskin, E; Grundy, W N; Singer, Y
2000-01-01
In this paper we present a method for classifying proteins into families using sparse Markov transducers (SMTs). Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Because substitutions of amino acids are common in protein families, incorporating wildcards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. We also present efficient data structures to improve the memory usage of the models. We evaluate SMTs by building protein family classifiers using the Pfam database and compare our results to previously published results.
Motzer, Robert J; Haas, Naomi B; Donskov, Frede; Gross-Goupil, Marine; Varlamov, Sergei; Kopyltsov, Evgeny; Lee, Jae Lyun; Melichar, Bohuslav; Rini, Brian I; Choueiri, Toni K; Zemanova, Milada; Wood, Lori A; Reaume, M Neil; Stenzl, Arnulf; Chowdhury, Simon; Lim, Ho Yeong; McDermott, Ray; Michael, Agnieszka; Bao, Weichao; Carrasco-Alfonso, Marlene J; Aimone, Paola; Voi, Maurizio; Doehn, Christian; Russo, Paul; Sternberg, Cora N
2017-09-13
Purpose This phase III trial evaluated the efficacy and safety of pazopanib versus placebo in patients with locally advanced renal cell carcinoma (RCC) at high risk for relapse after nephrectomy. Patients and Methods A total of 1,538 patients with resected pT2 (high grade) or ≥ pT3, including N1, clear cell RCC were randomly assigned to pazopanib or placebo for 1 year; 403 patients received a starting dose of 800 mg or placebo. To address toxicity attrition, the 800-mg starting dose was lowered to 600 mg, and the primary end point analysis was changed to disease-free survival (DFS) for pazopanib 600 mg versus placebo (n = 1,135). Primary analysis was performed after 350 DFS events in the intent-to-treat (ITT) pazopanib 600 mg group (ITT600mg), and DFS follow-up analysis was performed 12 months later. Secondary end point analyses included DFS with ITT pazopanib 800 mg (ITT800mg) and safety. Results The primary analysis results of DFS ITT600mg favored pazopanib but did not show a significant improvement over placebo (hazard ratio [HR], 0.86; 95% CI, 0.70 to 1.06; P = .165). The secondary analysis of DFS in ITT800mg (n = 403) yielded an HR of 0.69 (95% CI, 0.51 to 0.94). Follow-up analysis in ITT600mg yielded an HR of 0.94 (95% CI, 0.77 to 1.14). Increased ALT and AST were common adverse events leading to treatment discontinuation in the pazopanib 600 mg (ALT, 16%; AST, 5%) and 800 mg (ALT, 18%; AST, 7%) groups. Conclusion The results of the primary DFS analysis of pazopanib 600 mg showed no benefit over placebo in the adjuvant setting.
Ferayorni, Angelique; Yniguez, Robert; Bryson, Matt; Bulloch, Blake
2012-07-01
Lumbar puncture (LP) is an essential procedure in the emergency department (ED) for the evaluation of meningitis. Subcutaneous injection of lidocaine before LP for local anesthesia is not a pain-free procedure. The J-Tip device allows an intradermal needle-free jet injection of 1% buffered lidocaine. This study compares needle-free jet injection of lidocaine to saline in reducing pain before LP in infants. This is a randomized, double-blinded, placebo-controlled trial involving infants, younger than 3 months, presenting to the ED meeting clinical criteria for LP. All patients were administered the J-Tip and randomized to either treatment with 1% buffered lidocaine or an equivalent amount of sterile normal saline before LP. Vital signs were recorded during the procedure. Facial expressions as well as crying times were video recorded from start to finish. Independent reviewers assigned pain scores based on the validated Neonatal Facial Coding System with possible scores ranging from 0 to 5. A total of 55 patients were enrolled. Mean (SD) pain scores at the time of needle insertion were 4.1 (1.3) for the lidocaine group and 4.8 (0.5) for the saline group (P = 0.01). Length of cry was also shorter for those in the lidocaine group, 38.5 versus 48.8 seconds (P = 0.04). Overall, κ was 0.76 between 2 independent reviewers. Needle-free injection of 1% buffered lidocaine administered before needle insertion for LP in infants reduces pain and length of cry, compared with normal saline.
Sahmeddini, Mohammad Ali; Azemati, Simin; Motlagh, Ehsan Masoudi
2017-05-01
Postoperative pain control after cesarean section (C/S) is important because inadequate postoperative pain control can result in a prolonged hospital stay. In this study, we compared postoperative somatic wound pain control between patients receiving tramadol and bupivacaine, infiltrated at the wound site. In this randomized clinical trial, 98 patients, eligible for elective C/S under general anesthesia, were randomly allocated to 2 groups. Before wound closure, 20 cc of 0.025% bupivacaine and 2 mg/kg of tramadol, diluted to 20 cc, were infiltrated at the wound site in groups A and B, respectively. After surgery, the pain score was measured using the visual analogue scale (VAS). Additionally, 24-hour total morphine consumption, nausea and vomiting, and respiratory depression were compared after 2, 4, 8, 16, and 24 hours between the 2 groups. The data were analyzed using SPSS with the Student independent t test, χ(2) test, Fisher exact test, and repeated measure test. Postoperatively, there was no significant difference between these 2 groups in their VAS scores until 16 hours (P>0.05). However, at the 16(th) and 24(th) hours, the mean VAS scores were 3.20±2.24 and 2.51±2.55 in the bupivacaine group and 2.51±0.99 and 1.40±0.88 in the tramadol group, respectively (P<0.05). There was no difference in nausea and vomiting during the 24-hour period between the 2 groups. Also, no respiratory depression was detected in the both groups. Local infiltration of tramadol (2 mg/kg) at the incision site of C/S was effective in somatic wound pain relief without significant complications. IRCT2013070111662N2.
Sahmeddini, Mohammad Ali; Azemati, Simin; Motlagh, Ehsan Masoudi
2017-01-01
Background: Postoperative pain control after cesarean section (C/S) is important because inadequate postoperative pain control can result in a prolonged hospital stay. In this study, we compared postoperative somatic wound pain control between patients receiving tramadol and bupivacaine, infiltrated at the wound site. Methods: In this randomized clinical trial, 98 patients, eligible for elective C/S under general anesthesia, were randomly allocated to 2 groups. Before wound closure, 20 cc of 0.025% bupivacaine and 2 mg/kg of tramadol, diluted to 20 cc, were infiltrated at the wound site in groups A and B, respectively. After surgery, the pain score was measured using the visual analogue scale (VAS). Additionally, 24-hour total morphine consumption, nausea and vomiting, and respiratory depression were compared after 2, 4, 8, 16, and 24 hours between the 2 groups. The data were analyzed using SPSS with the Student independent t test, χ2 test, Fisher exact test, and repeated measure test. Results: Postoperatively, there was no significant difference between these 2 groups in their VAS scores until 16 hours (P>0.05). However, at the 16th and 24th hours, the mean VAS scores were 3.20±2.24 and 2.51±2.55 in the bupivacaine group and 2.51±0.99 and 1.40±0.88 in the tramadol group, respectively (P<0.05). There was no difference in nausea and vomiting during the 24-hour period between the 2 groups. Also, no respiratory depression was detected in the both groups. Conclusion: Local infiltration of tramadol (2 mg/kg) at the incision site of C/S was effective in somatic wound pain relief without significant complications. Trial Registration Number: IRCT2013070111662N2 PMID:28533571
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.
