Exact simulation of Brown-Resnick random fields at a finite number of locations
DEFF Research Database (Denmark)
Dieker, Ton; Mikosch, Thomas Valentin
2015-01-01
We propose an exact simulation method for Brown-Resnick random fields, building on new representations for these stationary max-stable fields. The main idea is to apply suitable changes of measure.......We propose an exact simulation method for Brown-Resnick random fields, building on new representations for these stationary max-stable fields. The main idea is to apply suitable changes of measure....
Multivariate Max-Stable Spatial Processes
Genton, Marc G.
2014-01-06
Analysis of spatial extremes is currently based on univariate processes. Max-stable processes allow the spatial dependence of extremes to be modelled and explicitly quantified, they are therefore widely adopted in applications. For a better understanding of extreme events of real processes, such as environmental phenomena, it may be useful to study several spatial variables simultaneously. To this end, we extend some theoretical results and applications of max-stable processes to the multivariate setting to analyze extreme events of several variables observed across space. In particular, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. Then, we define a Poisson process construction in the multivariate setting and introduce multivariate versions of the Smith Gaussian extremevalue, the Schlather extremal-Gaussian and extremal-t, and the BrownResnick models. Inferential aspects of those models based on composite likelihoods are developed. We present results of various Monte Carlo simulations and of an application to a dataset of summer daily temperature maxima and minima in Oklahoma, U.S.A., highlighting the utility of working with multivariate models in contrast to the univariate case. Based on joint work with Simone Padoan and Huiyan Sang.
Multivariate max-stable spatial processes
Genton, Marc G.
2015-02-11
Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.
Tukey max-stable processes for spatial extremes
Xu, Ganggang
2016-09-21
We propose a new type of max-stable process that we call the Tukey max-stable process for spatial extremes. It brings additional flexibility to modeling dependence structures among spatial extremes. The statistical properties of the Tukey max-stable process are demonstrated theoretically and numerically. Simulation studies and an application to Swiss rainfall data indicate the effectiveness of the proposed process. © 2016 Elsevier B.V.
A class of not max-stable extreme value distributions
Institute of Scientific and Technical Information of China (English)
JIANG Yue-xiang
2005-01-01
The sequences {Zi,n, l≤i≤n}, n≥l have multi-nomial distribution among i.i.d. random variables {X1,i, i≥1}, {X2,i,u≥l }, …, {Xm,i, i≥1 }. The extreme value distribution Gz(x) of this particular triangular array of i.i.d, random variables Z1,n, Z2Zn,n is discussed in this paper. We found a new type of not max-stable extreme value distributions, i) Gz(x) = r-1∏i=1ФAiαi(x) × Фαr (x);ii) Gz (x) = r-1∏i=1ψAiαi (x) × ψαr (x); iii) Gz (x) = r-1∏i=1 ∧Ai (λix) × A(x), r≥2, 0＜α1≤α2≤…≤αr and λi∈ (0,1] for i, l≤i≤r-1 which occur if Fj, …, Fm belong to the same MDA.
On the likelihood function of Gaussian max-stable processes
Genton, M. G.
2011-05-24
We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Conditional Sampling for Max-Stable Processes with a Mixed Moving Maxima Representation
Oesting, Marco
2012-01-01
This paper deals with the question of conditional sampling and prediction for the class of stationary max-stable processes which allow for a mixed moving maxima representation. We develop an exact procedure for conditional sampling using the Poisson point process structure of such processes. For explicit calculations we restrict ourselves to the one-dimensional case and use a finite number of shape functions satisfying some regularity conditions. For more general shape functions approximation techniques are presented. Our algorithm is applied to the Gaussian extreme value process and the Brown- Resnick process. Finally, we compare our computational results to other approaches.
Calibrating max-stable models of rainfall extremes at multiple timescales
Le, Phuong Dong; Leonard, Michael; Westra, Seth
2016-04-01
Understanding the probabilistic behaviour of extreme rainfall events is critical for estimating the risk of flooding, leading to better design of infrastructure and management of flood events. The majority of engineering design is based on estimates of the probability of extreme rainfall known as the Intensity-Frequency-Duration relationship (IDF). IDF curves are estimated at each rain gauge and are subsequently interpolated for application to ungauged locations. The pointwise nature of IDF estimates leads to difficulties, especially at sub-daily timescales, due to the sparseness of sub-daily extreme rainfall data. As a result there is greater uncertainty and potential for bias when estimating sub-daily extreme rainfall. By using a model that incorporates dependence between spatial extremes as well as across multiple timescales, there is considerable potential to improve estimates of extreme rainfall. The aim of this research is to develop max-stable models of extreme rainfall that have both spatial dependence as well as dependence across timescales. Max-stable processes are a direct extension of the univariate generalized extreme value (GEV) model into the spatial domain. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, Koutsoyiannis et al. (1998) proposed a mathematical framework which expresses the parameters as a function of timescale. This parameterization is important because it allows data to be incorporated from daily rainfall stations to improve estimates at sub-daily timescales. The approach therefore addresses the issue of sparseness for sub-daily stations by exploiting the denser network of daily stations. A case study in the Hawkesbury-Nepean catchment near Sydney is used, having 82 daily gauges (>50 years) and 13 sub-daily gauges (>24 years) over a region of 300 km x 300 km area. The max-stable
Freno, Antonino
2011-01-01
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an
Castruccio, Stefano
2016-01-01
In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of points is a very challenging problem and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.
High-order Composite Likelihood Inference for Max-Stable Distributions and Processes
Castruccio, Stefano
2015-09-29
In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.
Associative Hierarchical Random Fields.
Ladický, L'ubor; Russell, Chris; Kohli, Pushmeet; Torr, Philip H S
2014-06-01
This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.
Random walks, random fields, and disordered systems
Černý, Jiří; Kotecký, Roman
2015-01-01
Focusing on the mathematics that lies at the intersection of probability theory, statistical physics, combinatorics and computer science, this volume collects together lecture notes on recent developments in the area. The common ground of these subjects is perhaps best described by the three terms in the title: Random Walks, Random Fields and Disordered Systems. The specific topics covered include a study of Branching Brownian Motion from the perspective of disordered (spin-glass) systems, a detailed analysis of weakly self-avoiding random walks in four spatial dimensions via methods of field theory and the renormalization group, a study of phase transitions in disordered discrete structures using a rigorous version of the cavity method, a survey of recent work on interacting polymers in the ballisticity regime and, finally, a treatise on two-dimensional loop-soup models and their connection to conformally invariant systems and the Gaussian Free Field. The notes are aimed at early graduate students with a mod...
Markov Random Field Surface Reconstruction
DEFF Research Database (Denmark)
Paulsen, Rasmus Reinhold; Bærentzen, Jakob Andreas; Larsen, Rasmus
2010-01-01
) and knowledge about data (the observation model) in an orthogonal fashion. Local models that account for both scene-specific knowledge and physical properties of the scanning device are described. Furthermore, how the optimal distance field can be computed is demonstrated using conjugate gradients, sparse......A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaption of Markov Random Field regularization of a distance field. The Markov Random Field formulation allows us to integrate both knowledge about the type of surface we wish to reconstruct (the prior...
Random Fields and Spin Glasses
De Dominicis, Cirano; Giardina, Irene
2010-06-01
1. A brief introduction; 2. The Random Field Ising model; 3. The dynamical approach; 4. The p=2 spherical model; 5. Mean field spin glasses: one-step RSB; 6. The Sherrington-Kirkpatrick model; 7. Mean field via TAP equations; 8. Spin glass above D=6; 9. Propagators, mostly replicon; 10. Ward-Takahashi identities and Goldstone modes; 11. Alternative approaches and conclusions; Appendices; Index.
Random scalar fields and hyperuniformity
Ma, Zheng; Torquato, Salvatore
2017-06-01
Disordered many-particle hyperuniform systems are exotic amorphous states of matter that lie between crystals and liquids. Hyperuniform systems have attracted recent attention because they are endowed with novel transport and optical properties. Recently, the hyperuniformity concept has been generalized to characterize two-phase media, scalar fields, and random vector fields. In this paper, we devise methods to explicitly construct hyperuniform scalar fields. Specifically, we analyze spatial patterns generated from Gaussian random fields, which have been used to model the microwave background radiation and heterogeneous materials, the Cahn-Hilliard equation for spinodal decomposition, and Swift-Hohenberg equations that have been used to model emergent pattern formation, including Rayleigh-Bénard convection. We show that the Gaussian random scalar fields can be constructed to be hyperuniform. We also numerically study the time evolution of spinodal decomposition patterns and demonstrate that they are hyperuniform in the scaling regime. Moreover, we find that labyrinth-like patterns generated by the Swift-Hohenberg equation are effectively hyperuniform. We show that thresholding (level-cutting) a hyperuniform Gaussian random field to produce a two-phase random medium tends to destroy the hyperuniformity of the progenitor scalar field. We then propose guidelines to achieve effectively hyperuniform two-phase media derived from thresholded non-Gaussian fields. Our investigation paves the way for new research directions to characterize the large-structure spatial patterns that arise in physics, chemistry, biology, and ecology. Moreover, our theoretical results are expected to guide experimentalists to synthesize new classes of hyperuniform materials with novel physical properties via coarsening processes and using state-of-the-art techniques, such as stereolithography and 3D printing.
Variational Infinite Hidden Conditional Random Fields
Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja; Ghahramani, Zoubin
2015-01-01
Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of
Xu, Ganggang
2016-07-15
We propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States.
Zhang, Qiang; Xiao, Mingzhong; Singh, Vijay P.; Chen, Yongqin David
2014-11-01
Monthly precipitation extremes defined by monthly maximum one-day precipitation amount (Rx1day) and maximum consecutive five-day precipitation amount (Rx5day) were analyzed based on daily precipitation data covering a period of 1957 to 2010 across the Poyang Lake basin, the largest freshwater lake basin in the lower Yangtze River basin, China. Based on the max-stable, impacts of El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) on the annual and seasonal Rx1day and Rx5day regimes in the Poyang Lake basin were evaluated. Results indicated that annual Rx1day and Rx5day were influenced mainly by the ENSO events a year earlier and the relations between annual Rx1day and Rx5day and ENSO were statistically positive. However, influences of climate indices on the seasonal Rx1day and Rx5day are complicated when compared with the influences on annual Rx1day and Rx5day regimes. ENSO and PDO can enhance the spring Rx1day regime of the subsequent year and the same year, respectively, but IOD weakens spring Rx1day in the same year. In summer, ENSO can enhance Rx1day of the subsequent year. However, Rx1day in summer had a decreasing tendency. IOD and NAO influenced the autumn Rx1day in another way, i.e., IOD decreases autumn Rx1day of the subsequent year; while NAO enhances autumn Rx1day of the same year. Meanwhile, ENSO can amplify the summer Rx1day in terms of variability, while IOD and NAO can, respectively, influence autumn and winter Rx1day in terms of mean. Influences of climate indices on annual and seasonal Rx5day are similar to those on Rx1day. Results of this study are of great theoretical as well as practical merit in terms of evaluation of droughts and floods under the influences of climate indices.
Efficient robust conditional random fields.
Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A
2015-10-01
Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.
Sensing Random Electromagnetic Fields and Applications
2015-06-23
AFRL-OSR-VA-TR-2015-0172 SENSING RANDOM ELECTROMAGNETIC FIELDS AND APPLICATIONS Aristide Dogariu UNIVERSITY OF CENTRAL FLORIDA Final Report 06/23... Electromagnetic Fields and Applications 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK...14. ABSTRACT Random electromagnetic fields (REF) exist in all forms and one common origin is a result of the interaction of coherent fields with
Persistent Homology for Random Fields and Complexes
Adler, Robert J; Borman, Matthew S; Subag, Eliran; Weinberger, Shmuel
2010-01-01
We discuss and review recent developments in the area of applied algebraic topology, such as persistent homology and barcodes. In particular, we discuss how these are related to understanding more about manifold learning from random point cloud data, the algebraic structure of simplicial complexes determined by random vertices, and, in most detail, the algebraic topology of the excursion sets of random fields.
Approximating a harmonizable isotropic random field
Directory of Open Access Journals (Sweden)
Randall J. Swift
2001-01-01
Full Text Available The class of harmonizable fields is a natural extension of the class of stationary fields. This paper considers a stochastic series approximation of a harmonizable isotropic random field. This approximation is useful for numerical simulation of such a field.
Uniform dimension results for Gaussian random fields
Institute of Scientific and Technical Information of China (English)
2009-01-01
Let X = {X(t),t ∈ RN} be a Gaussian random field with values in Rd defined by X(t) =(X1(t),...,Xd(t)), t ∈ RN.(1) The properties of space and time anisotropy of X and their connections to uniform Hausdorff dimension results are discussed.It is shown that in general the uniform Hausdorff dimension result does not hold for the image sets of a space-anisotropic Gaussian random field X.When X is an(N,d)-Gaussian random field as in(1),where X1,...,Xd are independent copies of a real valued,centered Gaussian random field X0 which is anisotropic in the time variable.We establish uniform Hausdorff dimension results for the image sets of X.These results extend the corresponding results on one-dimensional Brownian motion,fractional Brownian motion and the Brownian sheet.
Concentration inequalities for random fields via coupling
Chazottes, J. R.; Collet, P.; Kuelske, C.; Redig, F.
We present a new and simple approach to concentration inequalities in the context of dependent random processes and random fields. Our method is based on coupling and does not use information inequalities. In case one has a uniform control on the coupling, one obtains exponential concentration
APLIKASI MARKOV RANDOM FIELD PADA MASALAH INDUSTRI
Directory of Open Access Journals (Sweden)
Siana Halim
2002-01-01
Full Text Available Markov chain in the stochastic process is widely used in the industrial problems particularly in the problem of determining the market share of products. In this paper we are going to extend the one in the random field so called the Markov Random Field and applied also in the market share problem with restriction the market is considered as a discrete lattice and Pott's models are going to be used as the potential function. Metropolis sampler is going to be used to determine the stability condition. Abstract in Bahasa Indonesia : Rantai Markov dalam proses stokastik seringkali digunakan dalam penyelesaian masalah industri khususnya dalam masalah penentuan market share. Dalam artikel ini akan dibahas perluasan Rantai Markov tesebut ke dalam sebuah random field yang disebut sebagai Markov Random Field (MRF yang juga akan diaplikasikan pada masalah market share dengan batasan daerah pemasarannya dianggap sebagai sebuah lattice diskrit dan fungsi potensial yang akan digunakan adalah Potts models. Akan digunakan Metropolis sampler untuk menentukan kondisi stabil. Kata kunci: proses stokastik, Markov Random Field, Gibbs Random Field, Potts model, Metropolis sampling.
Entropy estimates for simple random fields
DEFF Research Database (Denmark)
Forchhammer, Søren; Justesen, Jørn
1995-01-01
We consider the problem of determining the maximum entropy of a discrete random field on a lattice subject to certain local constraints on symbol configurations. The results are expected to be of interest in the analysis of digitized images and two dimensional codes. We shall present some examples...... of binary and ternary fields with simple constraints. Exact results on the entropies are known only in a few cases, but we shall present close bounds and estimates that are computationally efficient...
Relativistic diffusive motion in random electromagnetic fields
Energy Technology Data Exchange (ETDEWEB)
Haba, Z, E-mail: zhab@ift.uni.wroc.pl [Institute of Theoretical Physics, University of Wroclaw, 50-204 Wroclaw, Plac Maxa Borna 9 (Poland)
2011-08-19
We show that the relativistic dynamics in a Gaussian random electromagnetic field can be approximated by the relativistic diffusion of Schay and Dudley. Lorentz invariant dynamics in the proper time leads to the diffusion in the proper time. The dynamics in the laboratory time gives the diffusive transport equation corresponding to the Juettner equilibrium at the inverse temperature {beta}{sup -1} = mc{sup 2}. The diffusion constant is expressed by the field strength correlation function (Kubo's formula).
Random field estimation approach to robot dynamics
Rodriguez, Guillermo
1990-01-01
The difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.
Interfaces in Random Field Ising Systems
Seppälä, Eira
2001-03-01
Domain walls are studied in random field Ising magnets at T=0 in two and three dimensions using exact ground state calculations. In 2D below the random field strength dependent length scale Lb the walls exhibit a super-rough behavior with a roughness exponent greater than unity ζ ~= 1.20 ± 0.05. The nearest-neighbor height difference probability distribution depends on the system size below L_b. Above Lb domains become fractal, ζ ~= 1.(E. T. Seppälä, V. Petäjä, and M. J. Alava, Phys. Rev. E 58), R5217 (1998). The energy fluctuation exponent has a value θ=1, contradicting the exponent relation θ = 2ζ -1 due to the broken scale-invariance, below Lb and vanishes for system sizes above L_b. The broken scale-invariance should be manifest also in Kardar-Parisi-Zhang problem with random-field noise.(E. Frey, U. C. Täuber, and H. K. Janssen, Europhys. Lett. 47), 14 (1999). In 3D there exists a transition between ferromagnetic and paramagnetic phases at the critical random field strength (Δ/J)_c. Below (Δ/J)c the roughness exponent is also greater ζ ~= 0.73 ± 0.03 than the functional-renormalization-group calculation result ζ = (5-d)/3.(D. Fisher, Phys. Rev. Lett. 56), 1964 (1986).(P. Chauve, P. Le Doussal, and K. Wiese, cond-mat/0006056.) The height differences are system size dependent in 3D, as well. The behavior of the domain walls in 2D below Lb with a constant external field, i.e., the random-bulk wetting, is demonstrated.(E. T. Seppälä, I. Sillanpää, and M. J. Alava, unpublished.)
Conditional Random Fields for Image Labeling
Directory of Open Access Journals (Sweden)
Tong Liu
2016-01-01
Full Text Available With the rapid development and application of CRFs (Conditional Random Fields in computer vision, many researchers have made some outstanding progress in this domain because CRFs solve the classical version of the label bias problem with respect to MEMMs (maximum entropy Markov models and HMMs (hidden Markov models. This paper reviews the research development and status of object recognition with CRFs and especially introduces two main discrete optimization methods for image labeling with CRFs: graph cut and mean field approximation. This paper describes graph cut briefly while it introduces mean field approximation more detailedly which has a substantial speed of inference and is researched popularly in recent years.
Gaussian Markov random fields theory and applications
Rue, Havard
2005-01-01
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studies and, online, a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.
Ergodicity and Gaussianity for Spherical Random Fields
Marinucci, Domenico
2009-01-01
We investigate the relationship between ergodicity and asymptotic Gaussianity of isotropic spherical random fields, in the high-resolution (or high-frequency) limit. In particular, our results suggest that under a wide variety of circumstances the two conditions are equivalent, i.e. the sample angular power spectrum may converge to the population value if and only if the underlying field is asymptotically Gaussian, in the high frequency sense. These findings may shed some light on the role of Cosmic Variance in Cosmic Microwave Background (CMB) radiation data analysis.
Field and intensity correlation in random media
Sebbah; Pnini; Genack
2000-11-01
We have obtained the spectral and spatial field correlation functions, C(E)(Deltaomega) and C(E)(Deltax), respectively, from measurement of the microwave field spectrum at a series of points along a line on the output of a random dielectric medium. C(E)(Deltaomega) and C(E)(Deltax) are shown to be the Fourier transforms, respectively, of the time of flight distribution, obtained from pulsed measurements, and of the specific intensity. Unlike C(E)(Deltaomega), the imaginary part of C(E)(Deltax) is shown to vanish as a result of the isotropy of the correlation function in the output plane. The complex square of the field correlation function gives the short-range or C1 contribution to the intensity correlation function C. Longer-range contributions to the intensity correlation function are obtained directly by subtracting C1 from C and are in good agreement with theory.
Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields
Directory of Open Access Journals (Sweden)
Martin Schlather
2015-02-01
Full Text Available Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with cross- covariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matrn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.
Positive random fields for modeling material stiffness and compliance
DEFF Research Database (Denmark)
Hasofer, Abraham Michael; Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1998-01-01
with material properties modeled in terms of the considered random fields.The paper addsthe gamma field, the Fisher field, the beta field, and their reciprocal fields to the catalogue. These fields are all defined on the basis of sums of squares of independent standard Gaussian random variables.All the existing...... marginal moments and the correlation functions are obtained explicitly. Also an inverse Gaussian fieldis added to the catalogue. It is defined in terms of first passage times in correlated joint Brownian motions. Finally an n-dimensional random vector of positive components is defined such that it can...
Random Dieudonne modules, random p-divisible groups, and random curves over finite fields
Cais, Bryden; Zureick-Brown, David
2012-01-01
We describe a probability distribution on isomorphism classes of principally quasi-polarized p-divisible groups over a finite field k of characteristic p which can reasonably be thought of as "uniform distribution," and we compute the distribution of various statistics (p-corank, a-number, etc.) of p-divisible groups drawn from this distribution. It is then natural to ask to what extent the p-divisible groups attached to a randomly chosen hyperelliptic curve (resp. curve, resp. abelian variety) over k are uniformly distributed in this sense. For instance, one can ask whether the proportion of genus-g curves over F_p whose Jacobian is ordinary approaches the limit that such a heuristic would predict. This heuristic is analogous to conjectures of Cohen-Lenstra type for fields k of characteristic other than p, in which case the random p-divisible group is defined by a random matrix recording the action of Frobenius. Extensive numerical investigation reveals some cases of agreement with the heuristic and some int...
Efficient Incorporation of Markov Random Fields in Change Detection
DEFF Research Database (Denmark)
Aanæs, Henrik; Nielsen, Allan Aasbjerg; Carstensen, Jens Michael
2009-01-01
of noise, implying that the pixel-wise classifier is also noisy. There is thus a need for incorporating local homogeneity constraints into such a change detection framework. For this modelling task Markov Random Fields are suitable. Markov Random Fields have, however, previously been plagued by lack...
Sampling Strategies for Fast Updating of Gaussian Markov Random Fields
Brown, D. Andrew; McMahan, Christopher S.
2017-01-01
Gaussian Markov random fields (GMRFs) are popular for modeling temporal or spatial dependence in large areal datasets due to their ease of interpretation and computational convenience afforded by conditional independence and their sparse precision matrices needed for random variable generation. Using such models inside a Markov chain Monte Carlo algorithm requires repeatedly simulating random fields. This is a nontrivial issue, especially when the full conditional precision matrix depends on ...
Level sets and extrema of random processes and fields
Azais, Jean-Marc
2009-01-01
A timely and comprehensive treatment of random field theory with applications across diverse areas of study Level Sets and Extrema of Random Processes and Fields discusses how to understand the properties of the level sets of paths as well as how to compute the probability distribution of its extremal values, which are two general classes of problems that arise in the study of random processes and fields and in related applications. This book provides a unified and accessible approach to these two topics and their relationship to classical theory and Gaussian processes and fields, and the most modern research findings are also discussed. The authors begin with an introduction to the basic concepts of stochastic processes, including a modern review of Gaussian fields and their classical inequalities. Subsequent chapters are devoted to Rice formulas, regularity properties, and recent results on the tails of the distribution of the maximum. Finally, applications of random fields to various areas of mathematics a...
Supplementary Material for: Tukey g-and-h Random Fields
Xu, Ganggang
2016-01-01
We propose a new class of transGaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States. Supplementary materials for this article are available online.
Random field distributed Heisenberg model on a thin film geometry
Energy Technology Data Exchange (ETDEWEB)
Akıncı, Ümit, E-mail: umit.akinci@deu.edu.tr
2014-11-15
The effects of the bimodal random field distribution on the thermal and magnetic properties of the Heisenberg thin film have been investigated by making use of a two spin cluster with the decoupling approximation. Particular attention has been devoted to the obtaining of phase diagrams and magnetization behaviors. The physical behaviors of special as well as tricritical points are discussed for a wide range of selected Hamiltonian parameters. For example, it is found that when the strength of a magnetic field increases, the locations of the special point (which is the ratio of the surface exchange interaction and the exchange interaction of the inner layers that makes the critical temperature of the film independent of the thickness) in the related plane decrease. Moreover, tricritical behavior has been obtained for higher values of the magnetic field, and influences of the varying Hamiltonian parameters on its behavior have been elucidated in detail in order to have a better understanding of the mechanism underlying the considered system. - Highlights: • Effect of bimodal random field distribution within the Heisenberg model is investigated. • Phase diagrams of the random field Heisenberg model in a thin film geometry are obtained. • Effect of the random field on the magnetic properties is obtained. • Variation of the special point with random field is determined. • Variation of the tricritical point with random field is determined.
Positive random fields for modeling material stiffness and compliance
DEFF Research Database (Denmark)
Hasofer, Abraham Michael; Ditlevsen, Ove Dalager; Tarp-Johansen, Niels Jacob
1998-01-01
Positive random fields with known marginal properties and known correlation function are not numerous in the literature. The most prominent example is the log\\-normal field for which the complete distribution is known and for which the reciprocal field is also lognormal. It is of interest to supp...
Hidden Conditional Random Fields for Action Recognition
Chen, L.; van der Aa, N.P.; Tan, R.T.; Veltkamp, R.C.
2014-01-01
In the field of action recognition, the design of features has been explored extensively, but the choice of action classification methods is limited. Commonly used classification methods like k-Nearest Neighbors and Support Vector Machines assume conditional independency between features. In contras
Subquantum nonlocal correlations induced by the background random field
Khrennikov, Andrei
2011-10-01
We developed a purely field model of microphenomena—prequantum classical statistical field theory (PCSFT). This model not only reproduces important probabilistic predictions of quantum mechanics (QM) including correlations for entangled systems, but also gives a possibility to go beyond QM, i.e. to make predictions of phenomena that could be observed at the subquantum level. In this paper, we discuss one such prediction—the existence of nonlocal correlations between prequantum random fields corresponding to all quantum systems. (And by PCSFT, quantum systems are represented by classical Gaussian random fields and quantum observables by quadratic forms of these fields.) The source of these correlations is the common background field. Thus all prequantum random fields are 'entangled', but in the sense of classical signal theory. On the one hand, PCSFT demystifies quantum nonlocality by reducing it to nonlocal classical correlations based on the common random background. On the other hand, it demonstrates total generality of such correlations. They exist even for distinguishable quantum systems in factorizable states (by PCSFT terminology—for Gaussian random fields with covariance operators corresponding to factorizable quantum states).
A hybrid random field model for scalable statistical learning.
Freno, A; Trentin, E; Gori, M
2009-01-01
This paper introduces hybrid random fields, which are a class of probabilistic graphical models aimed at allowing for efficient structure learning in high-dimensional domains. Hybrid random fields, along with the learning algorithm we develop for them, are especially useful as a pseudo-likelihood estimation technique (rather than a technique for estimating strict joint probability distributions). In order to assess the generality of the proposed model, we prove that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. Once we establish this result, we develop a scalable algorithm for learning the structure of hybrid random fields, which we call 'Markov Blanket Merging'. On the one hand, we characterize some complexity properties of Markov Blanket Merging both from a theoretical and from the experimental point of view, using a series of synthetic benchmarks. On the other hand, we evaluate the accuracy of hybrid random fields (as learned via Markov Blanket Merging) by comparing them to various alternative statistical models in a number of pattern classification and link-prediction applications. As the results show, learning hybrid random fields by the Markov Blanket Merging algorithm not only reduces significantly the computational cost of structure learning with respect to several considered alternatives, but it also leads to models that are highly accurate as compared to the alternative ones.
Properties and simulation of α-permanental random fields
DEFF Research Database (Denmark)
Møller, Jesper; Rubak, Ege Holger
An α-permanental random field is briefly speaking a model for a collection of random variables with positive associations, where α is a positive number and the probability generating function is given in terms of a covariance or more general function so that density and moment expressions are given...... by certain α-permanents. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields and their potential applications. The purpose of this paper is first to summarize useful probabilistic results using the simplest possible setting...
Versatility and robustness of Gaussian random fields for modelling random media
Quintanilla, John A.; Chen, Jordan T.; Reidy, Richard F.; Allen, Andrew J.
2007-06-01
One of the authors (JAQ) has recently introduced a method of modelling random materials using excursion sets of Gaussian random fields. This method uses convex quadratic programming to find the optimal admissible field autocorrelation function, providing both theoretical and computational advantages over other techniques such as simulated annealing. In this paper, we discuss the application of this algorithm to model various aerogel systems given small-angle neutron scattering data. We also present new results concerning the robustness of this method.
Shape Modelling Using Markov Random Field Restoration of Point Correspondences
DEFF Research Database (Denmark)
Paulsen, Rasmus Reinhold; Hilger, Klaus Baggesen
2003-01-01
A method for building statistical point distribution models is proposed. The novelty in this paper is the adaption of Markov random field regularization of the correspondence field over the set of shapes. The new approach leads to a generative model that produces highly homogeneous polygonized sh...
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Aging in the two-dimensional random-field systems
Cheng, Xiang; Ma, Tianyu; Urazhdin, Sergei; Boettcher, Stefan
Random fields introduced into the classical Ising and Heisenberg spin models can roughen the energy landscape, leading to complex nonequilibrium dynamics. The effects of random fields on magnetism have been previously studied in the context of dilute antiferromagnets (AF), impure substrates, and magnetic alloys [ 1 ] . We utilized random-field spin models to simulate the observed magnetic aging in thin-film ferromagnet/antiferromagnet (F/AF) bilayers. Our experiments show extremely slow cooperative relaxation over a wide range of temperatures and magnetic fields [ 2 ] . In our simulations, the experimental system is coarse-grained into a random field Ising model on a 2D square lattice. Monte Carlo simulations indicate that aging processes may be associated with the glassy evolution of the magnetic domain walls, due to the pinning by the random fields. The scaling of the simulated aging agrees well with experiments. Both are consistent with either a small power-law or logarithmic dependence on time. We further discuss the topological effects on aging due to the dimensional crossover from the Ising to the Heisenberg regime. Supported through NSF grant DMR-1207431.
Field-theoretic simulations of random copolymers with structural rigidity.
Mao, Shifan; MacPherson, Quinn; Qin, Jian; Spakowitz, Andrew J
2017-04-12
Copolymers play an important role in a range of soft-materials applications and biological phenomena. Prevalent works on block copolymer phase behavior use flexible chain models and incorporate interactions using a mean-field approximation. However, when phase separation takes place on length scales comparable to a few monomers, the structural rigidity of the monomers becomes important. In addition, concentration fluctuations become significant at short length scales, rendering the mean-field approximation invalid. In this work, we use simulation to address the role of finite monomer rigidity and concentration fluctuations in microphase segregation of random copolymers. Using a field-theoretic Monte-Carlo simulation of semiflexible polymers with random chemical sequences, we generate phase diagrams for random copolymers. We find that the melt morphology of random copolymers strongly depends on chain flexibility and chemical sequence correlation. Chemically anti-correlated copolymers undergo first-order phase transitions to local lamellar structures. With increasing degree of chemical correlation, this first-order phase transition is softened, and melts form microphases with irregular shaped domains. Our simulations in the homogeneous phase exhibit agreement with the density-density correlation from mean-field theory. However, conditions near a phase transition result in deviations between simulation and mean-field theory for the density-density correlation and the critical wavemode. Chain rigidity and sequence randomness lead to frustration in the segregated phase, introducing heterogeneity in the resulting morphologies.
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Two Dimensional Honeycomb Materials: Random Fields, Dissipation and Fluctuations
Frederico, T.; Oliveira, O.; de Paula, W.; Hussein, M. S.; Cardoso, T. R.
2017-02-01
In this paper, we propose a method to describe the many-body problem of electrons in honeycomb materials via the introduction of random fields which are coupled to the electrons and have a Gaussian distribution. From a one-body approach to the problem, after integrating exactly the contribution of the random fields, one builds a non-hermitian and dissipative effective Hamiltonian with two-body interactions. Our approach introduces besides the usual average over the electron field a second average over the random fields. The interplay of two averages enables the definition of various types of Green's functions which allow the investigation of fluctuation-dissipation characteristics of the interactions that are a manifestation of the many-body problem. In the current work, we study only the dissipative term, through the perturbative analysis of the dynamics associated the effective Hamiltonian generated by two different kinds of couplings. For the cases analyzed, the eigenstates of the effective Hamiltonian are complex and, therefore, some of the states have a finite life time. Moreover, we also investigate, in the mean field approximation, the most general parity conserving coupling to the random fields and compute the width of charge carriers Γ as a function of the Fermi energy E F . The theoretical prediction for Γ( E F ) is compared to the available experimental data for graphene. The good agreement between Γ t h e o and Γ e x p suggests that description of the many-body problem associated to the electrons in honeycomb materials can indeed be done via the introduction of random fields.
Creep motion in a random-field Ising model.
Roters, L; Lübeck, S; Usadel, K D
2001-02-01
We analyze numerically a moving interface in the random-field Ising model which is driven by a magnetic field. Without thermal fluctuations the system displays a depinning phase transition, i.e., the interface is pinned below a certain critical value of the driving field. For finite temperatures the interface moves even for driving fields below the critical value. In this so-called creep regime the dependence of the interface velocity on the temperature is expected to obey an Arrhenius law. We investigate the details of this Arrhenius behavior in two and three dimensions and compare our results with predictions obtained from renormalization group approaches.
Generating Functionals for Quantum Field Theories with Random Potentials
Jain, Mudit
2015-01-01
We consider generating functionals for computing correlators in quantum field theories with random potentials. Examples of such theories include condensed matter systems with quenched disorder (e.g. spin glass) or cosmological systems in context of the string theory landscape (e.g. cosmic inflation). We use the so-called replica trick to define two different generating functionals for calculating correlators of the quantum fields averaged over a given distribution of random potentials. The first generating functional is appropriate for calculating averaged (in-out) amplitudes and involves a single replica of fields, but the replica limit is taken to an (unphysical) negative one number of fields outside of the path integral. When the number of replicas is doubled the generating functional can also be used for calculating averaged probabilities (squared amplitudes) using the in-in construction. The second generating functional involves an infinite number of replicas, but can be used for calculating both in-out ...
Magnetic properties of mixed Ising system with random field
Institute of Scientific and Technical Information of China (English)
Liang Ya-Qiu; Wei Guo-Zhu; Zhang Qi; Qiu Wei; Zang Shu-Liang
2004-01-01
A spin-1/2 and spin-3/2 mixed Ising system in a random field is studied by the use of effective-field theory with correlations. The phase diagrams and thermal behaviours of magnetizations are investigated numerically for the honeycomb lattice (z=3) and square lattice (z=4) respectively. The tricritical behaviours for both honeycomb and square lattices, as well as the reentrant behaviour for the square lattice are found.
Tail Approximations of Integrals of Gaussian Random Fields
Liu, Jingchen
2010-01-01
This paper develops asymptotic approximations of $P(\\int_T e^{f(t)}dt > b)$ as $b\\rightarrow \\infty$ for homogeneous smooth Gaussian random field, $f$, living on a compact $d$-dimensional Jordan measurable set $T$. The integral of exponent of Gaussian random field is an important random variable for many generic models in spatial point processes, portfolio risk analysis, asset pricing and so forth. The analysis technique consists of two steps: 1. evaluate the tail probability $P(\\int_\\Delta e^{f(t)}dt > b)$ over a small domain $\\Delta$ depending on $b$, where $mes(\\Delta) \\rightarrow 0$ as $b\\rightarrow \\infty$ and $mes(\\cdot)$ is the Lebesgue measure; 2. with $\\Delta$ appropriately chosen, we show that $P(\\int_T e^{f(t)}dt > b) =(1+o(1)) mes(T) mes^{-1}(\\Delta) P(\\int_\\Delta e^{f(t)}dt > b)$.
Closed form maximum likelihood estimator of conditional random fields
Zhu, Zhemin; Hiemstra, Djoerd; Apers, Peter M.G.; Wombacher, Andreas
2013-01-01
Training Conditional Random Fields (CRFs) can be very slow for big data. In this paper, we present a new training method for CRFs called {\\em Empirical Training} which is motivated by the concept of co-occurrence rate. We show that the standard training (unregularized) can have many maximum likeliho
Infinite conditional random fields for human behavior analysis
Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja
2013-01-01
Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF
Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
Bousmalis, K.; Zafeiriou, S.; Morency, L.P.; Pantic, Maja; Ghahramani, Z.
2013-01-01
Hidden Conditional Random Fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An infinite HCRF is an HCRF with a countably infinite number of hidden states, which rids us not only of the necessit
Anisotropic Fractional Brownian Random Fields as White Noise Functionals
Institute of Scientific and Technical Information of China (English)
Zhi-yuan Huang; Chu-jin Li
2005-01-01
This investigation aims at a new construction of anisotropic fractional Brownisn random fields by the white noise approach. Moreover, we investigate its distribution and sample properties (stationariness of increments, self-similarity, sample continuity) which will furnish some useful views to future applications.
A note on moving average models for Gaussian random fields
DEFF Research Database (Denmark)
Hansen, Linda Vadgård; Thorarinsdottir, Thordis L.
The class of moving average models offers a flexible modeling framework for Gaussian random fields with many well known models such as the Matérn covariance family and the Gaussian covariance falling under this framework. Moving average models may also be viewed as a kernel smoothing of a Lévy...
Nonstationary random acoustic and electromagnetic fields as wave diffusion processes
Arnaut, L R
2007-01-01
We investigate the effects of relatively rapid variations of the boundaries of an overmoded cavity on the stochastic properties of its interior acoustic or electromagnetic field. For quasi-static variations, this field can be represented as an ideal incoherent and statistically homogeneous isotropic random scalar or vector field, respectively. A physical model is constructed showing that the field dynamics can be characterized as a generalized diffusion process. The Langevin--It\\^{o} and Fokker--Planck equations are derived and their associated statistics and distributions for the complex analytic field, its magnitude and energy density are computed. The energy diffusion parameter is found to be proportional to the square of the ratio of the standard deviation of the source field to the characteristic time constant of the dynamic process, but is independent of the initial energy density, to first order. The energy drift vanishes in the asymptotic limit. The time-energy probability distribution is in general n...
Using convex quadratic programming to model random media with Gaussian random fields
Quintanilla, John A.; Jones, W. Max
2007-04-01
Excursion sets of Gaussian random fields (GRFs) have been frequently used in the literature to model two-phase random media with measurable phase autocorrelation functions. The goal of successful modeling is finding the optimal field autocorrelation function that best approximates the prescribed phase autocorrelation function. In this paper, we present a technique which uses convex quadratic programming to find the best admissible field autocorrelation function under a prescribed discretization. Unlike previous methods, this technique efficiently optimizes over all admissible field autocorrelation functions, instead of optimizing only over a predetermined parametrized family. The results from using this technique indicate that the GRF model is significantly more versatile than observed in previous studies. An application to modeling a base-catalyzed tetraethoxysilane aerogel system given small-angle neutron scattering data is also presented
Driving a Superconductor to Insulator Transition with Random Gauge Fields
Nguyen, H. Q.; Hollen, S. M.; Shainline, J.; Xu, J. M.; Valles, J. M.
2016-11-01
Typically the disorder that alters the interference of particle waves to produce Anderson localization is potential scattering from randomly placed impurities. Here we show that disorder in the form of random gauge fields that act directly on particle phases can also drive localization. We present evidence of a superfluid bose glass to insulator transition at a critical level of this gauge field disorder in a nano-patterned array of amorphous Bi islands. This transition shows signs of metallic transport near the critical point characterized by a resistance , indicative of a quantum phase transition. The critical disorder depends on interisland coupling in agreement with recent Quantum Monte Carlo simulations. We discuss how this disorder tuned SIT differs from the common frustration tuned SIT that also occurs in magnetic fields. Its discovery enables new high fidelity comparisons between theoretical and experimental studies of disorder effects on quantum critical systems.