NASA Astrophysics Data System (ADS)
Nickelsen, Daniel
2017-07-01
The statistics of velocity increments in homogeneous and isotropic turbulence exhibit universal features in the limit of infinite Reynolds numbers. After Kolmogorov’s scaling law from 1941, many turbulence models aim for capturing these universal features, some are known to have an equivalent formulation in terms of Markov processes. We derive the Markov process equivalent to the particularly successful scaling law postulated by She and Leveque. The Markov process is a jump process for velocity increments u(r) in scale r in which the jumps occur randomly but with deterministic width in u. From its master equation we establish a prescription to simulate the She-Leveque process and compare it with Kolmogorov scaling. To put the She-Leveque process into the context of other established turbulence models on the Markov level, we derive a diffusion process for u(r) using two properties of the Navier-Stokes equation. This diffusion process already includes Kolmogorov scaling, extended self-similarity and a class of random cascade models. The fluctuation theorem of this Markov process implies a ‘second law’ that puts a loose bound on the multipliers of the random cascade models. This bound explicitly allows for instances of inverse cascades, which are necessary to satisfy the fluctuation theorem. By adding a jump process to the diffusion process, we go beyond Kolmogorov scaling and formulate the most general scaling law for the class of Markov processes having both diffusion and jump parts. This Markov scaling law includes She-Leveque scaling and a scaling law derived by Yakhot.
Extreme event statistics in a drifting Markov chain
NASA Astrophysics Data System (ADS)
Kindermann, Farina; Hohmann, Michael; Lausch, Tobias; Mayer, Daniel; Schmidt, Felix; Widera, Artur
2017-07-01
We analyze extreme event statistics of experimentally realized Markov chains with various drifts. Our Markov chains are individual trajectories of a single atom diffusing in a one-dimensional periodic potential. Based on more than 500 individual atomic traces we verify the applicability of the Sparre Andersen theorem to our system despite the presence of a drift. We present detailed analysis of four different rare-event statistics for our system: the distributions of extreme values, of record values, of extreme value occurrence in the chain, and of the number of records in the chain. We observe that, for our data, the shape of the extreme event distributions is dominated by the underlying exponential distance distribution extracted from the atomic traces. Furthermore, we find that even small drifts influence the statistics of extreme events and record values, which is supported by numerical simulations, and we identify cases in which the drift can be determined without information about the underlying random variable distributions. Our results facilitate the use of extreme event statistics as a signal for small drifts in correlated trajectories.
Validity of the Markov approximation in ocean acoustics.
Henyey, Frank S; Ewart, Terry E
2006-01-01
Moment equations and path integrals for wave propagation in random media have been applied to many ocean acoustics problems. Both these techniques make use of the Markov approximation. The expansion parameter, which must be less than one for the Markov approximation to be valid, is the subject of this paper. There is a standard parameter (the Kubo number) which various authors have shown to be sufficient. Fourth moment equations have been successfully used to predict the experimentally measured frequency spectrum of intensity in the mid-ocean acoustic transmission experiment (MATE). Yet, in spite of this success, the Kubo number is greater than 1 for the measured index of refraction variability for MATE, arriving at a contradiction. Here, that contradiction is resolved by showing that the Kubo parameter is far too pessimistic for the ocean case. Using the methodology of van Kampen, another parameter is found which appears to be both necessary and sufficient, and is much smaller than the Kubo number when phase fluctuations are dominated by large scales in the medium. This parameter is shown to be small for the experimental regime of MATE, justifying the applications of the moment equations to that experiment.
PULSAR STATE SWITCHING FROM MARKOV TRANSITIONS AND STOCHASTIC RESONANCE
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.
Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung
2016-05-20
A facial sketch synthesis system is proposed featuring a two-dimensional direct combined model (2DDCM)-based facespecific Markov network. In contrast to 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 dataset 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 dataset. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.
Markov property of Gaussian states of canonical commutation relation algebras
NASA Astrophysics Data System (ADS)
Petz, Dénes; Pitrik, József
2009-11-01
The Markov property of Gaussian states of canonical commutation relation algebras is studied. The detailed description is given by the representing block matrix. The proof is short and allows infinite dimension. The relation to classical Gaussian Markov triplets is also described. The minimizer of relative entropy with respect to a Gaussian Markov state has the Markov property. The appendix contains formulas for the relative entropy.
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.
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
Chatterjee, Dattatreyo; Ghosh, Sudip Kumar; Sen, Sukanta; Sarkar, Saswati; Hazra, Avijit; De, Radharaman
2016-01-01
Epidermal dermatophyte infections most commonly manifest as tinea corporis or tinea cruris. Topical azole antifungals are commonly used in their treatment but literature suggests that most require twice-daily application and provide lower cure rates than the allylamine antifungal terbinafine. We conducted a head-to-head comparison of the effectiveness of the once-daily topical azole, sertaconazole, with terbinafine in these infections. We conducted a randomized, observer-blind, parallel group study (Clinical Trial Registry India [CTRI]/2014/09/005029) with adult patients of either sex presenting with localized lesions. The clinical diagnosis was confirmed by potassium hydroxide smear microscopy of skin scrapings. After baseline assessment of erythema, scaling, and pruritus, patients applied either of the two study drugs once daily for 2 weeks. If clinical cure was not seen at 2 weeks, but improvement was noted, application was continued for further 2 weeks. Patients deemed to be clinical failure at 2 weeks were switched to oral antifungals. Overall 88 patients on sertaconazole and 91 on terbinafine were analyzed. At 2 weeks, the clinical cure rates were comparable at 77.27% (95% confidence interval [CI]: 68.52%-86.03%) for sertaconazole and 73.63% (95% CI 64.57%-82.68%) for terbinafine (P = 0.606). Fourteen patients in either group improved and on further treatment showed complete healing by another 2 weeks. The final cure rate at 4 weeks was also comparable at 93.18% (95% CI 88.75%-97.62%) and 89.01% (95% CI 82.59%-95.44%), respectively (P = 0.914). At 2 weeks, 6 (6.82%) sertaconazole and 10 (10.99%) terbinafine recipients were considered as "clinical failure." Tolerability of both preparations was excellent. Despite the limitations of an observer-blind study without microbiological support, the results suggest that once-daily topical sertaconazole is as effective as terbinafine in localized tinea infections.
Pradeep, A R; Kumari, Minal; Rao, Nishanth S; Naik, Savitha B
2013-03-01
Alendronate (ALN), an aminobisphosphonate, is known to stimulate the formation of osteoblast precursors to promote osteoblastogenesis. The present study aims to explore the efficacy of 1% ALN gel as a local drug delivery system in adjunct to scaling and root planing (SRP) for the treatment of Class II furcation defects in comparison with placebo gel. A total of 69 mandibular Class II furcation defects were randomized and treated with either 1% ALN gel or placebo gel. Clinical parameters were recorded at baseline, 3 months, 6 months, and 12 months, and radiographic parameters were recorded at baseline, 6 months, and 12 months. Defect fill at baseline, 6 months, and 12 months was calculated on standardized radiographs using image analysis software. Mean probing depth (PD) reduction and mean relative vertical (RVCAL) and horizontal (RHCAL) clinical attachment level gain were shown to be greater in the ALN group than the placebo group at 3, 6, and 12 months. Furthermore, a significantly greater mean percentage of bone fill was found in the ALN group (32.11% ± 6.18%, 32.66% ± 5.86%), compared with the placebo group (2.71% ± 0.61%, 1.83% ± 1.51%), at 6 and 12 months, respectively. The results of the present study show that local delivery of 1% ALN into a Class II furcation defect stimulates a significant PD reduction, RVCAL and RHCAL gains, and improved bone fill compared with placebo gel as an adjunct to SRP. ALN can provide a new direction in management of furcation defects.
Zielinski, Stephanie M; Viveiros, Helena; Heetveld, Martin J; Swiontkowski, Marc F; Bhandari, Mohit; Patka, Peter; Van Lieshout, Esther M M
2012-01-08
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. 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. 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). 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. ClinicalTrials.gov: NCT00761813.
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
On Relaxation Algorithms Based on Markov Random Fields.
1987-07-10
C-0008., This contract support the Northeast Artificial Intelliglence Consortiu ( NAIC ). Trhis work was also supporte in part by U.S.Army Engineering...34continuation" (Fig. 4c) configurations simultaneously would be of little use, as the increases would tend to cancel each other out. The sensitivity of the...as are those obtained by applying 3X3 Kirsch operators with non- maximum suppression . The annealing schedule for the stochastic MAP follows the one
Quantification of heart rate variability by discrete nonstationary non-Markov stochastic processes
NASA Astrophysics Data System (ADS)
Yulmetyev, Renat; Hänggi, Peter; Gafarov, Fail
2002-04-01
We develop the statistical theory of discrete nonstationary non-Markov random processes in complex systems. The objective of this paper is to find the chain of finite-difference non-Markov kinetic equations for time correlation functions (TCF) in terms of nonstationary effects. The developed theory starts from careful analysis of time correlation through nonstationary dynamics of vectors of initial and final states and nonstationary normalized TCF. Using the projection operators technique we find the chain of finite-difference non-Markov kinetic equations for discrete nonstationary TCF and for the set of nonstationary discrete memory functions (MF's). The last one contains supplementary information about nonstationary properties of the complex system on the whole. Another relevant result of our theory is the construction of the set of dynamic parameters of nonstationarity, which contains some information of the nonstationarity effects. The full set of dynamic, spectral and kinetic parameters, and kinetic functions (TCF, short MF's statistical spectra of non-Markovity parameter, and statistical spectra of nonstationarity parameter) has made it possible to acquire the in-depth information about discreteness, non-Markov effects, long-range memory, and nonstationarity of the underlying processes. The developed theory is applied to analyze the long-time (Holter) series of RR intervals of human ECG's. We had two groups of patients: the healthy ones and the patients after myocardial infarction. In both groups we observed effects of fractality, standard and restricted self-organized criticality, and also a certain specific arrangement of spectral lines. The received results demonstrate that the power spectra of all orders (n=1,2,...) MF mn(t) exhibit the neatly expressed fractal features. We have found out that the full sets of non-Markov, discrete and nonstationary parameters can serve as reliable and powerful means of diagnosis of the cardiovascular system states and can
Quantification of heart rate variability by discrete nonstationary non-Markov stochastic processes.
Yulmetyev, Renat; Hänggi, Peter; Gafarov, Fail
2002-04-01
We develop the statistical theory of discrete nonstationary non-Markov random processes in complex systems. The objective of this paper is to find the chain of finite-difference non-Markov kinetic equations for time correlation functions (TCF) in terms of nonstationary effects. The developed theory starts from careful analysis of time correlation through nonstationary dynamics of vectors of initial and final states and nonstationary normalized TCF. Using the projection operators technique we find the chain of finite-difference non-Markov kinetic equations for discrete nonstationary TCF and for the set of nonstationary discrete memory functions (MF's). The last one contains supplementary information about nonstationary properties of the complex system on the whole. Another relevant result of our theory is the construction of the set of dynamic parameters of nonstationarity, which contains some information of the nonstationarity effects. The full set of dynamic, spectral and kinetic parameters, and kinetic functions (TCF, short MF's statistical spectra of non-Markovity parameter, and statistical spectra of nonstationarity parameter) has made it possible to acquire the in-depth information about discreteness, non-Markov effects, long-range memory, and nonstationarity of the underlying processes. The developed theory is applied to analyze the long-time (Holter) series of RR intervals of human ECG's. We had two groups of patients: the healthy ones and the patients after myocardial infarction. In both groups we observed effects of fractality, standard and restricted self-organized criticality, and also a certain specific arrangement of spectral lines. The received results demonstrate that the power spectra of all orders (n=1,2, ...) MF m(n)(t) exhibit the neatly expressed fractal features. We have found out that the full sets of non-Markov, discrete and nonstationary parameters can serve as reliable and powerful means of diagnosis of the cardiovascular system states and
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.
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.
Souchier, E; D'Acapito, F; Noé, P; Blaise, P; Bernard, M; Jousseaume, V
2015-10-07
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.
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 ui
Quantum Enhanced Inference in Markov Logic Networks
NASA Astrophysics Data System (ADS)
Wittek, Peter; Gogolin, Christian
2017-04-01
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
Markov Chain Estimation of Avian Seasonal Fecundity
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...
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…
Building Markov state models with solvent dynamics.
Gu, Chen; Chang, Huang-Wei; Maibaum, Lutz; Pande, Vijay S; Carlsson, Gunnar E; Guibas, Leonidas J
2013-01-01
Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.
Building Markov state models with solvent dynamics
2013-01-01
Background Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. Methods We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. Results We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. Conclusions In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses. PMID:23368418
Semi-Markov Unreliability Range Evaluator (SURE)
NASA Technical Reports Server (NTRS)
Butler, R. W.
1989-01-01
Analysis tool for reconfigurable, fault-tolerant systems, SURE provides efficient way to calculate accurate upper and lower bounds for death state probabilities for large class of semi-Markov models. Calculated bounds close enough for use in reliability studies of ultrareliable computer systems. Written in PASCAL for interactive execution and runs on DEC VAX computer under VMS.
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…
Markov process analysis of atom probe data
NASA Astrophysics Data System (ADS)
Wang, Qi; T, J. Kinkus; Ren, Dagang
1990-08-01
A geometry model of field evaporation process is set up; with this model the field evaporation process can be described as Markov process. Its application to the earliest stage of phase transition is studied. For comparison, Camus' system Fe-Cr 45 at.% is calculated agin, and the same result is extracted from our method and intimated in our experimental data.
Bias in Markov models of disease.
Faissol, Daniel M; Griffin, Paul M; Swann, Julie L
2009-08-01
We examine bias in Markov models of diseases, including both chronic and infectious diseases. We consider two common types of Markov disease models: ones where disease progression changes by severity of disease, and ones where progression of disease changes in time or by age. We find sufficient conditions for bias to exist in models with aggregated transition probabilities when compared to models with state/time dependent transition probabilities. We also find that when aggregating data to compute transition probabilities, bias increases with the degree of data aggregation. We illustrate by examining bias in Markov models of Hepatitis C, Alzheimer's disease, and lung cancer using medical data and find that the bias is significant depending on the method used to aggregate the data. A key implication is that by not incorporating state/time dependent transition probabilities, studies that use Markov models of diseases may be significantly overestimating or underestimating disease progression. This could potentially result in incorrect recommendations from cost-effectiveness studies and incorrect disease burden forecasts.