Three-dimensional extinction mapping using Gaussian random fields
Sale, S E
2014-01-01
We present a scheme for using stellar catalogues to map the three-dimensional distributions of extinction and dust within our Galaxy. Extinction is modelled as a Gaussian random field, whose covariance function is set by a simple physical model of the ISM that assumes a Kolmogorov-like power spectrum of turbulent fluctuations. As extinction is modelled as a random field, the spatial resolution of the resulting maps is set naturally by the data available; there is no need to impose any spatial binning. We verify the validity of our scheme by testing it on simulated extinction fields and show that its precision is significantly improved over previous dust-mapping efforts. The approach we describe here can make use of any photometric, spectroscopic or astrometric data; it is not limited to any particular survey. Consequently, it can be applied to a wide range of data from both existing and future surveys.
Cosmological fluctuations of a random field and radiation fluid
Energy Technology Data Exchange (ETDEWEB)
Bastero-Gil, Mar [Departamento de Física Teórica y del Cosmos, Campus de Fuentenueva, Universidad de Granada, Granada, 18071 (Spain); Berera, Arjun [SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3JZ (United Kingdom); Moss, Ian G. [School of Mathematics and Statistics, Newcastlle University, Newcastle upon Tyne, NE1 7RU (United Kingdom); Ramos, Rudnei O., E-mail: mbg@ugr.es, E-mail: ab@ph.ed.ac.uk, E-mail: ian.moss@ncl.ac.uk, E-mail: rudnei@uerj.br [Departamento de Física Teórica, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, RJ, 20550-013 Brazil (Brazil)
2014-05-01
A generalization of the random fluid hydrodynamic fluctuation theory due to Landau and Lifshitz is applied to describe cosmological fluctuations in systems with radiation and scalar fields. The viscous pressures, parametrized in terms of the bulk and shear viscosity coefficients, and the respective random fluctuations in the radiation fluid are combined with the stochastic and dissipative scalar evolution equation. This results in a complete set of equations describing the perturbations in both scalar and radiation fluids. These derived equations are then studied, as an example, in the context of warm inflation. Similar treatments can be done for other cosmological early universe scenarios involving thermal or statistical fluctuations.
Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields
DEFF Research Database (Denmark)
Thøgersen, Mikkel; Guerrero, Sergio Escalera; Gonzàlez, Jordi
2016-01-01
on stacked classifiers; the benefits are two fold: on one hand, the system scales well to consider different types of complex features and, on the other hand, the use of stacked classifiers makes the performance of the proposed technique more accurate. The proposed method consists of a random forest using...... a stacked random forest which gives the final predictions. The model is tested on the renown NYU-v2 dataset and the recently available SUNRGBD dataset. The approach shows that simple multimodal features with the power of using multi-class multi-scale stacked sequential learners (MMSSL) can achieve slight...... random offset features in combination with a conditional random field (CRF) acting on a simple linear iterative clustering (SLIC) superpixel segmentation. The predictions of the CRF are filtered spatially by a multi-scale decomposition before merging it with the original feature set and applying...
Conditional random field-based gesture recognition with depth information
Chung, Hyunsook; Yang, Hee-Deok
2013-01-01
Gesture recognition is useful for human-computer interaction. The difficulty of gesture recognition is that instances of gestures vary both in motion and shape in three-dimensional (3-D) space. We use depth information generated using Microsoft's Kinect in order to detect 3-D human body components and apply a threshold model with a conditional random field in order to recognize meaningful gestures using continuous motion information. Body gesture recognition is achieved through a framework consisting of two steps. First, a human subject is described by a set of features, encoding the angular relationship between body components in 3-D space. Second, a feature vector is recognized using a threshold model with a conditional random field. In order to show the performance of the proposed method, we use a public data set, the Microsoft Research Cambridge-12 Kinect gesture database. The experimental results demonstrate that the proposed method can efficiently and effectively recognize body gestures automatically.
Efficient approximation of random fields for numerical applications
Harbrecht, Helmut
2015-01-07
We consider the rapid computation of separable expansions for the approximation of random fields. We compare approaches based on techniques from the approximation of non-local operators on the one hand and based on the pivoted Cholesky decomposition on the other hand. We provide an a-posteriori error estimate for the pivoted Cholesky decomposition in terms of the trace. Numerical examples validate and quantify the considered methods.
Liouville field theory and log-correlated Random Energy Models
Cao, Xiangyu; Rosso, Alberto; Santachiara, Raoul
2016-01-01
An exact mapping is established between the $c\\geq25$ Liouville field theory (LFT) and the Gibbs measure statistics of a thermal particle in a 2D Gaussian Free Field plus a logarithmic confining potential. The probability distribution of the position of the minimum of the energy landscape is obtained exactly by combining the conformal bootstrap and one-step replica symmetry breaking methods. Operator product expansions in LFT allow to unveil novel universal behaviours of the log-correlated Random Energy class. High precision numerical tests are given.
Joint Conditional Random Field Filter for Multi-Object Tracking
Directory of Open Access Journals (Sweden)
Luo Ronghua
2011-03-01
Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.
Glassy behaviour of random field Ising spins on Bethe lattice in external magnetic field
Institute of Scientific and Technical Information of China (English)
Khalid Bannora; Galal Ismail; Wafaa Hassan
2011-01-01
The thermodynamics and the phase diagram of random field Ising model (RFIM) on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field (B). A Gaussian distribution of random field (hi) with zero mean and variance = H2RF is considered. The free-energy (F), the magnetization (M) and the order parameter (q) are investigated for several values of coordination number (z). The phase diagram shows several interesting behaviours and presents tricritical point at critical temperature TC = J/k and when HRF = 0 for finite z. The free-energy (F) values increase as T increases for different intensities of random field (HRF) and finite z. The internal energy (U) has a similar behaviour to that obtained from the Monte Carlo simulations. The ground state of magnetization decreases as the intensity of random field HRF increases. The ferromagnetic (FM)-paramagnetic (PM) phase boundary is clearly observed only when z →∞. While FM-PM-spin glass (SG) phase boundaries are present for finite z. The magnetic susceptibility (X) shows a sharp cusp at TC in a small random field for finite z and rounded different peaks on increasing HRF.
Visibility graphs of random scalar fields and spatial data
Lacasa, Lucas; Iacovacci, Jacopo
2017-07-01
We extend the family of visibility algorithms to map scalar fields of arbitrary dimension into graphs, enabling the analysis of spatially extended data structures as networks. We introduce several possible extensions and provide analytical results on the topological properties of the graphs associated to different types of real-valued matrices, which can be understood as the high and low disorder limits of real-valued scalar fields. In particular, we find a closed expression for the degree distribution of these graphs associated to uncorrelated random fields of generic dimension. This result holds independently of the field's marginal distribution and it directly yields a statistical randomness test, applicable in any dimension. We showcase its usefulness by discriminating spatial snapshots of two-dimensional white noise from snapshots of a two-dimensional lattice of diffusively coupled chaotic maps, a system that generates high dimensional spatiotemporal chaos. The range of potential applications of this combinatorial framework includes image processing in engineering, the description of surface growth in material science, soft matter or medicine, and the characterization of potential energy surfaces in chemistry, disordered systems, and high energy physics. An illustration on the applicability of this method for the classification of the different stages involved in carcinogenesis is briefly discussed.
Applications of random field theory to functional connectivity.
Worsley, K J; Cao, J; Paus, T; Petrides, M; Evans, A C
1998-01-01
Functional connectivity between two voxels or regions of voxels can be measured by the correlation between voxel measurements from either PET CBF or BOLD fMRI images in 3D. We propose to look at the entire 6D matrix of correlations between all voxels and search for 6D local maxima. The main result is a new theoretical formula based on random field theory for the p-value of these local maxima, which distinguishes true correlations from background noise. This can be applied to crosscorrelations between two different sets of images--such as activations under two different tasks, as well as autocorrelations within the same set of images.
Extremes in random fields a theory and its applications
Yakir, Benjamin
2013-01-01
Presents a useful new technique for analyzing the extreme-value behaviour of random fields Modern science typically involves the analysis of increasingly complex data. The extreme values that emerge in the statistical analysis of complex data are often of particular interest. This book focuses on the analytical approximations of the statistical significance of extreme values. Several relatively complex applications of the technique to problems that emerge in practical situations are presented. All the examples are difficult to analyze using classical methods, and as a result, the author pr
Markov random fields for static foreground classification in surveillance systems
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.
Ensemble renormalization group for the random-field hierarchical model.
Decelle, Aurélien; Parisi, Giorgio; Rocchi, Jacopo
2014-03-01
The renormalization group (RG) methods are still far from being completely understood in quenched disordered systems. In order to gain insight into the nature of the phase transition of these systems, it is common to investigate simple models. In this work we study a real-space RG transformation on the Dyson hierarchical lattice with a random field, which leads to a reconstruction of the RG flow and to an evaluation of the critical exponents of the model at T=0. We show that this method gives very accurate estimations of the critical exponents by comparing our results with those obtained by some of us using an independent method.
Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.
1992-01-01
The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
A Markov Random Field Model for Image Segmentation of Rice Planthopper in Rice Fields
Hongwei Yue; Ken Cai; Hanhui Lin; Hong Man; Zhaofeng Zeng
2016-01-01
It is meaningful to develop the automation segmentation of rice planthopper pests based on imaging technology in precision agriculture. However, rice planthopper images affected by light and complicated backgrounds in open rice fields make the segmentation difficult. This study proposed a segmentation approach of rice planthopper images based on the Markov random field to conduct effective segmentation. First, fractional order differential was introduced into the extraction proces...
Measuring marine oil spill extent by Markov Random Fields
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.
Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications
Directory of Open Access Journals (Sweden)
Martelli Pier
2009-10-01
Full Text Available Abstract Background Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs and Stochastic Grammars, are routinely applied. Results We introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs as an extension of Hidden Conditional Random Fields (HCRFs. GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows regular grammar rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins. Conclusion We show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications. Availability GRHCRF software is available under GPLv3 licence at the website http://www.biocomp.unibo.it/~savojard/biocrf-0.9.tar.gz.
Distributed estimation of a parametric field with random sensor placements
Alkhweldi, Marwan; Cao, Zhicheng; Schmid, Natalia A.
2015-05-01
This paper considers a problem of distributed function estimation in the case when sensor locations are modeled as Gaussian random variables. We consider a scenario where sensors are deployed in clusters with cluster centers known a priori (or estimated by a high performance GPS) and the average quadratic spread of sensors around the cluster center also known. Distributed sensors make noisy observations about an unknown parametric field generated by a physical object of interest (for example, magnetic field generated by a ferrous object and sensed by a network of magnetometers). Each sensor then performs local signal processing of its noisy observation and sends it to a central processor (called fusion center) in the wireless sensor network over parallel channels corrupted by fading and additive noise. The central processor combines the set of received signals to form an estimate of the unknown parametric field. In our numerical analysis, we involve a field shaped as a Gaussian bell. We experiment with the size of sensor clusters and with their number. A mean square error between the estimated parameters of the field and the true parameters used in simulations is involved as a performance measure. It can be shown that a relatively good estimate of the field can be obtained with only a small number of clusters. As the number of clusters increases, the estimation performance steadily improves. The results also indicate that, on the average, the number of clusters has more impact on the performance than the number of sensors per cluster, given the same size of the total network.
Quantum Coherence and Random Fields at Mesoscopic Scales
Energy Technology Data Exchange (ETDEWEB)
Rosenbaum, Thomas F. [Univ. of Chicago, IL (United States)
2016-03-01
We seek to explore and exploit model, disordered and geometrically frustrated magnets where coherent spin clusters stably detach themselves from their surroundings, leading to extreme sensitivity to finite frequency excitations and the ability to encode information. Global changes in either the spin concentration or the quantum tunneling probability via the application of an external magnetic field can tune the relative weights of quantum entanglement and random field effects on the mesoscopic scale. These same parameters can be harnessed to manipulate domain wall dynamics in the ferromagnetic state, with technological possibilities for magnetic information storage. Finally, extensions from quantum ferromagnets to antiferromagnets promise new insights into the physics of quantum fluctuations and effective dimensional reduction. A combination of ac susceptometry, dc magnetometry, noise measurements, hole burning, non-linear Fano experiments, and neutron diffraction as functions of temperature, magnetic field, frequency, excitation amplitude, dipole concentration, and disorder address issues of stability, overlap, coherence, and control. We have been especially interested in probing the evolution of the local order in the progression from spin liquid to spin glass to long-range-ordered magnet.
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.
A CONDITIONAL RANDOM FIELDS APPROACH TO BIOMEDICAL NAMED ENTITY RECOGNITION
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs (Conditional Random Fields) for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail,and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbreviation phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better results.
Speckle Optical Tweezers: Micromanipulation with Random Light Fields
Volpe, Giorgio; Callegari, Agnese; Volpe, Giovanni; Gigan, Sylvain
2014-01-01
Current optical manipulation techniques rely on carefully engineered setups and samples. Although similar conditions are routinely met in research laboratories, it is still a challenge to manipulate microparticles when the environment is not well controlled and known a priori, since optical imperfections and scattering limit the applicability of this technique to real-life situations, such as in biomedical or microfluidic applications. Nonetheless, scattering of coherent light by disordered structures gives rise to speckles, random diffraction patterns with well-defined statistical properties. Here, we experimentally demonstrate how speckle fields can become a versatile tool to efficiently perform fundamental optical manipulation tasks such as trapping, guiding and sorting. We anticipate that the simplicity of these "speckle optical tweezers" will greatly broaden the perspectives of optical manipulation for real-life applications.
Gaussian Random Field: Physical Origin of Sersic Profiles
Cen, Renyue
2014-01-01
While the Sersic profile family provide adequate fits for the surface brightness profiles of observed galaxies, the physical origin is unknown. We show that, if the cosmological density field are seeded by random gaussian fluctuations, as in the standard cold dark matter model, galaxies with steep central profiles have simultaneously extended envelopes of shallow profiles in the outskirts, whereas galaxies with shallow central profiles are accompanied by steep density profiles in the outskirts. These properties are in accord with those of the Sersic profile family. Moreover, galaxies with steep central profiles form their central regions in smaller denser subunits that possibly merge subsequently, which naturally leads to formation of bulges. In contrast, galaxies with shallow central profiles form their central regions in a coherent fashion without significant substructure, a necessary condition for disk galaxy formation. Thus, the scenario is self-consistent with respect to the correlation between observed ...
Strong mixing properties of max-infinitely divisible random fields
Dombry, Clément
2012-01-01
Let $\\eta=(\\eta(t))_{t\\in T}$ be a sample continuous max-infinitely random field on a locally compact metric space $T$. For a closed subset $S\\in T$, we note $\\eta_{S}$ the restriction of $\\eta$ to $S$. We consider $\\beta(S_1,S_2)$ the absolute regularity coefficient between $\\eta_{S_1}$ and $\\eta_{S_2}$, where $S_1,S_2$ are two disjoint closed subsets of $T$. Our main result is a simple upper bound for $\\beta(S_1,S_2)$ involving the exponent measure $\\mu$ of $\\eta$: we prove that $\\beta(S_1,S_2)\\leq 2\\int \\bbP[\\eta\
CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD
Institute of Scientific and Technical Information of China (English)
Xu Xuemei; Guo Yuanwei; Wang Zhenfei
2012-01-01
In order to overcome the disadvantages of low accuracy rate,high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection,this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling.This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm,and then,the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm.Finally,experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76％ and the average result can also reach 96.92％ for different type of image noise.
5th Seminar on Stochastic Processes, Random Fields and Applications
Russo, Francesco; Dozzi, Marco
2008-01-01
This volume contains twenty-eight refereed research or review papers presented at the 5th Seminar on Stochastic Processes, Random Fields and Applications, which took place at the Centro Stefano Franscini (Monte Verità) in Ascona, Switzerland, from May 30 to June 3, 2005. The seminar focused mainly on stochastic partial differential equations, random dynamical systems, infinite-dimensional analysis, approximation problems, and financial engineering. The book will be a valuable resource for researchers in stochastic analysis and professionals interested in stochastic methods in finance. Contributors: Y. Asai, J.-P. Aubin, C. Becker, M. Benaïm, H. Bessaih, S. Biagini, S. Bonaccorsi, N. Bouleau, N. Champagnat, G. Da Prato, R. Ferrière, F. Flandoli, P. Guasoni, V.B. Hallulli, D. Khoshnevisan, T. Komorowski, R. Léandre, P. Lescot, H. Lisei, J.A. López-Mimbela, V. Mandrekar, S. Méléard, A. Millet, H. Nagai, A.D. Neate, V. Orlovius, M. Pratelli, N. Privault, O. Raimond, M. Röckner, B. Rüdiger, W.J. Runggaldi...
Random field model reveals structure of the protein recombinational landscape.
Directory of Open Access Journals (Sweden)
Philip A Romero
Full Text Available We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative; more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation.
Numerical analysis for random temperature fields of embankment in cold regions
Institute of Scientific and Technical Information of China (English)
LIU ZhiQiang; LAI YuanMing; ZHANG MingYi; ZHANG XueFu
2007-01-01
The stochastic finite element equations for random temperature are obtained using the first-order perturbation technique taking into account the random thermal properties and boundary condition,based on heat transfer variational principle.The local average method for 2-D is used to discretize random fields.Then,the random temperature fields of embankment in cold regions are investigated on condition that the thermal properties and boundary condition are taken as random fields,respectively,by using the program,which is written by the methods.The expected value of temperature field and the standard deviation of the temperature field of embankment in cold regions are obtained and analyzed.
Numerical analysis for random temperature fields of embankment in cold regions
Institute of Scientific and Technical Information of China (English)
2007-01-01
The stochastic finite element equations for random temperature are obtained using the first-order per-turbation technique taking into account the random thermal properties and boundary condition, based on heat transfer variational principle. The local average method for 2-D is used to discretize random fields. Then, the random temperature fields of embankment in cold regions are investigated on condi-tion that the thermal properties and boundary condition are taken as random fields, respectively, by using the program, which is written by the methods. The expected value of temperature field and the standard deviation of the temperature field of embankment in cold regions are obtained and analyzed.
Simulation of heterogeneous two-phase media using random fields and level sets
Institute of Scientific and Technical Information of China (English)
George STEFANOU[1,2
2015-01-01
The accurate and efficient simulation of random heterogeneous media is important in the framework of modeling and design of complex materials across multiple length scales. It is usually assumed that the morphology of a random microstructure can be described as a non-Gaussian random field that is completely defined by its multivariate distribution. A particular kind of non-Gaussian random fields with great practical importance is that of translation fields resulting from a simple memory-less transformation of an underlying Gaussian field with known second-order statistics. This paper provides a critical examination of existing random field models of heterogeneous two-phase media with emphasis on level-cut random fields which are a special case of translation fields. The case of random level sets, often used to represent the geometry of physical systems, is also examined. Two numerical examples are provided to illustrate the basic features of the different approaches.
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.
A dissipative random velocity field for fully developed fluid turbulence
Chevillard, Laurent; Pereira, Rodrigo; Garban, Christophe
2016-11-01
We investigate the statistical properties, based on numerical simulations and analytical calculations, of a recently proposed stochastic model for the velocity field of an incompressible, homogeneous, isotropic and fully developed turbulent flow. A key step in the construction of this model is the introduction of some aspects of the vorticity stretching mechanism that governs the dynamics of fluid particles along their trajectory. An additional further phenomenological step aimed at including the long range correlated nature of turbulence makes this model depending on a single free parameter that can be estimated from experimental measurements. We confirm the realism of the model regarding the geometry of the velocity gradient tensor, the power-law behaviour of the moments of velocity increments, including the intermittent corrections, and the existence of energy transfers across scales. We quantify the dependence of these basic properties of turbulent flows on the free parameter and derive analytically the spectrum of exponents of the structure functions in a simplified non dissipative case. A perturbative expansion shows that energy transfers indeed take place, justifying the dissipative nature of this random field.
Khaneja, Mamta; Ghosh, Santanu; Gautam, Seema; Kumar, Prashant; Rawat, J S; Chaudhury, P K; Vankar, V D; Kumar, Vikram
2015-05-01
High field emission (FE) current density from carbon nanotube (CNT) arrays grown on lithographically patterned silicon substrates is reported. A typical patterned field emitter array consists of bundles of nanotubes separated by a fixed gap and spread over the entire emission area. Emission performance from such an array having randomly oriented nanotube growth within each bundle is reported for different bundle sizes and separations. One typical sample with aligned CNTs within the bundle is also examined for comparison. It is seen that the current density from an array having random nanotube growth within the bundles is appreciably higher as compared to its aligned counterpart. The influence of structure on FE current densities as revealed by Raman spectroscopy is also seen. It is also observed that current density depends on edge length and increases with the same for all samples under study. Highest current density of -100 mA cm(-2) at an applied field of 5 V/μm is achieved from the random growth patterned sample with a bundle size of 2 μm and spacing of 4 μm between the bundles.
A strong law of large numbers for harmonizable isotropic random fields
Directory of Open Access Journals (Sweden)
Randall J. Swift
1997-01-01
Full Text Available The class of harmonizable fields is a natural extension of the class of stationary fields. This paper considers a strong law of large numbers for the spherical average of a harmonizable isotropic random field.
Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields
Lee, J.; Chou, J.; Weger, R. C.; Welch, R. M.
1994-01-01
To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.
Gaussian conditional random fields for regression in remote sensing
Radosavljevic, Vladan
In recent years many remote sensing instruments of various properties have been employed in an attempt to better characterize important geophysical phenomena. Satellite instruments provide an exceptional opportunity for global long-term observations of the land, the biosphere, the atmosphere, and the oceans. The collected data are used for estimation and better understanding of geophysical parameters such as land cover type, atmospheric properties, or ocean temperature. Achieving accurate estimations of such parameters is an important requirement for development of models able to predict global climate changes. One of the most challenging climate research problems is estimation of global composition, load, and variability of aerosols, small airborne particles that reflect and absorb incoming solar radiation. The existing algorithm for aerosol prediction from satellite observations is deterministic and manually tuned by domain scientist. In contrast to domain-driven method, we show that aerosol prediction is achievable by completely data-driven approaches. These statistical methods consist of learning of nonlinear regression models to predict aerosol load using the satellite observations as inputs. Measurements from unevenly distributed ground-based sites over the world are used as proxy to ground-truth outputs. Although statistical methods achieve better accuracy than deterministic method this setup is appropriate when data are independently and identically distributed (IID). The IID assumption is often violated in remote sensing where data exhibit temporal, spatial, or spatio-temporal dependencies. In such cases, the traditional supervised learning approaches could result in a model with degraded accuracy. Conditional random fields (CRF) are widely used for predicting output variables that have some internal structure. Most of the CRF research has been done on structured classification where the outputs are discrete. We propose a CRF model for continuous outputs
Kouritzin, Michael A; Newton, Fraser; Wu, Biao
2013-04-01
Herein, we propose generating CAPTCHAs through random field simulation and give a novel, effective and efficient algorithm to do so. Indeed, we demonstrate that sufficient information about word tests for easy human recognition is contained in the site marginal probabilities and the site-to-nearby-site covariances and that these quantities can be embedded directly into certain conditional probabilities, designed for effective simulation. The CAPTCHAs are then partial random realizations of the random CAPTCHA word. We start with an initial random field (e.g., randomly scattered letter pieces) and use Gibbs resampling to re-simulate portions of the field repeatedly using these conditional probabilities until the word becomes human-readable. The residual randomness from the initial random field together with the random implementation of the CAPTCHA word provide significant resistance to attack. This results in a CAPTCHA, which is unrecognizable to modern optical character recognition but is recognized about 95% of the time in a human readability study.
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.
Conditional Random Fields for Pattern Recognition Applied to Structured Data
Directory of Open Access Journals (Sweden)
Tom Burr
2015-07-01
Full Text Available Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building or “natural” (such as a tree. Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs model structured data using the conditional distribution P(Y|X = x, without specifying a model for P(X, and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches in the output domain. Second, we identify research topics and present numerical examples.
IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES
Institute of Scientific and Technical Information of China (English)
纳瑟; 刘重庆
2002-01-01
This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K-means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.
Rigorously testing multialternative decision field theory against random utility models.
Berkowitsch, Nicolas A J; Scheibehenne, Benjamin; Rieskamp, Jörg
2014-06-01
Cognitive models of decision making aim to explain the process underlying observed choices. Here, we test a sequential sampling model of decision making, multialternative decision field theory (MDFT; Roe, Busemeyer, & Townsend, 2001), on empirical grounds and compare it against 2 established random utility models of choice: the probit and the logit model. Using a within-subject experimental design, participants in 2 studies repeatedly choose among sets of options (consumer products) described on several attributes. The results of Study 1 showed that all models predicted participants' choices equally well. In Study 2, in which the choice sets were explicitly designed to distinguish the models, MDFT had an advantage in predicting the observed choices. Study 2 further revealed the occurrence of multiple context effects within single participants, indicating an interdependent evaluation of choice options and correlations between different context effects. In sum, the results indicate that sequential sampling models can provide relevant insights into the cognitive process underlying preferential choices and thus can lead to better choice predictions.
Gaussian Random Field: Physical Origin of Sersic Profiles
Cen, Renyue
2014-08-01
While the Sersic profile family provides adequate fits for the surface brightness profiles of observed galaxies, its physical origin is unknown. We show that if the cosmological density field is seeded by random Gaussian fluctuations, as in the standard cold dark matter model, galaxies with steep central profiles have simultaneously extended envelopes of shallow profiles in the outskirts, whereas galaxies with shallow central profiles are accompanied by steep density profiles in the outskirts. These properties are in accord with those of the Sersic profile family. Moreover, galaxies with steep central profiles form their central regions in smaller denser subunits that possibly merge subsequently, which naturally leads to the formation of bulges. In contrast, galaxies with shallow central profiles form their central regions in a coherent fashion without significant substructure, a necessary condition for disk galaxy formation. Thus, the scenario is self-consistent with respect to the correlation between observed galaxy morphology and the Sersic index. We further predict that clusters of galaxies should display a similar trend, which should be verifiable observationally.
Hadjiagapiou, Ioannis A.
2014-03-01
The magnetic systems with disorder form an important class of systems, which are under intensive studies, since they reflect real systems. Such a class of systems is the spin glass one, which combines randomness and frustration. The Sherrington-Kirkpatrick Ising spin glass with random couplings in the presence of a random magnetic field is investigated in detail within the framework of the replica method. The two random variables (exchange integral interaction and random magnetic field) are drawn from a joint Gaussian probability density function characterized by a correlation coefficient ρ. The thermodynamic properties and phase diagrams are studied with respect to the natural parameters of both random components of the system contained in the probability density. The de Almeida-Thouless line is explored as a function of temperature, ρ and other system parameters. The entropy for zero temperature as well as for non zero temperatures is partly negative or positive, acquiring positive branches as h0 increases.
Magnetic Field Line Random Walk in Isotropic Turbulence with Zero Mean Field
Sonsrettee, W.; Subedi, P.; Ruffolo, D.; Matthaeus, W. H.; Snodin, A. P.; Wongpan, P.; Chuychai, P.
2015-01-01
In astrophysical plasmas, magnetic field lines often guide the motions of thermal and non-thermal particles. The field line random walk (FLRW) is typically considered to depend on the Kubo number R = (b/B 0)(l∥/l) for rms magnetic fluctuation b, large-scale mean field B 0, and parallel and perpendicular coherence scales l∥ and l, respectively. Here we examine the FLRW when R → ∞ by taking B 0 → 0 for finite bz (fluctuation component along B 0), which differs from the well-studied route with bz = 0 or bz Lt B 0 as the turbulence becomes quasi-two-dimensional (quasi-2D). Fluctuations with B 0 = 0 are typically isotropic, which serves as a reasonable model of interstellar turbulence. We use a non-perturbative analytic framework based on Corrsin's hypothesis to determine closed-form solutions for the asymptotic field line diffusion coefficient for three versions of the theory, which are directly related to the k -1 or k -2 moment of the power spectrum. We test these theories by performing computer simulations of the FLRW, obtaining the ratio of diffusion coefficients for two different parameterizations of a field line. Comparing this with theoretical ratios, the random ballistic decorrelation version of the theory agrees well with the simulations. All results exhibit an analog to Bohm diffusion. In the quasi-2D limit, previous works have shown that Corrsin-based theories deviate substantially from simulation results, but here we find that as B 0 → 0, they remain in reasonable agreement. We conclude that their applicability is limited not by large R, but rather by quasi-two-dimensionality.
MAGNETIC FIELD LINE RANDOM WALK IN ISOTROPIC TURBULENCE WITH ZERO MEAN FIELD
Energy Technology Data Exchange (ETDEWEB)
Sonsrettee, W.; Ruffolo, D.; Snodin, A. P.; Wongpan, P. [Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400 (Thailand); Subedi, P.; Matthaeus, W. H. [Bartol Research Institute, University of Delaware, Newark, DE 19716 (United States); Chuychai, P., E-mail: bturbulence@gmail.com, E-mail: david.ruf@mahidol.ac.th, E-mail: andrew.snodin@gmail.com, E-mail: pat.wongpan@postgrad.otago.ac.nz, E-mail: piyanate@gmail.com, E-mail: prasub@udel.edu, E-mail: whm@udel.edu [Thailand Center of Excellence in Physics, CHE, Ministry of Education, Bangkok 10400 (Thailand)
2015-01-01
In astrophysical plasmas, magnetic field lines often guide the motions of thermal and non-thermal particles. The field line random walk (FLRW) is typically considered to depend on the Kubo number R = (b/B {sub 0})(ℓ{sub ∥}/ℓ ) for rms magnetic fluctuation b, large-scale mean field B {sub 0}, and parallel and perpendicular coherence scales ℓ{sub ∥} and ℓ , respectively. Here we examine the FLRW when R → ∞ by taking B {sub 0} → 0 for finite b{sub z} (fluctuation component along B {sub 0}), which differs from the well-studied route with b{sub z} = 0 or b{sub z} << B {sub 0} as the turbulence becomes quasi-two-dimensional (quasi-2D). Fluctuations with B {sub 0} = 0 are typically isotropic, which serves as a reasonable model of interstellar turbulence. We use a non-perturbative analytic framework based on Corrsin's hypothesis to determine closed-form solutions for the asymptotic field line diffusion coefficient for three versions of the theory, which are directly related to the k {sup –1} or k {sup –2} moment of the power spectrum. We test these theories by performing computer simulations of the FLRW, obtaining the ratio of diffusion coefficients for two different parameterizations of a field line. Comparing this with theoretical ratios, the random ballistic decorrelation version of the theory agrees well with the simulations. All results exhibit an analog to Bohm diffusion. In the quasi-2D limit, previous works have shown that Corrsin-based theories deviate substantially from simulation results, but here we find that as B {sub 0} → 0, they remain in reasonable agreement. We conclude that their applicability is limited not by large R, but rather by quasi-two-dimensionality.
CONVERGENCE RATES IN THE STRONG LAWS OF ASYMPTOTICALLY NEGATIVELY ASSOCIATED RANDOM FIELDS
Institute of Scientific and Technical Information of China (English)
ZhangLixin; WangXiuyun
1999-01-01
In this paper, a notion of negative side p-mixing (p -mixing) which can be regardedas asymptotic negative association is defined, and some Rosenthal type inequalities for p -mix-ing random fields are established. The complete convergence and almost sure summability onthe convergence rates with respect to the strong law of large numbers are also discussed for p--mixing random fields. The results obtained extend those for negatively associated sequences andp" -mixing random fields.
Heterogeneous Web Data Extraction Algorithm Based On Modified Hidden Conditional Random Fields
Cui Cheng
2014-01-01
As it is of great importance to extract useful information from heterogeneous Web data, in this paper, we propose a novel heterogeneous Web data extraction algorithm using a modified hidden conditional random fields model. Considering the traditional linear chain based conditional random fields can not effectively solve the problem of complex and heterogeneous Web data extraction, we modify the standard hidden conditional random fields in three aspects, which are 1) Using the hidden Markov mo...
Phase Diagram and Tricritical Behavior of a Spin-2 Transverse Ising Model in a Random Field
Institute of Scientific and Technical Information of China (English)
LIANG Ya-Qiu; WEI Guo-Zhu; SONG Li-Li; SONG Guo-Li; ZANG Shu-Liang
2004-01-01
The phase diagrams of a spin-2 transverse Ising model with a random field on honeycomb, square, and simple-cubic lattices, respectively, are investigated within the framework of an effective-field theory with correlations.We find the behavior of the tricritical point and the reentrant phenomenon for the system with any coordination number z, when the applied random field is bimodal. The behavior of the tricritical point is also examined as a function of applied transverse field. The reentrant phenomenon comes from the competition between the transverse field and the random field.
Representation and prediction for locally harmonizable isotropic random fields
Directory of Open Access Journals (Sweden)
Randall J. Swift
1995-01-01
Full Text Available The class of harmonizable fields is a natural extension of the class of stationary fields. This paper considers fields whose increments are harmonizable and isotropic. Spectral representations are obtained for locally harmonizable isotropic fields. A linear least squares prediction for locally harmonizable isotropic fields is considered.
Bearing Fault Classification Based on Conditional Random Field
Directory of Open Access Journals (Sweden)
Guofeng Wang
2013-01-01
Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.
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.
Semikoz, V B
1994-01-01
We consider active-sterile neutrino conversions in the early universe hot plasma in the presence of a random magnetic field generated at the electroweak phase transition. Within a random field domain the magnetization asymmetry of the lepton antilepton plasma produced by a uniform constant magnetic field is huge in contrast to their small density asymmetry, leading to a drastic change in the active-sterile conversion rates. Assuming that the random field provides the seed for the galactic field one can estimate the restrictions from primordial nucleosynthesis. Requiring that the extra sterile \
Thermodynamical Properties of Spin-3／2 Ising Model in a Longitudinal Random Field with Crystal Field
Institute of Scientific and Technical Information of China (English)
LIANGYa-Qiu; WEIGuo-Zhu; ZHANGHong; SONGGuo-Li
2004-01-01
A theoretical study of a spin-3/2 Ising model in a longitudinal random field with crystal field is studied by using of the effective-field theory with correlations. The phase diagrams and the behavior of the tricritical point are investigated numerically for the honeycomb lattice when the random field is bimodal. In particular, the specific heat and the internal energy are examined in detail for the system with a crystal-field constant in the critical region where the ground-state configuration may change from the spin-3/2 state to the spin-1/2 state. We find many interesting phenomena in the system.
Fractional generalized Lévy random fields as white noise functionals
Institute of Scientific and Technical Information of China (English)
HUANG Zhiyuan; LI Peiyan
2007-01-01
In this paper, we construct the fractional generalized Lévy random fields (FGLRF) as tempered white noise functionals. We find that this white noise approach is very effective in investigating the properties of these fields. Under some conditions, the fractional Lévy fields in the usual sense are obtained. In addition, we also present a method to construct the anisotropic fractional generalized Lévy random fields (AFGLRF).
Magnetic Field Line Random Walk in Isotropic Turbulence with Varying Mean Field
Sonsrettee, W.; Subedi, P.; Ruffolo, D.; Matthaeus, W. H.; Snodin, A. P.; Wongpan, P.; Chuychai, P.; Rowlands, G.; Vyas, S.
2016-08-01
In astrophysical plasmas, the magnetic field line random walk (FLRW) plays an important role in guiding particle transport. The FLRW behavior is scaled by the Kubo number R=(b/{B}0)({{\\ell }}\\parallel /{{\\ell }}\\perp ) for rms magnetic fluctuation b, large-scale mean field {{\\boldsymbol{B}}}0, and coherence scales parallel ({{\\ell }}\\parallel ) and perpendicular ({{\\ell }}\\perp ) to {{\\boldsymbol{B}}}0. Here we use a nonperturbative analytic framework based on Corrsin’s hypothesis, together with direct computer simulations, to examine the R-scaling of the FLRW for varying B 0 with finite b and isotropic fluctuations with {{\\ell }}\\parallel /{{\\ell }}\\perp =1, instead of the well-studied route of varying {{\\ell }}\\parallel /{{\\ell }}\\perp for b \\ll {B}0. The FLRW for isotropic magnetic fluctuations is also of astrophysical interest regarding transport processes in the interstellar medium. With a mean field, fluctuations may have variance anisotropy, so we consider limiting cases of isotropic variance and transverse variance (with b z = 0). We obtain analytic theories, and closed-form solutions for extreme cases. Padé approximants are provided to interpolate all versions of theory and simulations to any B 0. We demonstrate that, for isotropic turbulence, Corrsin-based theories generally work well, and with increasing R there is a transition from quasilinear to Bohm diffusion. This holds even with b z = 0, when different routes to R\\to ∞ are mathematically equivalent; in contrast with previous studies, we find that a Corrsin-based theory with random ballistic decorrelation works well even up to R = 400, where the effects of trapping are barely perceptible in simulation results.
Reduction of the Random Variables of the Turbulent Wind Field
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Nielsen, Søren R.K.
2012-01-01
of the integral domain; this becomes increasingly difficult as the dimensions of the integral domain increase. On the other hand efficiency of the AMC methods is closely dependent on the design points of the problem. Presence of many random variables may increase the number of the design points, hence affects......Applicability of the Probability Density Evolution Method (PDEM) for realizing evolution of the probability density for the wind turbines has rather strict bounds on the basic number of the random variables involved in the model. The efficiency of most of the Advanced Monte Carlo (AMC) methods, i.......e. Importance Sampling (IS) or Subset Simulation (SS), will be deteriorated on problems with many random variables. The problem with PDEM is that a multidimensional integral has to be carried out over the space defined by the random variables of the system. The numerical procedure requires discretization...
Realization of random-field Ising ferromagnetism in a molecular magnet
Wen, Bo; Subedi, P.; Bo, Lin; Yeshurun, Y.; Sarachik, M. P.; Kent, A. D.; Millis, A. J.; Lampropoulos, C.; Christou, G.
2010-07-01
The longitudinal magnetic susceptibility of single crystals of the molecular magnet Mn12 -acetate obeys a Curie-Weiss law, indicating a transition to a ferromagnetic phase at ˜0.9K . With increasing magnetic field applied transverse to the easy axis, a marked change is observed in the temperature dependence of the susceptibility, and the suppression of ferromagnetism is considerably more rapid than predicted by mean-field theory for an ordered single crystal. Our results can instead be fit by a Hamiltonian for a random-field Ising ferromagnet in a transverse magnetic field, where the randomness derives from the intrinsic distribution of locally tilted magnetic easy axes known to exist in Mn12 -acetate crystals, suggesting that Mn12 -acetate is a realization of the random-field Ising model in which the random field may be tuned by a field applied transverse to the easy axis.
Mean field theory of directed polymers with random complex weights
Derrida, B.; Evans, M. R.; Speer, E. R.
1993-09-01
We show that for the problem of directed polymers on a tree with i.i.d. random complex weights on each bond, three possible phases can exist; the phase of a particular system is determined by the distribution ρ of the random weights. For each of these three phases, we give the expression of the free energy per unit length in the limit of infinitely long polymers. Our proofs require several hypotheses on the distribution ρ, most importantly, that the amplitude and the phase of each complex weight be statistically independent. The main steps of our proofs use bounds on noninteger moments of the partition function and self averaging properties of the free energy. We illustrate our results by some examples and discuss possible generalizations to a larger class of distributions, to Random Energy Models, and to the finite dimensional case. We note that our results are not in agreement with the predictions of a recent replica approach to a similar problem.