Happe, Svenja; Evers, Stefan; Thiedemann, Christian; Bunten, Sabine; Siegert, Rudolf
2016-11-15
Treatment of restless legs syndrome (RLS) is primarily based on drugs. Since many patients report improvement of symptoms due to cooling their legs, we examined the efficacy of cryotherapy in RLS. 35 patients (28 women, 60.9±12.5years) with idiopathic RLS and symptoms starting not later than 6pm were randomized into three groups: cold air chamber at -60°C (n=12); cold air chamber at -10°C (n=12); local cryotherapy at -17°C (n=11). After a two week baseline, the different therapies were applied three minutes daily at 6pm over two weeks, followed by a four week observation period. The patients completed several questionnaires regarding RLS symptoms, sleep, and quality of life on a weekly basis (IRLS, ESS), VAS and sleep/morning protocol were completed daily, MOSS/RLS-QLI were completed once in each period. Additionally, the PLM index was measured by a mobile device at the end of baseline, intervention, and follow-up. The IRLS score was chosen as primary efficacy parameter. At the end of follow-up, significant improvement of RLS symptoms and quality of life could be observed only in the -60°C group as compared to baseline (IRLS: p=0.009; RLS-QLI: p=0.006; ESS: p=0.020). Local cryotherapy led to improvement in quality of life (VAS4: p=0.028; RLS-QLI: p=0.014) and sleep quality (MOSS: p=0.020; MOSS2: p=0.022) but not in IRLS and ESS. In the -10°C group, the only significant effect was shortening of number of wake phases per night. Serious side-effects were not reported. Whole body cryotherapy at -60°C and, to a less extent, local cryotherapy seem to be a treatment option for RLS in addition to conventional pharmacological treatment. However, the exact mode of cryotherapy needs to be established. Copyright © 2016. Published by Elsevier B.V.
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
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.
Dimensional Reduction for the General Markov Model on Phylogenetic Trees.
Sumner, Jeremy G
2017-03-01
We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.
Α Markov model for longitudinal studies with incomplete dichotomous outcomes.
Efthimiou, Orestis; Welton, Nicky; Samara, Myrto; Leucht, Stefan; Salanti, Georgia
2017-03-01
Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time-dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data.
Acceleration of Markov chain Monte Carlo simulations through sequential updating
NASA Astrophysics Data System (ADS)
Ren, Ruichao; Orkoulas, G.
2006-02-01
Strict detailed balance is not necessary for Markov chain Monte Carlo simulations to converge to the correct equilibrium distribution. In this work, we propose a new algorithm which only satisfies the weaker balance condition, and it is shown analytically to have better mobility over the phase space than the Metropolis algorithm satisfying strict detailed balance. The new algorithm employs sequential updating and yields better sampling statistics than the Metropolis algorithm with random updating. We illustrate the efficiency of the new algorithm on the two-dimensional Ising model. The algorithm is shown to identify the correct equilibrium distribution and to converge faster than the Metropolis algorithm with strict detailed balance. The main advantages of the new algorithm are its simplicity and the feasibility of parallel implementation through domain decomposition.
Qualitative Analysis of Partially-Observable Markov Decision Processes
NASA Astrophysics Data System (ADS)
Chatterjee, Krishnendu; Doyen, Laurent; Henzinger, Thomas A.
We study observation-based strategies for partially-observable Markov decision processes (POMDPs) with parity objectives. An observation-based strategy relies on partial information about the history of a play, namely, on the past sequence of observations. We consider qualitative analysis problems: given a POMDP with a parity objective, decide whether there exists an observation-based strategy to achieve the objective with probability 1 (almost-sure winning), or with positive probability (positive winning). Our main results are twofold. First, we present a complete picture of the computational complexity of the qualitative analysis problem for POMDPs with parity objectives and its subclasses: safety, reachability, Büchi, and coBüchi objectives. We establish several upper and lower bounds that were not known in the literature. Second, we give optimal bounds (matching upper and lower bounds) for the memory required by pure and randomized observation-based strategies for each class of objectives.
Optimized nested Markov chain Monte Carlo sampling: theory
Coe, Joshua D; Shaw, M Sam; Sewell, Thomas D
2009-01-01
Metropolis Monte Carlo sampling of a reference potential is used to build a Markov chain in the isothermal-isobaric ensemble. At the endpoints of the chain, the energy is reevaluated at a different level of approximation (the 'full' energy) and a composite move encompassing all of the intervening steps is accepted on the basis of a modified Metropolis criterion. By manipulating the thermodynamic variables characterizing the reference system we maximize the average acceptance probability of composite moves, lengthening significantly the random walk made between consecutive evaluations of the full energy at a fixed acceptance probability. This provides maximally decorrelated samples of the full potential, thereby lowering the total number required to build ensemble averages of a given variance. The efficiency of the method is illustrated using model potentials appropriate to molecular fluids at high pressure. Implications for ab initio or density functional theory (DFT) treatment are discussed.
Α Markov model for longitudinal studies with incomplete dichotomous outcomes
Welton, Nicky; Samara, Myrto; Leucht, Stefan; Salanti, Georgia
2016-01-01
Missing outcome data constitute a serious threat to the validity and precision of inferences from randomized controlled trials. In this paper, we propose the use of a multistate Markov model for the analysis of incomplete individual patient data for a dichotomous outcome reported over a period of time. The model accounts for patients dropping out of the study and also for patients relapsing. The time of each observation is accounted for, and the model allows the estimation of time‐dependent relative treatment effects. We apply our methods to data from a study comparing the effectiveness of 2 pharmacological treatments for schizophrenia. The model jointly estimates the relative efficacy and the dropout rate and also allows for a wide range of clinically interesting inferences to be made. Assumptions about the missingness mechanism and the unobserved outcomes of patients dropping out can be incorporated into the analysis. The presented method constitutes a viable candidate for analyzing longitudinal, incomplete binary data. PMID:27917593
Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models.
Pinamonti, Giovanni; Zhao, Jianbo; Condon, David E; Paul, Fabian; Noè, Frank; Turner, Douglas H; Bussi, Giovanni
2017-02-14
Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely, atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. After properly removing structures that are artificially stabilized by known inaccuracies of the current RNA AMBER force field, the kinetic properties predicted are consistent with the time scales of previously reported relaxation experiments.
70 Gy versus 80 Gy in localized prostate cancer: 5-year results of GETUG 06 randomized trial.
Beckendorf, Véronique; Guerif, Stéphane; Le Prisé, 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-Léon; Luporsi, Elisabeth; Bey, Pierre
2011-07-15
To perform a randomized trial comparing 70 and 80 Gy radiotherapy for prostate cancer. 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. 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. High-dose radiotherapy provided a better 5-year biochemical outcome with slightly
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.
A mixed model for two-state Markov processes under panel observation.
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.