Path properties of lp-valued Gaussian random fields
Institute of Scientific and Technical Information of China (English)
Yong-Kab; CHOI
2007-01-01
)[16]Book S A,Shore T R.On large intervals in the Cs(o)rg(o)-Révész theorem on increments of a Wiener process.Z Wahrsch Verw Gebiete,46:1-11 (1978)[17]Choi Y K,K(o)no N.How big are the increments of a two-parameter Gaussian process? J Theoret Probab,12(1):105-129 (1999)[18]Lin Z Y,Choi Y K.Some limit theorems for fractional Levy Brownian fields.Stoch Proc & Their Appl,82:229-244 (1999)[19]Lin Z Y,Lee S H,Hwang K S et al.Some limit theorems on the increments of lp-valued multi-parameter Gaussian processes.Acta Math Sinica,English Ser,20(6):1019-1028 (2004)[20]Slepian D.The one-sided barrier problem for Gaussian noise.Bell System Tech J,41:463-501 (1962)[21]Choi Y K,Lin Z Y,Sung H S et al.Limit behaviors for the increments of a d-dimensional multi-parameter Gaussian process.J Korean Math Soc,42(6):1265-1278 (2005)[22]Leadbetter M R,Lindgren G,Rootzen H.Extremes and Related Properties of Random Sequences and Processes.New York:Springer-Verlag,1983[23]Li W V,Shao Q M.A normal comparison inequality and its applications.Probab Theory & Rel Fields,122:494-508 (2002)
A Markov random field-based approach to speckle reduction
Lankoande, Ousseini
One of the major factors plaguing the performance of any coherent imaging system in general, and synthetic-aperture-radar (SAR) and ultrasound-medical-imaging systems in particular, is the presence of signal-dependent speckle noise. Grainy in appearance, speckle noise is primarily due to the phase fluctuations of the electromagnetic returned signals. Existing multiplicative models for speckle noise do not possess the level of generality required to capture and exploit the inherent spatial-correlation characteristics of speckle noise. The capability of Markov random fields (MRFs) to model spatially correlated and signal-dependent phenomena makes them an excellent choice for modeling speckled images without the need to adopt a multiplicative-noise model. In addition, a MRF framework can lend itself to many image-processing strategies that are not predicated on any multiplicative- or additive-noise assumptions. We propose a new mathematical framework for modeling and mitigating speckle noise that combines the flexibility and generality of a MRF with the physical properties of speckle noise drawn from the statistical optics principles. In particular, Goodman's conditional probability density function (cpdf) of the intensity of any two points in the speckled image and the associated correlation function are used to derive the cpdf of any center pixel intensity given its four neighbors. An important parameter of the cpdf, the coherence factor, characterizes the spatial correlation between two given pixels. For the first-order MRF model used in this dissertation, the coherence factor is restricted to only one value, which is estimated from the data using a pseudo-likelihood method. Equipped with the cpdf, the Hammersley-Clifford theorem is utilized to derive a convex Gibbs energy function that characterizes the MRF. Based on the proposed model, we introduce two speckle-reduction algorithms. The first algorithm, termed the simulated-annealing-MRF (SAMRF), is based on
Fast vectorized algorithm for the Monte Carlo Simulation of the Random Field Ising Model
Rieger, H
1992-01-01
An algoritm for the simulation of the 3--dimensional random field Ising model with a binary distribution of the random fields is presented. It uses multi-spin coding and simulates 64 physically different systems simultaneously. On one processor of a Cray YMP it reaches a speed of 184 Million spin updates per second. For smaller field strength we present a version of the algorithm that can perform 242 Million spin updates per second on the same machine.
A Markov Random Field Model for Image Segmentation of Rice Planthopper in Rice Fields
Directory of Open Access Journals (Sweden)
Hongwei Yue
2016-04-01
Full Text Available It is meaningful to develop the automation segmentation of rice planthopper pests based on imaging technology in precision agriculture. However, rice planthopper images affected by light and complicated backgrounds in open rice fields make the segmentation difficult. This study proposed a segmentation approach of rice planthopper images based on the Markov random field to conduct effective segmentation. First, fractional order differential was introduced into the extraction process of image texture features to gain complete texture information of rice planthopper images. Observation data modeling was established by a combination of image color features and texture features to overcome the disadvantages of insufficient image texture information. Finally, the improved potential function models, the neighborhood relationship between the pixel labels, and the attributes of pixels were defined. The segmentation results were assessed by quantitative evaluation. The experiments showed that the proposed improved approach in the study was more robust, especially with the changes in the illumination condition. This approach can effectively improve segmentation accuracy and promote vision segmentation results of rice planthopper images.
Prediction of Geological Subsurfaces Based on Gaussian Random Field Models
Energy Technology Data Exchange (ETDEWEB)
Abrahamsen, Petter
1997-12-31
During the sixties, random functions became practical tools for predicting ore reserves with associated precision measures in the mining industry. This was the start of the geostatistical methods called kriging. These methods are used, for example, in petroleum exploration. This thesis reviews the possibilities for using Gaussian random functions in modelling of geological subsurfaces. It develops methods for including many sources of information and observations for precise prediction of the depth of geological subsurfaces. The simple properties of Gaussian distributions make it possible to calculate optimal predictors in the mean square sense. This is done in a discussion of kriging predictors. These predictors are then extended to deal with several subsurfaces simultaneously. It is shown how additional velocity observations can be used to improve predictions. The use of gradient data and even higher order derivatives are also considered and gradient data are used in an example. 130 refs., 44 figs., 12 tabs.
Remediating Computational Deficits at Third Grade: A Randomized Field Trial
Fuchs, Lynn S.; Powell, Sarah R.; Hamlett, Carol L.; Fuchs, Douglas; Cirino, Paul T.; FLETCHER, JACK M.
2008-01-01
The major purposes of this study were to assess the efficacy of tutoring to remediate 3rd-grade computational deficits and to explore whether remediation is differentially efficacious depending on whether students experience mathematics difficulty alone or concomitantly with reading difficulty. At 2 sites, 127 students were stratified on mathematics difficulty status and randomly assigned to 4 conditions: word recognition (control) tutoring or 1 of 3 computation tutoring conditions: fact retr...
Preventing Youth Violence and Dropout: A Randomized Field Experiment
Sara Heller; Harold A. Pollack; Roseanna Ander; Jens Ludwig
2013-01-01
Improving the long-term life outcomes of disadvantaged youth remains a top policy priority in the United States, although identifying successful interventions for adolescents - particularly males - has proven challenging. This paper reports results from a large randomized controlled trial of an intervention for disadvantaged male youth grades 7-10 from high-crime Chicago neighborhoods. The intervention was delivered by two local non-profits and included regular interactions with a pro-social ...
Segmentation of RGB-D indoor scenes by stacking random forests and conditional random fields
DEFF Research Database (Denmark)
Thøgersen, Mikkel; Guerrero, Sergio Escalera; Gonzàlez, Jordi;
2016-01-01
on stacked classifiers; the benefits are two fold: on one hand, the system scales well to consider different types of complex features and, on the other hand, the use of stacked classifiers makes the performance of the proposed technique more accurate. The proposed method consists of a random forest using...... a stacked random forest which gives the final predictions. The model is tested on the renown NYU-v2 dataset and the recently available SUNRGBD dataset. The approach shows that simple multimodal features with the power of using multi-class multi-scale stacked sequential learners (MMSSL) can achieve slight...
Development of a Random Field Model for Gas Plume Detection in Multiple LWIR Images.
Energy Technology Data Exchange (ETDEWEB)
Heasler, Patrick G.
2008-09-30
This report develops a random field model that describes gas plumes in LWIR remote sensing images. The random field model serves as a prior distribution that can be combined with LWIR data to produce a posterior that determines the probability that a gas plume exists in the scene and also maps the most probable location of any plume. The random field model is intended to work with a single pixel regression estimator--a regression model that estimates gas concentration on an individual pixel basis.
Generalized Whittle-Matern random field as a model of correlated fluctuations
Energy Technology Data Exchange (ETDEWEB)
Lim, S C [Faculty of Engineering, Multimedia University, Jalan Multimedia, Cyberjaya, 63100, Selangor Darul Ehsan (Malaysia); Teo, L P [Faculty of Information Technology, Multimedia University, Jalan Multimedia, Cyberjaya, 63100, Selangor Darul Ehsan (Malaysia)], E-mail: sclim@mmu.edu.my, E-mail: lpteo@mmu.edu.my
2009-03-13
This paper considers a generalization of the Gaussian random field with covariance function of the Whittle-Matern family. Such a random field can be obtained as the solution to the fractional stochastic differential equation with two fractional orders. Asymptotic properties of the covariance functions belonging to this generalized Whittle-Matern family are studied, which are used to deduce the sample path properties of the random field. The Whittle-Matern field has been widely used in modeling geostatistical data such as sea beam data, wind speed, field temperature and soil data. In this paper we show that the generalized Whittle-Matern field provides a more flexible model for wind speed data.
Rare-event Analysis and Computational Methods for Stochastic Systems Driven by Random Fields
2014-12-29
random elliptic differential equations . The equation has been employed to describe physics systems in areas as diverse as material science, fluid...probabilities associated with random fields and the associated random elliptic differential equations . The equation has been employed to describe physics...Received Paper 1.00 3.00 4.00 Gongjun Xu, Guang Lin, Jingchen Liu. Rare-Event Simulation for the Stochastic Korteweg--de Vries Equation , SIAM/ASA
On Closely Coupled Dipoles in a Random Field
DEFF Research Database (Denmark)
Andersen, Jørgen Bach; Vincent, L.
2006-01-01
Reception of partially correlated fields by two closely coupled electrical dipoles is discussed as a function of load impedances and open-circuit correlations. Two local maxima of the power may be achieved for two different load impedances, but in those cases the output correlations are high...
Average Number of Coherent Modes for Pulse Random Fields
Lazaruk, A M; Lazaruk, Alexander M.; Karelin, Nikolay V.
1997-01-01
Some consequences of spatio-temporal symmetry for the deterministic decomposition of complex light fields into factorized components are considered. This enables to reveal interrelations between spatial and temporal coherence properties of wave. An estimation of average number of the decomposition terms is obtained in the case of statistical ensemble of light pulses.
Institute of Scientific and Technical Information of China (English)
Erhan Albayrak
2013-01-01
The spin-1 Blume-Capel model with transverse Ω and longitudinal external magnetic fields h,in addition to a longitudinal random crystal field D,is studied in the mean-field approximation.It is assumed that the crystal field is either turned on with probability p or turned off with probability 1-p on the sites of a square lattice.Phase diagrams are then calculated on the reduced temperature crystal field planes for given values of γ =-Ω/J and p at zero h.Thus,the effect of changing γ and p are illustrated on the phase diagrams in great detail and interesting results are observed.
Thermodynamical Properties of Spin-3/2 Ising Model in a Longitudinal Random Field with Crystal Field
Institute of Scientific and Technical Information of China (English)
LIANG Ya-Qiu; WEI Guo-Zhu; ZHANG Hong; SONG Guo-Li
2004-01-01
A theoretical study of a spin-3/2 Ising model in a longitudinal random field with crystal field is studiedby using of the effective-field theory with correlations. The phase diagrams and the behavior of the tricritical point areinvestigated numerically for the honeycomb lattice when the randorm field is bimodal. In particular, the specific heatand the internal energy are examined in detail for the system with a crystal-field constant in the critical region wherethe ground-state configuration may change from the spin-3/2 state to the spin-1/2 state. We find many interestingphenomena in the system.
Modulation of electromagnetic fields by a depolarizer of random polarizer array
DEFF Research Database (Denmark)
Ma, Ning; Hanson, Steen Grüner; Wang, Wei
2016-01-01
The statistical properties of the electric fields with random changes of the polarization state in space generated by a depolarizer are investigated on the basis of the coherence matrix. The depolarizer is a polarizer array composed of a multitude of contiguous square cells of polarizers with ran......The statistical properties of the electric fields with random changes of the polarization state in space generated by a depolarizer are investigated on the basis of the coherence matrix. The depolarizer is a polarizer array composed of a multitude of contiguous square cells of polarizers...... with randomly distributed polarization angles, where the incident fields experience a random polarization modulation after passing through the depolarizer. The propagation of the modulated electric fields through any quadratic optical system is examined within the framework of the complex ABCD matrix to show...
Initialization of Markov Random Field Clustering of Large Remote Sensing Images
Tran, T.N.; Wehrens, H.R.M.J.; Hoekman, D.H.; Buydens, L.M.C.
2005-01-01
Markov random field (MRF) clustering, utilizing both spectral and spatial interpixel dependency information, often improves classification accuracy for remote sensing images, such as multichannel polarimetric synthetic aperture radar (SAR) images. However, it is heavily sensitive to initial conditio
Active sterile neutrino conversions in a supernova with random magnetic fields
Pastor, S; Valle, José W F; Pastor, S; Semikoz, V; Valle, Jose W F
1995-01-01
{Large enough random magnetic fields may affect in an important way neutrino conversion rates, even in the case where neutrinos have zero transition magnetic moments. We consider their effect in the case of active to sterile \
Continuous Markov Random Fields for Robust Stereo Estimation
Yamaguchi, Koichiro; McAllester, David; Urtasun, Raquel
2012-01-01
In this paper we present a novel slanted-plane MRF model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as the one of inference in a hybrid MRF composed of both continuous (i.e., slanted 3D planes) and discrete (i.e., occlusion boundaries) random variables. This allows us to define potentials encoding the ownership of the pixels that compose the boundary between segments, as well as potentials encoding which junctions are physically possible. Our approach outperforms the state-of-the-art on Middlebury high resolution imagery as well as in the more challenging KITTI dataset, while being more efficient than existing slanted plane MRF-based methods, taking on average 2 minutes to perform inference on high resolution imagery.
DEFF Research Database (Denmark)
Wang, W.; Hanson, Steen Grüner; Miyamoto, Y.;
2005-01-01
We present the first direct experimental evidence of the local properties of optical vortices in a random laser speckle field. We have observed the Berry anisotropy ellipse describing the anisotropic squeezing of phase lines close to vortex cores and quantitatively verified the Dennis angular...... momentum rule for its phase. Some statistics associated with vortices, such as density, anisotropy ellipse eccentricity, and its relation to zero crossings of real and imaginary parts of the random field, are also investigated by experiments....
Phase Diagrams and Tricritical Behaviour of the Spin-2 Ising Model in a Longitudinal Random Field
Institute of Scientific and Technical Information of China (English)
LIANG Ya-Qiu; WEI Guo-Zhu; ZHANG Qi; SONG Guo-Li
2004-01-01
@@ Within the framework of the effective-field theory with correlations, we study the ferromagnetic spin-2 randomfield Ising model (RFIM) in the presence of a crystal field on honeycomb (z = 3), square (z = 4) and simple cubic (z = 6) lattices. The effects of the crystal field and the longitudinal random field on the phase diagrams are investigated. Some characteristic features of the phase diagrams, such as the tricritical phenomena, reentrant phenomena and existence of two tricritical points, are found.
Random Field Satisfying a Linear Partial Differential Equation with Random Forcing Term.
1984-01-01
physical approximation 12 may be taken equal to 1. For k = 0, the experimental spectral density function of the random fluctuations of the pressure in deep...fa e , tlpt 2 in R , and the power spectral density function is 1 0 y(v) - 27 2 2’ y I a +v Proof: In that case da = X 0dT and r(tl,t2) =- e , the...power spectral density function of the solution X is V R___ 1 __ 2 I The second order properties are the same for every Poisson, Gaussian, Levy centered
Revisiting Boltzmann learning: parameter estimation in Markov random fields
DEFF Research Database (Denmark)
Hansen, Lars Kai; Andersen, Lars Nonboe; Kjems, Ulrik
1996-01-01
This article presents a generalization of the Boltzmann machine that allows us to use the learning rule for a much wider class of maximum likelihood and maximum a posteriori problems, including both supervised and unsupervised learning. Furthermore, the approach allows us to discuss regularization...... and generalization in the context of Boltzmann machines. We provide an illustrative example concerning parameter estimation in an inhomogeneous Markov field. The regularized adaptation produces a parameter set that closely resembles the “teacher” parameters, hence, will produce segmentations that closely reproduce...
Random walk study of electron motion in helium in crossed electromagnetic fields
Englert, G. W.
1972-01-01
Random walk theory, previously adapted to electron motion in the presence of an electric field, is extended to include a transverse magnetic field. In principle, the random walk approach avoids mathematical complexity and concomitant simplifying assumptions and permits determination of energy distributions and transport coefficients within the accuracy of available collisional cross section data. Application is made to a weakly ionized helium gas. Time of relaxation of electron energy distribution, determined by the random walk, is described by simple expressions based on energy exchange between the electron and an effective electric field. The restrictive effect of the magnetic field on electron motion, which increases the required number of collisions per walk to reach a terminal steady state condition, as well as the effect of the magnetic field on electron transport coefficients and mean energy can be quite adequately described by expressions involving only the Hall parameter.
Arnaut, L R
2006-01-01
Using a TE/TM decomposition for an angular plane-wave spectrum of free random electromagnetic waves and matched boundary conditions, we derive the probability density function for the energy density of the vector electric field in the presence of a semi-infinite isotropic medium. The theoretical analysis is illustrated with calculations and results for good electric conductors and for a lossless dielectric half-space. The influence of the permittivity and conductivity on the intensity, random polarization, statistical distribution and standard deviation of the field is investigated, both for incident plus reflected fields and for refracted fields. External refraction is found to result in compression of the fluctuations of the random field.
Synchronization in the random-field Kuramoto model on complex networks
Lopes, M. A.; Lopes, E. M.; Yoon, S.; Mendes, J. F. F.; Goltsev, A. V.
2016-07-01
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fields with uniformly distributed directions and homogeneous and heterogeneous (Gaussian) field magnitude distribution. In our analysis, we apply the Ott-Antonsen method and the annealed-network approximation to find the critical behavior of the order parameter. In the case of homogeneous fields, we find a tricritical point above which a second-order phase transition gives place to a first-order phase transition when the network is either fully connected or scale-free with the degree exponent γ >5 . Interestingly, for scale-free networks with 2 networks, although the fields increase the critical coupling for γ >3 . Our simulations support these analytical results.
Metastable minima of the Heisenberg spin glass in a random magnetic field
Sharma, Auditya; Yeo, Joonhyun; Moore, M. A.
2016-11-01
We have studied zero-temperature metastable minima in classical m -vector component spin glasses in the presence of m -component random fields for two models, the Sherrington-Kirkpatrick (SK) model and the Viana-Bray (VB) model. For the SK model we have calculated analytically its complexity (the log of the number of minima) for both the annealed case where one averages the number of minima before taking the log and the quenched case where one averages the complexity itself, both for fields above and below the de Almeida-Thouless (AT) field, which is finite for m >2 . We have done numerical quenches starting from a random initial state (infinite temperature state) by putting spins parallel to their local fields until there is no further decrease of the energy and found that in zero field it always produces minima that have zero overlap with each other. For the m =2 and m =3 cases in the SK model the final energy reached in the quench is very close to the energy Ec at which the overlap of the states would acquire replica symmetry-breaking features. These minima have marginal stability and will have long-range correlations between them. In the SK limit we have analytically studied the density of states ρ (λ ) of the Hessian matrix in the annealed approximation. Despite the fact that in the presence of a random field there are no continuous symmetries, the spectrum extends down to zero with the usual √{λ } form for the density of states for fields below the AT field. However, when the random field is larger than the AT field, there is a gap in the spectrum, which closes up as the AT field is approached. The VB model behaves differently and seems rather similar to studies of the three-dimensional Heisenberg spin glass in a random vector field.
A dissipative random velocity field for fully developed fluid turbulence
Pereira, Rodrigo M; Chevillard, Laurent
2015-01-01
We investigate the statistical properties, based on numerical simulations and analytical calculations, of a recently proposed stochastic model for the velocity field of an incompressible, homogeneous, isotropic and fully developed turbulent flow. A key step in the construction of this model is the introduction of some aspects of the vorticity stretching mechanism that governs the dynamics of fluid particles along their trajectory. An additional further phenomenological step aimed at including the long range correlated nature of turbulence makes this model depending on a single free parameter $\\gamma$ that can be estimated from experimental measurements. We confirm the realism of the model regarding the geometry of the velocity gradient tensor, the power-law behaviour of the moments of velocity increments (i.e. the structure functions), including the intermittent corrections, and the existence of energy transfers across scales. We quantify the dependence of these basic properties of turbulent flows on the free...
CRITICAL BEHAVIOR OF S-3／2 ISING MODEL IN RANDOM LONGITUDINAL AND TRANSVERSE FIELDS
Institute of Scientific and Technical Information of China (English)
宋为基
1995-01-01
The phase diagrams and the other crtical properties of S-3/2 Ising model in random longitudinal and transverse fields(RLIM) are dicussed with the approximate scheme combined by mean-field renormalization group theory(MFRG) and the discretized path-integral representation(DPIR).
A Remark on the Lifshitz Tail for Schrödinger Operator with Random Magnetic Field
Indian Academy of Sciences (India)
Shu Nakamura
2002-02-01
In this note, we consider the Lifshitz singularity for Schrödinger operator with ergodic random magnetic field. A key estimate is an energy bound for magnetic Schrödinger operators as discussed in Nakamura [8]. Here we remove a technical assumption in [8], namely, the uniform boundedness of the magnetic field.
Scheerlinck, N.; Verboven, P.; Stigter, J.D.; Baerdenmaeker, de J.; Impe, van J.F.; Nicolai, B.A.
2000-01-01
A first-order perturbation algorithm for the computation of mean values and variances of transient temperature and moisture fields during coupled heat and mass transfer problems with random field parameters has been developed and implemented. The algorithm is based on the Galerkin finite-element dis
Mean-field theory of random-site q-state Potts models
van Enter, Aernout; Hemmen, Jan Leonard van; Pospiech, C.
1988-01-01
A class of random-site mean-field Potts models is introduced and solved exactly. The bifurcation properties of the resulting mean-field equations are analysed in detail. Particular emphasis is put on the relation between the solutions and the underlying symmetries of the model. It turns out that, in
Modeling of nonlinear microscopy of localized field enhancements in random metal nanostructures
DEFF Research Database (Denmark)
Beermann, Jonas; Bozhevolnyi, Sergey I.; Coello, Victor
2006-01-01
Nonlinear microscopy of localized field enhancements in random metal nanostructures with a tightly focused laser beam scanning over a sample surface is modeled by making use of analytic representations of the Green dyadic in the near- and far-field regions, with the latter being approximated...... by the part describing the scattering via excitation of surface plasmon polaritons. The developed approach is applied to scanning second-harmonic (SH) microscopy of small gold spheres placed randomly on a gold surface. We calculate self-consistent fundamental harmonic (FH) and SH field distributions...
Magnetic properties of a transverse spin- Ising model with random longitudinal field
Liang, Ya-Qiu; Wei, Guo-Zhu; Song, Guo-Li
2004-12-01
Within the framework of the effective-field theory with correlations, a spin- transverse Ising model in the longitudinal random-field on a honeycomb lattice is studied. The phase diagrams and the behavior of the tricritical point are examined. The possible re-entrance phenomena displayed by the system due to the competition effects that occur for the appropriate ranges of the random and transverse field are investigated. The longitudinal and transverse magnetizations, the longitudinal quadrupolar moments and internal energy are given numerically for a honeycomb lattice (z = 3).
Phase diagram of the classical Heisenberg model in a trimodal random field distribution
Santos-Filho, A.; Albuquerque, D. F. de; Santos-Filho, J. B.; Batista, T. S. Araujo
2016-11-01
The classical spin 1 / 2 Heisenberg model on a simple cubic lattice, with fluctuating bond interactions between nearest neighbors and in the presence of a random magnetic field, is investigated by effective field theory based on two-spin cluster. The random field is drawn from the asymmetric and anisotropic trimodal probability distribution. The fluctuating bond is extracted from the symmetric and anisotropic bimodal probability. We estimate the transition temperatures, and the phase diagram in the Tc- h, Tc- p and Tc - α planes. We observe that the temperature of the tricritical point decreases with the increase of disorder in exchange interactions until the system ceases to display tricritical behavior. The disorder of the interactions and reentrant phenomena depends on the trimodal distribution of the random field.
DEFF Research Database (Denmark)
Franchin, P.; Ditlevsen, Ove Dalager; Kiureghian, Armen Der
2002-01-01
The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals...... are considered to be Gaussian. Conventional FORM analysis yields the linearization point of the idealized limit-state surface. A model correction factor is then introduced to push the idealized limit-state surface onto the actual limit-state surface. A few iterations yield a good approximation of the reliability...... reliability method; Model correction factor method; Nataf field integration; Non-Gaussion random field; Random field integration; Structural reliability; Pile foundation reliability...
Effects of the random field on the magnetic behavior of nanowires with core/shell morphology
Energy Technology Data Exchange (ETDEWEB)
Zaim, A., E-mail: ah_zaim@yahoo.fr [Laboratoire Physique des Matériaux et Modélisation des Systèmes (LP2MS), Unité Associée au CNRST-URAC: 08, Faculty of Sciences, University Moulay Ismail, B.P. 11201, Zitoune, Meknes (Morocco); Kerouad, M., E-mail: kerouad@fs-umi.ac.ma [Laboratoire Physique des Matériaux et Modélisation des Systèmes (LP2MS), Unité Associée au CNRST-URAC: 08, Faculty of Sciences, University Moulay Ismail, B.P. 11201, Zitoune, Meknes (Morocco); Boughrara, M. [Laboratoire Physique des Matériaux et Modélisation des Systèmes (LP2MS), Unité Associée au CNRST-URAC: 08, Faculty of Sciences, University Moulay Ismail, B.P. 11201, Zitoune, Meknes (Morocco)
2013-04-15
We have used the effective field theory based on probability distribution method to investigate the hysteresis behavior of the magnetic nanowires with core/shell morphology in a random magnetic field. The hysteresis curves are obtained for different values of the random magnetic field, both ferromagnetic and antiferromagnetic couplings between the shell and the core are considered. A number of characteristic behaviors are find, such as the existence of double or triple hysteresis loops for appropriate values of the system parameters affected by the random magnetic field, temperature, and interfacial coupling. -- Highlights: ► The hysteresis behavior of the Ising nanowires has been studied by EFT. ► The effects of the random field on the hysteresis loops have been examined. ► The hysteresis loops are obtained for different values of the interfacial coupling constant. ► The triple hysteresis loops occur for the larger antiferromagnetic coupling constant. ► The dependence of the coercive field on the temperature is investigated.
The limiting behavior of the estimated parameters in a misspecified random field regression model
DEFF Research Database (Denmark)
Dahl, Christian Møller; Qin, Yu
convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully......This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection...... of nonlinear functions and it has the added advantage that there is no "curse of dimensionality."Contrary to existing literature on the asymptotic properties of the estimated parameters in random field models our results do not require that the explanatory variables are sampled on a grid. However...
Randomness in highly reflective silver nanoparticles and their localized optical fields
Naruse, Makoto; Yasuda, Hideki; Tate, Naoya; Ohtsu, Motoichi; Naya, Masayuki
2014-01-01
Reflection of near-infrared light is important for preventing heat transfer in energy saving applications. A large-area, mass-producible reflector that contains randomly distributed disk-shaped silver nanoparticles and that exhibits high reflection at near-infrared wavelengths was demonstrated. Although resonant coupling between incident light and the nanostructure of the reflector plays some role, what is more important is the geometrical randomness of the nanoparticles, which serves as the origin of a particle-dependent localization and hierarchical distribution of optical near-fields in the vicinity of the nanostructure. Here we show and clarified the unique optical near-field processes associated with the randomness seen in experimentally fabricated silver nanostructures by adapting a rigorous theory of optical near-fields based on an angular spectrum and detailed electromagnetic calculations.
Destruction of first-order phase transition in a random-field Ising model
Energy Technology Data Exchange (ETDEWEB)
Crokidakis, Nuno; Nobre, Fernando D [Centro Brasileiro de Pesquisas Fisicas, Rua Xavier Sigaud 150, 22290-180, Rio de Janeiro-RJ (Brazil)], E-mail: nuno@if.uff.br, E-mail: fdnobre@cbpf.br
2008-04-09
The phase transitions that occur in an infinite-range-interaction Ising ferromagnet in the presence of a double Gaussian random magnetic field are analyzed. Such random fields are defined as a superposition of two Gaussian distributions, presenting the same width {sigma}. It is argued that this distribution is more appropriate for a theoretical description of real systems than other simpler cases, i.e. the bimodal ({sigma} = 0) and single Gaussian distributions. It is shown that a low-temperature first-order phase transition may be destroyed for increasing values of {sigma}, similarly to what happens in the compound Fe{sub x}Mg{sub 1-x}Cl{sub 2}, whose finite-temperature first-order phase transition is presumably destroyed by an increase in the field randomness.
A test for stationarity of spatio-temporal random fields on planar and spherical domains
Jun, Mikyoung
2012-01-01
A formal test for weak stationarity of spatial and spatio-temporal random fields is proposed. We consider the cases where the spatial domain is planar or spherical, and we do not require distributional assumptions for the random fields. The method can be applied to univariate or to multivariate random fields. Our test is based on the asymptotic normality of certain statistics that are functions of estimators of covariances at certain spatial and temporal lags under weak stationarity. Simulation results for spatial as well as spatio-temporal cases on the two types of spatial domains are reported. We describe the results of testing the stationarity of Pacific wind data, and of testing the axial symmetry of climate model errors for surface temperature using the NOAA GFDL model outputs and the observations from the Climate Research Unit in East Anglia and the Hadley Centre.
Experimental realization of random-field Ising ferromagnetism in a molecular magnet
Sarachik, Myriam P.
2011-03-01
The longitudinal magnetic susceptibility of single crystals of the molecular magnet Mn 12 -acetate obeys a Curie-Weiss law, indicating a transition to a ferromagnetic phase at ~ 0.9 K [1,2]. With increasing magnetic field applied transverse to the easy axis, a marked change is observed in the temperature dependence of the susceptibility, with a considerably more rapid suppression of the Curie-Weiss temperature than predicted by mean-field theory for an ordered single crystal. Our results can instead be fit by a Hamiltonian for a random-field Ising ferromagnet in a transverse magnetic field, where the randomness derives from the intrinsic distribution of locally tilted magnetic easy axes known to exist in Mn 12 -acetate crystals. Mn 12 -ac and other single molecule magnets may thus serve as clean model systems for the study of random field ferromagnetism where the random fields are controllable and considerably larger than typical hyperfine fields. This discovery promises to enable widespread and convenient experimental study of magnetism in a random field in a broad class of new materials. Work performed by and in collaboration with: Bo Wen, and Lin Bo, Physics Dept. City College of New York, CUNY (funded by NSF-DMR-0451605), P. Subedi and A. D. Kent, Physics Dept., NYU, (funded by NSF-DMR-0506946 and ARO-W911NF-08-1-0364) Y. Yeshurun, Physics Dept., Bar Ilan U, (funded by Deutsche Forschungsgemeinschaft), A. J. Millis, Physics Dept. Columbia U. (funded by DMR DMR-0705847), C. Lampropoulos and G. Christou, Chemistry Dept., U. of Florida (funded by NSF -CHE-0910472). Funding provided by NSF award DMR-0451605.
Return probability and recurrence for the random walk driven by two-dimensional Gaussian free field
Biskup, Marek; Ding, Jian; Goswami, Subhajit
2016-01-01
Given any $\\gamma>0$ and for $\\eta=\\{\\eta_v\\}_{v\\in \\mathbb Z^2}$ denoting a sample of the two-dimensional discrete Gaussian free field on $\\mathbb Z^2$ pinned at the origin, we consider the random walk on $\\mathbb Z^2$ among random conductances where the conductance of edge $(u, v)$ is given by $\\mathrm{e}^{\\gamma(\\eta_u + \\eta_v)}$. We show that, for almost every $\\eta$, this random walk is recurrent and that, with probability tending to 1 as $T\\to \\infty$, the return probability at time $2...
Dynamics of Crowd Behaviors: From Complex Plane to Quantum Random Fields
Ivancevic, Vladimir G.; Reid, Darryn J.
2015-11-01
The following sections are included: * Complex Plane Dynamics of Crowds and Groups * Introduction * Complex-Valued Dynamics of Crowd and Group Behaviors * Kähler Geometry of Crowd and Group Dynamics * Computer Simulations of Crowds and Croups Dynamics * Braids of Agents' Behaviors in the Complex Plane * Hilbert-Space Control of Crowds and Groups Dynamics * Quantum Random Fields: A Unique Framework for Simulation, Optimization, Control and Learning * Introduction * Adaptive Quantum Oscillator * Optimization and Learning on Banach and Hilbert Spaces * Appendix * Complex-Valued Image Processing * Linear Integral Equations * Riemann-Liouville Fractional Calculus * Rigorous Geometric Quantization * Supervised Machine-Learning Methods * First-Order Logic and Quantum Random Fields
THE DECISION OF THE OPTIMAL PARAMETERS IN MARKOV RANDOM FIELDS OF IMAGES BY GENETIC ALGORITHM
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters.Focusing on the shortcomings in present methods,a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field.The detailed procedure is discussed.On the basis of the parameters solved by genetic algorithms,some experim ents on classification of aerial images are given.Experimental results show that the proposed method is effective and the classification results are satisfactory.
The limiting behavior of the estimated parameters in a misspecified random field regression model
DEFF Research Database (Denmark)
Dahl, Christian Møller; Qin, Yu
, as a consequence the random field model specification introduces non-stationarity and non-ergodicity in the misspecified model and it becomes non-trivial, relative to the existing literature, to establish the limiting behavior of the estimated parameters. The asymptotic results are obtained by applying some...... convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully...
Excursion sets of infinitely divisible random fields with convolution equivalent Lévy measure
DEFF Research Database (Denmark)
Rønn-Nielsen, Anders; Jensen, Eva B. Vedel
We consider a continuous, infinitely divisible random field in R d , d = 1, 2, 3, given as an integral of a kernel function with respect to a Lévy basis with convolution equivalent Lévy measure. For a large class of such random fields we compute the asymptotic probability that the excursion set a...... at level x contains some rotation of an object with fixed radius as x → ∞. Our main result is that the asymptotic probability is equivalent to the right tail of the underlying Lévy measure...
Extremes of random fields over arbitrary domains with application to concrete rupture stresses
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
2004-01-01
To find the exact probability distribution of the global maximum or minimum of a random field within a bounded domain is a pending problem even for Gaussian fields. Except for very special examples of fields, recourse must be taken to approximate reasoning or asymptotic considerations to be judged....... In this paper, the distribution function is obtained in general for Gaussian fields over arbitrary bounded domains with piecewise continuous and differentiable boundaries, and as in earlier works the distribution function is tested against empirical distribution functions obtained by simulation of sample...
Carbon nanotube field emitters on KOVAR substrate modified by random pattern
Energy Technology Data Exchange (ETDEWEB)
Park, Seol Ah; Song, Eun-Ho; Kang, Byung Hyun; Ju, Byeong-Kwon, E-mail: bkju@korea.ac.kr [Korea University, Display and Nanosystem Laboratory, College of Engineering (Korea, Republic of)
2015-07-15
We investigated the field emission characteristics of patterned carbon nanotubes (CNTs) on KOVAR substrates with different surface morphologies. The substrate with a micro-sized random pattern was fabricated through chemical wet etching, whereas the substrate with a nano-sized random pattern was formed by surface roughening process of polymer and chemical wet etching. The field emission characteristics of these substrates were the compared with those of non-treated substrates. It was clearly revealed that the field emission characteristics of CNTs were influenced by the surface morphology of the cathode substrate. When the surface of cathode was modified by random pattern, the modified substrate provided a large surface area and a wider print area. Also, the modified surface morphology of the cathode provided strong adhesion between the CNT paste and the cathode. Particularly, the substrate with the nano-sized random pattern showed that the turn-on field value decreases and the field enhancement factor value improves as compared with non-treated substrate.
Disorder fingerprint: Intensity distributions in the near field of random media
Naraghi, R. Rezvani; Sukhov, S.; Dogariu, A.
2016-11-01
The structural morphology of complex dielectric media determines their functionalities by driving the statistical properties of the electromagnetic fields. Our controlled experiments and full electromagnetic calculations that go beyond common dipolar approximations demonstrate that the specific characteristics of disorder lead to non-Rayleigh statistics of detected intensity, which can be directly accessed in the near field of random media and can be unambiguously related to the short-range correlations of disorder.
Modeling and statistical analysis of non-Gaussian random fields with heavy-tailed distributions
Nezhadhaghighi, Mohsen Ghasemi; Nakhlband, Abbas
2017-04-01
In this paper, we investigate and develop an alternative approach to the numerical analysis and characterization of random fluctuations with the heavy-tailed probability distribution function (PDF), such as turbulent heat flow and solar flare fluctuations. We identify the heavy-tailed random fluctuations based on the scaling properties of the tail exponent of the PDF, power-law growth of q th order correlation function, and the self-similar properties of the contour lines in two-dimensional random fields. Moreover, this work leads to a substitution for the fractional Edwards-Wilkinson (EW) equation that works in the presence of μ -stable Lévy noise. Our proposed model explains the configuration dynamics of the systems with heavy-tailed correlated random fluctuations. We also present an alternative solution to the fractional EW equation in the presence of μ -stable Lévy noise in the steady state, which is implemented numerically, using the μ -stable fractional Lévy motion. Based on the analysis of the self-similar properties of contour loops, we numerically show that the scaling properties of contour loop ensembles can qualitatively and quantitatively distinguish non-Gaussian random fields from Gaussian random fluctuations.
Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel
2016-07-01
A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.
Using Conditional Random Fields to Extract Contexts and Answers of Questions from Online Forums
DEFF Research Database (Denmark)
Ding, Shilin; Cong, Gao; Lin, Chin-Yew;
2008-01-01
on Conditional Random Fields (CRFs) to detect the contexts and answers of questions from forum threads. We improve the basic framework by Skip-chain CRFs and 2D CRFs to better accommodate the features of forums for better performance. Experimental results show that our techniques are very promising....
Kernel conditional ordinal random fields for temporal segmentation of facial action units
Rudovic, Ognjen; Pavlovic, Vladimir; Pantic, Maja
2012-01-01
We consider the problem of automated recognition of temporal segments (neutral, onset, apex and offset) of Facial Action Units. To this end, we propose the Laplacian-regularized Kernel Conditional Ordinal Random Field model. In contrast to standard modeling approaches to recognition of AUs’ temporal
Using Conditional Random Fields to Extract Contexts and Answers of Questions from Online Forums
DEFF Research Database (Denmark)
Ding, Shilin; Cong, Gao; Lin, Chin-Yew
2008-01-01
on Conditional Random Fields (CRFs) to detect the contexts and answers of questions from forum threads. We improve the basic framework by Skip-chain CRFs and 2D CRFs to better accommodate the features of forums for better performance. Experimental results show that our techniques are very promising....
van Kasteren, T.L.M.; Noulas, A.K.; Kröse, B.J.A.; Smit, G.J.M.; Epema, D.H.J.; Lew, M.S.
2008-01-01
Conditional Random Fields are a discriminative probabilistic model which recently gained popularity in applications that require modeling nonindependent observation sequences. In this work, we present the basic advantages of this model over generative models and argue about its suitability in the do
Separate Training for Conditional Random Fields Using Co-occurrence Rate Factorization
Zhu, Zhemin; Hiemstra, Djoerd; Apers, Peter M.G.; Wombacher, Andreas
2012-01-01
The standard training method of Conditional Random Fields (CRFs) is very slow for large-scale applications. As an alternative, piecewise training divides the full graph into pieces, trains them independently, and combines the learned weights at test time. In this paper, we present separate training
A Central Limit Theorem for the Volumes of High Excursions of Stationary Associated Random Fields
Directory of Open Access Journals (Sweden)
Vadim Demichev
2015-05-01
Full Text Available We prove that under certain conditions the excursion sets volumes of stationary positively associated random fields converge after rescaling to the normal distribution as the excursion level and the size of the observation window grow. In addition, we provide a number of examples.
Modelling Distributed Shape Priors by Gibbs Random Fields of Second Order
Flach, Boris
2011-01-01
We analyse the potential of Gibbs Random Fields for shape prior modelling. We show that the expressive power of second order GRFs is already sufficient to express simple shapes and spatial relations between them simultaneously. This allows to model and recognise complex shapes as spatial compositions of simpler parts.
Joint modeling of ChIP-seq data via a Markov random field model
Bao, Yanchun; Vinciotti, Veronica; Wit, Ernst; 't Hoen, Peter A C
2014-01-01
Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spat
Luan, Nana
2011-01-01
Let $X= \\{X(t), t \\in \\R^N\\}$ be a Gaussian random field with values in $\\R^d$ defined by \\[ X(t) = \\big(X_1(t),..., X_d(t)\\big),\\qquad t \\in \\R^N, \\] where $X_1, ..., X_d$ are independent copies of a real-valued, centered, anisotropic Gaussian random field $X_0$ which has stationary increments and the property of strong local nondeterminism. In this paper we determine the exact Hausdorff measure function for the range $X([0, 1]^N)$. We also provide a sufficient condition for a Gaussian random field with stationary increments to be strongly locally nondeterministic. This condition is given in terms of the spectral measures of the Gaussian random fields which may contain either an absolutely continuous or discrete part. This result strengthens and extends significantly the related theorems of Berman (1973, 1988), Pitt (1978) and Xiao (2007, 2009), and will have wider applicability beyond the scope of the present paper.
Random fields of initial out of straightness leading to column buckling
DEFF Research Database (Denmark)
Kala, Zdeněk; Valeš, Jan; Jönsson, Jeppe
2017-01-01
The elastic load-carrying capacity and buckling trajectory of steel columns under compression with open and hollow cross-sections, whose axis is curved by spatial random fields, are studied in the article. As a result of the spatial curvature of the axis the cross-sections are subjected to compre...
Exponential Distribution for the Occurrence of Rare Patterns in Gibbsian Random Fields
Abadi, M.; Chazottes, J.-R.; Redig, F.; Verbitskiy, E.
2004-01-01
We study the distribution of the occurrence of rare patterns in sufficiently mixing Gibbs random fields on the lattice Z^d, d ≥ 2. A typical example is the high temperature Ising model. This distribution is shown to converge to an exponential law as the size of the pattern diverges. Our analysis not
Modulation of electromagnetic fields by a depolarizer of random polarizer array
DEFF Research Database (Denmark)
Ma, Ning; Hanson, Steen Grüner; Wang, Wei
2016-01-01
The statistical properties of the electric fields with random changes of the polarization state in space generated by a depolarizer are investigated on the basis of the coherence matrix. The depolarizer is a polarizer array composed of a multitude of contiguous square cells of polarizers with ran...
High Performance Ambipolar Field-Effect Transistor of Random Network Carbon Nanotubes
Bisri, Satria Zulkarnaen; Gao, Jia; Derenskyi, Vladimir; Gomulya, Widianta; Iezhokin, Igor; Gordiichuk, Pavlo; Herrmann, Andreas; Loi, Maria Antonietta
2012-01-01
Ambipolar field-effect transistors of random network carbon nanotubes are fabricated from an enriched dispersion utilizing a conjugated polymer as the selective purifying medium. The devices exhibit high mobility values for both holes and electrons (3 cm(2)/V.s) with a high on/off ratio (10(6)). The
Information Extraction from Research Papers based on Conditional Random Field Model
Directory of Open Access Journals (Sweden)
Zhu Shuxin
2013-01-01
Full Text Available With the increasing use of CiteSeer academic search engines, the accuracy of such systems has become more and more important. The paper adopts the improved particle swarm optimization algorithm for training conditional random field model and applies it into the research papers’ title and citation retrieval. The improved particl swarm optimization algorithm brings the particle swarm aggregation to prevent particle swarm from being plunged into local convergence too early, and uses the linear inertia factor and learning factor to update particle rate. It can control algorithm in infinite iteration by the iteration between particle relative position change rate. The results of which using the standard research papers’ heads and references to evaluate the trained conditional random field model shows that compared with traditionally conditional random field model and Hidden Markov Model, the conditional random field model ,optimized and trained by improved particle swarm, has been better ameliorated in the aspect of F1 mean error and word error rate.
Is Bonferroni correction more sensitive than Random Field Theory for most fMRI studies?
Tierney, Tim M; Carmichael, David W
2016-01-01
Random Field Theory has been used in the fMRI literature to address the multiple comparisons problem. The method provides an analytical solution for the computation of precise p-values when its assumptions are met. When its assumptions are not met the thresholds generated by Random Field Theory can be more conservative than Bonferroni corrections, which are arguably too stringent for use in fMRI. As this has been well documented theoretically it is surprising that a majority of current studies (~80%) would not meet the assumptions of Random Field Theory and therefore would have reduced sensitivity. Specifically most data is not smooth enough to meet the good lattice assumption. Current studies smooth data on average by twice the voxel size which is rarely sufficient to meet the good lattice assumption. The amount of smoothing required for Random Field Theory to produce accurate p-values increases with image resolution and decreases with degrees of freedom. There is no rule of thumb that is valid for all study...
Magalhaes, S. G.; Zimmer, F. M.; Coqblin, B.
2012-12-01
We study here the influence of a random applied magnetic field on the competition between the Kondo effect, the spin glass phase and a ferromagnetic order in disordered cerium systems such as CeNi1-xCux. The model used here takes an intrasite Kondo coupling and an intersite random coupling; both the intersite random coupling and the random magnetic field are described within the Sherrington-Kirkpatrick model and the one-step replica symmetry breaking procedure is also used here. We present phase diagrams giving Temperature versus the Kondo exchange parameter and the random magnetic field makes decrease particularly the importance of the spin glass and ferromagnetic phases.
On Non-Parametric Field Estimation using Randomly Deployed, Noisy, Binary Sensors
Wang, Ye
2007-01-01
We consider the problem of reconstructing a deterministic data field from binary quantized noisy observations of sensors randomly deployed over the field domain. Our focus is on the extremes of lack of control in the sensor deployment, arbitrariness and lack of knowledge of the noise distribution, and low-precision and unreliability in the sensors. These adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. We propose a simple estimator that reconstructs the entire data field from these unreliable, binary quantized, noisy observations. Under the assumption of a bounded amplitude field, we prove almost sure and mean-square convergence of the estimator to the actual field as the number of sensors tends to infinity. For fields with bounded-variation, Sobolev differentiable, or finite-dimensionality properties, we derive specific mean squared error...
Phase Diagram and Tricritical Behavior of a Spin-2 Transverse Ising Model in aRandom Field
Institute of Scientific and Technical Information of China (English)
LIANGYa-Qiu; WEIGuo-Zhu; SONGLi-Li; SONGGuo-Li; ZANGShu-Liang
2004-01-01
The phase diagrams of a spin-2 transverse Ising model with a random field on honeycomb, square, and simple-cubic lattices, respectively, are investigated within the framework of an effective-field theory with correlations.We find the behavior of the tricritical point and the reentrant phenomenon for the system with any coordination number z, when the applied random field is bimodal. The behavior of the tricritical point is also examined as a function of applied transverse field. The reentrant phenomenon comes from the competition between the transverse field and the random field.
Exact Partition Function for the Random Walk of an Electrostatic Field
Directory of Open Access Journals (Sweden)
Gabriel González
2017-01-01
Full Text Available The partition function for the random walk of an electrostatic field produced by several static parallel infinite charged planes in which the charge distribution could be either ±σ is obtained. We find the electrostatic energy of the system and show that it can be analyzed through generalized Dyck paths. The relation between the electrostatic field and generalized Dyck paths allows us to sum overall possible electrostatic field configurations and is used for obtaining the partition function of the system. We illustrate our results with one example.
Directory of Open Access Journals (Sweden)
Xiaofeng Sun
2017-08-01
Full Text Available As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks-based classification method.
The van Hemmen model and effect of random crystalline anisotropy field
Energy Technology Data Exchange (ETDEWEB)
Morais, Denes M. de [Instituto de Física, Universidade Federal de Mato Grosso, 78060-900 Cuiabá, Mato Grosso (Brazil); Godoy, Mauricio, E-mail: mgodoy@fisica.ufmt.br [Instituto de Física, Universidade Federal de Mato Grosso, 78060-900 Cuiabá, Mato Grosso (Brazil); Arruda, Alberto S. de, E-mail: aarruda@fisica.ufmt.br [Instituto de Física, Universidade Federal de Mato Grosso, 78060-900 Cuiabá, Mato Grosso (Brazil); Silva, Jonathas N. da [Universidade Estadual Paulista, 14800-901, Araraquara, São Paulo (Brazil); Ricardo de Sousa, J. [Instituto Nacional de Sistemas Complexos, Departamento de Fisica, Universidade Federal do Amazona, 69077-000, Manaus, Amazonas (Brazil)
2016-01-15
In this work, we have presented the generalized phase diagrams of the van Hemmen model for spin S=1 in the presence of an anisotropic term of random crystalline field. In order to study the critical behavior of the phase transitions, we employed a mean-field Curie–Weiss approach, which allows calculation of the free energy and the equations of state of the model. The phase diagrams obtained here displayed tricritical behavior, with second-order phase transition lines separated from the first-order phase transition lines by a tricritical point. - Highlights: • Several phase diagrams are obtained for the model. • The influence of the random crystalline anisotropy field on the model is investigated. • Three ordered (spin-glass, ferromagnetic and mixed) phases are found. • The tricritical behavior is examined.
Controlling dispersion forces between small particles with artificially created random light fields
Bruegger, Georges; Scheffold, Frank; Saenz, Juan Jose
2015-01-01
Appropriate combinations of laser beams can be used to trap and manipulate small particles with "optical tweezers" as well as to induce significant "optical binding" forces between particles. These interaction forces are usually strongly anisotropic depending on the interference landscape of the external fields. This is in contrast with the familiar isotropic, translationally invariant, van der Waals and, in general, Casimir-Lifshitz interactions between neutral bodies arising from random electromagnetic waves generated by equilibrium quantum and thermal fluctuations. Here we show, both theoretically and experimentally, that dispersion forces between small colloidal particles can also be induced and controlled using artificially created fluctuating light fields. Using optical tweezers as gauge, we present experimental evidence for the predicted isotropic attractive interactions between dielectric microspheres induced by laser-generated, random light fields. These light induced interactions open a path towards...
Maximizing Entropy of Pickard Random Fields for 2x2 Binary Constraints
DEFF Research Database (Denmark)
Søgaard, Jacob; Forchhammer, Søren
2014-01-01
This paper considers the problem of maximizing the entropy of two-dimensional (2D) Pickard Random Fields (PRF) subject to constraints. We consider binary Pickard Random Fields, which provides a 2D causal finite context model and use it to define stationary probabilities for 2x2 squares, thus...... allowing us to calculate the entropy of the field. All possible binary 2x2 constraints are considered and all constraints are categorized into groups according to their properties. For constraints which can be modeled by a PRF approach and with positive entropy, we characterize and provide statistics...... of the maximum PRF entropy. As examples, we consider the well known hard square constraint along with a few other constraints....
Field quality control of an earth dam: random versus purposive sampling
Energy Technology Data Exchange (ETDEWEB)
Kotzias, P.C.; Stamatopoulos, A.C.
1996-08-01
Two sampling operations, the random and purposive techniques, for field quality control of an earth dam were presented. Each technique was analyzed and their similarities and differences were compared. Each took into consideration the attributes or variables such as strength, temperature, slump, air content, density, and water content. The purposive operation needed much less field sampling and testing than the random operation. The advantages and disadvantages of each technique were described. It was concluded that both techniques could be used individually or jointly on the same project as long as the extent of the application, with all rules and underlying assumptions, were recognized for each case and were strictly adhered to in the field. 19 refs., 4 tabs., 2 figs.
Certified Randomness from a Two-Level System in a Relativistic Quantum Field
Thinh, Le Phuc; Martin-Martinez, Eduardo
2016-01-01
Randomness is an indispensable resource in modern science and information technology. Fortunately, an experimentally simple procedure exists to generate randomness with well-characterized devices: measuring a quantum system in a basis complementary to its preparation. Towards realizing this goal one may consider using atoms or superconducting qubits, promising candidates for quantum information processing. However, their unavoidable interaction with the electromagnetic field affects their dynamics. At large time scales, this can result in decoherence. Smaller time scales in principle avoid this problem, but may not be well analysed under the usual rotating wave and single-mode approximation (RWA and SMA) which break the relativistic nature of quantum field theory. Here, we use a fully relativistic analysis to quantify the information that an adversary with access to the field could get on the result of an atomic measurement. Surprisingly, we find that the adversary's guessing probability is not minimized for ...
Levitation of Extended States in a Random Magnetic Field with a Finite Mean
Institute of Scientific and Technical Information of China (English)
LIU Wen-Sheng; LEI Xiao-Lin
2004-01-01
We study the localization properties of electrons in a two-dimensional system in a random magnetic field B(r) = Bo + δB(r) with the average Bo and the amplitude of the magnetic field fluctuations δB. The localization length of the system is calculated by using the finite-size scaling method combined with the transfer-matrix technique.Inthe case of weak δB, we find that the random magnetic field system is equivalent to the integer quantum Hall effect system, namely, the energy band splits into a series of disorder broadened Landau bands, at the centers of which states are extended with the localization length exponent v = 2.34 ± 0.02. With increasing δB, the extended states float up in energy, which is similar to the levitation scenario proposed for the integer quantum Hall effect.
Zaim, N.; Zaim, A.; Kerouad, M.
2017-02-01
In this work, the magnetic behavior of the cylindrical nanowire, consisting of a ferromagnetic core of spin-1 atoms surrounded by a ferromagnetic shell of spin-1 atoms is studied in the presence of a random crystal field interaction. Based on Metropolis algorithm, the Monte Carlo simulation has been used to investigate the effects of the concentration of the random crystal field p, the crystal field D and the shell exchange interaction Js on the phase diagrams and the hysteresis behavior of the system. Some characteristic behaviors have been found, such as the first and second-order phase transitions joined by tricritical point for appropriate values of the system parameters, triple and isolated critical points can be also found. Depending on the Hamiltonian parameters, single, double and para hysteresis regions are explicitly determined.
The Stochastic Galerkin Method for Darcy Flow Problem with Log-Normal Random Field Coefficients
Directory of Open Access Journals (Sweden)
Michal Beres
2017-01-01
Full Text Available This article presents a study of the Stochastic Galerkin Method (SGM applied to the Darcy flow problem with a log-normally distributed random material field given by a mean value and an autocovariance function. We divide the solution of the problem into two parts. The first one is the decomposition of a random field into a sum of products of a random vector and a function of spatial coordinates; this can be achieved using the Karhunen-Loeve expansion. The second part is the solution of the problem using SGM. SGM is a simple extension of the Galerkin method in which the random variables represent additional problem dimensions. For the discretization of the problem, we use a finite element basis for spatial variables and a polynomial chaos discretization for random variables. The results of SGM can be utilised for the analysis of the problem, such as the examination of the average flow, or as a tool for the Bayesian approach to inverse problems.
Predictors of Consent in a Randomized Field Study in Child Welfare.
McDonald, Tom; Bhattarai, Jackie; Akin, Becci
2017-01-01
Randomized controlled trials (RCTs) are often viewed as the "gold standard" for proving the efficacy and effectiveness of new interventions. However, some are skeptical of the generalizability of the findings that RCTs produce. The characteristics of those willing to participate in research studies have the potential to affect the generalizability of its findings. This study examined factors that could influence consent among families recruited to participate in a randomized field trial in a real-world child welfare setting. This study tested the Parent Management Training Oregon Model for children in foster care with serious emotional disturbance. It employed a post-randomization consent design, whereby the entire sample of eligible participants, not just those who are willing to consent to randomization, are included in the sample. Initial eligibility assessment data and data from the federally mandated reporting system for public child welfare agencies provided the pool of potential predictors of consent. Bivariate and multivariate analyses were conducted to identify statistically significant predictors of consent. Being a dual reunification family was the most significant factor in predicting consent. Unmarried individuals, younger, female parents, cases where parental incarceration was the reason for removal and cases where the removal reason was not due to their children's behavioral problem(s) were also more likely to participate. As one of the first research studies to examine predictors of consent to a randomized field study in child welfare settings, results presented here can act as a preliminary guide for conducting RCTs in child welfare settings.
Dynamic attack zone of air-to-air missile after being launched in random wind field
Institute of Scientific and Technical Information of China (English)
Hui Yaoluo; Nan Ying; Chen Shaodong; Ding Quanxin; Wu Shengliang
2015-01-01
A new concept is presented for air-to-air missile which is dynamic attack zone after being launched in random wind field. This new concept can be used to obtain the 4-dimensional (4-D) information regarding the dynamic envelope of an air-to-air missile at any flight time aimed at different flight targets considering influences of random wind, in the situation of flight fighters coop-erated with missiles fighting against each other. Based on an air-to-air missile model, some typical cases of dynamic attack zone after being launched in random wind field were numerically simulated. Compared with the simulation results of traditional dynamic envelope, the properties of dynamic attack zone after being launched are as follows. The 4-D dynamic attack zone after being launched is inside traditional maximum dynamic envelope, but its forane boundary is usually not inside tra-ditional no-escape dynamic envelope;Traditional dynamic attack zone can just be reliably used at launch time, while dynamic envelope after being launched can be reliably and accurately used dur-ing any flight antagonism time. Traditional envelope is a special case of dynamic envelope after being launched when the dynamic envelope is calculated at the launch time;the dynamic envelope after being launched can be influenced by the random wind field.
On Polynomial Lieb-Robinson Bounds for the XY Chain in a Decaying Random Field
Gebert, Martin; Lemm, Marius
2016-08-01
We consider the isotropic XY quantum spin chain in a random external field in the z direction, with single site distributions given by i.i.d. random variables times the critical decaying envelope j^{-1/2}. Our motivation is the study of many-body localization. We investigate transport properties in terms of polynomial Lieb-Robinson (PLR) bounds. We prove a zero-velocity PLR bound for large disorder strength λ and for small λ we show a partial converse, which suggests the existence of a transition to non-trivial transport in the model.
FINITE VARIANCE OF THE NUMBER OF STATIONARY POINTS OF A GAUSSIAN RANDOM FIELD
Estrade, Anne; Fournier, Julie
2015-01-01
Let X be a real-valued stationary Gaussian random field defined on $R^d$ (d ≥ 1), with almost every realization of class $C^2$. This paper is concerned with the random variable giving the number of points in $T$ (a compact set of $R^d$) where the gradient $X'$ takes a fixed value $v\\in R^d$, $N_{X'}(T, v) = \\{t \\in T : X'(t) = v\\}$. More precisely, it deals with the finiteness of the variance of $N_{X'} (T, v)$, under some non-degeneracy hypothesis on $X$. For d = 1, the so-called " Geman con...
Random pinning glass transition: hallmarks, mean-field theory and renormalization group analysis.
Cammarota, Chiara; Biroli, Giulio
2013-03-28
We present a detailed analysis of glass transitions induced by pinning particles at random from an equilibrium configuration. We first develop a mean-field analysis based on the study of p-spin spherical disordered models and then obtain the three-dimensional critical behavior by the Migdal-Kadanoff real space renormalization group method. We unveil the important physical differences with the case in which particles are pinned from a random (or very high temperature) configuration. We contrast the pinning particles approach to the ones based on biasing dynamical trajectories with respect to their activity and on coupling to equilibrium configurations. Finally, we discuss numerical and experimental tests.
Randomized Field Trials and Internal Validity: Not So Fast My Friend.
Directory of Open Access Journals (Sweden)
James H. McMillan
2007-12-01
Full Text Available The purpose of this article is to summarize eight potential threats to internal validity that occur with randomized field trial (RFT studies. Depending on specific contextual factors, RFTs do not necessarily result in strong internal validity. Of particular concern is whether the unit of random assignment is the same as the number of replications of the intervention, threats as a result of local history, and subject effects. The eight threats are described, with suggestions for providing adequate monitoring to know if they rise to the level of likely or plausible threat to internal validity.
3D morphology of a random field from its 2D cross-section
Makarenko, Irina; Shukurov, Anvar
2014-01-01
We show that both aspect ratios of randomly oriented triaxial ellipsoids (representing isosurfaces of an isotropic 3D random field) can be determined from a single 2D cross-section of their sample using the probability distribution of the filamentarity F of the structures seen in the cross-section (F=0 for a circle and F=1 for a line). The probability distribution of F has a robust form with a sharp maximum and truncation that are sensitive to the ellipsoids' aspect ratios. We show that the aspect ratios of triaxial ellipsoids with randomly distributed dimensions can still be recovered from the probability distribution of F. This method is applicable to many shape recognition and classification problems, here illustrated with neutral hydrogen density in the turbulent interstellar medium of the Milky Way. The gas distribution is shown to be filamentary with the mean axis ratio 1:2:20.
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.
Spatial risk measures and applications to max-stable processes
Koch, Erwan
2015-01-01
The risk of extreme environmental events is of great importance for both the authorities and the insurance industry. This paper concerns risk measures in a spatial setting, in order to introduce the spatial features of damages stemming from environmental events in the measure of the risk. We develop a new concept of spatial risk measure, based on the spatially aggregated loss over the region of interest, and propose an adapted set of axioms for these spatial risk measures. These axioms quanti...
Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields
Institute of Scientific and Technical Information of China (English)
Siddhartha Shakya; John McCall
2007-01-01
This paper presents a Markov random field (MRP) approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields (DEUM). DEUM is a subclass of estimation of distribution algorithms (EDAs) where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are: 1) use of MRF models, 2) fitness modeling approach to estimating the parameter of the model and 3) Monte Carlo approach to sampling from the model.
Estimation of random fields from network observations. Technical report No. 26
Energy Technology Data Exchange (ETDEWEB)
Cabannes, A F
1979-06-01
When one has observed a random field Z at some points and recorded its values (network observations), a natural problem is to estimate Z at points where there are no observations. This dissertation deals first with this problem in an abstract setting, in m dimensions; later, it considers the estimation of a spatial two-dimensional random field. The problem then is one of constructing an estimated map of Z over a geographic area. For a given network of stations the quality of a map depends on the method of estimation. But for the given method of estimation the quality of a map depends on the choice of locations for the stations. This is the problem of network design. Both the study of methods of estimation and the problem of network design are addressed. 16 figures. (RWR)
A Study and Comparative Analysis of Conditional Random Fields for Intrusion Detection
Directory of Open Access Journals (Sweden)
Deepa Guleria
2012-07-01
Full Text Available Intrusion detection systems are an important component of defensive measures protecting computer systems and networks from abuse. Intrusion detection plays one of the key roles in computer security techniques and is one of the prime areas of research. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. An intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper we study the Machine Learning and data mining techniques to solve Intrusion Detection problems within computer networks and compare the various approaches with conditional random fields and address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach.
A Modified FCM Classifier Constrained by Conditional Random Field Model for Remote Sensing Imagery
Directory of Open Access Journals (Sweden)
WANG Shaoyu
2016-12-01
Full Text Available Remote sensing imagery has abundant spatial correlation information, but traditional pixel-based clustering algorithms don't take the spatial information into account, therefore the results are often not good. To this issue, a modified FCM classifier constrained by conditional random field model is proposed. Adjacent pixels' priori classified information will have a constraint on the classification of the center pixel, thus extracting spatial correlation information. Spectral information and spatial correlation information are considered at the same time when clustering based on second order conditional random field. What's more, the global optimal inference of pixel's classified posterior probability can be get using loopy belief propagation. The experiment shows that the proposed algorithm can effectively maintain the shape feature of the object, and the classification accuracy is higher than traditional algorithms.
Features of randomized electric-field assisted domain inversion in lithium tantalate
Stivala, Salvatore; Curcio, Luciano; Oliveri, Roberto L; Busacca, Alessandro C; Assanto, Gaetano; 10.1364/OE.19.025780
2012-01-01
We report on bulk and guided-wave second-harmonic generation via random Quasi-Phase-Matching in Lithium Tantalate. By acquiring the far-field profiles at several wavelengths, we extract statistical information on the distribution of the quadratic nonlinearity as well as its average period, both at the surface and in the bulk of the sample. By investigating the distribution in the two regions we demonstrate a non-invasive approach to the study of poling dynamics.
Magnetoresistance of a two-dimensional electron gas in a random magnetic field
DEFF Research Database (Denmark)
Smith, Anders; Taboryski, Rafael Jozef; Hansen, Luise Theil
1994-01-01
We report magnetoresistance measurements on a two-dimensional electron gas made from a high-mobility GaAs/AlxGa1-xAs heterostructure, where the externally applied magnetic field was expelled from regions of the semiconductor by means of superconducting lead grains randomly distributed on the surf...... on the surface of the sample. A theoretical explanation in excellent agreement with the experiment is given within the framework of the semiclassical Boltzmann equation. © 1994 The American Physical Society...
Ilic, Velimir M; Todorovic, Branimir T; Stankovic, Miomir S
2010-01-01
The paper proposes a new recursive algorithm for the exact computation of the linear chain conditional random fields gradient. The algorithm is an instance of the Entropy Message Passing (EMP), introduced in our previous work, and has the purpose to enhance memory efficiency when applied to long observation sequences. Unlike the traditional algorithm based on the forward and the backward recursions, the memory complexity of our algorithm does not depend on the sequence length, having the same computational complexity as the standard algorithm.
A Fast Variational Approach for Learning Markov Random Field Language Models
2015-01-01
our class of models. Learning undirected graphical models is challenging because of the global nor- malization constant, or partition function. We...503–528, 1989. Marcus, Mitchell P, Marcinkiewicz, Mary Ann, and San- torini, Beatrice. Building a large annotated corpus of english : The penn treebank...A Fast Variational Approach for Learning Markov Random Field Language Models Yacine Jernite JERNITE@CS.NYU.EDU CIMS, New York University, 251 Mercer
Prediction of the spatial occurrence of fire induced spalling in concrete slabs using random fields
Directory of Open Access Journals (Sweden)
Van Coile R.
2013-09-01
Full Text Available As the loss of concrete cover can significantly influence the reliability of concrete elements during fire, spalling should be taken into account when performing reliability calculations. However, the occurrence and spatial variation of spalling are highly uncertain. A first step towards a probabilistic analysis of spalling is made by combining existing deterministic models with a stochastic representation of the concrete tensile strength and by using random fields to model the tensile strength spatial variation.
Gustavsson, K
2013-01-01
We calculate the Lyapunov exponents describing spatial clustering of particles advected in one- and two-dimensional random velocity fields at finite Kubo number Ku (a dimensionless parameter characterising the correlation time of the velocity field). In one dimension we obtain accurate results up to Ku ~ 1 by resummation of a perturbation expansion in Ku. At large Kubo numbers we compute the Lyapunov exponent by taking into account the fact that the particles follow the minima of the potential function corresponding to the velocity field. In two dimensions we compute the first four non-vanishing terms in the small-Ku expansion of the Lyapunov exponents. For large Kubo numbers we estimate the Lyapunov exponents by assuming that the particles sample stagnation points of the velocity field with det A > 0 and Tr A < 0 where A is the matrix of flow-velocity gradients.
Recursive Estimation of Gauss-Markov Random Fields Indexed over 1-D Space
Vats, Divyanshu
2009-01-01
This paper presents optimal recursive estimators for vector valued Gauss-Markov random \\emph{fields} taking values in $\\R^M$ and indexed by (intervals of) $\\R$ or $\\Z$. These 1-D fields are often called reciprocal processes. They correspond to two point boundary value fields, i.e., they have boundary conditions given at the end points of the indexing interval. To obtain the recursive estimators, we derive two classes of recursive representations for reciprocal processes. The first class transforms the Gaussian reciprocal process into a Gauss-Markov \\emph{process}, from which we derive forward and backwards recursive representations. The second representation folds the index set and transforms the original \\emph{field} taking values in $\\R^M$ into a Markov \\emph{process} taking values in $\\R^{2M}$. The folding corresponds to recursing the reciprocal process from the boundary points and telescoping inwards. From these recursive representations, Kalman filters and recursive smoothers are promptly derived.
Sensor selection for parameterized random field estimation in wireless sensor networks
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...
Anomalous diffusion and Levy random walk of magnetic field lines in three dimensional turbulence
Energy Technology Data Exchange (ETDEWEB)
Zimbardo, G.; Veltri, P.; Basile, G.; Principato, S. [Dipartimento di Fisica, Universita della Calabria, I-87030 Arcavacata di Rende (Italy)
1995-07-01
The transport of magnetic field lines is studied numerically where three dimensional (3-D) magnetic fluctuations, with a power law spectrum, and periodic over the simulation box are superimposed on an average uniform magnetic field. The weak and the strong turbulence regime, {delta}{ital B}{similar_to}{ital B}{sub 0}, are investigated. In the weak turbulence case, magnetic flux tubes are separated from each other by percolating layers in which field lines undergo a chaotic motion. In this regime the field lines may exhibit Levy, rather than Gaussian, random walk, changing from Levy flights to trapped motion. The anomalous diffusion laws {l_angle}{Delta}{ital x}{sup 2}{sub {ital i}}{r_angle}{proportional_to}{ital s}{sup {alpha}} with {alpha}{gt}1 and {alpha}{lt}1, are obtained for a number of cases, and the non-Gaussian character of the field line random walk is pointed out by computing the kurtosis. Increasing the fluctuation level, and, therefore stochasticity, normal diffusion ({alpha}{congruent}1) is recovered and the kurtoses reach their Gaussian value. However, the numerical results show that neither the quasi-linear theory nor the two dimensional percolation theory can be safely extrapolated to the considered 3-D strong turbulence regime. {copyright} {ital 1995} {ital American} {ital Institute} {ital of} {ital Physics}.
Schwinger-Dyson equations in large-N quantum field theories and nonlinear random processes
Buividovich, P V
2010-01-01
We study stochastic methods for solving Schwinger-Dyson equations in large-N quantum field theories. Expectation values of single-trace operators are sampled by stationary probability distributions of so-called nonlinear random processes. The set of all histories of such processes corresponds to the set of all planar diagrams in the perturbative expansion of the theory. We describe stochastic algorithms for summation of planar diagrams in matrix-valued scalar field theory and in the Weingarten model of random planar surfaces on the lattice. For compact field variables, the method does not converge in the physically most interesting weak-coupling limit. In this case one can absorb the divergences into the self-consistent redefinition of expansion parameters. Stochastic solution of the self-consistency conditions can be implemented as a random process with memory. We illustrate this idea on the example of two-dimensional O(N) sigma-model. Extension to non-Abelian lattice gauge theories is discussed.
Exact mean field concept to compute defect energetics in random alloys on rigid lattices
Bonny, G.; Castin, N.; Pascuet, M. I.; Çelik, Y.
2017-07-01
In modern materials science modeling, the evolution of the energetics of random alloys with composition are desirable input parameters for several meso-scale and continuum scale models. When using atomistic methods to parameterize the above mentioned concentration dependent function, a mean field theory can significantly reduce the computational burden associated to obtaining the desired statistics in a random alloy. In this work, a mean field concept is developed to obtain the energetics of point-defect clusters in perfect random alloys. It is demonstrated that for a rigid lattice the concept is mathematically exact. In addition to the accuracy of the presented method, it is also computationally efficient as a small box can be used and perfect statistics are obtained in a single run. The method is illustrated by computing the formation and binding energy of solute and vacancy pairs in FeCr and FeW binaries. Also, the dissociation energy of small vacancy clusters was computed in FeCr and FeCr-2%W alloys, which are considered model alloys for Eurofer steels. As a result, it was concluded that the dissociation energy is not expected to vary by more than 0.1 eV in the 0-10% Cr and 0-2% W composition range. The present mean field concept can be directly applied to parameterize meso-scale models, such as cluster dynamics and object kinetic Monte Carlo models.
Spectral turning bands for efficient Gaussian random fields generation on GPUs and accelerators
Hunger, L.; Cosenza, B.; Kimeswenger, S.; Fahringer, T.
2015-11-01
A random field (RF) is a set of correlated random variables associated with different spatial locations. RF generation algorithms are of crucial importance for many scientific areas, such as astrophysics, geostatistics, computer graphics, and many others. Current approaches commonly make use of 3D fast Fourier transform (FFT), which does not scale well for RF bigger than the available memory; they are also limited to regular rectilinear meshes. We introduce random field generation with the turning band method (RAFT), an RF generation algorithm based on the turning band method that is optimized for massively parallel hardware such as GPUs and accelerators. Our algorithm replaces the 3D FFT with a lower-order, one-dimensional FFT followed by a projection step and is further optimized with loop unrolling and blocking. RAFT can easily generate RF on non-regular (non-uniform) meshes and efficiently produce fields with mesh sizes bigger than the available device memory by using a streaming, out-of-core approach. Our algorithm generates RF with the correct statistical behavior and is tested on a variety of modern hardware, such as NVIDIA Tesla, AMD FirePro and Intel Phi. RAFT is faster than the traditional methods on regular meshes and has been successfully applied to two real case scenarios: planetary nebulae and cosmological simulations.
Vector solution for the mean electromagnetic fields in a layer of random particles
Lang, R. H.; Seker, S. S.; Levine, D. M.
1986-01-01
The mean electromagnetic fields are found in a layer of randomly oriented particles lying over a half space. A matrix-dyadic formulation of Maxwell's equations is employed in conjunction with the Foldy-Lax approximation to obtain equations for the mean fields. A two variable perturbation procedure, valid in the limit of small fractional volume, is then used to derive uncoupled equations for the slowly varying amplitudes of the mean wave. These equations are solved to obtain explicit expressions for the mean electromagnetic fields in the slab region in the general case of arbitrarily oriented particles and arbitrary polarization of the incident radiation. Numerical examples are given for the application to remote sensing of vegetation.
Khrennikov, Andrei
2017-02-01
The scientific methodology based on two descriptive levels, ontic (reality as it is) and epistemic (observational), is briefly presented. Following Schrödinger, we point to the possible gap between these two descriptions. Our main aim is to show that, although ontic entities may be unaccessible for observations, they can be useful for clarification of the physical nature of operational epistemic entities. We illustrate this thesis by the concrete example: starting with the concrete ontic model preceding quantum mechanics (the latter is treated as an epistemic model), namely, prequantum classical statistical field theory (PCSFT), we propose the natural physical interpretation for the basic quantum mechanical entity-the quantum state ("wave function"). The correspondence PCSFT ↦ QM is not straightforward, it couples the covariance operators of classical (prequantum) random fields with the quantum density operators. We use this correspondence to clarify the physical meaning of the pure quantum state and the superposition principle-by using the formalism of classical field correlations.
Potts q-color field theory and scaling random cluster model
Delfino, Gesualdo
2011-01-01
We study structural properties of the q-color Potts field theory which, for real values of q, describes the scaling limit of the random cluster model. We show that the number of independent n-point Potts spin correlators coincides with that of independent n-point cluster connectivities and is given by generalized Bell numbers. Only a subset of these spin correlators enters the determination of the Potts magnetic properties for q integer. The structure of the operator product expansion of the spin fields for generic q is also identified. For the two-dimensional case, we analyze the duality relation between spin and kink field correlators, both for the bulk and boundary cases, obtaining in particular a sum rule for the kink-kink elastic scattering amplitudes.
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
DEFF Research Database (Denmark)
Barrera Figueroa, Salvador; Henriquez, Vicente Cutanda; Jacobsen, Finn
2006-01-01
The difference between the random-incidence sensitivity of a microphone and the diffusefield sensitivity is that according to the definition of the former, plane waves coming from different angles of incidence impinge successively onto the microphone under free-field conditions, whereas according...... to the definition of the latter, a number of plane waves coming from random directions and having random phases impinge simultaneously upon the microphone. The random-incidence sensitivity can be estimated using measurements made in an anechoic chamber, while the diffuse-field sensitivity requires a reverberation...
Fisher, D S; Le Doussal, P; Monthus, C
2001-12-01
The nonequilibrium dynamics of classical random Ising spin chains with nonconserved magnetization are studied using an asymptotically exact real space renormalization group (RSRG). We focus on random field Ising model (RFIM) spin chains with and without a uniform applied field, as well as on Ising spin glass chains in an applied field. For the RFIM we consider a universal regime where the random field and the temperature are both much smaller than the exchange coupling. In this regime, the Imry-Ma length that sets the scale of the equilibrium correlations is large and the coarsening of domains from random initial conditions (e.g., a quench from high temperature) occurs over a wide range of length scales. The two types of domain walls that occur diffuse in opposite random potentials, of the form studied by Sinai, and domain walls annihilate when they meet. Using the RSRG we compute many universal asymptotic properties of both the nonequilibrium dynamics and the equilibrium limit. We find that the configurations of the domain walls converge rapidly toward a set of system-specific time-dependent positions that are independent of the initial conditions. Thus the behavior of this nonequilibrium system is pseudodeterministic at long times because of the broad distributions of barriers that occur on the long length scales involved. Specifically, we obtain the time dependence of the energy, the magnetization, and the distribution of domain sizes (found to be statistically independent). The equilibrium limits agree with known exact results. We obtain the exact scaling form of the two-point equal time correlation function and the two-time autocorrelations . We also compute the persistence properties of a single spin, of local magnetization, and of domains. The analogous quantities for the +/-J Ising spin glass in an applied field are obtained from the RFIM via a gauge transformation. In addition to these we compute the two-point two-time correlation function which can in
Combination of fractional Brownian random field and lacunarity for iris recognition
Liu, Kai; Zhou, Weidong; Wang, Yu
2011-10-01
Feature extraction plays a vital role in iris recognition, affecting the performance of iris recognition algorithm strongly. In this paper, we present an individual recognition algorithm using fractal dimension based on fractional Brownian random field and lacunarity in feature extraction. Making use of the fractal feature of iris, such as self-similarity and random patterns, fractal dimension can extract texture information effectively. Lacunarity overcomes the limitation of fractal dimension that fractal sets with different textures may share the same fractal dimension value. The combination of fractal dimension and lacunarity makes the feature extraction more comprehensive and distinguishable. The experimental results show that this recognition algorithm can obtain great performance on CASIA 1.0 iris database
Submicron structure random field on granular soil material with retinex algorithm optimization
Liang, Yu; Tao, Chenyuan; Zhou, Bingcheng; Huang, Shuai; Huang, Linchong
2017-06-01
In this paper, a Retinex scale optimized image enhancement algorithm is proposed, which can enhance the micro vision image and eliminate the influence of the uneven illumination. Based on that, a random geometric model of the microstructure of granular materials is established with Monte-Carlo method, the numerical simulation including consolidation process of granular materials is compared with the experimental data. The results have proved that the random field method with Retinex image enhancement algorithm is effective, the image of microstructure of granular materials becomes clear and the contrast ratio is improved, after using Retinex image enhancement algorithm to enhance the CT image. The fidelity of enhanced image is higher than that dealing with other method, which have explained that the algorithm can preserve the microstructure information of the image well. The result of numerical simulation is similar with the one obtained from conventional three axis consolidation test, which proves that the simulation result is reliable.
Directory of Open Access Journals (Sweden)
Thierry Géraud
2004-12-01
Full Text Available We present a fast method for road network extraction in satellite images. It can be seen as a transposition of the segmentation scheme Ã¢Â€Âœwatershed transform + region adjacency graph + Markov random fieldsÃ¢Â€Â to the extraction of curvilinear objects. Many road extractors which are composed of two stages can be found in the literature. The first one acts like a filter that can decide from a local analysis, at every image point, if there is a road or not. The second stage aims at obtaining the road network structure. In the method we propose to rely on a Ã¢Â€ÂœpotentialÃ¢Â€Â image, that is, unstructured image data that can be derived from any road extractor filter. In such a potential image, the value assigned to a point is a measure of its likelihood to be located in the middle of a road. A filtering step applied on the potential image relies on the area closing operator followed by the watershed transform to obtain a connected line which encloses the road network. Then a graph describing adjacency relationships between watershed lines is built. Defining Markov random fields upon this graph, associated with an energetic model of road networks, leads to the expression of road network extraction as a global energy minimization problem. This method can easily be adapted to other image processing fields, where the recognition of curvilinear structures is involved.