Chatterjee, Dattatreyo; Ghosh, Sudip Kumar; Sen, Sukanta; Sarkar, Saswati; Hazra, Avijit; De, Radharaman
2016-01-01
Objective: Epidermal dermatophyte infections most commonly manifest as tinea corporis or tinea cruris. Topical azole antifungals are commonly used in their treatment but literature suggests that most require twice-daily application and provide lower cure rates than the allylamine antifungal terbinafine. We conducted a head-to-head comparison of the effectiveness of the once-daily topical azole, sertaconazole, with terbinafine in these infections. Materials and Methods: We conducted a randomized, observer-blind, parallel group study (Clinical Trial Registry India [CTRI]/2014/09/005029) with adult patients of either sex presenting with localized lesions. The clinical diagnosis was confirmed by potassium hydroxide smear microscopy of skin scrapings. After baseline assessment of erythema, scaling, and pruritus, patients applied either of the two study drugs once daily for 2 weeks. If clinical cure was not seen at 2 weeks, but improvement was noted, application was continued for further 2 weeks. Patients deemed to be clinical failure at 2 weeks were switched to oral antifungals. Results: Overall 88 patients on sertaconazole and 91 on terbinafine were analyzed. At 2 weeks, the clinical cure rates were comparable at 77.27% (95% confidence interval [CI]: 68.52%–86.03%) for sertaconazole and 73.63% (95% CI 64.57%–82.68%) for terbinafine (P = 0.606). Fourteen patients in either group improved and on further treatment showed complete healing by another 2 weeks. The final cure rate at 4 weeks was also comparable at 93.18% (95% CI 88.75%–97.62%) and 89.01% (95% CI 82.59%–95.44%), respectively (P = 0.914). At 2 weeks, 6 (6.82%) sertaconazole and 10 (10.99%) terbinafine recipients were considered as “clinical failure.” Tolerability of both preparations was excellent. Conclusion: Despite the limitations of an observer-blind study without microbiological support, the results suggest that once-daily topical sertaconazole is as effective as terbinafine in localized tinea
Ma, Junxun; Yao, Sheng; Li, Xiao-Song; Kang, Huan-Rong; Yao, Fang-Fang; Du, Nan
2015-10-01
Locally advanced gastric cancer (LAGC) is best treated with surgical resection. Bevacizumab in combination with chemotherapy has shown promising results in treating advanced gastric cancer. This study aimed to investigate the efficacy of neoadjuvant chemotherapy using the docetaxel/oxaliplatin/5-FU (DOF) regimen and bevacizumab in LAGC patients.Eighty LAGC patients were randomized to receive DOF alone (n = 40) or DOF plus bevacizumab (n = 40) as neoadjuvant therapy before surgery. The lesions were evaluated at baseline and during treatment. Circulating tumor cells (CTCs) were counted using the FISH test. Patients were followed up for 3 years to analyze the disease-free survival (DFS) and overall survival (OS).The total response rate was significantly higher in the DOF plus bevacizumab group than the DOF group (65% vs 42.5%, P = 0.0436). The addition of bevacizumab significantly increased the surgical resection rate and the R0 resection rate (P < 0.05). The DOF plus bevacizumab group showed significantly greater reduction in CTC counts after neoadjuvant therapy in comparison with the DOF group (P = 0.0335). Although the DOF plus bevacizumab group had significantly improved DFS than the DOF group (15.2 months vs 12.3 months, P = 0.013), the 2 groups did not differ significantly in OS (17.6 ± 1.8 months vs 16.4 ± 1.9 months, P = 0.776. Cox proportional model analysis showed that number of metastatic lymph nodes, CTC reduction, R0 resection, and neoadjuvant therapy are independent prognostic factors for patients with LAGC.Neoadjuvant of DOF regimen plus bevacizumab can improve the R0 resection rate and DFS in LAGC. These beneficial effects might be associated with the reduction in CTC counts.
Baujat, Bertrand; Audry, Helene; Bourhis, Jean; Chan, Anthony T.C.; Onat, Haluk; Chua, Daniel T.T.; Kwong, Dora L.W.; Al-Sarraf, Muhyi; Chi, K.-H.; Hareyama, Masato; Leung, Sing F.; Thephamongkhol, Kullathorn; Pignon, Jean-Pierre . E-mail: jppignon@igr.fr
2006-01-01
Objectives: To study the effect of adding chemotherapy to radiotherapy (RT) on overall survival and event-free survival for patients with nasopharyngeal carcinoma. Methods and Materials: This meta-analysis used updated individual patient data from randomized trials comparing chemotherapy plus RT with RT alone in locally advanced nasopharyngeal carcinoma. The log-rank test, stratified by trial, was used for comparisons, and the hazard ratios of death and failure were calculated. Results: Eight trials with 1753 patients were included. One trial with a 2 x 2 design was counted twice in the analysis. The analysis included 11 comparisons using the data from 1975 patients. The median follow-up was 6 years. The pooled hazard ratio of death was 0.82 (95% confidence interval, 0.71-0.94; p = 0.006), corresponding to an absolute survival benefit of 6% at 5 years from the addition of chemotherapy (from 56% to 62%). The pooled hazard ratio of tumor failure or death was 0.76 (95% confidence interval, 0.67-0.86; p < 0.0001), corresponding to an absolute event-free survival benefit of 10% at 5 years from the addition of chemotherapy (from 42% to 52%). A significant interaction was observed between the timing of chemotherapy and overall survival (p = 0.005), explaining the heterogeneity observed in the treatment effect (p = 0.03), with the highest benefit resulting from concomitant chemotherapy. Conclusion: Chemotherapy led to a small, but significant, benefit for overall survival and event-free survival. This benefit was essentially observed when chemotherapy was administered concomitantly with RT.
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.
Phase-Type Approximations for Wear Processes in A Semi-Markov Environment
2004-03-01
identically distributed exponential random variables, is equivalent to the absorption time of an underlying k-state Markov process. As noted by Perros ...the Coxian distribution is that it can exactly represent any distribution having a rational Laplace transform [23]. Moreover, Perros [23] gives the...Performance Evaluation (TOOLS 2003), 200-217. 23. Perros , H. (1994). Queueing Networks with Blocking. Oxford University Press, New York. 24. Ro, C.W
Inferring species interactions from co-occurrence data with Markov networks.
Harris, David J
2016-12-01
Inferring species interactions from co-occurrence data is one of the most controversial tasks in community ecology. One difficulty is that a single pairwise interaction can ripple through an ecological network and produce surprising indirect consequences. For example, the negative correlation between two competing species can be reversed in the presence of a third species that outcompetes both of them. Here, I apply models from statistical physics, called Markov networks or Markov random fields, that can predict the direct and indirect consequences of any possible species interaction matrix. Interactions in these models can be estimated from observed co-occurrence rates via maximum likelihood, controlling for indirect effects. Using simulated landscapes with known interactions, I evaluated Markov networks and six existing approaches. Markov networks consistently outperformed the other methods, correctly isolating direct interactions between species pairs even when indirect interactions or abiotic factors largely overpowered them. Two computationally efficient approximations, which controlled for indirect effects with partial correlations or generalized linear models, also performed well. Null models showed no evidence of being able to control for indirect effects, and reliably yielded incorrect inferences when such effects were present.
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.