Certified randomness from a two-level system in a relativistic quantum field
Thinh, Le Phuc; Bancal, Jean-Daniel; Martín-Martínez, Eduardo
2016-08-01
Randomness is an indispensable resource in modern science and information technology. Fortunately, an experimentally simple procedure exists to generate randomness with well-characterized devices: measuring a quantum system in a basis complementary to its preparation. Towards realizing this goal one may consider using atoms or superconducting qubits, promising candidates for quantum information processing. However, their unavoidable interaction with the electromagnetic field affects their dynamics. At large time scales, this can result in decoherence. Smaller time scales in principle avoid this problem, but may not be well analyzed under the usual rotating wave and single mode approximation (RWA and SMA) which break the relativistic nature of quantum field theory. Here, we use a fully relativistic analysis to quantify the information that an adversary with access to the field could get on the result of an atomic measurement. Surprisingly, we find that the adversary's guessing probability is not minimized for atoms initially prepared in the ground state (an intuition derived from the RWA and SMA model).
Random-field Ising model: Insight from zero-temperature simulations
Directory of Open Access Journals (Sweden)
P.E. Theodorakis
2014-12-01
Full Text Available We enlighten some critical aspects of the three-dimensional (d=3 random-field Ising model (RFIM from simulations performed at zero temperature. We consider two different, in terms of the field distribution, versions of model, namely a Gaussian RFIM and an equal-weight trimodal RFIM. By implementing a computational approach that maps the ground-state of the system to the maximum-flow optimization problem of a network, we employ the most up-to-date version of the push-relabel algorithm and simulate large ensembles of disorder realizations of both models for a broad range of random-field values and systems sizes V=LxLxL, where L denotes linear lattice size and Lmax=156. Using as finite-size measures the sample-to-sample fluctuations of various quantities of physical and technical origin, and the primitive operations of the push-relabel algorithm, we propose, for both types of distributions, estimates of the critical field hmax and the critical exponent ν of the correlation length, the latter clearly suggesting that both models share the same universality class. Additional simulations of the Gaussian RFIM at the best-known value of the critical field provide the magnetic exponent ratio β/ν with high accuracy and clear out the controversial issue of the critical exponent α of the specific heat. Finally, we discuss the infinite-limit size extrapolation of energy- and order-parameter-based noise to signal ratios related to the self-averaging properties of the model, as well as the critical slowing down aspects of the algorithm.
Statistical Shape Modelling and Markov Random Field Restoration (invited tutorial and exercise)
DEFF Research Database (Denmark)
Hilger, Klaus Baggesen
This tutorial focuses on statistical shape analysis using point distribution models (PDM) which is widely used in modelling biological shape variability over a set of annotated training data. Furthermore, Active Shape Models (ASM) and Active Appearance Models (AAM) are based on PDMs and have proven...... themselves a generic holistic tool in various segmentation and simulation studies. Finding a basis of homologous points is a fundamental issue in PDMs which effects both alignment and decomposition of the training data, and may be aided by Markov Random Field Restoration (MRF) of the correspondence...
Conditional Random Field-Based Offline Map Matching for Indoor Environments
Directory of Open Access Journals (Sweden)
Safaa Bataineh
2016-08-01
Full Text Available In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF. The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm.
Automatic Recognition of Chinese Personal Name Using Conditional Random Fields and Knowledge Base
Directory of Open Access Journals (Sweden)
Chuan Gu
2015-01-01
Full Text Available According to the features of Chinese personal name, we present an approach for Chinese personal name recognition based on conditional random fields (CRF and knowledge base in this paper. The method builds multiple features of CRF model by adopting Chinese character as processing unit, selects useful features based on selection algorithm of knowledge base and incremental feature template, and finally implements the automatic recognition of Chinese personal name from Chinese document. The experimental results on open real corpus demonstrated the effectiveness of our method and obtained high accuracy rate and high recall rate of recognition.
Institute of Scientific and Technical Information of China (English)
Xi F. XU
2015-01-01
The Green-function-based multiscale stochastic finite element method （MSFEM） has been formulated based on the stochastic variational principle. In this study a fast computing procedure based on the MSFEM is developed to solve random field geotechnical problems with a typical coefficient of variance less than 1. A unique fast computing advantage of the procedure enables computation performed only on those locations of interest, therefore saving a lot of computation. The numerical example on soil settlement shows that the procedure achieves significant computing efficiency compared with Monte Carlo method.
Conditional Random Field-Based Offline Map Matching for Indoor Environments.
Bataineh, Safaa; Bahillo, Alfonso; Díez, Luis Enrique; Onieva, Enrique; Bataineh, Ikram
2016-08-16
In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm.
Reduced Wiener Chaos representation of random fields via basis adaptation and projection
Energy Technology Data Exchange (ETDEWEB)
Tsilifis, Panagiotis, E-mail: tsilifis@usc.edu [Department of Mathematics, University of Southern California, Los Angeles, CA 90089 (United States); Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States); Ghanem, Roger G., E-mail: ghanem@usc.edu [Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States)
2017-07-15
A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptation capabilities of these expansions where the core idea is to rotate the basis of the underlying Gaussian Hilbert space, in order to achieve reduced functional representations that concentrate the induced probability measure in a lower dimensional subspace. For a smooth family of rotations along the domain of interest, the uncorrelated Gaussian inputs are transformed into a Gaussian process, thus introducing a mesoscale that captures intermediate characteristics of the quantity of interest.
Theory of Distribution Estimation of Hyperparameters in Markov Random Field Models
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.
Integrals of random fields treated by the model correction factor method
DEFF Research Database (Denmark)
Franchin, P.; Ditlevsen, Ove Dalager; Kiureghian, Armen Der
2002-01-01
The model correction factor method (MCFM) is used in conjunction with the first-order reliability method (FORM) to solve structural reliability problems involving integrals of non-Gaussian random fields. The approach replaces the limit-state function with an idealized one, in which the integrals...... are considered to be Gaussian. Conventional FORM analysis yields the linearization point of the idealized limit-state surface. A model correction factor is then introduced to push the idealized limit-state surface onto the actual limit-state surface. A few iterations yield a good approximation of the reliability...
Ashayeri-Panah, Mitra; Eftekhar, Fereshteh; Ghamsari, Maryam Mobarak; Parvin, Mahmood; Feizabadi, Mohammad Mehdi
2013-01-01
In this study, the discriminatory power of pulsed field gel electrophoresis (PFGE) and random amplified polymorphic DNA (RAPD) methods for subtyping of 54 clinical isolates of Klebsiella pneumoniae were compared. All isolates were typeable by RAPD, while 3.6% of them were not typeable by PFGE. The repeatability of both typing methods were 100% with satisfying reproducibility (≥ 95%). Although the discriminatory power of PFGE was greater than RAPD, both methods showed sufficient discriminatory power (DI > 0.95) which reflects the heterogeneity among the K. pneumoniae isolates. An optimized RAPD protocol is less technically demanding and time consuming that makes it a reliable typing method and competitive with PFGE. PMID:24516423
Bykov, Andrei M; Ellison, Donald C
2008-01-01
Non-thermal X-ray emission in some supernova remnants originates from synchrotron radiation of ultra-relativistic particles in turbulent magnetic fields. We address the effect of a random magnetic field on synchrotron emission images and spectra. A random magnetic field is simulated to construct synchrotron emission maps of a source with a steady distribution of ultra-relativistic electrons. Non-steady localized structures (dots, clumps and filaments), in which the magnetic field reaches exceptionally high values, typically arise in the random field sample. These magnetic field concentrations dominate the synchrotron emission (integrated along the line of sight) from the highest energy electrons in the cut-off regime of the distribution, resulting in an evolving, intermittent, clumpy appearance. The simulated structures resemble those observed in X-ray images of some young supernova remnants. The lifetime of X-ray clumps can be short enough to be consistent with that observed even in the case of a steady part...
Khrennikov, Andrei
2016-01-01
The scientific methodology based on two descriptive levels, ontic (reality as it is ) and epistemic (observational), is briefly presented. Following Schr\\"odinger, we point to the possible gap between these two descriptions. Our main aim is to show that, although ontic entities may be inaccessible for observations, they can be useful for clarification of the physical nature of operational epistemic entities. We illustrate this thesis by the concrete example: starting with the concrete ontic model preceding quantum mechanics (the latter is treated as an epistemic model), namely, prequantum classical statistical field theory (PCSFT), we propose the natural physical interpretation for the basic quantum mechanical entity - the quantum state ("wave function"). The correspondence PCSFT to QM is not straightforward, it couples the covariance operators of classical (prequantum) random fields with the quantum density operators. We use this correspondence to clarify the physical meaning of the pure quantum state and th...
A Unified 3D Mesh Segmentation Framework Based on Markov Random Field
Directory of Open Access Journals (Sweden)
Z.F. Shi
2012-04-01
Full Text Available 3D Mesh segmentation has become an important research field in computer graphics during the past decades. Many geometry based and semantic oriented approaches for 3D mesh segmentation has been presented. In this paper, we present a definition of mesh segmentation according to labeling problem. Inspired by the Markov Random Field (MRF based image segmentation, we propose a new framework of 3D mesh segmentation based on MRF and use graph cuts to solve it. Any features of 3D mesh can be integrated into the segmentation framework. Experimental results show that the noise and over-segmentation are avoided. It also demonstrates that the proposed scheme has the capability of combining the geometric and topology information of the 3D mesh.
Energy Technology Data Exchange (ETDEWEB)
Papusoi, C. [Spintec, URA 2512 CEA/CNRS, 17 rue des Martyrs, 38054 Grenoble (France)], E-mail: cristian_papusoi@yahoo.com; Conraux, Y.; Prejbeanu, I.L. [Crocus Technology, 5 Robert Schumann, BP 1510, 38025 Grenoble (France); Sousa, R.; Dieny, B. [Spintec, URA 2512 CEA/CNRS, 17 rue des Martyrs, 38054 Grenoble (France)
2009-08-15
The minimum applied field H{sub SW} required to reverse the magnetic moment of the ferromagnetic/antiferromagnetic storage layer of a thermally assisted magnetic random access memory (TA-MRAM) device during the application of a heating electric pulse is investigated as a function of pulse power P{sub HP} and duration {delta}. For the same power of the heating pulse P{sub HP} (or, equivalently, for the same temperature of the storage layer), H{sub SW} increases with decreasing heating time {delta}. This behavior is consistently interpreted by a thermally activated propagating domain-wall switching model, corroborated by a real-time study of switching. The increase of H{sub SW} with decreasing pulse width introduces a constraint for the minimum power consumption of a TA-MRAM where writing combines heating and magnetic field application.
Integrating BC & GC Models In Computing Stereo Disparity As Markov Random Field
Directory of Open Access Journals (Sweden)
Hongsheng Zhang
2006-11-01
Full Text Available Belief propagation and graph cuts have emerged as powerful tools for computing efficient approximate solution to stereo disparity field modelled as the Markov random field (MRF. These algorithms have provided the best performance based on results on a standard data set. However, employment of the brightness constancy (BC assumption severely limits the range of their applications. Previously, augmenting the BC with gradient constancy (GC assumption has shown to produce a more robust optical flow algorithm. In this paper, these constraints are integrated within the MRF framework to devise an enhanced global method that broadens the application domains for stereo computation. Results of experiments with both semi-synthetic data and more challenging ocean images are presented to illustrate that the proposed method generally outperforms earlier dense optical flow and stereo algorithms.
Correlation functions of the one-dimensional random field Ising model at zero temperature
Farhi, E; Farhi, Edward; Gutmann, Sam
1993-01-01
We consider the one-dimensional random field Ising model, where the spin-spin coupling, $J$, is ferromagnetic and the external field is chosen to be $+h$ with probability $p$ and $-h$ with probability $1-p$. At zero temperature, we calculate an exact expression for the correlation length of the quenched average of the correlation function $\\langle s_0 s_n \\rangle - \\langle s_0 \\rangle \\langle s_n \\rangle$ in the case that $2J/h$ is not an integer. The result is a discontinuous function of $2J/h$. When $p = {1 \\over 2}$, we also place a bound on the correlation length of the quenched average of the correlation function $\\langle s_0 s_n \\rangle$.
Statistical Shape Modelling and Markov Random Field Restoration (invited tutorial and exercise)
DEFF Research Database (Denmark)
Hilger, Klaus Baggesen
This tutorial focuses on statistical shape analysis using point distribution models (PDM) which is widely used in modelling biological shape variability over a set of annotated training data. Furthermore, Active Shape Models (ASM) and Active Appearance Models (AAM) are based on PDMs and have prov...... using Markov random field relaxation of a spectral classifier. Keywords: the Ising model, the Potts model, stochastic sampling, discriminant analysis, expectation maximization.......This tutorial focuses on statistical shape analysis using point distribution models (PDM) which is widely used in modelling biological shape variability over a set of annotated training data. Furthermore, Active Shape Models (ASM) and Active Appearance Models (AAM) are based on PDMs and have proven...... deformation field between shapes. The tutorial demonstrates both generative active shape and appearance models, and MRF restoration on 3D polygonized surfaces. ''Exercise: Spectral-Spatial classification of multivariate images'' From annotated training data this exercise applies spatial image restoration...
Rotation and Scale Space Random Fields and the Gaussian Kinematic Formula
Adler, Robert J; Taylor, Jonathan E
2011-01-01
We provide a new approach, along with extensions, to results in two important papers of Worsley, Siegmund and coworkers closely tied to the statistical analysis of fMRI (functional magnetic resonance imaging) brain data. These papers studied approximations for the exceedence probabilities of scale and rotation space random fields, the latter playing an important role in the statistical analysis of fMRI data. The techniques used there came either from the Euler characteristic heuristic or via tube formulae, and to a large extent were carefully attuned to the specific examples of the paper. This paper treats the same problem, but via calculations arising from the so-called Gaussian kinematic formula. This allows for extensions of the Worsley-Siegmund results to a wide class of non-Gaussian cases. In addition, it allows one to obtain results for rotation space random fields in any dimension via reasonably straightforward Riemannian geometric calculations, whereas previously only the two-dimensional case could be...
Seeking mathematics success for college students: a randomized field trial of an adapted approach
Gula, Taras; Hoessler, Carolyn; Maciejewski, Wes
2015-11-01
Many students enter the Canadian college system with insufficient mathematical ability and leave the system with little improvement. Those students who enter with poor mathematics ability typically take a developmental mathematics course as their first and possibly only mathematics course. The educational experiences that comprise a developmental mathematics course vary widely and are, too often, ineffective at improving students' ability. This trend is concerning, since low mathematics ability is known to be related to lower rates of success in subsequent courses. To date, little attention has been paid to the selection of an instructional approach to consistently apply across developmental mathematics courses. Prior research suggests that an appropriate instructional method would involve explicit instruction and practising mathematical procedures linked to a mathematical concept. This study reports on a randomized field trial of a developmental mathematics approach at a college in Ontario, Canada. The new approach is an adaptation of the JUMP Math program, an explicit instruction method designed for primary and secondary school curriculae, to the college learning environment. In this study, a subset of courses was assigned to JUMP Math and the remainder was taught in the same style as in the previous years. We found consistent, modest improvement in the JUMP Math sections compared to the non-JUMP sections, after accounting for potential covariates. The findings from this randomized field trial, along with prior research on effective education for developmental mathematics students, suggest that JUMP Math is a promising way to improve college student outcomes.
Institute of Scientific and Technical Information of China (English)
Hongyan Wang; Xiaobo Zhou
2013-01-01
By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules,Chromatin marks have been proposed to regulate gene expression,a property that has motivated researchers to link these marks to cis-regulatory elements.With the help of next generation sequencing technologies,we can now correlate one specific chromatin mark with regulatory elements (e.g.enhancers or promoters) and also build tools,such as hidden Markov models,to gain insight into mark combinations.However,hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain.Here,we employed two graphical probabilistic models,namely the linear conditional random field model and multivariate hidden Markov model,to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks.Both models revealed chromatin states that may correspond to enhancers and promoters,transcribed regions,transcriptional elongation,and low-signal regions.We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements,such as promoter-,enhancer-,and transcriptional elongation-associated regions,which gives us a better choice.
GENERAL: Mean-field Theory for Some Bus Transport Networks with Random Overlapping Clique Structure
Yang, Xu-Hua; Sun, Bao; Wang, Bo; Sun, You-Xian
2010-04-01
Transport networks, such as railway networks and airport networks, are a kind of random network with complex topology. Recently, more and more scholars paid attention to various kinds of transport networks and try to explore their inherent characteristics. Here we study the exponential properties of a recently introduced Bus Transport Networks (BTNs) evolution model with random overlapping clique structure, which gives a possible explanation for the observed exponential distribution of the connectivities of some BTNs of three major cities in China. Applying mean-field theory, we analyze the BTNs model and prove that this model has the character of exponential distribution of the connectivities, and develop a method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the exponents. By comparing mean-field based theoretic results with the statistical data of real BTNs, we observe that, as a whole, both of their data show similar character of exponential distribution of the connectivities, and their exponents have same order of magnitude, which show the availability of the analytical result of this paper.
Scene-Layout Compatible Conditional Random Field for Classifying Terrestrial Laser Point Clouds
Luo, C.; Sohn, G.
2014-08-01
Terrestrial Laser Scanning (TLS) rapidly becomes a primary surveying tool due to its fast acquisition of highly dense threedimensional point clouds. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. Conditional Random Field (CRF) is a well-known discriminative classifier, which integrates local appearance of the observation (laser point) with spatial interactions among its neighbouring points in classification process. Typical CRFs employ generic label consistency using short-range dependency only, which often causes locality problem. In this paper, we present a multi-range and asymmetric Conditional Random Field (CRF) (maCRF), which adopts a priori information of scene-layout compatibility addressing long-range dependency. The proposed CRF constructs two graphical models, one for enhancing a local labelling smoothness within short-range (srCRF) and the other for favouring a global and asymmetric regularity of spatial arrangement between different object classes within long-range (lrCRF). This maCRF classifier assumes two graphical models (srCRF and lrCRF) are independent of each other. Final labelling decision was accomplished by probabilistically combining prediction results obtained from two CRF models. We validated maCRF's performance with TLS point clouds acquired from RIEGL LMS-Z390i scanner using cross validation. Experiment results demonstrate that synergetic classification improvement can be achievable by incorporating two CRF models.
Risk Prediction Modeling of Sequencing Data Using a Forward Random Field Method.
Wen, Yalu; He, Zihuai; Li, Ming; Lu, Qing
2016-02-19
With the advance in high-throughput sequencing technology, it is feasible to investigate the role of common and rare variants in disease risk prediction. While the new technology holds great promise to improve disease prediction, the massive amount of data and low frequency of rare variants pose great analytical challenges on risk prediction modeling. In this paper, we develop a forward random field method (FRF) for risk prediction modeling using sequencing data. In FRF, subjects' phenotypes are treated as stochastic realizations of a random field on a genetic space formed by subjects' genotypes, and an individual's phenotype can be predicted by adjacent subjects with similar genotypes. The FRF method allows for multiple similarity measures and candidate genes in the model, and adaptively chooses the optimal similarity measure and disease-associated genes to reflect the underlying disease model. It also avoids the specification of the threshold of rare variants and allows for different directions and magnitudes of genetic effects. Through simulations, we demonstrate the FRF method attains higher or comparable accuracy over commonly used support vector machine based methods under various disease models. We further illustrate the FRF method with an application to the sequencing data obtained from the Dallas Heart Study.
Adaptive Multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model
Navarro, C A; Deng, Youjin
2015-01-01
The study of disordered spin systems through Monte Carlo simulations has proven to be a hard task due to the adverse energy landscape present at the low temperature regime, making it difficult for the simulation to escape from a local minimum. Replica based algorithms such as the Exchange Monte Carlo (also known as parallel tempering) are effective at overcoming this problem, reaching equilibrium on disordered spin systems such as the Spin Glass or Random Field models, by exchanging information between replicas of neighbor temperatures. In this work we present a multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The implementation is based on a two-level parallelization scheme that allows the method to scale its performance in the presence of faster and GPUs as well as multiple GPUs. In addition, we modified the original algorithm by adapting the set of temperatures according to the exchange rate observed from short trial runs, leading to an increased exchange rate...
da Silva, W. P.; de Arruda, P. H. Z.; Tunes, T. M.; Godoy, M.; de Arruda, A. S.
2017-01-01
We have studied the effects of the random single-ion anisotropy and random magnetic field in the phase diagram and in the thermodynamic properties of the spin-3/2 Blume-Capel model via Curie-Weiss mean-field approximation. The phase diagrams were built in the planes temperature versus single-ion anisotropy, temperature versus magnetic field, temperature versus random parameters and the dependencies of magnetization were plotted versus temperature and single-ion anisotropy. These diagrams show that, in the space (D / J - T / J) , the type (first- or second-order) of the phase transition between the ferromagnetic and paramagnetic phases is dependent on the random parameters. Therefore, within these conditions the model presents tricritical behavior. For large values, and a certain critical value of the random parameters, the phase transition is only of second-order, but it is of first-order within the ordered phase, between the phase with m = 1 / 2 and m = 3 / 2 , which ends in a terminal critical point.
Turbulence generation by a shock wave interacting with a random density inhomogeneity field
Huete Ruiz de Lira, C.
2010-12-01
When a planar shock wave interacts with a random pattern of pre-shock density non-uniformities, it generates an anisotropic turbulent velocity/vorticity field. This turbulence plays an important role in the early stages of the mixing process in a compressed fluid. This situation emerges naturally in a shock interaction with weakly inhomogeneous deuterium-wicked foam targets in inertial confinement fusion and with density clumps/clouds in astrophysics. We present an exact small-amplitude linear theory describing such an interaction. It is based on the exact theory of time and space evolution of the perturbed quantities behind a corrugated shock front for a single-mode pre-shock non-uniformity. Appropriate mode averaging in two dimensions results in closed analytical expressions for the turbulent kinetic energy, degree of anisotropy of velocity and vorticity fields in the shocked fluid, shock amplification of the density non-uniformity and sonic energy flux radiated downstream. These explicit formulae are further simplified in the important asymptotic limits of weak/strong shocks and highly compressible fluids. A comparison with the related problem of a shock interacting with a pre-shock isotropic vorticity field is also presented.
Table Extraction from Web Pages Using Conditional Random Fields to Extract Toponym Related Data
Luthfi Hanifah, Hayyu’; Akbar, Saiful
2017-01-01
Table is one of the ways to visualize information on web pages. The abundant number of web pages that compose the World Wide Web has been the motivation of information extraction and information retrieval research, including the research for table extraction. Besides, there is a need for a system which is designed to specifically handle location-related information. Based on this background, this research is conducted to provide a way to extract location-related data from web tables so that it can be used in the development of Geographic Information Retrieval (GIR) system. The location-related data will be identified by the toponym (location name). In this research, a rule-based approach with gazetteer is used to recognize toponym from web table. Meanwhile, to extract data from a table, a combination of rule-based approach and statistical-based approach is used. On the statistical-based approach, Conditional Random Fields (CRF) model is used to understand the schema of the table. The result of table extraction is presented on JSON format. If a web table contains toponym, a field will be added on the JSON document to store the toponym values. This field can be used to index the table data in accordance to the toponym, which then can be used in the development of GIR system.
Stochastic generation of explicit pore structures by thresholding Gaussian random fields
Hyman, Jeffrey D.; Winter, C. Larrabee
2014-11-01
We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1]) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone.
Morais, C. V.; Zimmer, F. M.; Lazo, M. J.; Magalhães, S. G.; Nobre, F. D.
2016-06-01
The behavior of the nonlinear susceptibility χ3 and its relation to the spin-glass transition temperature Tf in the presence of random fields are investigated. To accomplish this task, the Sherrington-Kirkpatrick model is studied through the replica formalism, within a one-step replica-symmetry-breaking procedure. In addition, the dependence of the Almeida-Thouless eigenvalue λAT (replicon) on the random fields is analyzed. Particularly, in the absence of random fields, the temperature Tf can be traced by a divergence in the spin-glass susceptibility χSG, which presents a term inversely proportional to the replicon λAT. As a result of a relation between χSG and χ3, the latter also presents a divergence at Tf, which comes as a direct consequence of λAT=0 at Tf. However, our results show that, in the presence of random fields, χ3 presents a rounded maximum at a temperature T* which does not coincide with the spin-glass transition temperature Tf (i.e., T*>Tf for a given applied random field). Thus, the maximum value of χ3 at T* reflects the effects of the random fields in the paramagnetic phase instead of the nontrivial ergodicity breaking associated with the spin-glass phase transition. It is also shown that χ3 still maintains a dependence on the replicon λAT, although in a more complicated way as compared with the case without random fields. These results are discussed in view of recent observations in the LiHoxY1 -xF4 compound.
A Markov random field approach for modeling spatio-temporal evolution of microstructures
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.
Li, Edward; Shafiee, Mohammad Javad; Haider, Masoom A; Wong, Alexander
2015-01-01
Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long acquisition times for MRIs due to patient motion, as well as result in great patient discomfort. Reducing MRI acquisition time can reduce patient discomfort and as a result reduces motion artifacts from the acquisition process. Compressive sensing strategies, when applied to MRI, have been demonstrated to be effective at decreasing acquisition times significantly by sparsely sampling the \\emph{k}-space during the acquisition process. However, such a strategy requires advanced reconstruction algorithms to produce high quality and reliable images from compressive sensing MRI. This paper proposes a new reconstruction approach based on cross-domain stochastically fully connected conditional random fields (CD-SFCRF) for compressive sensing MRI. The CD-SFCRF introduces constraints in both \\emph{k}-space and spatial domains within a stochas...
High energy X-ray phase and dark-field imaging using a random absorption mask
Wang, Hongchang; Kashyap, Yogesh; Cai, Biao; Sawhney, Kawal
2016-07-01
High energy X-ray imaging has unique advantage over conventional X-ray imaging, since it enables higher penetration into materials with significantly reduced radiation damage. However, the absorption contrast in high energy region is considerably low due to the reduced X-ray absorption cross section for most materials. Even though the X-ray phase and dark-field imaging techniques can provide substantially increased contrast and complementary information, fabricating dedicated optics for high energies still remain a challenge. To address this issue, we present an alternative X-ray imaging approach to produce transmission, phase and scattering signals at high X-ray energies by using a random absorption mask. Importantly, in addition to the synchrotron radiation source, this approach has been demonstrated for practical imaging application with a laboratory-based microfocus X-ray source. This new imaging method could be potentially useful for studying thick samples or heavy materials for advanced research in materials science.
Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes
Directory of Open Access Journals (Sweden)
Yu Tong
2015-03-01
Full Text Available As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.
Directory of Open Access Journals (Sweden)
Ming Xu
2015-01-01
Full Text Available In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.
SkipCor: skip-mention coreference resolution using linear-chain conditional random fields.
Directory of Open Access Journals (Sweden)
Slavko Žitnik
Full Text Available Coreference resolution tries to identify all expressions (called mentions in observed text that refer to the same entity. Beside entity extraction and relation extraction, it represents one of the three complementary tasks in Information Extraction. In this paper we describe a novel coreference resolution system SkipCor that reformulates the problem as a sequence labeling task. None of the existing supervised, unsupervised, pairwise or sequence-based models are similar to our approach, which only uses linear-chain conditional random fields and supports high scalability with fast model training and inference, and a straightforward parallelization. We evaluate the proposed system against the ACE 2004, CoNLL 2012 and SemEval 2010 benchmark datasets. SkipCor clearly outperforms two baseline systems that detect coreferentiality using the same features as SkipCor. The obtained results are at least comparable to the current state-of-the-art in coreference resolution.
Random magnetic fields inducing solar neutrino spin-flavor precession in a three generation context
Guzzo, M M; Peres, O L G
2005-01-01
We study the effect of random magnetic fields in the spin-flavor precession of solar neutrinos in a three generation context, when a non-vanishing transition magnetic moment is assumed. While this kind of precession is strongly constrained when the magnetic moment involves the first family, such constraints do not apply if we suppose a transition magnetic moment between the second and third families. In this scenario we can have a large non-electron anti-neutrino flux arriving on Earth, which can lead to some interesting phenomenological consequences, as, for instance, the suppression of day-night asymmetry. We have analyzed the high energy solar neutrino data and the KamLAND experiment to constrain the solar mixing angle, and solar mass difference, and we have found a larger shift of allowed values.
Efficient Hierarchical Markov Random Fields for Object Detection on a Mobile Robot
Lea, Colin S
2011-01-01
Object detection and classification using video is necessary for intelligent planning and navigation on a mobile robot. However, current methods can be too slow or not sufficient for distinguishing multiple classes. Techniques that rely on binary (foreground/background) labels incorrectly identify areas with multiple overlapping objects as single segment. We propose two Hierarchical Markov Random Field models in efforts to distinguish connected objects using tiered, binary label sets. Near-realtime performance has been achieved using efficient optimization methods which runs up to 11 frames per second on a dual core 2.2 Ghz processor. Evaluation of both models is done using footage taken from a robot obstacle course at the 2010 Intelligent Ground Vehicle Competition.
RANDOM AND SYSTEMATIC FIELD ERRORS IN THE SNS RING: A STUDY OF THEIR EFFECTS AND COMPENSATION
Energy Technology Data Exchange (ETDEWEB)
GARDNER,C.J.; LEE,Y.Y.; WENG,W.T.
1998-06-22
The Accumulator Ring for the proposed Spallation Neutron Source (SNS) [l] is to accept a 1 ms beam pulse from a 1 GeV Proton Linac at a repetition rate of 60 Hz. For each beam pulse, 10{sup 14} protons (some 1,000 turns) are to be accumulated via charge-exchange injection and then promptly extracted to an external target for the production of neutrons by spallation. At this very high intensity, stringent limits (less than two parts in 10,000 per pulse) on beam loss during accumulation must be imposed in order to keep activation of ring components at an acceptable level. To stay within the desired limit, the effects of random and systematic field errors in the ring require careful attention. This paper describes the authors studies of these effects and the magnetic corrector schemes for their compensation.
Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields
Directory of Open Access Journals (Sweden)
Hee-Deok Yang
2014-12-01
Full Text Available Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D space. In this research, we use 3D depth information from hand motions, generated from Microsoft’s Kinect sensor and apply a hierarchical conditional random field (CRF that recognizes hand signs from the hand motions. The proposed method uses a hierarchical CRF to detect candidate segments of signs using hand motions, and then a BoostMap embedding method to verify the hand shapes of the segmented signs. Experiments demonstrated that the proposed method could recognize signs from signed sentence data at a rate of 90.4%.
System for Detecting Potential Lost Person based on Conditional Random Field
Kusuma, R. S.; Saptawati, G. A. P.
2017-01-01
Global Positioning System (GPS) technology has been used widely in transsportation industry to help company in managing taxis. The most popular GPS utilization for taxi company is to identify the position of taxis and monitor theirs the mobility. Nowdays, data collected from GPS tracker is combined with data from taxi meter are analyzed to provide region information regarding potential passengers. Zicheng Liao’s proposed a system based on GPS taxi data to detect anomalous area/region which was then interpreted as region with to predict rare passengers. The system was developed based on conditional random field (CRF) method and features position, velocity, passenger loading information. Our research was aimed to develop tool based on GPS data to detect potential lost person. We motivated by Liao research and modified the algorithms and features of CRF. Our experiments showed that the system has precision of 98.86% and recall of 87.478%.
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.
Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints
Institute of Scientific and Technical Information of China (English)
LI Jia; WAN ChengKai; ZHANG DianYong; MIAO ZhenJiang; YUAN BaoZong
2009-01-01
Currently, many vision-based motion capture systems require passive markers attached to key lca-tions on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field (MRF) and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints Into the pose estimation process. The ad-vantages of incorporating color constraints are demonstrated by experimental results on several video sequences.
Time-dependent Relativistic Mean-field Theory and Random Phase Approximation
Institute of Scientific and Technical Information of China (English)
P.Ring; D.Vretenar; A.Wandelt; NguyenVanGiai; MAZhong-yu; CAOLi-gang
2001-01-01
The relativistic random phase approximation (RRPA) is derived from the time-dependent relativistic mean field (TD RMF) theory in the limit of small amplitude oscillations. In the no-sea approximation of the RMF theory, the RRPA configuration space includes not only the usual particle-hole ph-states, but also ah configurations, i.e. pairs formed from occupied states in the Fermi sea and empty negative-energy states in the Dirac sea. The contribution of the negative energy states to the RRPA matrices is examined in a schematic model, and the large effect of Dirac sea states on isoscalar strength distributions is illustrated for the giant monopole resonance in 116Sn. It is shown that
Cross-covariance functions for multivariate random fields based on latent dimensions
Apanasovich, T. V.
2010-02-16
The problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness parameters, in each component. We discuss estimation of these models and perform a small simulation study to demonstrate our approach. We illustrate our methodology on a trivariate spatio-temporal pollution dataset from California and demonstrate that our cross-covariance performs better than other competing models. © 2010 Biometrika Trust.
Document page structure learning for fixed-layout e-books using conditional random fields
Tao, Xin; Tang, Zhi; Xu, Canhui
2013-12-01
In this paper, a model is proposed to learn logical structure of fixed-layout document pages by combining support vector machine (SVM) and conditional random fields (CRF). Features related to each logical label and their dependencies are extracted from various original Portable Document Format (PDF) attributes. Both local evidence and contextual dependencies are integrated in the proposed model so as to achieve better logical labeling performance. With the merits of SVM as local discriminative classifier and CRF modeling contextual correlations of adjacent fragments, it is capable of resolving the ambiguities of semantic labels. The experimental results show that CRF based models with both tree and chain graph structures outperform the SVM model with an increase of macro-averaged F1 by about 10%.
Broadcast News Story Segmentation Using Conditional Random Fields and Multimodal Features
Wang, Xiaoxuan; Xie, Lei; Lu, Mimi; Ma, Bin; Chng, Eng Siong; Li, Haizhou
In this paper, we propose integration of multimodal features using conditional random fields (CRFs) for the segmentation of broadcast news stories. We study story boundary cues from lexical, audio and video modalities, where lexical features consist of lexical similarity, chain strength and overall cohesiveness; acoustic features involve pause duration, pitch, speaker change and audio event type; and visual features contain shot boundaries, anchor faces and news title captions. These features are extracted in a sequence of boundary candidate positions in the broadcast news. A linear-chain CRF is used to detect each candidate as boundary/non-boundary tags based on the multimodal features. Important interlabel relations and contextual feature information are effectively captured by the sequential learning framework of CRFs. Story segmentation experiments show that the CRF approach outperforms other popular classifiers, including decision trees (DTs), Bayesian networks (BNs), naive Bayesian classifiers (NBs), multilayer perception (MLP), support vector machines (SVMs) and maximum entropy (ME) classifiers.
Quantum Phase Transition in the Two-Dimensional Random Transverse-Field Ising Model
Pich, C.; Young, A. P.
1998-03-01
We study the quantum phase transition in the random transverse-field Ising model by Monte Carlo simulations. In one-dimension it has been established that this system has the following striking behavior: (i) the dynamical exponent is infinite, and (ii) the exponents for the divergence of the average and typical correlation lengths are different. An important issue is whether this behavior is special to one-dimension or whether similar behavior persists in higher dimensions. Here we attempt to answer this question by studies of the two-dimensional model. Our simulations use the Wolff cluster algorithm and the results are analyzed by anisotropic finite size scaling, paying particular attention to the Binder ratio of moments of the order parameter distribution and the distribution of the spin-spin correlation functions for various distances.
Depinning transition and thermal fluctuations in the random-field Ising model.
Roters, L; Hucht, A; Lübeck, S; Nowak, U; Usadel, K D
1999-11-01
We analyze the depinning transition of a driven interface in the three-dimensional (3D) random field Ising model (RFIM) with quenched disorder by means of Monte Carlo simulations. The interface initially built into the system is perpendicular to the [111] direction of a simple cubic lattice. We introduce an algorithm which is capable of simulating such an interface independent of the considered dimension and time scale. This algorithm is applied to the 3D RFIM to study both the depinning transition and the influence of thermal fluctuations on this transition. It turns out that in the RFIM characteristics of the depinning transition depend crucially on the existence of overhangs. Our analysis yields critical exponents of the interface velocity, the correlation length, and the thermal rounding of the transition. We find numerical evidence for a scaling relation for these exponents and the dimension d of the system.
ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm.
Passera, Katia M; Potepan, Paolo; Brambilla, Luca; Mainardi, Luca T
2008-01-01
In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy.
A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos
Wu, Baoyuan
2016-10-25
Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.
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.
Scaling Conditional Random Fields by One-Against-the-Other Decomposition
Institute of Scientific and Technical Information of China (English)
Hai Zhao; Chunyu Kie
2008-01-01
As a powerful sequence labeling model, conditional random fields (CRFs) have had successful applications in many natural language processing (NLP) tasks. However, the high complexity of CRFs training only allows a very small tag (or label) set, because the training becomes intractable as the tag set enlarges. This paper proposes an improved decomposed training and joint decoding algorithm for CRF learning. Instead of training a single CRF model for all tags, it trains a binary sub-CRF independently for each tag. An optimal tag sequence is then produced by a joint decoding algorithm based on the probabilistic output of all sub-CRFs involved. To test its effectiveness, we apply this approach to tackling Chinese word segmentation (CWS) as a sequence labeling problem. Our evaluation shows that it can reduce the computational cost of this language processing task by 40-50% without any significant performance loss on various large-scale data sets.
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Truyen, Tran The; Bui, Hung H; Venkatesh, Svetha
2010-01-01
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirectedMarkov chains tomodel complex hierarchical, nestedMarkov processes. It is parameterised in a discriminative framework and has polynomial time algorithms for learning and inference. Importantly, we consider partiallysupervised learning and propose algorithms for generalised partially-supervised learning and constrained inference. We demonstrate the HSCRF in two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. We show that the HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.
A Markov Random Field Model-Based Approach to Natural Image Matting
Institute of Scientific and Technical Information of China (English)
Sheng-You Lin; Jiao-Ying Shi
2007-01-01
This paper proposes a Markov Random Field (MRF) model-based approach to natural image matting with complex scenes.After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions.In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel.All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori (MAP) estimation problem.Simulated annealing is used to find the optimal MAP estimation.The better results can be obtained under the same user-interactions when images are complex.Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.
Hausdorff-type Measures of the Sample Path of Gaussian Random Fields
Institute of Scientific and Technical Information of China (English)
Zhen-long Chen; San-yang Liu
2005-01-01
Let φ be a Hausdorff measure function and ∧ be an infinite increasing sequence of positive integers.The Hausdorff-type measure φ - m∧ associated to φ and ∧ is studied. Let X(t)(t ∈ RN) be certain Gaussian random fields in Rd. We give the exact Hausdorff measure of the graph set GrX([0, 1]N), and evaluate the exact φ - m∧ measure of the image and graph set of X(t). A necessary and sufficient condition on the sequence ∧ is given so that the usual Hausdorff measure function for X([0, 1]N) and GrX([0,1]N) are still the correct measure functions. If the sequence ∧ increases faster, then some smaller measure functions will give positive and finite (φ, ∧)-Hausdorff measure for X([0, 1]N) and GTX([0, 1]N).
Anomalous transport in fluid field with random waiting time depending on the preceding jump length
Zhang, Hong; Li, Guo-Hua
2016-11-01
Anomalous (or non-Fickian) transport behaviors of particles have been widely observed in complex porous media. To capture the energy-dependent characteristics of non-Fickian transport of a particle in flow fields, in the present paper a generalized continuous time random walk model whose waiting time probability distribution depends on the preceding jump length is introduced, and the corresponding master equation in Fourier-Laplace space for the distribution of particles is derived. As examples, two generalized advection-dispersion equations for Gaussian distribution and lévy flight with the probability density function of waiting time being quadratic dependent on the preceding jump length are obtained by applying the derived master equation. Project supported by the Foundation for Young Key Teachers of Chengdu University of Technology, China (Grant No. KYGG201414) and the Opening Foundation of Geomathematics Key Laboratory of Sichuan Province, China (Grant No. scsxdz2013009).