Liu, An-An; Li, Kang; Kanade, Takeo
2012-02-01
We propose a semi-Markov model trained in a max-margin learning framework for mitosis event segmentation in large-scale time-lapse phase contrast microscopy image sequences of stem cell populations. Our method consists of three steps. First, we apply a constrained optimization based microscopy image segmentation method that exploits phase contrast optics to extract candidate subsequences in the input image sequence that contains mitosis events. Then, we apply a max-margin hidden conditional random field (MM-HCRF) classifier learned from human-annotated mitotic and nonmitotic sequences to classify each candidate subsequence as a mitosis or not. Finally, a max-margin semi-Markov model (MM-SMM) trained on manually-segmented mitotic sequences is utilized to reinforce the mitosis classification results, and to further segment each mitosis into four predefined temporal stages. The proposed method outperforms the event-detection CRF model recently reported by Huh as well as several other competing methods in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells. For mitosis detection, an overall precision of 95.8% and a recall of 88.1% were achieved. For mitosis segmentation, the mean and standard deviation for the localization errors of the start and end points of all mitosis stages were well below 1 and 2 frames, respectively. In particular, an overall temporal location error of 0.73 ± 1.29 frames was achieved for locating daughter cell birth events.
NASA Astrophysics Data System (ADS)
Ma, Jie; Wang, Lin-Wang
2015-03-01
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. Using linear-scaling ab-initiomethods, we have calculated the electronic structures of the tetragonal phase of CH3NH3PbI3 with randomly orientated organic molecules in large supercells up to ~20,000 atoms. Due to 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. We find that the charge densities of the conduction-band minimum and the valence-band maximum are localized separately 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. We have also proposed a model to explain the charge localization.
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.
A critical appraisal of Markov state models
NASA Astrophysics Data System (ADS)
Schütte, Ch.; Sarich, M.
2015-09-01
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a molecular system in equilibrium has gained a lot of attention recently. The last 10 years have seen an ever increasing publication activity on how to construct Markov State Models (MSMs) for very different molecular systems ranging from peptides to proteins, from RNA to DNA, and via molecular sensors to molecular aggregation. Simultaneously the accompanying theory behind MSM building and approximation quality has been developed well beyond the concepts and ideas used in practical applications. This article reviews the main theoretical results, provides links to crucial new developments, outlines the full power of MSM building today, and discusses the essential limitations still to overcome.
Markov state models based on milestoning
NASA Astrophysics Data System (ADS)
Schütte, Christof; Noé, Frank; Lu, Jianfeng; Sarich, Marco; Vanden-Eijnden, Eric
2011-05-01
Markov state models (MSMs) have become the tool of choice to analyze large amounts of molecular dynamics data by approximating them as a Markov jump process between suitably predefined states. Here we investigate "Core Set MSMs," a new type of MSMs that build on metastable core sets acting as milestones for tracing the rare event kinetics. We present a thorough analysis of Core Set MSMs based on the existing milestoning framework, Bayesian estimation methods and Transition Path Theory (TPT). We show that Core Set MSMs can be used to extract phenomenological rate constants between the metastable sets of the system and to approximate the evolution of certain key observables. The performance of Core Set MSMs in comparison to standard MSMs is analyzed and illustrated on a toy example and in the context of the torsion angle dynamics of alanine dipeptide.
Metastability for Markov processes with detailed balance.
Larralde, Hernán; Leyvraz, François
2005-04-29
We present a definition for metastable states applicable to arbitrary finite state Markov processes satisfying detailed balance. In particular, we identify a crucial condition that distinguishes metastable states from other slow decaying modes and which allows us to show that our definition has several desirable properties similar to those postulated in the restricted ensemble approach. The intuitive physical meaning of this condition is simply that the total equilibrium probability of finding the system in the metastable state is negligible.
Metastability for Markov Processes with Detailed Balance
NASA Astrophysics Data System (ADS)
Larralde, Hernán; Leyvraz, François
2005-04-01
We present a definition for metastable states applicable to arbitrary finite state Markov processes satisfying detailed balance. In particular, we identify a crucial condition that distinguishes metastable states from other slow decaying modes and which allows us to show that our definition has several desirable properties similar to those postulated in the restricted ensemble approach. The intuitive physical meaning of this condition is simply that the total equilibrium probability of finding the system in the metastable state is negligible.
The cutoff phenomenon in finite Markov chains.
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
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.
Laparoscopic task recognition using Hidden Markov Models.
Dosis, Aristotelis; Bello, Fernando; Gillies, Duncan; Undre, Shabnam; Aggarwal, Rajesh; Darzi, Ara
2005-01-01
Surgical skills assessment has been paid increased attention over the last few years. Stochastic models such as Hidden Markov Models have recently been adapted to surgery to discriminate levels of expertise. Based on our previous work combining synchronized video and motion analysis we present preliminary results of a HMM laparoscopic task recognizer which aims to model hand manipulations and to identify and recognize simple surgical tasks.
On Measures Driven by Markov Chains
NASA Astrophysics Data System (ADS)
Heurteaux, Yanick; Stos, Andrzej
2014-12-01
We study measures on which are driven by a finite Markov chain and which generalize the famous Bernoulli products.We propose a hands-on approach to determine the structure function and to prove that the multifractal formalism is satisfied. Formulas for the dimension of the measures and for the Hausdorff dimension of their supports are also provided. Finally, we identify the measures with maximal dimension.
Hidden Markov Model Analysis of Multichromophore Photobleaching
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
Constructing Dynamic Event Trees from Markov Models
Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood
2006-05-01
In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: a) partitioning the process variable state space into magnitude intervals (cells), b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank
A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models
NASA Astrophysics Data System (ADS)
Nicholl, P.; Amira, A.; Bouchaffra, D.; Perrott, R. H.
2007-12-01
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy.
Markov chains and semi-Markov models in time-to-event analysis
Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.
2014-01-01
A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062
NASA Technical Reports Server (NTRS)
English, Thomas
2005-01-01
A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.
Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model
NASA Astrophysics Data System (ADS)
Nawaz, Muhammad Atif; Curtis, Andrew
2017-02-01
Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of 2-D spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2-D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P- and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell-facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations - an assumption referred to as `localized likelihoods'. The hidden state (facies) at a location cannot be determined solely by the observation at that
Bayesian Inversion of Seismic Attributes for Geological Facies using a Hidden Markov Model
NASA Astrophysics Data System (ADS)
Nawaz, Muhammad Atif; Curtis, Andrew
2016-11-01
Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of two-dimensional spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P- and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell - facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations - an assumption referred to as localized likelihoods. The hidden state (facies) at a location cannot be determined solely by the
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.
Cook, Richard J; Yi, Grace Y; Lee, Ker-Ai; Gladman, Dafna D
2004-06-01
Clustered progressive chronic disease processes arise when interest lies in modeling damage in paired organ systems (e.g., kidneys, eyes), in diseases manifest in different organ systems, or in systemic conditions for which damage may occur in several locations of the body. Multistate Markov models have considerable appeal for modeling damage in such settings, particularly when patients are only under intermittent observation. Generalizations are necessary, however, to deal with the fact that processes within subjects may not be independent. We describe a conditional Markov model in which the clustering in processes within subjects is addressed by the use of multiplicative random effects for each transition intensity. The random effects for the different transition intensities may be correlated within subjects, but are assumed to be independent for different subjects. We apply the mixed Markov model to a motivating data set of patients with psoriatic arthritis, and characterize the progressive course of damage in joints of the hand. A generalization to accommodate a subpopulation of "stayers" and extensions which facilitate regression are indicated and illustrated.
Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.
Mathew, B; Bauer, A M; Koistinen, P; Reetz, T C; Léon, J; Sillanpää, M J
2012-10-01
Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.
Recursive recovery of Markov transition probabilities from boundary value data
Patch, Sarah Kathyrn
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 x 4 problem. Finally, the smallest nontrivial problem in three dimensions, the 2 x 2 x 2 problem, is solved.
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.
NASA Astrophysics Data System (ADS)
Staňová, Sidónia; Soták, Ján; Hudec, Norbert
2009-08-01
Methods based on the Markov Chains can be easily applied in the evaluation of order in sedimentary sequences. In this contribution Markov Chain analysis was applied to analysis of turbiditic formation of the Outer Western Carpathians in NW Slovakia, although it also has broader utilization in the interpretation of sedimentary sequences from other depositional environments. Non-random facies transitions were determined in the investigated strata and compared to the standard deep-water facies models to provide statistical evidence for the sedimentological interpretation of depositional processes. As a result, six genetic facies types, interpreted in terms of depositional processes, were identified. They comprise deposits of density flows, turbidity flows, suspension fallout as well as units which resulted from syn- or post-depositional deformation.
Is anoxic depolarisation associated with an ADC threshold? A Markov chain Monte Carlo analysis.
King, Martin D; Crowder, Martin J; Hand, David J; Harris, Neil G; Williams, Stephen R; Obrenovitch, Tihomir P; Gadian, David G
2005-12-01
A Bayesian nonlinear hierarchical random coefficients model was used in a reanalysis of a previously published longitudinal study of the extracellular direct current (DC)-potential and apparent diffusion coefficient (ADC) responses to focal ischaemia. The main purpose was to examine the data for evidence of an ADC threshold for anoxic depolarisation. A Markov chain Monte Carlo simulation approach was adopted. The Metropolis algorithm was used to generate three parallel Markov chains and thus obtain a sampled posterior probability distribution for each of the DC-potential and ADC model parameters, together with a number of derived parameters. The latter were used in a subsequent threshold analysis. The analysis provided no evidence indicating a consistent and reproducible ADC threshold for anoxic depolarisation.
Segmentation of brain tumors in 4D MR images using the hidden Markov model.
Solomon, Jeffrey; Butman, John A; Sood, Arun
2006-12-01
Tumor size is an objective measure that is used to evaluate the effectiveness of anticancer agents. Responses to therapy are categorized as complete response, partial response, stable disease and progressive disease. Implicit in this scheme is the change in the tumor over time; however, most tumor segmentation algorithms do not use temporal information. Here we introduce an automated method using probabilistic reasoning over both space and time to segment brain tumors from 4D spatio-temporal MRI data. The 3D expectation-maximization method is extended using the hidden Markov model to infer tumor classification based on previous and subsequent segmentation results. Spatial coherence via a Markov Random Field was included in the 3D spatial model. Simulated images as well as patient images from three independent sources were used to validate this method. The sensitivity and specificity of tumor segmentation using this spatio-temporal model is improved over commonly used spatial or temporal models alone.
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.
A Hidden Markov Model of Daily Precipitation over Western Colombia.
NASA Astrophysics Data System (ADS)
Rojo Hernández, Julián; Lall, Upmanu; Mesa Sanchez, Oscar
2017-04-01
A Hidden Markov Model of Daily Precipitation over Western Colombia. The western Pacific coast of Colombia (Chocó Region) is among the rainiest on earth, largely due to low level jet activity and orographic lifting along the western Andes. A hidden Markov model (HMM) is used to characterize daily rainfall occurrence at 250 gauge stations over the Western Pacific coast and Andean plateau in Colombia during the wet season (September - November) from 1970 to 2015. Four ''hidden'' rainfall states are identified, with the first pair representing wet and dry conditions at all stations, and the second pair North-West to South-East gradients in rainfall occurrence. Using the ERA-Interim reanalysis data (1979-2012) we show that the first pair of states are associated with low level jet convergence and divergence, while the second pair is associated with South Atlantic Convergence Zone activity and local convection. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal and interannual time scales, exhibiting a strong relationship with ENSO. Finally, a nonhomogeneous HMM (NHMM) is then used to simulate daily precipitation occurrence at the 250 stations, using the ERA-Interim vertically integrated moisture flux anomalies (two weeks lagged) and monthly means of the sea surface temperatures (one month lagged). Simulations from the NHMM are found to reproduce the relationship between the ENSO and the western Colombian precipitation. The NHMM simulations are also able to capture interannual changes in daily rainfall occurrence and dry-wet frequencies at some individual stations. It is suggested that a) HMM provides a useful tool that contributes to characterizing the Colombian's Hydro-Meteorology and it's anomalies during the ENSO, and b) the NHMM is an important tool to produce station-scale daily rainfall sequence scenarios for input into hydrological models.
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
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.
Optimal Markov approximations and generalized embeddings
NASA Astrophysics Data System (ADS)
Holstein, Detlef; Kantz, Holger
2009-05-01
Based on information theory, we present a method to determine an optimal Markov approximation for modeling and prediction from time series data. The method finds a balance between minimal modeling errors by taking as much as possible memory into account and minimal statistical errors by working in embedding spaces of rather small dimension. A key ingredient is an estimate of the statistical error of entropy estimates. The method is illustrated with several examples, and the consequences for prediction are evaluated by means of the root-mean-squared prediction error for point prediction.
Markov chain Monte Carlo without likelihoods.
Marjoram, Paul; Molitor, John; Plagnol, Vincent; Tavare, Simon
2003-12-23
Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likelihoods. It can also be used in frequentist applications, in particular for maximum-likelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.
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.
Algorithms for the Markov entropy decomposition
NASA Astrophysics Data System (ADS)
Ferris, Andrew J.; Poulin, David
2013-05-01
The Markov entropy decomposition (MED) is a recently proposed, cluster-based simulation method for finite temperature quantum systems with arbitrary geometry. In this paper, we detail numerical algorithms for performing the required steps of the MED, principally solving a minimization problem with a preconditioned Newton's algorithm, as well as how to extract global susceptibilities and thermal responses. We demonstrate the power of the method with the spin-1/2 XXZ model on the 2D square lattice, including the extraction of critical points and details of each phase. Although the method shares some qualitative similarities with exact diagonalization, we show that the MED is both more accurate and significantly more flexible.
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.
NASA Astrophysics Data System (ADS)
Ababaei, Behnam; Sohrabi, Teymour; Mirzaei, Farhad
2014-10-01
Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.