Zhang, Y.; Li, F.; Zhang, S.; Hao, W.; Zhu, T.; Yuan, L.; Xiao, F.
2017-09-01
In this paper, Statistical Distribution based Conditional Random Fields (STA-CRF) algorithm is exploited for improving marginal ice-water classification. Pixel level ice concentration is presented as the comparison of methods based on CRF. Furthermore, in order to explore the effective statistical distribution model to be integrated into STA-CRF, five statistical distribution models are investigated. The STA-CRF methods are tested on 2 scenes around Prydz Bay and Adélie Depression, where contain a variety of ice types during melt season. Experimental results indicate that the proposed method can resolve sea ice edge well in Marginal Ice Zone (MIZ) and show a robust distinction of ice and water.
A Markov-random-field-based filter for speckle reduction in ultrasound imagery
Lankoande, Ousseini; Hayat, Majeed M.; Santhanam, Balu
2009-02-01
The coherent nature of ultrasound imaging inherently produces the notorious signal-dependent speckle noise. Recently, a novel approach based upon embedding the statistical and physical properties of speckle patterns into a Markov-random-field (MRF) framework was developed and demonstrated by the authors in the context of synthetic-aperture radar imaging. The contributions of this work are twofold. First, the MRF approach is extended to include a pseudo maximum-likelihood estimator of a key model parameter, making the approach fully autonomous. Second, the capability of the extended approach, called the modified MRF-based conditional-expectation approach (MRFCEA), in denoising real ultrasound imagery is demonstrated. The proposed MRFCEA approach offers superior performance over existing methods by reducing speckle noise without compromising the spatial resolution. In addition, MRFCEA is autonomous, contrary to existing methods such as the enhanced-Frost or the modified-Lee, which require user's input.
Sign language recognition with the Kinect sensor based on conditional random fields.
Yang, Hee-Deok
2014-12-24
Sign language is a visual language used by deaf people. One difficulty of sign language recognition is that sign instances of vary in both motion and shape in three-dimensional (3D) space. In this research, we use 3D depth information from hand motions, generated from Microsoft's Kinect sensor and apply a hierarchical conditional random field (CRF) that recognizes hand signs from the hand motions. The proposed method uses a hierarchical CRF to detect candidate segments of signs using hand motions, and then a BoostMap embedding method to verify the hand shapes of the segmented signs. Experiments demonstrated that the proposed method could recognize signs from signed sentence data at a rate of 90.4%.
Directory of Open Access Journals (Sweden)
Mitra Ashayeri-Panah
2013-09-01
Full Text Available In this study, the discriminatory power of pulsed field gel electrophoresis (PFGE and random amplified polymorphic DNA (RAPD methods for subtyping of 54 clinical isolates of Klebsiella pneumoniae were compared. All isolates were typeable by RAPD, while 3.6% of them were not typeable by PFGE. The repeatability of both typing methods were 100% with satisfying reproducibility (≥ 95%. Although the discriminatory power of PFGE was greater than RAPD, both methods showed sufficient discriminatory power (DI > 0.95 which reflects the heterogeneity among the K. pneumoniae isolates. An optimized RAPD protocol is less technically demanding and time consuming that makes it a reliable typing method and competitive with PFGE.
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.
A Tutorial of the Poisson Random Field Model in Population Genetics
Directory of Open Access Journals (Sweden)
Praveen Sethupathy
2008-01-01
Full Text Available Population genetics is the study of allele frequency changes driven by various evolutionary forces such as mutation, natural selection, and random genetic drift. Although natural selection is widely recognized as a bona-fide phenomenon, the extent to which it drives evolution continues to remain unclear and controversial. Various qualitative techniques, or so-called “tests of neutrality”, have been introduced to detect signatures of natural selection. A decade and a half ago, Stanley Sawyer and Daniel Hartl provided a mathematical framework, referred to as the Poisson random field (PRF, with which to determine quantitatively the intensity of selection on a particular gene or genomic region. The recent availability of large-scale genetic polymorphism data has sparked widespread interest in genome-wide investigations of natural selection. To that end, the original PRF model is of particular interest for geneticists and evolutionary genomicists. In this article, we will provide a tutorial of the mathematical derivation of the original Sawyer and Hartl PRF model.
The monomer-dimer problem and moment Lyapunov exponents of homogeneous Gaussian random fields
Vladimirov, Igor G
2012-01-01
We consider an "elastic" version of the statistical mechanical monomer-dimer problem on the n-dimensional integer lattice. Our setting includes the classical "rigid" formulation as a special case and extends it by allowing each dimer to consist of particles at arbitrarily distant sites of the lattice, with the energy of interaction between the particles in a dimer depending on their relative position. We reduce the free energy of the elastic dimer-monomer (EDM) system per lattice site in the thermodynamic limit to the moment Lyapunov exponent (MLE) of a homogeneous Gaussian random field (GRF) whose mean value and covariance function are the Boltzmann factors associated with the monomer energy and dimer potential. In particular, the classical monomer-dimer problem becomes related to the MLE of a moving average GRF. We outline an approach to recursive computation of the partition function for "Manhattan" EDM systems where the dimer potential is a weighted l1-distance and the auxiliary GRF is a Markov random fie...
Liu, Hui-Fang; Yang, Lin; He, Hong-Chen; Zhou, Jun; Liu, Ying; Wang, Chun-Yan; Wu, Yuan-Chao; He, Cheng-Qi
2013-05-01
A randomized, active-controlled clinical trial was conducted to examine the effect of pulsed electromagnetic fields (PEMFs) on women with postmenopausal osteoporosis (PMO) in southwest China. Forty-four participants were randomly assigned to receive alendronate or one course of PEMFs treatment. The primary endpoint was the mean percentage change in bone mineral density of the lumbar spine (BMDL), and secondary endpoints were the mean percentage changes in left proximal femur bone mineral density (BMDF), serum 25OH vitamin D3 (25(OH)D) concentrations, total lower-extremity manual muscle test (LE MMT) score, and Berg Balance Scale (BBS) score. The BMDL, BMDF, total LE MMT score and BBS score were recorded at baseline, 5, 12, and 24 weeks. Serum concentrations of 25(OH)D were measured at baseline and 5 weeks. Using a mixed linear model, there was no significant treatment difference between the two groups in the BMDL, BMDF, total LE MMT score, and BBS score (P ≥ 0.05). For 25(OH)D concentrations, the effects were also comparable between the two groups (P ≥ 0.05) with the Mann-Whitney's U-test. These results suggested that a course of PEMFs treatment with specific parameters was as effective as alendronate in treating PMO within 24 weeks.
Role of polar nanoregions with weak random fields in Pb-based perovskite ferroelectrics
Helal, M. A.; Aftabuzzaman, M.; Tsukada, S.; Kojima, S.
2017-01-01
In relaxor ferroelectrics, the role of randomly orientated polar nanoregions (PNRs) with weak random fields (RFs) is one of the most puzzling issues of materials science. The relaxation time of polarization fluctuations of PNRs, which manifests themselves as a central peak (CP) in inelastic light scattering, is the important physical quantity to understand the dynamics of PNRs. Here, the angular and temperature dependences of depolarized and polarized CPs in 0.44Pb(Mg1/3Nb2/3)O3-0.56PbTiO3 single crystals with weak RFs have been studied by Raman and Brillouin scattering, respectively. The CPs observed in Raman scattering show the very clear angular dependence which is consistent with the local tetragonal symmetry. It is different from the well-known local rhombohedral symmetry with strong RFs for Pb(Mg1/3Nb2/3)O3. In Brillouin scattering, depolarized and polarized CPs show two relaxation processes corresponding to transverse and longitudinal fluctuations of PNRs. The remarkable slowing down towards the Curie temperature was observed for transverse fluctuations in local tetragonal symmetry. PMID:28300152
Extended power-law scaling of heavy-tailed random fields or processes
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A. Guadagnini
2012-06-01
Full Text Available We analyze the scaling behaviors of two log permeability data sets showing heavy-tailed frequency distributions in three and two spatial dimensions, respectively. One set consists of 1-m scale pneumatic packer test data from six vertical and inclined boreholes spanning a decameters scale block of unsaturated fractured tuffs near Superior, Arizona, the other of pneumatic minipermeameter data measured at a spacing of 15 cm along two horizontal transects on a 21 m long outcrop of lower-shoreface bioturbated sandstone near Escalante, Utah. Order q sample structure functions of each data set scale as a power ξ (q of separation scale or lag, s, over limited ranges of s. A procedure known as Extended Self-Similarity (ESS extends this range to all lags and yields a nonlinear (concave functional relationship between ξ (q and q. Whereas the literature tends to associate extended and nonlinear power-law scaling with multifractals or fractional Laplace motions, we have shown elsewhere that (a ESS of data having a normal frequency distribution is theoretically consistent with (Gaussian truncated (additive, self-affine, monofractal fractional Brownian motion (tfBm, the latter being unique in predicting a breakdown in power-law scaling at small and large lags, and (b nonlinear power-law scaling of data having either normal or heavy-tailed frequency distributions is consistent with samples from sub-Gaussian random fields or processes subordinated to tfBm, stemming from lack of ergodicity which causes sample moments to scale differently than do their ensemble counterparts. Here we (i demonstrate that the above two data sets are consistent with sub-Gaussian random fields subordinated to tfBm and (ii provide maximum likelihood estimates of parameters characterizing the corresponding Lévy stable subordinators and tfBm functions.
Crop Type Mapping from a Sequence of Terrasar-X Images with Dynamic Conditional Random Fields
Kenduiywo, B. K.; Bargiel, D.; Soergel, U.
2016-06-01
Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It
Stochastic generation of explicit pore structures by thresholding Gaussian random fields
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Hyman, Jeffrey D., E-mail: jhyman@lanl.gov [Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721-0089 (United States); Computational Earth Science, Earth and Environmental Sciences (EES-16), and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87544 (United States); Winter, C. Larrabee, E-mail: winter@email.arizona.edu [Program in Applied Mathematics, University of Arizona, Tucson, AZ 85721-0089 (United States); Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721-0011 (United States)
2014-11-15
We provide a description and computational investigation of an efficient method to stochastically generate realistic pore structures. Smolarkiewicz and Winter introduced this specific method in pores resolving simulation of Darcy flows (Smolarkiewicz and Winter, 2010 [1]) without giving a complete formal description or analysis of the method, or indicating how to control the parameterization of the ensemble. We address both issues in this paper. The method consists of two steps. First, a realization of a correlated Gaussian field, or topography, is produced by convolving a prescribed kernel with an initial field of independent, identically distributed random variables. The intrinsic length scales of the kernel determine the correlation structure of the topography. Next, a sample pore space is generated by applying a level threshold to the Gaussian field realization: points are assigned to the void phase or the solid phase depending on whether the topography over them is above or below the threshold. Hence, the topology and geometry of the pore space depend on the form of the kernel and the level threshold. Manipulating these two user prescribed quantities allows good control of pore space observables, in particular the Minkowski functionals. Extensions of the method to generate media with multiple pore structures and preferential flow directions are also discussed. To demonstrate its usefulness, the method is used to generate a pore space with physical and hydrological properties similar to a sample of Berea sandstone. -- Graphical abstract: -- Highlights: •An efficient method to stochastically generate realistic pore structures is provided. •Samples are generated by applying a level threshold to a Gaussian field realization. •Two user prescribed quantities determine the topology and geometry of the pore space. •Multiple pore structures and preferential flow directions can be produced. •A pore space based on Berea sandstone is generated.
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
IMPLEMENTATION OF THE MARKOV RANDOM FIELD FOR URBAN LAND COVER CLASSIFICATION OF UAV VHIR DATA
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Jati Pratomo
2016-10-01
Full Text Available The usage of Unmanned Aerial Vehicle (UAV has grown rapidly in various fields, such as urban planning, search and rescue, and surveillance. Capturing images from UAV has many advantages compared with satellite imagery. For instance, higher spatial resolution and less impact from atmospheric variations can be obtained. However, there are difficulties in classifying urban features, due to the complexity of the urban land covers. The usage of Maximum Likelihood Classification (MLC has limitations since it is based on the assumption of the normal distribution of pixel values, where, in fact, urban features are not normally distributed. There are advantages in using the Markov Random Field (MRF for urban land cover classification as it assumes that neighboring pixels have a higher probability to be classified in the same class rather than a different class. This research aimed to determine the impact of the smoothness (λ and the updating temperature (Tupd on the accuracy result (κ in MRF. We used a UAV VHIR sized 587 square meters, with six-centimetre resolution, taken in Bogor Regency, Indonesia. The result showed that the kappa value (κ increases proportionally with the smoothness (λ until it reaches the maximum (κ, then the value drops. The usage of higher (Tupd has resulted in better (κ although it also led to a higher Standard Deviations (SD. Using the most optimal parameter, MRF resulted in slightly higher (κ compared with MLC.
Turbulence generation by a shock wave interacting with a random density inhomogeneity field
de Lira, Cesar Huete Ruiz
2010-01-01
When a planar shock wave interacts with a random pattern of pre-shock density non-uniformities, it generates an anisotropic turbulent velocity/vorticity field. This turbulence plays an important role at the early stages of the mixing process in the compressed fluid. This situation emerges naturally in shock interaction with weakly inhomogeneous deuterium-wicked foam targets in Inertial Confinement Fusion (ICF) and with density clumps/clouds in astrophysics. We present an exact small-amplitude linear theory describing such interaction. It is based on the exact theory of time and space evolution of the perturbed quantities behind a corrugated shock front for a single-mode pre-shock non-uniformity. Appropriate mode averaging in 2D results in closed analytical expressions for the turbulent kinetic energy, degree of anisotropy of velocity and vorticity fields in the shocked fluid, shock amplification of the density non-uniformity, and sonic energy flux radiated downstream. These explicit formulas are further simpl...
Multi-fidelity Gaussian process regression for prediction of random fields
Energy Technology Data Exchange (ETDEWEB)
Parussini, L. [Department of Engineering and Architecture, University of Trieste (Italy); Venturi, D., E-mail: venturi@ucsc.edu [Department of Applied Mathematics and Statistics, University of California Santa Cruz (United States); Perdikaris, P. [Department of Mechanical Engineering, Massachusetts Institute of Technology (United States); Karniadakis, G.E. [Division of Applied Mathematics, Brown University (United States)
2017-05-01
We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.
Multilayer Markov Random Field models for change detection in optical remote sensing images
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.
Distributed Denial of Service Attacks Detection Method Based on Conditional Random Fields
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Shi-wen CHEN
2013-04-01
Full Text Available In order to detect distributed denial of service (DDoS attacks accurately and efficiently, a new detection model based on conditional random fields (CRF was proposed. The CRF based model incorporates the signature-based and anomaly-based detection methods to a hybrid system. The selected features include source IP entropy, destination IP entropy, source port entropy, destination port entropy, protocol number and etc. The CRF based model combines these IP flow entropies and other fingerprints into a normalize entropy as the feature vectors to depict the states of the monitoring traffic. The training method of the detection model uses the L-BFGS algorithm. And the model only needs to inspect the IP header fields of each packet, which makes it possible for real-time implementation even on real time network traffic. The CRF based model may have the ability to detect new form of forthcoming attacks, because it is independent from any specific DDoS flooding attack tools. The experiment results show that the CRF based method has higher detection accuracy and sensitivity as well as lower false positive alarms. Experiment with KDD CUP1999, DARPA 2000 and generated attack datasets, the CRF based model outperforms other well-known detection methods such as Naïve Bayes, KNN, SVM and etc. The accuracy goes beyond 95.0% and the false alarm rate is less than 5.0%. Meanwhile, the CRF based model is robust under massive background network traffic.
A Novel Microaneurysms Detection Method Based on Local Applying of Markov Random Field.
Ganjee, Razieh; Azmi, Reza; Moghadam, Mohsen Ebrahimi
2016-03-01
Diabetic Retinopathy (DR) is one of the most common complications of long-term diabetes. It is a progressive disease and by damaging retina, it finally results in blindness of patients. Since Microaneurysms (MAs) appear as a first sign of DR in retina, early detection of this lesion is an essential step in automatic detection of DR. In this paper, a new MAs detection method is presented. The proposed approach consists of two main steps. In the first step, the MA candidates are detected based on local applying of Markov random field model (MRF). In the second step, these candidate regions are categorized to identify the correct MAs using 23 features based on shape, intensity and Gaussian distribution of MAs intensity. The proposed method is evaluated on DIARETDB1 which is a standard and publicly available database in this field. Evaluation of the proposed method on this database resulted in the average sensitivity of 0.82 for a confidence level of 75 as a ground truth. The results show that our method is able to detect the low contrast MAs with the background while its performance is still comparable to other state of the art approaches.
Super-rough glassy phase of the random field XY model in two dimensions.
Perret, Anthony; Ristivojevic, Zoran; Le Doussal, Pierre; Schehr, Grégory; Wiese, Kay J
2012-10-12
We study both analytically, using the renormalization group (RG) to two loop order, and numerically, using an exact polynomial algorithm, the disorder-induced glass phase of the two-dimensional XY model with quenched random symmetry-breaking fields and without vortices. In the super-rough glassy phase, i.e., below the critical temperature T(c), the disorder and thermally averaged correlation function B(r) of the phase field θ(x), B(r)=([θ(x)-θ(x+r)](2)) behaves, for r > a, as B(r) is approximately equal to A(τ)ln(2)(r/a) where r=|r| and a is a microscopic length scale. We derive the RG equations up to cubic order in τ=(T(c)-T)/T(c) and predict the universal amplitude A(τ)=2τ(2)-2τ(3)+O(τ(4)). The universality of A(τ) results from nontrivial cancellations between nonuniversal constants of RG equations. Using an exact polynomial algorithm on an equivalent dimer version of the model we compute A(τ) numerically and obtain a remarkable agreement with our analytical prediction, up to τ≈0.5.
Institute of Scientific and Technical Information of China (English)
ZHANGYa-Nan; YANShi-Lei
2003-01-01
We study the thermodynamic properties of random transverse field mixed spin system in the presence of single-ion anisotropy on a square lattice. By making use of the effective field theory and a cutting approximation, the detailed phase diagrams are described and some interesting results are found under trimodal random transverse field distribution. A small single-ion anisotropy can magnify magnetic ordering region at low temperatures and existence of a large transverse field can assist the occurrence of reentrant phenomena. With increasing disorder, second-order phase transitions are shown to change into first-order phase transitions. The trajectory of the tricritical point in the phase space as a function of disorder is presented. These indicate a strong correlation with the corresponding to trimodal transverse field distribution.
Random-field Potts model for the polar domains of lead magnesium niobate and lead scandium tantalate
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Qian, H.; Bursill, L.A
1997-06-01
A random filed Potts model is used to establish the spatial relationship between the nanoscale distribution of charges chemical defects and nanoscale polar domains for the perovskite-based relaxor materials lead magnesium niobate (PMN) and lead scandium tantalate (PST). The random fields are not set stochastically but are determined initially by the distribution of B-site cations (Mg, Nb) or (Sc, Ta) generated by Monte Carlo NNNI-model simulations for the chemical defects. An appropriate random field Potts model is derived and algorithms developed for a 2D lattice. It is shown that the local fields are strongly correlated with the chemical domain walls and that polar domains as a function of decreasing temperature is simulated for the two cases of PMN and PST. The dynamics of the polar clusters is also discussed. 33 refs., 9 figs.
Energy Technology Data Exchange (ETDEWEB)
Zentner, I. [IMSIA, UMR EDF-ENSTA-CNRS-CEA 9219, Université Paris-Saclay, 828 Boulevard des Maréchaux, 91762 Palaiseau Cedex (France); Ferré, G., E-mail: gregoire.ferre@ponts.org [CERMICS – Ecole des Ponts ParisTech, 6 et 8 avenue Blaise Pascal, Cité Descartes, Champs sur Marne, 77455 Marne la Vallée Cedex 2 (France); Poirion, F. [Department of Structural Dynamics and Aeroelasticity, ONERA, BP 72, 29 avenue de la Division Leclerc, 92322 Chatillon Cedex (France); Benoit, M. [Institut de Recherche sur les Phénomènes Hors Equilibre (IRPHE), UMR 7342 (CNRS, Aix-Marseille Université, Ecole Centrale Marseille), 49 rue Frédéric Joliot-Curie, BP 146, 13384 Marseille Cedex 13 (France)
2016-06-01
In this paper, a new method for the identification and simulation of non-Gaussian and non-stationary stochastic fields given a database is proposed. It is based on two successive biorthogonal decompositions aiming at representing spatio–temporal stochastic fields. The proposed double expansion allows to build the model even in the case of large-size problems by separating the time, space and random parts of the field. A Gaussian kernel estimator is used to simulate the high dimensional set of random variables appearing in the decomposition. The capability of the method to reproduce the non-stationary and non-Gaussian features of random phenomena is illustrated by applications to earthquakes (seismic ground motion) and sea states (wave heights).
Transverse eV ion heating by random electric field fluctuations in the plasmasphere
Artemyev, A. V.; Mourenas, D.; Agapitov, O. V.; Blum, L.
2017-02-01
Charged particle acceleration in the Earth inner magnetosphere is believed to be mainly due to the local resonant wave-particle interaction or particle transport processes. However, the Van Allen Probes have recently provided interesting evidence of a relatively slow transverse heating of eV ions at distances about 2-3 Earth radii during quiet times. Waves that are able to resonantly interact with such very cold ions are generally rare in this region of space, called the plasmasphere. Thus, non-resonant wave-particle interactions are expected to play an important role in the observed ion heating. We demonstrate that stochastic heating by random transverse electric field fluctuations of whistler (and possibly electromagnetic ion cyclotron) waves could explain this weak and slow transverse heating of H+ and O+ ions in the inner magnetosphere. The essential element of the proposed model of ion heating is the presence of trains of random whistler (hiss) wave packets, with significant amplitude modulations produced by strong wave damping, rapid wave growth, or a superposition of wave packets of different frequencies, phases, and amplitudes. Such characteristics correspond to measured characteristics of hiss waves in this region. Using test particle simulations with typical wave and plasma parameters, we demonstrate that the corresponding stochastic transverse ion heating reaches 0.07-0.2 eV/h for protons and 0.007-0.015 eV/h for O+ ions. This global temperature increase of the Maxwellian ion population from an initial Ti˜0.3 eV could potentially explain the observations.
Transverse eV Ion Heating by Random Electric Field Fluctuations in the Plasmasphere
Artemyev, A. V.; Mourenas, D.; Agapitov, O. V.; Blum, L.
2017-01-01
Charged particle acceleration in the Earth inner magnetosphere is believed to be mainly due to the local resonant wave-particle interaction or particle transport processes. However, the Van Allen Probes have recently provided interesting evidence of a relatively slow transverse heating of eV ions at distances about 2-3 Earth radii during quiet times. Waves that are able to resonantly interact with such very cold ions are generally rare in this region of space, called the plasmasphere. Thus, non-resonant wave-particle interactions are expected to play an important role in the observed ion heating. We demonstrate that stochastic heating by random transverse electric field fluctuations of whistler (and possibly electromagnetic ion cyclotron) waves could explain this weak and slow transverse heating of H+ and O+ ions in the inner magnetosphere. The essential element of the proposed model of ion heating is the presence of trains of random whistler (hiss) wave packets, with significant amplitude modulations produced by strong wave damping, rapid wave growth, or a superposition of wave packets of different frequencies, phases, and amplitudes. Such characteristics correspond to measured characteristics of hiss waves in this region. Using test particle simulations with typical wave and plasma parameters, we demonstrate that the corresponding stochastic transverse ion heating reaches 0.07-0.2 eV/h for protons and 0.007-0.015 eV/h for O+ ions. This global temperature increase of the Maxwellian ion population from an initial Ti approx. 0.3 eV could potentially explain the observations.
Extraction of semantic biomedical relations from text using conditional random fields
Directory of Open Access Journals (Sweden)
Stetter Martin
2008-04-01
Full Text Available Abstract Background The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition. Results We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. Conclusion We extend the framework of Conditional Random Fields towards the annotation of semantic relations from
Markov random field modelling for fluid distributions from the seismic velocity structures
Kuwatani, T.; Nagata, K.; Okada, M.; Toriumi, M.
2011-12-01
Recent development of geophysical observations, such as seismic tomography, seismic reflection method and geomagnetic method, provide us detailed images of the earth's interior. However, it has still been difficult to interpret these data geologically, including predicting lithology and fluid distributions, mainly because (1) available data usually have large noise and uncertainty, and (2) the number of observable parameters is usually smaller than the number of target parameters. Therefore, the statistical analyses of geophysical data sets are essential for the objective and quantitative geological interpretation. We propose the use of Markov random field (MRF) model to geophysical image data as an alternative to classical deterministic approaches. The MRF model is a Bayesian stochastic model using a generalized form of Markov Chains, and is often applied to the analysis of images, particularly in the detection of visual patterns or textures. The MRF model assumes that the spatial gradients of physical properties are relatively small compared to the observational noises. By hyperparameter estimation, the variances of noises can be appropriately estimated only from available data sets without prior information about observational noises. In this study, we try to image the fluid distributions based on the seismic velocity structure by using the Markov random field model. According to Nakajima et al. (2005), seismic velocities (Vp and Vs) are expressed as functions of porosity and pore geometry using the unified formulation proposed by Takei (2002). Additionally, the spatial continuity of porosity and pore geometry is incorporated by Gaussian Markov Chains as prior probabilities. The most probable estimation can be obtained by maximizing the posterior probability of the fluid distribution given the observed velocity structures. In the present study, the steepest descent method was implemented in order to minimize the free energy (i.e. maximize the posterior
Zhang, Yifan
2016-08-18
For face naming in TV series or movies, a typical way is using subtitles/script alignment to get the time stamps of the names, and tagging them to the faces. We study the problem of face naming in videos when subtitles are not available. To this end, we divide the problem into two tasks: face clustering which groups the faces depicting a certain person into a cluster, and name assignment which associates a name to each face. Each task is formulated as a structured prediction problem and modeled by a hidden conditional random field (HCRF) model. We argue that the two tasks are correlated problems whose outputs can provide prior knowledge of the target prediction for each other. The two HCRFs are coupled in a unified graphical model called coupled HCRF where the joint dependence of the cluster labels and face name association is naturally embedded in the correlation between the two HCRFs. We provide an effective algorithm to optimize the two HCRFs iteratively and the performance of the two tasks on real-world data set can be both improved.
Diaz, P. M. A.; Feitosa, R. Q.; Sanches, I. D.; Costa, G. A. O. P.
2016-06-01
This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.
Tao, R.; Tang, H.
Chocolate is one of the most popular food types and flavors in the world. Unfortunately, at present, chocolate products contain too much fat, leading to obesity. For example, a typical molding chocolate has various fat up to 40% in total and chocolate for covering ice cream has fat 50 -60%. Especially, as children are the leading chocolate consumers, reducing the fat level in chocolate products to make them healthier is important and urgent. While this issue was called into attention and elaborated in articles and books decades ago and led to some patent applications, no actual solution was found unfortunately. Why is reducing fat in chocolate so difficult? What is the underlying physical mechanism? We have found that this issue is deeply related to the basic science of soft matters, especially to their viscosity and maximally random jammed (MRJ) density φx. All chocolate productions are handling liquid chocolate, a suspension with cocoa solid particles in melted fat, mainly cocoa butter. The fat level cannot be lower than 1-φxin order to have liquid chocolate to flow. Here we show that that with application of an electric field to liquid chocolate, we can aggregate the suspended particles into prolate spheroids. This microstructure change reduces liquid chocolate's viscosity along the flow direction and increases its MRJ density significantly. Hence the fat level in chocolate can be effectively reduced. We are looking forward to a new class of healthier and tasteful chocolate coming to the market soon. Dept. of Physics, Temple Univ, Philadelphia, PA 19122.
Recognition of 3-D objects based on Markov random field models
Institute of Scientific and Technical Information of China (English)
HUANG Ying; DING Xiao-qing; WANG Sheng-jin
2006-01-01
The recognition of 3-D objects is quite a difficult task for computer vision systems.This paper presents a new object framework,which utilizes densely sampled grids with different resolutions to represent the local information of the input image.A Markov random field model is then created to model the geometric distribution of the object key nodes.Flexible matching,which aims to find the accurate correspondence map between the key points of two images,is performed by combining the local similarities and the geometric relations together using the highest confidence first method.Afterwards,a global similarity is calculated for object recognition. Experimental results on Coil-100 object database,which consists of 7 200 images of 100 objects,are presented.When the numbers of templates vary from 4,8,18 to 36 for each object,and the remaining images compose the test sets,the object recognition rates are 95.75 %,99.30 %,100.0 % and 100.0 %,respectively.The excellent recognition performance is much better than those of the other cited references,which indicates that our approach is well-suited for appearance-based object recognition.
Chang, Ju Yong
2016-08-01
We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by matching input features with a training dataset. In addition to the conventional skeletal joint-based features, the appearance information near the active hand in an RGB image is exploited to capture the detailed motion of fingers. The estimated likelihood function is then used as the unary term for our CRF model. The smoothness term is also incorporated to enforce the temporal coherence of our solution. Frame-wise recognition results can then be obtained by applying an efficient dynamic programming technique. To estimate the parameters of the proposed CRF model, we incorporate the structured support vector machine (SSVM) framework that can perform efficient structured learning by using large-scale datasets. Experimental results demonstrate that our method provides effective gesture recognition results for challenging real gesture datasets. By scoring 0.8563 in the mean Jaccard index, our method has obtained the state-of-the-art results for the gesture recognition track of the 2014 ChaLearn Looking at People (LAP) Challenge.
Scene Segmentation with Low-Dimensional Semantic Representations and Conditional Random Fields
Directory of Open Access Journals (Sweden)
Triggs Bill
2010-01-01
Full Text Available This paper presents a fast, precise, and highly scalable semantic segmentation algorithm that incorporates several kinds of local appearance features, example-based spatial layout priors, and neighborhood-level and global contextual information. The method works at the level of image patches. In the first stage, codebook-based local appearance features are regularized and reduced in dimension using latent topic models, combined with spatial pyramid matching based spatial layout features, and fed into logistic regression classifiers to produce an initial patch level labeling. In the second stage, these labels are combined with patch-neighborhood and global aggregate features using either a second layer of Logistic Regression or a Conditional Random Field. Finally, the patch-level results are refined to pixel level using MRF or over-segmentation based methods. The CRF is trained using a fast Maximum Margin approach. Comparative experiments on four multi-class segmentation datasets show that each of the above elements improves the results, leading to a scalable algorithm that is both faster and more accurate than existing patch-level approaches.
Directory of Open Access Journals (Sweden)
Hoffmann Nico
2016-09-01
Full Text Available Intraoperative thermal neuroimaging is a novel intraoperative imaging technique for the characterization of perfusion disorders, neural activity and other pathological changes of the brain. It bases on the correlation of (sub-cortical metabolism and perfusion with the emitted heat of the cortical surface. In order to minimize required computational resources and prevent unwanted artefacts in subsequent data analysis workflows foreground detection is a important preprocessing technique to differentiate pixels representing the cerebral cortex from background objects. We propose an efficient classification framework that integrates characteristic dynamic thermal behaviour into this classification task to include additional discriminative features. The first stage of our framework consists of learning this representation of characteristic thermal time-frequency behaviour. This representation models latent interconnections in the time-frequency domain that cover specific, yet a priori unknown, thermal properties of the cortex. In a second stage these features are then used to classify each pixel’s state with conditional random fields. We quantitatively evaluate several approaches to learning high-level features and their impact to the overall prediction accuracy. The introduction of high-level features leads to a significant accuracy improvement compared to a baseline classifier.
Real-time Gaussian Markov random-field-based ground tracking for ground penetrating radar data
Bradbury, Kyle; Torrione, Peter A.; Collins, Leslie
2009-05-01
Current ground penetrating radar algorithms for landmine detection require accurate estimates of the location of the air/ground interface to maintain high levels of performance. However, the presence of surface clutter, natural soil roughness, and antenna motion lead to uncertainty in these estimates. Previous work on improving estimates of the location of the air/ground interface have focused on one-dimensional filtering techniques to localize the air/ground interface. In this work, we propose an algorithm for interface localization using a 2- D Gaussian Markov random field (GMRF). The GMRF provides a statistical model of the surface structure, which enables the application of statistical optimization techniques. In this work, the ground location is inferred using iterated conditional modes (ICM) optimization which maximizes the conditional pseudo-likelihood of the GMRF at a point, conditioned on its neighbors. To illustrate the efficacy of the proposed interface localization approach, pre-screener performance with and without the proposed ground localization algorithm is compared. We show that accurate localization of the air/ground interface provides the potential for future performance improvements.
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.
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.
Subject-adaptive real-time sleep stage classification based on conditional random field.
Luo, Gang; Min, Wanli
2007-10-11
Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject S(new) with limited training data D(new), we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from D(new) a regulated estimate of CRF's parameters. Using sleep recordings from human subjects, we show that even without any D(new), our sleep stager can achieve an average classification accuracy of 70% on S(new). This accuracy increases with the size of D(new) and eventually becomes close to the theoretical limit.
Beyond-mean-field corrections within the second random-phase approximation
Grasso, M.; Gambacurta, D.; Engel, J.
2016-06-01
A subtraction procedure, introduced to overcome double-counting problems in beyond-mean-field theories, is used in the second random-phase approximation (SRPA). Doublecounting problems arise in the energy-density functional framework in all cases where effective interactions tailored at leading order are used for higher-order calculations, such as those done in the SRPA model. It was recently shown that this subtraction procedure also guarantees that the stability condition related to the Thouless theorem is verified in extended RPA models. We discuss applications of the subtraction procedure, introduced within the SRPA model, to the nucleus 16O. The application of the subtraction procedure leads to: (i) stable results that are weakly cutoff dependent; (ii) a considerable upwards correction of the SRPA spectra (which were systematically shifted downwards by several MeV with respect to RPA spectra, in all previous calculations). With this important implementation of the model, many applications may be foreseen to analyze the genuine impact of 2 particle-2 hole configurations (without any cutoff dependences and anomalous shifts) on the excitation spectra of medium-mass and heavy nuclei.
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/.
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.
Gaussian random field power spectrum and the S\\'ersic law
Nipoti, Carlo
2015-01-01
The surface-brightness profiles of galaxies are well described by the S\\'ersic law: systems with high S\\'ersic index m have steep central profiles and shallow outer profiles, while systems with low m have shallow central profiles and steep outer profiles. R. Cen (2014, ApJL, 790, L24) has conjectured that these profiles arise naturally in the standard cosmological model with initial density fluctuations represented by a Gaussian random field (GRF). We explore and confirm this hypothesis with N-body simulations of dissipationless collapses in which the initial conditions are generated from GRFs with different power spectra. The numerical results show that GRFs with more power on small scales lead to systems with higher m. In our purely dissipationless simulations the S\\'ersic index is in the range 2
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
Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection
Directory of Open Access Journals (Sweden)
Lizhen Wu
2012-01-01
Full Text Available Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images.
Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model
Directory of Open Access Journals (Sweden)
Yu Guo
2014-01-01
Full Text Available 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.
Directory of Open Access Journals (Sweden)
Min Chen
2011-04-01
Full Text Available Genome-wide association studies (GWAS examine a large number of markers across the genome to identify associations between genetic variants and disease. Most published studies examine only single markers, which may be less informative than considering multiple markers and multiple genes jointly because genes may interact with each other to affect disease risk. Much knowledge has been accumulated in the literature on biological pathways and interactions. It is conceivable that appropriate incorporation of such prior knowledge may improve the likelihood of making genuine discoveries. Although a number of methods have been developed recently to prioritize genes using prior biological knowledge, such as pathways, most methods treat genes in a specific pathway as an exchangeable set without considering the topological structure of a pathway. However, how genes are related with each other in a pathway may be very informative to identify association signals. To make use of the connectivity information among genes in a pathway in GWAS analysis, we propose a Markov Random Field (MRF model to incorporate pathway topology for association analysis. We show that the conditional distribution of our MRF model takes on a simple logistic regression form, and we propose an iterated conditional modes algorithm as well as a decision theoretic approach for statistical inference of each gene's association with disease. Simulation studies show that our proposed framework is more effective to identify genes associated with disease than a single gene-based method. We also illustrate the usefulness of our approach through its applications to a real data example.
Heterogeneous Memorized Continuous Time Random Walks in an External Force Fields
Wang, Jun; Zhou, Ji; Lv, Long-Jin; Qiu, Wei-Yuan; Ren, Fu-Yao
2014-09-01
In this paper, we study the anomalous diffusion of a particle in an external force field whose motion is governed by nonrenewal continuous time random walks with correlated memorized waiting times, which involves Reimann-Liouville fractional derivative or Reimann-Liouville fractional integral. We show that the mean squared displacement of the test particle which is dependent on its location of the form (El-Wakil and Zahran, Chaos Solitons Fractals, 12, 1929-1935, 2001) where is the anomalous exponent, the diffusion exponent is dependent on the model parameters. We obtain the Fokker-Planck-type dynamic equations, and their stationary solutions are of the Boltzmann-Gibbs form. These processes obey a generalized Einstein-Stokes-Smoluchowski relation and the second Einstein relation. We observe that the asymptotic behavior of waiting times and subordinations are of stretched Gaussian distributions. We also discuss the time averaged in the case of an harmonic potential, and show that the process exhibits aging and ergodicity breaking.
Segmentation of cone-beam CT using a hidden Markov random field with informative priors
Moores, M.; Hargrave, C.; Harden, F.; Mengersen, K.
2014-03-01
Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.
Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin
2016-01-01
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223.252.210:8080/SS/cdr.html PMID:27777244
Nuzhnaya, Tatyana; Bakic, Predrag; Kontos, Despina; Megalooikonomou, Vasileios; Ling, Haibin
2012-02-01
This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching structures with the underlying image texture. Morphological variability of the breast ductal network is associated with subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia. In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast anatomy phantoms.
Random-field Ising model on isometric lattices: Ground states and non-Porod scattering
Bupathy, Arunkumar; Banerjee, Varsha; Puri, Sanjay
2016-01-01
We use a computationally efficient graph cut method to obtain ground state morphologies of the random-field Ising model (RFIM) on (i) simple cubic (SC), (ii) body-centered cubic (BCC), and (iii) face-centered cubic (FCC) lattices. We determine the critical disorder strength Δc at zero temperature with high accuracy. For the SC lattice, our estimate (Δc=2.278 ±0.002 ) is consistent with earlier reports. For the BCC and FCC lattices, Δc=3.316 ±0.002 and 5.160 ±0.002 , respectively, which are the most accurate estimates in the literature to date. The small-r behavior of the correlation function exhibits a cusp regime characterized by a cusp exponent α signifying fractal interfaces. In the paramagnetic phase, α =0.5 ±0.01 for all three lattices. In the ferromagnetic phase, the cusp exponent shows small variations due to the lattice structure. Consequently, the interfacial energy Ei(L ) for an interface of size L is significantly different for the three lattices. This has important implications for nonequilibrium properties.
Singular-potential random-matrix model arising in mean-field glassy systems.
Akemann, Gernot; Villamaina, Dario; Vivo, Pierpaolo
2014-06-01
We consider an invariant random matrix ensemble where the standard Gaussian potential is distorted by an additional single pole of arbitrary fixed order. Potentials with first- and second-order poles have been considered previously and found applications in quantum chaos and number theory. Here we present an application to mean-field glassy systems. We derive and solve the loop equation in the planar limit for the corresponding class of potentials. We find that the resulting mean or macroscopic spectral density is generally supported on two disconnected intervals lying on the two sides of the repulsive pole, whose edge points can be completely determined imposing the additional constraint of traceless matrices on average. For an unbounded potential with an attractive pole, we also find a possible one-cut solution for certain values of the couplings, which is ruled out when the traceless condition is imposed. Motivated by the calculation of the distribution of the spin-glass susceptibility in the Sherrington-Kirkpatrick spin-glass model, we consider in detail a second-order pole for a zero-trace model and provide the most explicit solution in this case. In the limit of a vanishing pole, we recover the standard semicircle. Working in the planar limit, our results apply to matrices with orthogonal, unitary, and symplectic invariance. Numerical simulations and an independent analytical Coulomb fluid calculation for symmetric potentials provide an excellent confirmation of our results.
A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking.
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Mohammad Javad Shafiee
Full Text Available In this work, we introduce a deep-structured conditional random field (DS-CRF model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.
Zhang, Yongsheng; Xiong, Hongkai; He, Zhihai; Yu, Songyu
2010-07-01
An important issue in Wyner-Ziv video coding is the reconstruction of Wyner-Ziv frames with decoded bit-planes. So far, there are two major approaches: the Maximum a Posteriori (MAP) reconstruction and the Minimum Mean Square Error (MMSE) reconstruction algorithms. However, these approaches do not exploit smoothness constraints in natural images. In this paper, we model a Wyner-Ziv frame by Markov random fields (MRFs), and produce reconstruction results by finding an MAP estimation of the MRF model. In the MRF model, the energy function consists of two terms: a data term, MSE distortion metric in this paper, measuring the statistical correlation between side-information and the source, and a smoothness term enforcing spatial coherence. In order to better describe the spatial constraints of images, we propose a context-adaptive smoothness term by analyzing the correspondence between the output of Slepian-Wolf decoding and successive frames available at decoders. The significance of the smoothness term varies in accordance with the spatial variation within different regions. To some extent, the proposed approach is an extension to the MAP and MMSE approaches by exploiting the intrinsic smoothness characteristic of natural images. Experimental results demonstrate a considerable performance gain compared with the MAP and MMSE approaches.
Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features
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Sirinoot Boonsuk
2014-01-01
Full Text Available Spoken language recognition (SLR has been of increasing interest in multilingual speech recognition for identifying the languages of speech utterances. Most existing SLR approaches apply statistical modeling techniques with acoustic and phonotactic features. Among the popular approaches, the acoustic approach has become of greater interest than others because it does not require any prior language-specific knowledge. Previous research on the acoustic approach has shown less interest in applying linguistic knowledge; it was only used as supplementary features, while the current state-of-the-art system assumes independency among features. This paper proposes an SLR system based on the latent-dynamic conditional random field (LDCRF model using phonological features (PFs. We use PFs to represent acoustic characteristics and linguistic knowledge. The LDCRF model was employed to capture the dynamics of the PFs sequences for language classification. Baseline systems were conducted to evaluate the features and methods including Gaussian mixture model (GMM based systems using PFs, GMM using cepstral features, and the CRF model using PFs. Evaluated on the NIST LRE 2007 corpus, the proposed method showed an improvement over the baseline systems. Additionally, it showed comparable result with the acoustic system based on i-vector. This research demonstrates that utilizing PFs can enhance the performance.
Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random field modeling
Institute of Scientific and Technical Information of China (English)
Yuan Zhan; He You; Cai Fuqing
2013-01-01
Synthetic aperture radar (SAR) image is severely affected by multiplicative speckle noise, which greatly complicates the edge detection. In this paper, by incorporating the discontinuity-adaptive Markov random field (DAMRF) and maximum a posteriori (MAP) estimation criterion into edge detection, a Bayesian edge detector for SAR imagery is accordingly developed. In the pro-posed detector, the DAMRF is used as the a priori distribution of the local mean reflectivity, and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient-descent method. Four normalized ratios constructed in different directions are computed, based on which two edge strength maps (ESMs) are formed. The final edge detection result is achieved by fusing the results of two thresholded ESMs. The experimental results with synthetic and real SAR images show that the proposed detector could efficiently detect edges in SAR images, and achieve better performance than two popular detectors in terms of Pratt’s figure of merit and visual evaluation in most cases.
Singular-potential random-matrix model arising in mean-field glassy systems
Akemann, Gernot; Villamaina, Dario; Vivo, Pierpaolo
2014-06-01
We consider an invariant random matrix ensemble where the standard Gaussian potential is distorted by an additional single pole of arbitrary fixed order. Potentials with first- and second-order poles have been considered previously and found applications in quantum chaos and number theory. Here we present an application to mean-field glassy systems. We derive and solve the loop equation in the planar limit for the corresponding class of potentials. We find that the resulting mean or macroscopic spectral density is generally supported on two disconnected intervals lying on the two sides of the repulsive pole, whose edge points can be completely determined imposing the additional constraint of traceless matrices on average. For an unbounded potential with an attractive pole, we also find a possible one-cut solution for certain values of the couplings, which is ruled out when the traceless condition is imposed. Motivated by the calculation of the distribution of the spin-glass susceptibility in the Sherrington-Kirkpatrick spin-glass model, we consider in detail a second-order pole for a zero-trace model and provide the most explicit solution in this case. In the limit of a vanishing pole, we recover the standard semicircle. Working in the planar limit, our results apply to matrices with orthogonal, unitary, and symplectic invariance. Numerical simulations and an independent analytical Coulomb fluid calculation for symmetric potentials provide an excellent confirmation of our results.
Human fixation detection model in video compressed domain based on Markov random field
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.
Wang, Zhiyong; Xu, Jinbo
2011-07-01
Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE. zywang@ttic.edu; j3xu@ttic.edu Supplementary data are available at Bioinformatics online.
Analysis of tree stand horizontal structure using random point field methods
Directory of Open Access Journals (Sweden)
O. P. Sekretenko
2015-06-01
Full Text Available This paper uses the model approach to analyze the horizontal structure of forest stands. The main types of models of random point fields and statistical procedures that can be used to analyze spatial patterns of trees of uneven and even-aged stands are described. We show how modern methods of spatial statistics can be used to address one of the objectives of forestry – to clarify the laws of natural thinning of forest stand and the corresponding changes in its spatial structure over time. Studying natural forest thinning, we describe the consecutive stages of modeling: selection of the appropriate parametric model, parameter estimation and generation of point patterns in accordance with the selected model, the selection of statistical functions to describe the horizontal structure of forest stands and testing of statistical hypotheses. We show the possibilities of a specialized software package, spatstat, which is designed to meet the challenges of spatial statistics and provides software support for modern methods of analysis of spatial data. We show that a model of stand thinning that does not consider inter-tree interaction can project the size distribution of the trees properly, but the spatial pattern of the modeled stand is not quite consistent with observed data. Using data of three even-aged pine forest stands of 25, 55, and 90-years old, we demonstrate that the spatial point process models are useful for combining measurements in the forest stands of different ages to study the forest stand natural thinning.
Institute of Scientific and Technical Information of China (English)
ZHANG Ya-Nan; YAN Shi-Lei
2003-01-01
We study the thermodynamic properties of random transverse field mixed spin system in the presence ofsingle-ion anisotropy on a square lattice. By making use of the effective field theory and a cutting approximation, thedetailed phase diagrams are described and some interesting results are found under trimodal random transverse fielddistribution. A smallsingle-ion anisotropy can magnify magnetic ordering region at low temperatures and existence ofa large transverse field can assist the occurrence of reentrant phenomena. With increasing disorder, second-order phasetransitions are shown to change into first-order phase transitions. The trajectory of the tricritical point in the phasespace as a function of disorder is presented. These indicate a strong correlation with the corresponding to trimodaltransverse field distribution.
DEFF Research Database (Denmark)
Thamsborg, G; Florescu, A; Oturai, P
2005-01-01
OBJECTIVE: The investigation aimed at determining the effectiveness of pulsed electromagnetic fields (PEMF) in the treatment of osteoarthritis (OA) of the knee by conducting a randomized, double-blind, placebo-controlled clinical trial. DESIGN: The trial consisted of 2h daily treatment 5 days per...
Roeser, Robert W.; Schonert-Reichl, Kimberly A.; Jha, Amishi; Cullen, Margaret; Wallace, Linda; Wilensky, Rona; Oberle, Eva; Thomson, Kimberly; Taylor, Cynthia; Harrison, Jessica
2013-01-01
The effects of randomization to mindfulness training (MT) or to a waitlist-control condition on psychological and physiological indicators of teachers' occupational stress and burnout were examined in 2 field trials. The sample included 113 elementary and secondary school teachers (89% female) from Canada and the United States. Measures were…
Vink, R L C; Fischer, T; Binder, K
2010-11-01
In systems belonging to the universality class of the random field Ising model, the standard hyperscaling relation between critical exponents does not hold, but is replaced with a modified hyperscaling relation. As a result, standard formulations of finite-size scaling near critical points break down. In this work, the consequences of modified hyperscaling are analyzed in detail. The most striking outcome is that the free-energy cost ΔF of interface formation at the critical point is no longer a universal constant, but instead increases as a power law with system size, ΔF∝L(θ), with θ as the violation of hyperscaling critical exponent and L as the linear extension of the system. This modified behavior facilitates a number of numerical approaches that can be used to locate critical points in random field systems from finite-size simulation data. We test and confirm the approaches on two random field systems in three dimensions, namely, the random field Ising model and the demixing transition in the Widom-Rowlinson fluid with quenched obstacles.
Yan, Yuan
2017-07-13
Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
Hida, Kazuo
2006-07-01
The multiple reentrant quantum phase transitions in the S=1/2 antiferromagnetic Heisenberg chains with random bond alternation in the magnetic field are investigated by the density matrix renormalization group method combined with interchain mean field approximation. It is assumed that odd numbered bonds are antiferromagnetic with strength J and even numbered bonds can take the values JS and JW (JS > J > JW > 0) randomly with the probabilities p and 1- p, respectively. The pure version ( p=0 and 1) of this model has a spin gap but exhibits a field-induced antiferromagnetism in the presence of interchain coupling if Zeeman energy due to the magnetic field exceeds the spin gap. For 0 < p < 1, antiferromagnetism is induced by randomness at the small field region where the ground state is disordered due to the spin gap in the pure version. At the same time, this model exhibits randomness-induced plateaus at several values of magnetization. The antiferromagnetism is destroyed on the plateaus. As a consequence, we find a series of reentrant quantum phase transitions between transverse antiferromagnetic phases and disordered plateau phases with the increase of magnetic field for a moderate strength of interchain coupling. Above the main plateaus, the magnetization curve consists of a series of small plateaus and jumps between them. It is also found that antiferromagnetism is induced by infinitesimal interchain coupling at the jumps between the small plateaus. We conclude that this antiferromagnetism is supported by the mixing of low-lying excited states by the staggered interchain mean field even though the spin correlation function is short ranged in the ground state of each chain.
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 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 fields is
A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
Li, Ming; He, Zihuai; Zhang, Min; Zhan, Xiaowei; Wei, Changshuai; Elston, Robert C.; Lu, Qing
2017-01-01
With the advance of high-throughput sequencing technologies, it has become feasible to investigate the influence of the entire spectrum of sequencing variations on complex human diseases. Although association studies utilizing the new sequencing technologies hold great promise to unravel novel genetic variants, especially rare genetic variants that contribute to human diseases, the statistical analysis of high-dimensional sequencing data remains a challenge. Advanced analytical methods are in great need to facilitate high-dimensional sequencing data analyses. In this article, we propose a generalized genetic random field (GGRF) method for association analyses of sequencing data. Like other similarity-based methods (e.g., SIMreg and SKAT), the new method has the advantages of avoiding the need to specify thresholds for rare variants and allowing for testing multiple variants acting in different directions and magnitude of effects. The method is built on the generalized estimating equation framework and thus accommodates a variety of disease phenotypes (e.g., quantitative and binary phenotypes). Moreover, it has a nice asymptotic property, and can be applied to small-scale sequencing data without need for small-sample adjustment. Through simulations, we demonstrate that the proposed GGRF attains an improved or comparable power over a commonly used method, SKAT, under various disease scenarios, especially when rare variants play a significant role in disease etiology. We further illustrate GGRF with an application to a real dataset from the Dallas Heart Study. By using GGRF, we were able to detect the association of two candidate genes, ANGPTL3 and ANGPTL4, with serum triglyceride. PMID:24482034
Directory of Open Access Journals (Sweden)
Kuan Y Chang
Full Text Available Antimicrobial peptides (AMPs are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew's correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1 coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion.
Application of Markov random fields to landmine detection in ground penetrating radar data
Torrione, Peter A.; Collins, Leslie
2008-04-01
Recent advances in ground penetrating radar (GPR) design and fabrication have resulted in improved fidelity responses from relatively small, shallow-buried objects like landmines and improvised explosive devices. As the responses measured with GPR improve, more and more advanced processing techniques can be brought to bear on the problem of target identification in GPR data. From an electromagnetic point of view, the problem of target detection in GPR signal processing is reducible to inferring the presence or absence of changes in the electromagnetic properties of soils and thus the presence or absence of buried targets. Problems arise because the algorithms required for the full electromagnetic inversion of GPR signals are extremely computationally expensive, and usually rely on assumptions of electromagnetically constant transmission media; these problems typically make the real-time implementation of purely electromagnetic-inspired algorithms infeasible. On the other hand, purely statistical or signal-processing inspired approaches to target identification in GPR often lack a solid theoretical basis in the underlying physics, which is fundamental to understanding responses in GPR. In this work, we propose a model for responses in time-domain ground penetrating radar that attempts to incorporate the underlying physics of the problem, but avoids several of the issues inherent in assuming constant media with known electrical parameters by imposing a statistical model over the observed parameters of interest in A-scans - namely the signal gains, times of arrival, etc. The spatial requirements of the proposed statistical model suggests the application of Markov random field (MRF) distributions which provide expressive, but computationally simple models of spatial interactions. In this work we will explore the application of physics-based MRF's as generative models for time-domain GPR data, the pre-screening algorithms that this model motivates, and discuss how the
IMPROVING MARKOV RANDOM FIELD BASED SUPER RESOLUTION MAPPING THROUGH FUZZY PARAMETER INTEGRATION
Directory of Open Access Journals (Sweden)
D. R . Welikanna
2012-07-01
Full Text Available The objective of this study was to improve the Markov Random Field (MRF based Super Resolution Mapping (SRM technique to account for the vague land-cover interpretations (class mixture and the intermediate conditions in an urban area. The algorithm has been improved to integrate the fuzzy mean and fuzzy covariance measurements, to a MRF based SRM scheme to optimize the classification results. The technique was tested on a WORLDVIEW-2 data set, acquired over a highway construction area, in Colombo, Sri Lanka. Based on the visual interpretation of the image, three major land-cover types of this area were identified for the study; those were vegetation, soil and exposed grass and impervious surface with low medium and high albedo. The membership values for each pixel were determined from training samples through Spectral Angle Mapper (SAM technique. The compulsory fuzzy mean and the covariance measurements were derived using these membership grades, and subsequently was applied in MRF based SRM technique. The primary reference data was generated using Maximum Likelihood Classification (MLC performed on the same data which was resampled to 1m resolution. The scale factor was set to be (S = 2, to generate SRM of 1m resolution. The smoothening parameter (λ which balances the prior and likelihood energy terms were tested in the range from 0.3 to 0.9. SRM were generated using fuzzy MRF and the conventional MRF models respectively. Results suggest that the fuzzy integrated model has improved the results with an overall accuracy of 85.60% and kappa value of 0.78 between the optimal results and the reference data, while in the conventional case it was 77.81% of overall accuracy with kappa being 0.65. Among the two MRF models, fuzzy parameter integrated model shows the highest agreement with class fractions from the reference image with a smallest average _MAE (MAE, Mean Absolute Error of 0.03.
Directory of Open Access Journals (Sweden)
Gupta Anupam
2009-12-01
Full Text Available Background : Pressure ulcers are one of the most common complications in health care settings. Still there are no optimal protocols to manage the pressure ulcers. Aim : To assess the effectiveness of pulsed electromagnetic field therapy (PEMF in healing of pressure ulcers in patients with neurological disorders. Design : Randomized double blind control trial. Setting : Neurological rehabilitation department in a university research hospital. Participants : Twelve patients (M:F, 9:3 having neurological disorders, with age between 12-50 years (mean 30.16611.32 yrs and 24 pressure ulcers. Intervention : Six patients with 13 ulcers received PEMF therapy and the remaining 6 patients with 11 ulcers received sham treatment, for 30 sessions (45 minutes each using the equipment ′Pulsatron′. The frequency of PEMF was set at 1 Hz with sine waves and current intensity of 30 mili ampere. Whole body exposure was given in both the groups. Outcome Measures : Bates-Jensen wound assessment tool (BJWAT score was used as main outcome measure and scores at the end of session were compared with initial scores and analyzed. Similarly National Pressure Ulcer Advisory Panel (NPUAP scores were compared and analyzed as secondary outcome measure. Results : Thirteen ulcers were in stage IV and 11 were in stage III at the start of the study. Significant healing of ulcers was noted, BJWAT scores, in both the treatment and sham groups (P < 0.001 and 0.003 respectively at the completion of the study. However, when comparing between the groups, healing was not significant (P = 0.361. Similarly trend was noted with NPUAP scores with no significant difference between the treatment and sham groups (P = 0.649 at the completion of study. Conclusions : No significant difference in pressure ulcer healing was observed between PEMF treatment and sham group in this study.
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
Adaptive multi-GPU Exchange Monte Carlo for the 3D Random Field Ising Model
Navarro, Cristóbal A.; Huang, Wei; Deng, Youjin
2016-08-01
This work presents an adaptive multi-GPU Exchange Monte Carlo approach for the simulation of the 3D Random Field Ising Model (RFIM). The design is based on a two-level parallelization. The first level, spin-level parallelism, maps the parallel computation as optimal 3D thread-blocks that simulate blocks of spins in shared memory with minimal halo surface, assuming a constant block volume. The second level, replica-level parallelism, uses multi-GPU computation to handle the simulation of an ensemble of replicas. CUDA's concurrent kernel execution feature is used in order to fill the occupancy of each GPU with many replicas, providing a performance boost that is more notorious at the smallest values of L. In addition to the two-level parallel design, the work proposes an adaptive multi-GPU approach that dynamically builds a proper temperature set free of exchange bottlenecks. The strategy is based on mid-point insertions at the temperature gaps where the exchange rate is most compromised. The extra work generated by the insertions is balanced across the GPUs independently of where the mid-point insertions were performed. Performance results show that spin-level performance is approximately two orders of magnitude faster than a single-core CPU version and one order of magnitude faster than a parallel multi-core CPU version running on 16-cores. Multi-GPU performance is highly convenient under a weak scaling setting, reaching up to 99 % efficiency as long as the number of GPUs and L increase together. The combination of the adaptive approach with the parallel multi-GPU design has extended our possibilities of simulation to sizes of L = 32 , 64 for a workstation with two GPUs. Sizes beyond L = 64 can eventually be studied using larger multi-GPU systems.
Spatial analysis in a Markov random field framework: The case of burning oil wells in Kuwait
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.
A stochastically fully connected conditional random field framework for super resolution OCT
Boroomand, A.; Tan, B.; Wong, A.; Bizheva, K.
2017-02-01
A number of factors can degrade the resolution and contrast of OCT images, such as: (1) changes of the OCT pointspread function (PSF) resulting from wavelength dependent scattering and absorption of light along the imaging depth (2) speckle noise, as well as (3) motion artifacts. We propose a new Super Resolution OCT (SR OCT) imaging framework that takes advantage of a Stochastically Fully Connected Conditional Random Field (SF-CRF) model to generate a Super Resolved OCT (SR OCT) image of higher quality from a set of Low-Resolution OCT (LR OCT) images. The proposed SF-CRF SR OCT imaging is able to simultaneously compensate for all of the factors mentioned above, that degrade the OCT image quality, using a unified computational framework. The proposed SF-CRF SR OCT imaging framework was tested on a set of simulated LR human retinal OCT images generated from a high resolution, high contrast retinal image, and on a set of in-vivo, high resolution, high contrast rat retinal OCT images. The reconstructed SR OCT images show considerably higher spatial resolution, less speckle noise and higher contrast compared to other tested methods. Visual assessment of the results demonstrated the usefulness of the proposed approach in better preservation of fine details and structures of the imaged sample, retaining biological tissue boundaries while reducing speckle noise using a unified computational framework. Quantitative evaluation using both Contrast to Noise Ratio (CNR) and Edge Preservation (EP) parameter also showed superior performance of the proposed SF-CRF SR OCT approach compared to other image processing approaches.
Pradhan, M.; Suryadarma, D.; Beatty, A.; Wong, M.; Alishjabana, A.; Gaduh, A.
2011-01-01
This study evaluates the effect of four randomized interventions aimed at strengthening school committees, and subsequently improving learning outcomes, in public primary schools in Indonesia. All study schools were randomly allocated to either a control group receiving no intervention, or to treatm
Khrennikov, Andrei
2014-11-01
This paper is a contribution to the project "emergent quantum mechanics" unifying a variety of attempts to treat quantum mechanics (QMs) as emergent from other theories pretending on finer descriptions of quantum phenomena. More concretely it is about an attempt to model detection probabilities predicted by QM for single photon states by using classical random fields interacting with detectors of the threshold type. Continuous field model, prequantum classical statistical field theory (PCSFT), was developed in recent years and its predictions about probabilities and correlations match well with QM. The main problem is to develop the corresponding measurement theory which would describe the transition from continuous fields to discrete events, "clicks of detectors". Some success was achieved and the click-probabilities for quantum observables can be derived from PCSFT by modeling interaction of fields with the threshold type detectors. However, already for the coefficient of second-order coherence g2(0) calculations are too complicated and only an estimation of g2(0) was obtained. In this paper, we present results of numerical simulation based on PCSFT and modeling of interaction with threshold type detectors. The "prequantum random field" interacting with a detector is modeled as the Brownian motion in the space of classical fields (Wiener process in complex Hilbert space). Simulation for g2(0) shows that this coefficient approaches zero with increase of the number of detections.
Albert, Lena; Rottensteiner, Franz; Heipke, Christian
2017-08-01
We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intra-layer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by inter-layer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the super-pixels has an influence on the level of detail of the classification result, but also on the
Limitations of a True Random Number Generator in a Field Programmable Gate Array
2007-12-01
more hardware than addition. The first two numbers for a sequence from a Fibonacci generator must be supplied and generally m is usually chosen as...congruential generator with a lagged Fibonacci generator. The two generators should produce a good random sequence and tested to make sure the resulting...9 Figure 3 Von Neumann and Xor Corrector for Unbiasing Random Sequences ............. 10 Figure 4 Four Bit Linear
Zi, Bin; Zhou, Bin
2016-07-01
For the prediction of dynamic response field of the luffing system of an automobile crane (LSOAAC) with random and interval parameters, a hybrid uncertain model is introduced. In the hybrid uncertain model, the parameters with certain probability distribution are modeled as random variables, whereas, the parameters with lower and upper bounds are modeled as interval variables instead of given precise values. Based on the hybrid uncertain model, the hybrid uncertain dynamic response equilibrium equation, in which different random and interval parameters are simultaneously included in input and output terms, is constructed. Then a modified hybrid uncertain analysis method (MHUAM) is proposed. In the MHUAM, based on random interval perturbation method, the first-order Taylor series expansion and the first-order Neumann series, the dynamic response expression of the LSOAAC is developed. Moreover, the mathematical characteristics of extrema of bounds of dynamic response are determined by random interval moment method and monotonic analysis technique. Compared with the hybrid Monte Carlo method (HMCM) and interval perturbation method (IPM), numerical results show the feasibility and efficiency of the MHUAM for solving the hybrid LSOAAC problems. The effects of different uncertain models and parameters on the LSOAAC response field are also investigated deeply, and numerical results indicate that the impact made by the randomness in the thrust of the luffing cylinder F is larger than that made by the gravity of the weight in suspension Q . In addition, the impact made by the uncertainty in the displacement between the lower end of the lifting arm and the luffing cylinder a is larger than that made by the length of the lifting arm L .
Lucas, Andrew; Schalm, Koenraad
2014-01-01
We compute the direct current resistivity of a scale-invariant, $d$-dimensional strange metal with dynamic critical exponent $z$ and hyperscaling-violating exponent $\\theta$, weakly perturbed by a scalar operator coupled to random-field disorder that locally breaks a $\\mathbb{Z}_2$ symmetry. Independent calculations via Einstein-Maxwell-Dilaton holography and memory matrix methods lead to the same results. We show that random field disorder has a strong effect on resistivity: charge carriers in the infrared are easily depleted, as the relaxation time for momentum is surprisingly small. In the course of our holographic calculation we introduce a non-trivial dilaton coupling to the disordered scalar, allowing us to study a strongly-coupled scale invariant theory with $\\theta \
Metwally, N.; Eleuch, H.; Obada, A.-S.
2016-10-01
The entangled behavior of different dimensional systems driven by classical external random field is investigated. The amount of the survival entanglement between the components of each system is quantified. There are different behaviors of entanglement that come into view decay, sudden death, sudden birth and long-lived entanglement. The maximum entangled states which can be generated from any of theses suggested systems are much fragile than the partially entangled ones. The systems of larger dimensions are more robust than those of smaller dimensions systems, where the entanglement decay smoothly, gradually and may vanish for a very short time. For the class of $2\\times 3$ dimensional system, the one parameter family is found to be more robust than the two parameters family. Although the entanglement of driven $ 2 \\times 3$ dimensional system is very sensitive to the classical external random field, one can use them to generate a long-lived entanglement.
Directory of Open Access Journals (Sweden)
Phil Diamond
2003-01-01
Full Text Available Sensitivity of output of a linear operator to its input can be quantified in various ways. In Control Theory, the input is usually interpreted as disturbance and the output is to be minimized in some sense. In stochastic worst-case design settings, the disturbance is considered random with imprecisely known probability distribution. The prior set of probability measures can be chosen so as to quantify how far the disturbance deviates from the white-noise hypothesis of Linear Quadratic Gaussian control. Such deviation can be measured by the minimal Kullback-Leibler informational divergence from the Gaussian distributions with zero mean and scalar covariance matrices. The resulting anisotropy functional is defined for finite power random vectors. Originally, anisotropy was introduced for directionally generic random vectors as the relative entropy of the normalized vector with respect to the uniform distribution on the unit sphere. The associated a-anisotropic norm of a matrix is then its maximum root mean square or average energy gain with respect to finite power or directionally generic inputs whose anisotropy is bounded above by a≥0. We give a systematic comparison of the anisotropy functionals and the associated norms. These are considered for unboundedly growing fragments of homogeneous Gaussian random fields on multidimensional integer lattice to yield mean anisotropy. Correspondingly, the anisotropic norms of finite matrices are extended to bounded linear translation invariant operators over such fields.
Brouard, Olivier; Delannay, Fabrice; Ricordel, Vincent; Barba, Dominique
2008-01-01
International audience; In this paper, we proposed a Markov Random field sequence segmentation and regions tracking model, which aims at combining color, texture, and motion features. First a motion-based segmentation is realized. The global motion of the video sequence is estimated and compensated. From the remaining motion information, the motion segmentation is achieved. Then, we use a Markovian approach to update and track over time the video objects. By video object, we mean typically, a...
Roters, L; Lübeck, S; Usadel, K D
2002-12-01
We investigate the depinning transition for driven interfaces in the random-field Ising model for various dimensions. We consider the order parameter as a function of the control parameter (driving field) and examine the effect of thermal fluctuations. Although thermal fluctuations drive the system away from criticality, the order parameter obeys a certain scaling law for sufficiently low temperatures and the corresponding exponents are determined. Our results suggest that the so-called upper critical dimension of the depinning transition is five and that the systems belongs to the universality class of the quenched Edward-Wilkinson equation.
Fu, Chuan-Ji; Zhu, Qin-Sheng; Wu, Shao-Yi
2010-06-01
Based on algebraic dynamics and the concept of the concurrence of the entanglement, we investigate the evolutive properties of the two-qubit entanglement that formed by Heisenberg XXX models under a time-depending external held. For this system, the property of the concurrence that is only dependent on the coupling constant J and total values of the external field is proved. Furthermore, we found that the thermal concurrence of the system under a static random external field is a function of the coupling constant J, temperature T, and the magnitude of external held.
Energy Technology Data Exchange (ETDEWEB)
Mooney, V. (Univ. of California, Irvine (USA))
1990-07-01
A randomized double-blind prospective study of pulsed electromagnetic fields for lumbar interbody fusions was performed on 195 subjects. There were 98 subjects in the active group and 97 subjects in the placebo group. A brace containing equipment to induce an electromagnetic field was applied to patients undergoing interbody fusion in the active group, and a sham brace was used in the control group. In the active group there was a 92% success rate, while the control group had a 65% success rate (P greater than 0.005). The effectiveness of bone graft stimulation with the device is thus established.
Wan, W. M. V.; Lee, H. C.; Hui, P. M.; Yu, K. W.
1996-08-01
The effective response of random media consisting of two different kinds of strongly nonlinear materials with strong power-law nonlinearity is studied. Each component satisfies current density and electric-field relation of the form J=χ\\|E\\|βE. A simple self-consistent mean-field theory, which leads to a simple way in determining the average local electric field in each constituent, is introduced. Each component is assumed to have a conductivity depending on the averaged local electric field. The averaged local electric field is then determined self-consistently. Numerical simulations of the system are carried out on random nonlinear resistor networks. Theoretical results are compared with simulation data, and excellent agreements are found. Results are also compared with the Hashin-Shtrikman lower bound proposed by Ponte Castaneda et al. [Phys. Rev. B 46, 4387 (1992)]. It is found that the present theory, at small contrasts of χ between the two components, gives a result identical to that of Ponte Castaneda et al. up to second order of the contrast. The crossover and scaling behavior of the effective response near the percolation threshold as suggested by the present theory are discussed and demonstrated.
Aftabuzzaman, Md; Helal, Md Al; Dec, Jan; Kleemann, Wolfgang; Kojima, Seiji
2017-10-01
The elastic properties of uniaxial relaxor Sr x Ba1‑ x Nb2O6 (x = 0.70, SBN70) single crystals with strong random fields (RFs) were studied by Brillouin scattering spectroscopy as functions of temperature and external electric field along the [001] direction. A remarkable diffuseness of a ferroelectric phase transition was observed both on zero field heating and zero field cooling. The analysis of elastic anomaly shows the stretched critical slowing down of polar nanoregions (PNRs). Under 3.0 kV/cm, a complete alignment of nanodomains and an enhancement of the long-range ferroelectric order were observed below the Curie temperature T C = 23 °C. The alignment of quasistatic PNRs above T C was also observed under a sufficiently strong electric field. In a field-dependent measurement, a mixed state consisting of field-induced macrodomains and nanodomains caused by RFs was observed at 3.4 kV/cm. This mixed state persisted up to 9.0 kV/cm due to the incomplete switching of nanodomains to the macro/single domain state.
Ismail, G.; Bannora, K.; Hassan, S.
2004-12-01
A one-dimensional system of six coupled random field Ising spins is studied by Glauber dynamics. The nearest-neighbour and next-nearest-neighbour interactions are taken into consideration. Two distributions of random fields (RF) - binary and Gaussian distribution - are investigated. We consider four cases of exchange couplings: ferro-ferromagnetic (F-F), ferro-antiferromagnetic (F-AF), antiferro-ferromagnetic (AF-F) and antiferro-antiferromagnetic (AF-AF). The system is fully frustrated, undergoes a zero-temperature phase transition and has multiple local energy minima in both distributions. The dynamics of the four systems are solved exactly in both distributions of RF. The effects of random fields are discussed. The number of diverging relaxation times is equal to the number of energy minima minus one. The longest relaxation times verify the Arrhenius law with energy barrier determined by the energy needed to invert the ground-state spin configuration. At low temperatures, according to the Arrhenius law, the spectrum of relaxation times shows a double-peak distribution on a logarithmic scale. In the Gaussian distribution of RF the energy-barrier distribution is continuous while it is quasi-discrete in the binary distribution.
van Nierop, Lotte E; Slottje, Pauline; Kingma, Herman; Kromhout, Hans
2013-07-01
We assessed postural body sway performance after exposure to movement induced time-varying magnetic fields in the static magnetic stray field in front of a 7 Tesla (T) magnetic resonance imaging scanner. Using a double blind randomized crossover design, 30 healthy volunteers performed two balance tasks (i.e., standing with eyes closed and feet in parallel and then in tandem position) after standardized head movements in a sham, low exposure (on average 0.24 T static magnetic stray field and 0.49 T·s(-1) time-varying magnetic field) and high exposure condition (0.37 T and 0.70 T·s(-1)). Personal exposure to static magnetic stray fields and time-varying magnetic fields was measured with a personal dosimeter. Postural body sway was expressed in sway path, area, and velocity. Mixed-effects model regression analysis showed that postural body sway in the parallel task was negatively affected (P static magnetic stray field and time-varying magnetic field exposure. In addition, practical safety implications of these findings, e.g., for surgeons and others working near magnetic resonance imaging scanners need to be investigated.
Thermal vacancies in random alloys in the single-site mean-field approximation
Ruban, A. V.
2016-04-01
A formalism for the vacancy formation energies in random alloys within the single-site mean-filed approximation, where vacancy-vacancy interaction is neglected, is outlined. It is shown that the alloy configurational entropy can substantially reduce the concentration of vacancies at high temperatures. The energetics of vacancies in random Cu0.5Ni0.5 alloy is considered as a numerical example illustrating the developed formalism. It is shown that the effective formation energy increases with temperature, however, in this particular system it is still below the mean value of the vacancy formation energy, which would correspond to the vacancy formation energy in a homogeneous model of a random alloy, such as given by the coherent potential approximation.
Stochastic modeling and vibration analysis of rotating beams considering geometric random fields
Choi, Chan Kyu; Yoo, Hong Hee
2017-02-01
Geometric parameters such as the thickness and width of a beam are random for various reasons including manufacturing tolerance and operation wear. Due to these random parameter properties, the vibration characteristics of the structure are also random. In this paper, we derive equations of motion to conduct stochastic vibration analysis of a rotating beam using the assumed mode method and stochastic spectral method. The accuracy of the proposed method is first verified by comparing analysis results to those obtained with Monte-Carlo simulation (MCS). The efficiency of the proposed method is then compared to that of MCS. Finally, probability densities of various modal and transient response characteristics of rotating beams are obtained with the proposed method.
Thermal vacancies in random alloys in the single-site mean-field approximation
Ruban, Andrei V
2015-01-01
A formalism for the vacancy formation energies in random alloys is outlined within the single-site mean-filed approximation where vacancy-vacancy interaction is neglected. It is shown that alloy entropy (without vacancies) can substantially reduce the concentration of vacancies at high temperatures. The energetics of vacancies in random Cu_0.5Ni_0.5 alloy is considered as a numerical example illustrating the developed formalism. It is shown that the effective formation energy is increases with temperature, however, in this particular system it is still below the mean value of the vacancy formation energy due to a large dispersion of the local vacancy formation energies.
Mean-field and non-mean-field behaviors in scale-free networks with random Boolean dynamics
Energy Technology Data Exchange (ETDEWEB)
Castro e Silva, A [Departamento de Fisica, Universidade Federal de Ouro Preto, Campus Universitario, 35.400-000 Ouro Preto, Minas Gerais (Brazil); Kamphorst Leal da Silva, J, E-mail: alcidescs@gmail.co, E-mail: jaff@fisica.ufmg.b [Departamento de Fisica, Universidade Federal de Minas Gerais, Caixa Postal 702, 30.161-970, Belo Horizonte, Minas Gerais (Brazil)
2010-06-04
We study two types of simplified Boolean dynamics in scale-free networks, both with a synchronous update. Assigning only the Boolean functions AND and XOR to the nodes with probabilities 1 - p and p, respectively, we are able to analyze the density of 1's and the Hamming distance on the network by numerical simulations and by a mean-field approximation (annealed approximation). We show that the behavior is quite different if the node always enters in the dynamics as its own input (self-regulation) or not. The same conclusion holds for the Kauffman NK model. Moreover, the simulation results and the mean-field ones (i) agree well when there is no self-regulation and (ii) disagree for small p when self-regulation is present in the model.
Mean-Field and Non-Mean-Field Behaviors in Scale-free Networks with Random Boolean Dynamics
Silva, A Castro e
2009-01-01
We study two types of simplified Boolean dynamics over scale-free networks, both with synchronous update. Assigning only Boolean functions AND and XOR to the nodes with probability $1-p$ and $p$, respectively, we are able to analyze the density of 1's and the Hamming distance on the network by numerical simulations and by a mean-field approximation (annealed approximation). We show that the behavior is quite different if the node always enters in the dynamic as its own input (self-regulation) or not. The same conclusion holds for the Kauffman KN model. Moreover, the simulation results and the mean-field ones (i) agree well when there is no self-regulation, and (ii) disagree for small $p$ when self-regulation is present in the model.
The Covariance of Scalar Fields Scattered by Pressure-Release Randomly Rough Surfaces.
1987-12-01
The Mean Scattered Pressure .- The surfaces to be dealt with will be random stationary, ’. described by "(x",y"). "(x",y") is a sample of a...developed in Chapter 3 when the surface involved is the fully developed, non-directional ocean spectrum -see Appendix. For convience we list the most
Explaining Feast or Famine in Randomized Field Trials: Medical Science and Criminology Compared.
Shepherd, Jonathan P.
2003-01-01
Discusses the contrast between the frequency of randomized clinical trials in the health sciences and the relative famine of such studies in criminology. Attributes this difference to the contexts in which research is done and the difference in the status of situational research in the two disciplines. (SLD)
Explaining Feast or Famine in Randomized Field Trials: Medical Science and Criminology Compared.
Shepherd, Jonathan P.
2003-01-01
Discusses the contrast between the frequency of randomized clinical trials in the health sciences and the relative famine of such studies in criminology. Attributes this difference to the contexts in which research is done and the difference in the status of situational research in the two disciplines. (SLD)
A portable high-quality random number generator for lattice field theory simulations
Lüscher, Martin
1994-01-01
The theory underlying a proposed random number generator for numerical simulations in elementary particle physics and statistical mechanics is discussed. The generator is based on an algorithm introduced by Marsaglia and Zaman, with an important added feature leading to demonstrably good statistical properties. It can be implemented exactly on any computer complying with the IEEE--754 standard for single precision floating point arithmetic.
A Portable High-Quality Random Number Generator for Lattice Field Theory Simulations
Luescher, Martin
1993-01-01
The theory underlying a proposed random number generator for numerical simulations in elementary particle physics and statistical mechanics is discussed. The generator is based on an algorithm introduced by Marsaglia and Zaman, with an important added feature leading to demonstrably good statistical properties. It can be implemented exactly on any computer complying with the IEEE--754 standard for single precision floating point arithmetic.
Asadollahi, Malihe; Jabraeili, Mahnaz; Mahallei, Majid; Asgari Jafarabadi, Mohammad; Ebrahimi, Sakine
2016-01-01
Introduction: Hospitalization in neonatal intensive care unit may leads to many stresses for premature infants. Since premature infants cannot properly process stressors, identifying interventions that reduce the stress level for them is seems necessary. The aim of present study was to compare the effects of Field massage and Gentle Human Touch (GHT) techniques on the urine level of cortisol, as an indicator of stress in preterm infants. Methods: This randomized, controlled clinical trial was carried out in Al-Zahra hospital, Tabriz. A total of 84 premature infants were randomly assigned into three groups. First groups were touched by their mothers three times a day (15 minutes in each session) for 5 days by GHT technique. The second group was received 15 minutes Field massage with sunflower oil three times a day by their mothers for 5 days. The third group received routine care. In all groups, 24-hours urine samples were collected in the first and sixth day after the intervention and analyzed for cortisol level. Data were analyzed by SPSS software. Results: There were significant differences between mean of changes in cortisol level between GHT and control groups and Field massage and control groups (0.026). Conclusion: Although the massage with Field technique resulted in a significant reduction in blood cortisol level, but the GHT technique have also a similar effect. So, both methods are recommended for decreasing of stress in preterm infants. PMID:27752484
Waldorp, Lourens J
2016-01-01
It was recently shown how graphs can be used to provide descriptions of psychopathologies, where symptoms of, say, depression, affect each other and certain configurations determine whether someone could fall into a sudden depression. To analyse changes over time and characterise possible future behaviour is rather difficult for large graphs. We describe the dynamics of networks using one-dimensional discrete time dynamical systems theory obtained from a mean field approach to (elementary) probabilistic cellular automata (PCA). Often the mean field approach is used on a regular graph (a grid or torus) where each node has the same number of edges and the same probability of becoming active. We show that we can use variations of the mean field of the grid to describe the dynamics of the PCA on a random and small-world graph. Bifurcation diagrams for the mean field of the grid, random, and small-world graphs indicate possible phase transitions for certain parameter settings. Extensive simulations indicate for di...