Vernon, C C; Hand, J W; Field, S B; Machin, D; Whaley, J B; van der Zee, J; van Putten, W L; van Rhoon, G C; van Dijk, J D; González González, D; Liu, F F; Goodman, P; Sherar, M
1996-07-01
Claims for the value of hyperthermia as an adjunct to radiotherapy in the treatment of cancer have mostly been based on small Phase I or II trials. To test the benefit of this form of treatment, randomized Phase III trials were needed. Five randomized trials addressing this question were started between 1988 and 1991. In these trials, patients were eligible if they had advanced primary or recurrent breast cancer, and local radiotherapy was indicated in preference to surgery. In addition, heating of the lesions and treatment with a prescribed (re)irradiation schedule had to be feasible and informed consent was obtained. The primary endpoint of all trials was local complete response. Slow recruitment led to a decision to collaborate and combine the trial results in one analysis, and report them simultaneously in one publication. Interim analyses were carried out and the trials were closed to recruitment when a previously agreed statistically significant difference in complete response rate was observed in the two larger trials. We report on pretreatment characteristics, the treatments received, the local response observed, duration of response, time to local failure, distant progression and survival, and treatment toxicity of the 306 patients randomized. The overall CR rate for RT alone was 41% and for the combined treatment arm was 59%, giving, after stratification by trial, an odds ratio of 2.3. Not all trials demonstrated an advantage for the combined treatment, although the 95% confidence intervals of the different trials all contain the pooled odds ratio. The greatest effect was observed in patients with recurrent lesions in previously irradiated areas, where further irradiation was limited to low doses. The combined result of the five trials has demonstrated the efficacy of hyperthermia as an adjunct to radiotherapy for treatment of recurrent breast cancer. The implication of these encouraging results is that hyperthermia appears to have an important role in
Milani, Amin S; Zand, Vahid; Abdollahi, Amir A; Froughreyhani, Mohammad; Zakeri-Milani, Parvin; Jafarabadi, Mohammad A
2016-07-01
This study compared the effect of local pressure and topical lidocaine-prilocaine (EMLA) cream on pain during infiltration injection for maxillary canine teeth. A total of 140 volunteer students participated in this split-mouth design randomized clinical trial. The subjects were randomly divided into four groups (n = 35). Before administration of anesthesia, in each group, one side was randomly selected as the experimental and the opposite side as the control. In group 1, finger pressure was applied on the alveolar mucosa on the experimental side and on the tooth crown on the control side. In group 2, 5% EMLA cream and placebo; in group 3, finger pressure and 5% EMLA cream; and in group 4, 5% EMLA cream and 20% benzocaine gel were applied. In all the groups, a buccal infiltration procedure was carried out. Pain during injection was recorded with visual analog scale (VAS). Wilcoxon and McNemar tests were used for statistical analysis of the results. Statistical significance was set at p < 0.05. The results showed that EMLA reduced the injection pain significantly more than benzocaine (p = 0.02). Also, injection pain was significantly lower with the use of EMLA in comparison to placebo (p = 0.00). Application of local pressure reduced the injection pain, but the difference from the control side was not significant (p = 0.05). Furthermore, the difference between application of local pressure and EMLA was not statistically significant (p = 0.08). Topical anesthesia of 5% EMLA was more effective than 20% benzocaine in reducing pain severity during infiltration injection. However, it was not significantly different in comparison to the application of local pressure.
NASA Astrophysics Data System (ADS)
Sato, Haruo; Fehler, Mike; Saito, Tatsuhiko
2004-06-01
Wave trains in high-frequency seismograms of local earthquakes are mostly composed of incoherent waves that are scattered by distributed heterogeneities within the lithosphere. Their phase variations are very complex; however, their wave envelopes are systematic, frequency-dependent, and vary regionally. Stochastic approaches are superior to deterministic wave-theoretical approaches for modeling wave envelopes in random media. The time width of a wavelet is broadened with increasing travel distance mostly because of diffraction caused by the long-wavelength components of random velocity inhomogeneity. The Markov approximation for the parabolic wave equation is effective for the synthesis of envelopes for random media whose spectra are poor in short-wavelength components; however, we have to consider the contribution of large-angle nonisotropic scattering if the random media are rich in short-wavelength inhomogeneities. Multiple nonisotropic scattering can be reliably modeled as isotropic scattering by using an effective isotropic scattering coefficient given by the momentum transfer scattering coefficient, which is a reciprocal of the transport mean free path. It is mostly controlled by the short-wavelength spectra of random media. We propose a hybrid method for the synthesis of whole wave envelopes that uses the envelope derived from the Markov approximation as a propagator in the radiative transfer integral equation for isotropic scattering. The envelopes resulting from the hybrid method agree well with ensemble average envelopes calculated by averaging envelopes from individual finite difference simulations of the wave equation for a suite of random media.
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.
Neyman, Markov processes and survival analysis.
Yang, Grace
2013-07-01
J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.
Stochastic seismic tomography by interacting Markov chains
NASA Astrophysics Data System (ADS)
Bottero, Alexis; Gesret, Alexandrine; Romary, Thomas; Noble, Mark; Maisons, Christophe
2016-10-01
Markov chain Monte Carlo sampling methods are widely used for non-linear Bayesian inversion where no analytical expression for the forward relation between data and model parameters is available. Contrary to the linear(ized) approaches, they naturally allow to evaluate the uncertainties on the model found. Nevertheless their use is problematic in high-dimensional model spaces especially when the computational cost of the forward problem is significant and/or the a posteriori distribution is multimodal. In this case, the chain can stay stuck in one of the modes and hence not provide an exhaustive sampling of the distribution of interest. We present here a still relatively unknown algorithm that allows interaction between several Markov chains at different temperatures. These interactions (based on importance resampling) ensure a robust sampling of any posterior distribution and thus provide a way to efficiently tackle complex fully non-linear inverse problems. The algorithm is easy to implement and is well adapted to run on parallel supercomputers. In this paper, the algorithm is first introduced and applied to a synthetic multimodal distribution in order to demonstrate its robustness and efficiency compared to a simulated annealing method. It is then applied in the framework of first arrival traveltime seismic tomography on real data recorded in the context of hydraulic fracturing. To carry out this study a wavelet-based adaptive model parametrization has been used. This allows to integrate the a priori information provided by sonic logs and to reduce optimally the dimension of the problem.
Equilibrium Control Policies for Markov Chains
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.
A Markov model of the Indus script
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
Non-Markov effects in intersecting sprays
NASA Astrophysics Data System (ADS)
Panchagnula, Mahesh; Kumaran, Dhivyaraja; Deevi, Sri Vallabha; Tangirala, Arun
2016-11-01
Sprays have been assumed to follow a Markov process. In this study, we revisit that assumption relying on experimental data from intersecting and non-intersecting sprays. A phase Doppler Particle Analyzer (PDPA) is used to measure particle diameter and velocity at various axial locations in the intersection region of two sprays. Measurements of single sprays, with one nozzle turned off alternatively are also obtained at the same locations. This data, treated as an unstructured time series is classified into three bins each for diameter (small, medium, large) and velocity (slow, medium, fast). Conditional probability analysis on this binned data showed a higher static correlation between droplet velocities, while diameter correlation is significantly alleviated (reduced) in intersecting sprays, compared to single sprays. Further analysis using serial correlation measures: auto-correlation function (ACF) and partial auto-correlation function (PACF) shows that the lagged correlations in droplet velocity are enhanced while those in the droplet diameter are significantly debilitated in intersecting sprays. We show that sprays are not necessarily Markov processes and that memory persists, even though curtailed to fewer lags in case of size, and enhanced in case of droplet velocity.
Monitoring volcano activity through Hidden Markov Model
NASA Astrophysics Data System (ADS)
Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.
2013-12-01
During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.
A Markov model of the Indus script.
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.