Institute of Scientific and Technical Information of China (English)
Xinguo Yang; Tianlong Wu; Xu Cheng
2008-01-01
The authors investigated the heterogeneous size patterns and dynamic growth of the ramet population of Panicum virgatum, a clonal caespitose plant, limited to the space occupied by a ramet bunch and the time of the ramet yearly life cycle, to understand the ecology of clonal caespitose plants in the field, where the ramet bunch generally consisted of more than one genet. Dynamic life tables for ramet populations were established by the replacement of living ramets at the present time with "dead" ones in past time. These tables revealed stable coexisting patterns of isometric and allometric growing processes of ramets in mass and height respectively, which approximately followed the historic trajectory of a density-independent population. The ecology of clonal caespitose plants is further discussed based on the competitively random growth of ramet individuals, including the scale of foraging behavior. In the field, the ramet population ecol-ogy of switchgrass may be a statistical result of competitively random growth of ramet individuals. The foraging behavior of a ramet population could then be presented as a process in which ramet individuals competed with each other for light and grew randomly, whileat the same time a relatively stable dynamic growth pattern was apparent at the level of the ramet population, and the functional leaves were placed properly in time and space.
Seismic spatial effects on long-span bridge response in nonstationary inhomogeneous random fields
Jiahao, Lin; Yahui, Zhang; Yan, Zhao
2005-06-01
The long-span bridge response to nonstationary multiple seismic random excitations is investigated using the PEM (pseudo excitation method). This method transforms the nonstationary random response analysis into ordinary direct dynamic analysis, and therefore, the analysis can be solved conveniently using the Newmark, Wilson-θ schemes or the precise integration method. Numerical results of the seismic response for an actual long-span bridge using the proposed PEM are given and compared with the results based on the conventional stationary analysis. From the numerical comparisons, it was found that both the seismic spatial effect and the nonstationary effect are quite important, and that both stationary and nonstationary seismic analysis should pay special attention to the wave passage effect.
Institute of Scientific and Technical Information of China (English)
林家浩; 张亚辉; 赵岩
2004-01-01
The seismic analysis of long-span bridges subjected to multiple ground excitations is an important problem.The conventional response spectrum method neglects the spatial effects of ground motion, and therefore may result in questionable conclusions. The random vibration approach has been regarded as more reliable. Unfortunately, so far,computational difficulties have not yet been satisfactorily resolved. In this paper, an accurate and efficient random vibration approach - pseudo excitation method (PEM), by which the above difficulties are overcome, is presented. It has been successfully used in the three dimensional seismic analysis of a number of long-span bridges with thousands of degrees of freedom and dozens of supports. The numerical results of a typical bridge show that the seismic spatial etfects, particularly the wave passage effect, are sometimes quite important in evaluating the safety of long-span bridges.
Seismic spatial effects on long-span bridge response in nonstationary inhomogeneous random fields
Institute of Scientific and Technical Information of China (English)
Lin Jiahao; Zhang Yahui; Zhao Yan
2005-01-01
The long-span bridge response to nonstationary multiple seismic random excitations is investigated using the PEM (pseudo excitation method). This method transforms the nonstationary random response analysis into ordinary direct dynamic analysis, and therefore, the analysis can be solved conveniently using the Newmark, Wilson-θ schemes or the precise integration method. Numerical results of the seismic response for an actual long-span bridge using the proposed PEM are given and compared with the results based on the conventional stationary analysis. From the numerical comparisons, it was found that both the seismic spatial effect and the nonstationary effect are quite important, and that both stationary and nonstationary seismic analysis should pay special attention to the wave passage effect.
Jiahao, Lin; Yahui, Zhang; Yan, Zhao
2004-12-01
The seismic analysis of long-span bridges subjected to multiple ground excitations is an important problem. The conventional response spectrum method neglects the spatial effects of ground motion, and therefore may result in questionable conclusions. The random vibration approach has been regarded as more reliable. Unfortunately, so far, computational difficulties have not yet been satisfactorily resolved. In this paper, an accurate and efficient random vibration approach — pseudo excitation method (PEM), by which the above difficulties are overcome, is presented. It has been successfully used in the three dimensional seismic analysis of a number of long-span bridges with thousands of degrees of freedom and dozens of supports. The numerical results of a typical bridge show that the seismic spatial effects, particularly the wave passage effect, are sometimes quite important in evaluating the safety of long-span bridges.
A portable high-quality random number generator for lattice field theory simulations
Lüscher, Martin
1994-02-01
The theory underlying a proposed random number generator for numerical simulations in elementary particle physics and statistical mechanics is discussed. The generator is based on an algorithm introduced by Marsaglia and Zaman, with an important added feature leading to demonstrably good statistical properties. It can be implemented exactly on any computer complying with the IEEE-754 standard for single-precision floating-point arithmetic.
Beardslee, Joseph A; Sadtler, Bryce; Lewis, Nathan S
2012-11-27
External magnetic fields have been used to vertically align ensembles of silicon microwires coated with ferromagnetic nickel films. X-ray diffraction and image analysis techniques were used to quantify the degree of vertical orientation of the microwires. The degree of vertical alignment and the minimum field strength required for alignment were evaluated as a function of the wire length, coating thickness, magnetic history, and substrate surface properties. Nearly 100% of 100 μm long, 2 μm diameter, Si microwires that had been coated with 300 nm of Ni could be vertically aligned by a 300 G magnetic field. For wires ranging from 40 to 60 μm in length, as the length of the wire increased, a higher degree of alignment was observed at lower field strengths, consistent with an increase in the available magnetic torque. Microwires that had been exposed to a magnetic sweep up to 300 G remained magnetized and, therefore, aligned more readily during subsequent magnetic field alignment sweeps. Alignment of the Ni-coated Si microwires occurred at lower field strengths on hydrophilic Si substrates than on hydrophobic Si substrates. The magnetic field alignment approach provides a pathway for the directed assembly of solution-grown semiconductor wires into vertical arrays, with potential applications in solar cells as well as in other electronic devices that utilize nano- and microscale components as active elements.
Directory of Open Access Journals (Sweden)
Zehr Emilie S
2012-06-01
Full Text Available Abstract Background Haemophilus parasuis is the causative agent of Glässer’s disease and is a pathogen of swine in high-health status herds. Reports on serotyping of field strains from outbreaks describe that approximately 30% of them are nontypeable and therefore cannot be traced. Molecular typing methods have been used as alternatives to serotyping. This study was done to compare random amplified polymorphic DNA (RAPD profiles and whole cell protein (WCP lysate profiles as methods for distinguishing H. parasuis reference strains and field isolates. Results The DNA and WCP lysate profiles of 15 reference strains and 31 field isolates of H. parasuis were analyzed using the Dice and neighbor joining algorithms. The results revealed unique and reproducible DNA and protein profiles among the reference strains and field isolates studied. Simpson’s index of diversity showed significant discrimination between isolates when three 10mer primers were combined for the RAPD method and also when both the RAPD and WCP lysate typing methods were combined. Conclusions The RAPD profiles seen among the reference strains and field isolates did not appear to change over time which may reflect a lack of DNA mutations in the genes of the samples. The recent field isolates had different WCP lysate profiles than the reference strains, possibly because the number of passages of the type strains may affect their protein expression.
Mödden, Claudia; Behrens, Marion; Damke, Iris; Eilers, Norbert; Kastrup, Andreas; Hildebrandt, Helmut
2012-06-01
Compensatory and restorative treatments have been developed to improve visual field defects after stroke. However, no controlled trials have compared these interventions with standard occupational therapy (OT). A total of 45 stroke participants with visual field defect admitted for inpatient rehabilitation were randomized to restorative computerized training (RT) using computer-based stimulation of border areas of their visual field defects or to a computer-based compensatory therapy (CT) teaching a visual search strategy. OT, in which different compensation strategies were used to train for activities of daily living, served as standard treatment for the active control group. Each treatment group received 15 single sessions of 30 minutes distributed over 3 weeks. The primary outcome measures were visual field expansion for RT, visual search performance for CT, and reading performance for both treatments. Visual conjunction search, alertness, and the Barthel Index were secondary outcomes. Compared with OT, CT resulted in a better visual search performance, and RT did not result in a larger expansion of the visual field. Intragroup pre-post comparisons demonstrated that CT improved all defined outcome parameters and RT several, whereas OT only improved one. CT improved functional deficits after visual field loss compared with standard OT and may be the intervention of choice during inpatient rehabilitation. A larger trial that includes lesion location in the analysis is recommended.
Brémaud, Pierre
2017-01-01
The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book. .
Pruemper, G
2007-01-01
In molecular photofragmentation processes by soft X-rays, a number of ionic fragments can be produced, each having a different abundance and correlation with the emitted electron kinetic energy. For investigating these fragmentation processes, electron-ion and electron-ion-ion coincidence experiments, in which the kinetic energy of electrons are analyzed using an electrostatic analyzer while the mass of the ions is analyzed using a pulsed electric field, are very powerful. For such measurements, however, the contribution of random coincidences is substantial and affects the data in a non-trivial way. Simple intuitive subtraction methods cannot be applied. In the present paper, we describe these electron-ion and electron-ion-ion coincidence experiments together with a subtraction method for the contribution from random coincidences. We provide a comprehensive set of equations for the data treatment, including equations for the calculation of error-bars. We demonstrate the method by applying it to the fragmenta...
Mixed spin Ising model with four-spin interaction and random crystal field
Energy Technology Data Exchange (ETDEWEB)
Benayad, N., E-mail: n.benayad@fsac.ac.ma [Groupe de Mecanique Statistique, Laboratoire de physique theorique et appliquee, Faculte des sciences-Aien Chock, Universite Hassan II-Casablanca, B.P 5366 Maarif, Casablanca 20100 (Morocco); Laboratoire de physique des hautes energies et de la matiere condensee, Faculte des sciences-Aien Chock, Universite Hassan II-Casablanca, B.P 5366 Maarif, Casablanca 20100 (Morocco); Ghliyem, M. [Groupe de Mecanique Statistique, Laboratoire de physique theorique et appliquee, Faculte des sciences-Aien Chock, Universite Hassan II-Casablanca, B.P 5366 Maarif, Casablanca 20100 (Morocco); Laboratoire de physique des hautes energies et de la matiere condensee, Faculte des sciences-Aien Chock, Universite Hassan II-Casablanca, B.P 5366 Maarif, Casablanca 20100 (Morocco)
2012-01-01
The effects of fluctuations of the crystal field on the phase diagram of the mixed spin-1/2 and spin-1 Ising model with four-spin interactions are investigated within the finite cluster approximation based on a single-site cluster theory. The state equations are derived for the two-dimensional square lattice. It has been found that the system exhibits a variety of interesting features resulting from the fluctuation of the crystal field interactions. In particular, for low mean value D of the crystal field, the critical temperature is not very sensitive to fluctuations and all transitions are of second order for any value of the four-spin interactions. But for relatively high D, the transition temperature depends on the fluctuation of the crystal field, and the system undergoes tricritical behaviour for any strength of the four-spin interactions. We have also found that the model may exhibit reentrance for appropriate values of the system parameters.
Adiabatic hydrodynamic modes in dielectric environment in a random electric field
Stupka, Anton
2016-01-01
Dielectric is considered in the electric field that has equal to zero the first moment and different from zero the second moment of strength in an equilibrium. The equations of ideal hydrodynamics are obtained in such a field for the case of the neglect of dissipative effects. A new variable - the second moment of electric field strength is included in the Euler equation. A temporal equation for this variable is obtained on the basis of Maxwell equations in the hydrodynamic approximation. Adiabatic one-dimensional waves of small amplitude are studied in this system. Proceeding from the theoretical estimation of the intracrystalline field in an ionic crystal the good consent of the obtained numerical values of transversal velocity of this wave with transversal velocity of sound for isotropic crystals of alkali halides is found.
Directory of Open Access Journals (Sweden)
M Kariuki Njenga
2015-03-01
Full Text Available BACKGROUND: Although livestock vaccination is effective in preventing Rift Valley fever (RVF epidemics, there are concerns about safety and effectiveness of the only commercially available RVF Smithburn vaccine. We conducted a randomized controlled field trial to evaluate the immunogenicity and safety of the new RVF Clone 13 vaccine, recently registered in South Africa. METHODS: In a blinded randomized controlled field trial, 404 animals (85 cattle, 168 sheep, and 151 goats in three farms in Kenya were divided into three groups. Group A included males and non-pregnant females that were randomized and assigned to two groups; one vaccinated with RVF Clone 13 and the other given placebo. Groups B included animals in 1st half of pregnancy, and group C animals in 2nd half of pregnancy, which were also randomized and either vaccinated and given placebo. Animals were monitored for one year and virus antibodies titers assessed on days 14, 28, 56, 183 and 365. RESULTS: In vaccinated goats (N = 72, 72% developed anti-RVF virus IgM antibodies and 97% neutralizing IgG antibodies. In vaccinated sheep (N = 77, 84% developed IgM and 91% neutralizing IgG antibodies. Vaccinated cattle (N = 42 did not develop IgM antibodies but 67% developed neutralizing IgG antibodies. At day 14 post-vaccination, the odds of being seropositive for IgG in the vaccine group was 3.6 (95% CI, 1.5 - 9.2 in cattle, 90.0 (95% CI, 25.1 - 579.2 in goats, and 40.0 (95% CI, 16.5 - 110.5 in sheep. Abortion was observed in one vaccinated goat but histopathologic analysis did not indicate RVF virus infection. There was no evidence of teratogenicity in vaccinated or placebo animals. CONCLUSIONS: The results suggest RVF Clone 13 vaccine is safe to use and has high (>90% immunogenicity in sheep and goats but moderate (> 65% immunogenicity in cattle.
Boutron, Isabelle; Altman, Douglas G; Hopewell, Sally; Vera-Badillo, Francisco; Tannock, Ian; Ravaud, Philippe
2014-12-20
We aimed to assess the impact of spin (ie, reporting to convince readers that the beneficial effect of the experimental treatment is greater than shown by the results) on the interpretation of results of abstracts of randomized controlled trials (RCTs) in the field of cancer. We performed a two-arm, parallel-group RCT. We selected a sample of published RCTs with statistically nonsignificant primary outcome and with spin in the abstract conclusion. Two versions of these abstracts were used-the original with spin and a rewritten version without spin. Participants were clinician corresponding authors of articles reporting RCTs, investigators of trials, and reviewers of French national grants. The primary outcome was clinicians' interpretation of the beneficial effect of the experimental treatment (0 to 10 scale). Participants were blinded to study hypothesis. Three hundred clinicians were randomly assigned using a Web-based system; 150 clinicians assessed an abstract with spin and 150 assessed an abstract without spin. For abstracts with spin, the experimental treatment was rated as being more beneficial (mean difference, 0.71; 95% CI, 0.07 to 1.35; P = .030), the trial was rated as being less rigorous (mean difference, -0.59; 95% CI, -1.13 to 0.05; P = .034), and clinicians were more interested in reading the full-text article (mean difference, 0.77; 95% CI, 0.08 to 1.47; P = .029). There was no statistically significant difference in the clinicians' rating of the importance of the study or the need to run another trial. Spin in abstracts can have an impact on clinicians' interpretation of the trial results. © 2014 by American Society of Clinical Oncology.
Stenull, O; Janssen, H K
2001-03-01
We study the multifractal moments of the current distribution in randomly diluted resistor networks near the percolation threshold. When an external current is applied between two terminals x and x(') of the network, the lth multifractal moment scales as M((l))(I)(x,x(')) approximately equal /x-x'/(psi(l)/nu), where nu is the correlation length exponent of the isotropic percolation universality class. By applying our concept of master operators [Europhys. Lett. 51, 539 (2000)] we calculate the family of multifractal exponents [psi(l)] for l>or=0 to two-loop order. We find that our result is in good agreement with numerical data for three dimensions.
Sumedha; Jana, Nabin Kumar
2017-01-01
In this paper we solve the Blume-Capel model on a complete graph in the presence of random crystal field with a distribution, P≤ft({{ Δ }i}\\right)=pδ ≤ft({{ Δ }i}- Δ \\right)+(1-p)δ ≤ft({{ Δ }i}+ Δ \\right) , using large deviation techniques. We find that the first order transition of the pure system is destroyed for 0.046 0.954) even at zero temperature.
Near Field Heat Transfer between Random Composite Materials: Applications and Limitations
Santiago, Eva Yazmin; Esquivel-Sirvent, Raul
2017-02-01
We present a theoretical study of the limits and bounds of using effective medium approximations in the calculation of the near field radiative heat transfer between a composite system made of Au nanoparticles in a SiC host and an homogeneous SiC slab. The effective dielectric function of the composite slab is calculated using three different approximations: Maxwell-Garnett, Bruggeman, and Looyenga's. In addition, we considered an empirical fit to the effective dielectric function by Grundquist and Hunderi. We show that the calculated value of the heat flux in the near field is dependent on the model, and the difference in the effective dielectric function is larger around the plasmonic response of the Au nanoparticles. This, in turn, accounts for the difference in the near field radiative heat flux. For all values of filling fractions, the Looyenga approximation gives a lower bound for the heat flux.
Near field heat transfer between random composite materials. Applications and limitations
Energy Technology Data Exchange (ETDEWEB)
Santiago, Eva Yazmin; Esquivel-Sirvent, Raul [Univ. Nacional Autonoma de Mexico (Mexico). Inst. de Fisica
2017-05-01
We present a theoretical study of the limits and bounds of using effective medium approximations in the calculation of the near field radiative heat transfer between a composite system made of Au nanoparticles in a SiC host and an homogeneous SiC slab. The effective dielectric function of the composite slab is calculated using three different approximations: Maxwell-Garnett, Bruggeman, and Looyenga's. In addition, we considered an empirical fit to the effective dielectric function by Grundquist and Hunderi. We show that the calculated value of the heat flux in the near field is dependent on the model, and the difference in the effective dielectric function is larger around the plasmonic response of the Au nanoparticles. This, in turn, accounts for the difference in the near field radiative heat flux. For all values of filling fractions, the Looyenga approximation gives a lower bound for the heat flux.
The radiative transfer of synchrotron radiation through a compressed random magnetic field
Cawthorne, T V
2014-01-01
This paper examines the radiative transfer of synchrotron radiation in the presence of a magnetic field configuration resulting from the compression of a highly disordered magnetic field. It is shown that, provided Faraday rotation and circular polarization can be neglected, the radiative transfer equations for synchrotron radiation separate for this configuration, and the intensities and polarization values for sources that are uniform on large scales can be found straightforwardly in the case where opacity is significant. Although the emission and absorption coefficients must, in general, be obtained numerically, the process is much simpler than a full numerical solution to the transfer equations. Some illustrative results are given and an interesting effect, whereby the polarization increases while the magnetic field distribution becomes less strongly confined to the plane of compression, is discussed. The results are of importance for the interpretation of polarization near the edges of lobes in radio gal...
Wang, Zhiguang; Zhang, Yue; Wang, Yaojin; Li, Yanxi; Luo, Haosu; Li, Jiefang; Viehland, Dwight
2014-08-26
Magnetization-based memories, e.g., hard drive and magnetoresistive random-access memory (MRAM), use bistable magnetic domains in patterned nanomagnets for information recording. Electric field (E) tunable magnetic anisotropy can lower the energy barrier between two distinct magnetic states, promising reduced power consumption and increased recording density. However, integration of magnetoelectric heterostructure into MRAM is a highly challenging task owing to the particular architecture requirements of each component. Here, we show an epitaxial growth of self-assembled CoFe2O4 nanostripes with bistable in-plane magnetizations on Pb(Mg,Nb)O3-PbTiO3 (PMN-PT) substrates, where the magnetic switching can be triggered by E-induced elastic strain effect. An unprecedented magnetic coercive field change of up to 600 Oe was observed with increasing E. A near 180° magnetization rotation can be activated by E in the vicinity of the magnetic coercive field. These findings might help to solve the 1/2-selection problem in traditional MRAM by providing reduced magnetic coercive field in E field selected memory cells.
Iacobelli, Giulio; Kuelske, Christof
We consider a general class of disordered mean-field models where both the spin variables and disorder variables eta take finitely many values. To investigate the size-dependence in the phase-transition regime we construct the metastate describing the probabilities to find a large system close to a
A story about estimation of a random field of boulders from incomplete seismic measurements
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
2005-01-01
deposits along the tunnel line. By use of this important distribution information and of the observed homogeneity of the seismic point source field together with the physical properties of diffraction it became possible to make the wanted prediction. During the excavation the found boulders were counted...
Estimating adhesive seed-dispersal distances : field experiments and correlated random walks
Mouissie, AM; Lengkeek, W; van Diggelen, R
1. In this study we aimed to estimate distance distributions of adhesively dispersed seeds and the factors that determine them. 2. Seed attachment and detachment were studied using field experiments with a real sheep, a sheep dummy and a cattle dummy. Seed-retention data were used in correlated
Adams, Roy J; Saleheen, Nazir; Thomaz, Edison; Parate, Abhinav; Kumar, Santosh; Marlin, Benjamin M
2016-06-01
The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance relative to a hierarchical pairwise CRF.
Wang, Yonggang; Hui, Cong; Liu, Chong; Xu, Chao
2016-04-01
The contribution of this paper is proposing a new entropy extraction mechanism based on sampling phase jitter in ring oscillators to make a high throughput true random number generator in a field programmable gate array (FPGA) practical. Starting from experimental observation and analysis of the entropy source in FPGA, a multi-phase sampling method is exploited to harvest the clock jitter with a maximum entropy and fast sampling speed. This parametrized design is implemented in a Xilinx Artix-7 FPGA, where the carry chains in the FPGA are explored to realize the precise phase shifting. The generator circuit is simple and resource-saving, so that multiple generation channels can run in parallel to scale the output throughput for specific applications. The prototype integrates 64 circuit units in the FPGA to provide a total output throughput of 7.68 Gbps, which meets the requirement of current high-speed quantum key distribution systems. The randomness evaluation, as well as its robustness to ambient temperature, confirms that the new method in a purely digital fashion can provide high-speed high-quality random bit sequences for a variety of embedded applications.
The random field model of the spatial distribution of heavy vehicle loads on long-span bridges
Chen, Zhicheng; Bao, Yuequan; Li, Hui
2016-04-01
A stochastic model based on Markov random field is proposed to model the spatial distribution of vehicle loads on longspan bridges. The bridge deck is divided into a finite set of discrete grid cells, each cell has two states according to whether the cell is occupied by the heavy vehicle load or not, then a four-neighbor lattice-structured undirected graphical model with each node corresponding to a cell state variable is proposed to model the location distribution of heavy vehicle loads on the bridge deck. The node potential is defined to quantitatively describe the randomness of node state, and the edge potential is defined to quantitatively describe the correlation of the connected node pair. The junction tree algorithm is employed to obtain the systematic solutions of inference problems of the graphical model. A marked random variable is assigned to each node to represent the amplitude of the total weight of vehicle applied on the corresponding cell of the bridge deck. The rationality of the model is validated by a Monte Carlo simulation of a learned model based on monitored data of a cable-stayed bridge.
Wang, Yonggang; Hui, Cong; Liu, Chong; Xu, Chao
2016-04-01
The contribution of this paper is proposing a new entropy extraction mechanism based on sampling phase jitter in ring oscillators to make a high throughput true random number generator in a field programmable gate array (FPGA) practical. Starting from experimental observation and analysis of the entropy source in FPGA, a multi-phase sampling method is exploited to harvest the clock jitter with a maximum entropy and fast sampling speed. This parametrized design is implemented in a Xilinx Artix-7 FPGA, where the carry chains in the FPGA are explored to realize the precise phase shifting. The generator circuit is simple and resource-saving, so that multiple generation channels can run in parallel to scale the output throughput for specific applications. The prototype integrates 64 circuit units in the FPGA to provide a total output throughput of 7.68 Gbps, which meets the requirement of current high-speed quantum key distribution systems. The randomness evaluation, as well as its robustness to ambient temperature, confirms that the new method in a purely digital fashion can provide high-speed high-quality random bit sequences for a variety of embedded applications.
N. Renders (Nicole); Y. Romling; A.F. van Belkum (Alex); H.A. Verbrugh (Henri)
1996-01-01
textabstractEighty-seven strains of Pseudomonas aeruginosa were typed by random amplification of polymorphic DNA (RAPD) and pulsed-field gel electrophoresis (PFGE) of macrorestriction fragments. Stains were clustered on the basis of interpretative criteria as presented
N. Renders (Nicole); Y. Romling; A.F. van Belkum (Alex); H.A. Verbrugh (Henri)
1996-01-01
textabstractEighty-seven strains of Pseudomonas aeruginosa were typed by random amplification of polymorphic DNA (RAPD) and pulsed-field gel electrophoresis (PFGE) of macrorestriction fragments. Stains were clustered on the basis of interpretative criteria as presented
Precise asymptotics in the law of the logarithm for random fields in Hilbert space
Institute of Scientific and Technical Information of China (English)
FU Ke-ang; ZHANG Li-xin
2007-01-01
Consider the positive d-dimensional lattice Zd+ (d≥2) with partial ordering ≤, let {XK; K∈Zd+} be i.i.d. random variables taking values in a real separable Hilbert space (H, ‖·‖) with mean zero and covariance operator ∑, and set partial sums SN =∑K≤NXK, K, N ∈ Zd+. Under some moment conditions, we obtain the precise asymptotics of a kind of weighted infinite series for partial sums SN as ε↘0 by using the truncation and approximation methods. The results are related to the convergence rates of the law of the logarithm in Hilbert space, and they also extend the results of (Gut and Spǎtaru, 2003).
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.
Li, Lijun; Lee, Inyeal; Youn, Doo-Hyeb; Kim, Gil-Ho
2017-02-01
We investigate the hopping conduction and random telegraph signal caused by various species of interface charge scatterers in a MoS2 multilayer field-effect transistor. The temperature dependence of the channel resistivity shows that at low temperatures and low carrier densities the carrier transport is via Mott variable range hopping with a hopping length changing from 41 to 80 nm. The hopping conduction was due to electron tunneling through localized band tail states formed by the scatterers located in the vicinity of the MoS2 layer. In the temperature range of 40-70 K, we observed random telegraph signal (RTS) that is caused by the capture and emission of a carrier by the interface traps that are located away from the layer. These traps form strong potential that interact with the layer and change the potential profile of the electron system. The characteristics of RTS depend strongly on gate bias and temperature, as well as the application of a magnetic field.
Gaussian Random Fields Methods for Fork-Join Network with Synchronization Constraints
2014-12-22
research papers, under review in Mathematics of Operations Research (minor revision) and Annals of Applied Probability. One paper was on the finalist...2.00 1.00 Hongyuan Lu, Guodong Pang. Heavy Traffic Limits for A Fork-Join Network In the Halfin-Whitt Regime, Annals of Applied Probability (11 2014...fellowships for further studies in science , mathematics, engineering or technology fields: Student Metrics This section only applies to graduating
Cordero-Grande, Lucilio; Vegas-Sánchez-Ferrero, Gonzalo; Casaseca-de-la-Higuera, Pablo; Alberola-López, Carlos
2012-04-01
This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.
Energy Technology Data Exchange (ETDEWEB)
Zimbardo, G.
2005-07-01
Plasma transport in the presence of turbulence depends on a variety of parameters like the fluctuation level ? B/B0, the ratio between the particle Larmor radius and the turbulence correlation lengths, and the turbulence anisotropy. In this presentation, we review the results of numerical simulations of plasma and magnetic field line transport in the case of anisotropic magnetic turbulence, for parameter values close to those of the solar wind. We assume a uniform background magnetic field B0 = B0ez and a Fourier representation for magnetic fluctuations, with wavectors forming any angle with respect to B0. The energy density spectrum is a power law, and in k space the constant amplitude surfaces are ellipsoids, described by the correlation lengths lx, ly, lz, which quantify the anisotropy of turbulence. For magnetic field lines, we find that transport perpendicular to the background field depends on the Kubo number R = ? B B0 lz lx . For small Kubo numbers, R ? 1, we find anomalous, non Gaussian transport regimes (both sub and superdiffusive) which can be described as a Levy random walk. Increasing the Kubo number, i.e., the fluctuation level ? B/B0 and/or the ratio lz/lx, we find first a quasilinear and then a percolative regime, both corresponding to Gaussian diffusion. For particles, we find that transport parallel and perpendicular to the background magnetic field heavily depends on the turbulence anisotropy and on the particle Larmor radius. For turbulence levels typical of the solar wind, ? B/B0 ? 0.5 ?1, when the ratio between the particle Larmor radius and the turbulence correlation lengths is small, anomalous regimes are found in the case lz/lx ? 1, with Levy random walk (superdiffusion) along the magnetic field and subdiffusion in the perpendicular directions. Conversely, for lz/lx > 1 normal, Gaussian diffusion is found. Increasing the ratio between the particle Larmor radius and the turbulence correlation lengths, the parallel superdiffusion is
Energy Technology Data Exchange (ETDEWEB)
Zimbardo, Gaetano [Dipartimento di Fisica, Universita della Calabria, Ponte P. Bucci, Cubo 31C, I-87036 Arcavacata di Rende (Italy)
2005-12-15
Plasma transport in the presence of turbulence depends on a variety of parameters such as the fluctuation level, {delta}B/B{sub 0}, the ratio between the particle Larmor radius and the turbulence correlation length, and the turbulence anisotropy. In this paper, we present the results of numerical simulations of plasma and magnetic field line transport in the case of anisotropic magnetic turbulence, for parameter values close to those of the solar wind. We assume a uniform background magnetic field B{sub 0} = B{sub 0} e{sub z} and a Fourier representation for magnetic fluctuations, which includes wavectors oblique with respect to B{sub 0}. The energy density spectrum is a power law, and in k space it is described by the correlation lengths l{sub x}, l{sub y}, l{sub z}, which quantify the anisotropy of turbulence. For magnetic field lines, transport perpendicular to the background field depends on the Kubo number R ({delta}B/B{sub 0}) (l{sub z}/l{sub x}). For small Kubo numbers, R << 1, anomalous, non-Gaussian transport regimes (both sub- and superdiffusive) are found, which can be described as a Levy random walk. Increasing the Kubo number, i.e. the fluctuation level, {delta}B/B{sub 0}, or the ratio l{sub z}/l{sub x}, we find first a quasilinear regime and then a percolative regime, both corresponding to Gaussian diffusion. For particles, we find that transport parallel and perpendicular to the background magnetic field depends heavily on the turbulence anisotropy and on the particle Larmor radius. For turbulence levels typical of the solar wind, {delta}B/B{sub 0}{approx_equal} 0.5-1, when the ratio between the particle Larmor radius and the turbulence correlation lengths is small, anomalous regimes are found in the case l{sub z}/l{sub x} {<=} 1, with a Levy random walk (superdiffusion) along the magnetic field and subdiffusion in the perpendicular directions. Conversely, for l{sub z}/l{sub x} > 1 normal Gaussian diffusion is found. A possible expression for
Lu, B.; Darmon, M.; Leymarie, N.; Chatillon, S.; Potel, C.
2012-05-01
In-service inspection of Sodium-Cooled Fast Reactors (SFR) requires the development of non-destructive techniques adapted to the harsh environment conditions and the examination complexity. From past experiences, ultrasonic techniques are considered as suitable candidates. The ultrasonic telemetry is a technique used to constantly insure the safe functioning of reactor inner components by determining their exact position: it consists in measuring the time of flight of the ultrasonic response obtained after propagation of a pulse emitted by a transducer and its interaction with the targets. While in-service the sodium flow creates turbulences that lead to temperature inhomogeneities, which translates into ultrasonic velocity inhomogeneities. These velocity variations could directly impact the accuracy of the target locating by introducing time of flight variations. A stochastic simulation model has been developed to calculate the propagation of ultrasonic waves in such an inhomogeneous medium. Using this approach, the travel time is randomly generated by a stochastic process whose inputs are the statistical moments of travel times known analytically. The stochastic model predicts beam deviations due to velocity inhomogeneities, which are similar to those provided by a determinist method, such as the ray method.
Explaining feast or famine in randomized field trials. Medical science and criminology compared.
Shepherd, Jonathan P
2003-06-01
A feast of randomized controlled trials (RCTs) in medical science and comparative famine in criminology can be explained in terms of cultural and structural factors. Of central importance is the context in which the evaluation of interventions is done and the difference in status of situational research in the two disciplines. Evaluation of medical interventions has traditionally been led by practitioner (clinical) academics. This is not the case in criminal justice, where theory has had higher status than intervention research. Medical science has advanced in, or closely associated with, university teaching hospitals, but links between criminology and criminal justice services are far more tenuous. The late development of situational crime prevention seems extraordinary from a medical perspective, as does the absence of university police schools in the United Kingdom and elsewhere. These structural and cultural factors explain concentration of expectation, resource, and RCT productivity in medical science. The Campbell Collaboration and the Academy of Experimental Criminology are forces which are reducing this polarization of feast and famine in RCTs. But unless scientific criminology is embedded in university schools which are responsible for the education and training of law, probation, and police practitioners, convergence in terms of RCTs and implementation of findings in practice seems unlikely.
Mualla, Firas; Scholl, Simon; Sommerfeldt, Bjorn; Maier, Andreas; Hornegger, Joachim
2013-12-01
We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.
Empirically based gamma-distributed random wall pressure field in silo
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Berntsen, Kasper Nikolaj
1999-01-01
Measurements show that silo wall pressures exhibit large fluctuations in time and space during discharge of the silo. This observation is important for the design of the silowall because spatial pressure variations may impose substantial bending moments in the silo wall that otherwise may be small...... or vanishing do to the carrying ability of the membrane forces in the silo wall. Information about the stochastic properties of this pressure variation cannot be obtained from any existing continuum model for the silo medium flowing within the confinement of the silo walls. Therefore the modeling must...... presently be tied to statistical analysis of the empirical evidence combined with simple mechanical rinciples. It is shown that an explicitely constructed gamma distribution type of field in equilibrium with itself fits well to themeasurements made in the Swedish Karpalund silo....
Critical behavior of mean-field spin glasses on a dilute random graph
Energy Technology Data Exchange (ETDEWEB)
De Sanctis, Luca [Dipartimento di Matematica e di Psicologia, Universita di Bologna, P.zza di Porta San Donato 5, 40126 Bologna (Italy); Barra, Adriano; Folli, Viola [Dipartimento di Fisica, Universita La Sapienza, P.le Aldo Moro 5, 00185 Roma (Italy)], E-mail: desanctis@dm.unibo.it, E-mail: adriano.barra@roma1.infn.it, E-mail: viola.folli@roma1.infn.it
2008-05-30
We provide a rigorous strategy to find the critical exponents of the overlaps for dilute spin glasses, in the absence of an external field. Such a strategy is based on the expansion of a suitably perturbed average of the overlaps, which is used in the formulation of the free energy as the difference between a cavity part and the derivative of the free energy itself, considered as a function of the connectivity of the model. We assume the validity of certain reasonable approximations, equivalent to assuming a second-order transition, e.g. that higher powers of overlap monomials are of smaller magnitude near the critical point, of which we do not provide a rigorous proof.
Makowski, Piotr L; Domanski, Andrzej W
2013-09-01
An efficient simulation technique is proposed for computing propagation of uniformly polarized statistically stationary fields in linear nonimage-forming systems that includes dispersion of linear birefringence to all orders. The method is based on the discrete-time Fourier transformation of modified frequency profiles of the spectral Stokes parameters. It works under the condition that all (linearly) birefringent sections present in the system are described by the same phase birefringence dispersion curve, being a monotonic function of the optical frequency within the bandwidth of the light. We demonstrate the technique as a supplement for the Mueller-Stokes matrix formalism extended to any uniformly polarized polychromatic illumination. Accuracy of its numerical implementation has been verified by using parameters of a Lyot depolarizer made of a highly birefringent and dispersive monomode photonic crystal fiber.
Directory of Open Access Journals (Sweden)
Ningzhi Li
2017-06-01
Full Text Available In vivo13C magnetic resonance spectroscopy (MRS is a unique and effective tool for studying dynamic human brain metabolism and the cycling of neurotransmitters. One of the major technical challenges for in vivo13C-MRS is the high radio frequency (RF power necessary for heteronuclear decoupling. In the common practice of in vivo13C-MRS, alkanyl carbons are detected in the spectra range of 10–65 ppm. The amplitude of decoupling pulses has to be significantly greater than the large one-bond 1H-13C scalar coupling (1JCH = 125–145 Hz. Two main proton decoupling methods have been developed: broadband stochastic decoupling and coherent composite or adiabatic pulse decoupling (e.g., WALTZ; the latter is widely used because of its efficiency and superb performance under inhomogeneous B1 field. Because the RF power required for proton decoupling increases quadratically with field strength, in vivo13C-MRS using coherent decoupling is often limited to low magnetic fields [<=4 Tesla (T] to keep the local and averaged specific absorption rate (SAR under the safety guidelines established by the International Electrotechnical Commission (IEC and the US Food and Drug Administration (FDA. Alternately, carboxylic/amide carbons are coupled to protons via weak long-range 1H-13C scalar couplings, which can be decoupled using low RF power broadband stochastic decoupling. Recently, the carboxylic/amide 13C-MRS technique using low power random RF heteronuclear decoupling was safely applied to human brain studies at 7T. Here, we review the two major decoupling methods and the carboxylic/amide 13C-MRS with low power decoupling strategy. Further decreases in RF power deposition by frequency-domain windowing and time-domain random under-sampling are also discussed. Low RF power decoupling opens the possibility of performing in vivo13C experiments of human brain at very high magnetic fields (such as 11.7T, where signal-to-noise ratio as well as spatial and temporal
Directory of Open Access Journals (Sweden)
Daniela Oliveira F. do Amaral
2014-07-01
Full Text Available Conditional Random Fields (CRF é um método probabilístico de predição estruturada que tem sido amplamente aplicado em diversas áreas, tais como a de Processamento da Linguagem Natural (PLN, incluindo o Reconhecimento de Entidades Nomeadas (REN, visão computacional e bioinformática. Nesse sentido, propõe-se a realização da tarefa de REN aplicando o método CRF e, sequencialmente, é feita uma avaliação do seu desempenho com base no corpus do HAREM. Conclui-se que, nos testes realizados, o sistema NERP-CRF obteve os melhores resultados de Precisão quando comparado com os sistemas avaliados no mesmo corpus, com plenas condições de ser um sistema competitivo e eficaz.
SubPatch: random kd-tree on a sub-sampled patch set for nearest neighbor field estimation
Pedersoli, Fabrizio; Benini, Sergio; Adami, Nicola; Okuda, Masahiro; Leonardi, Riccardo
2015-02-01
We propose a new method to compute the approximate nearest-neighbors field (ANNF) between image pairs using random kd-tree and patch set sub-sampling. By exploiting image coherence we demonstrate that it is possible to reduce the number of patches on which we compute the ANNF, while maintaining high overall accuracy on the final result. Information on missing patches is then recovered by interpolation and propagation of good matches. The introduction of the sub-sampling factor on patch sets also allows for setting the desired trade off between accuracy and speed, providing a flexibility that lacks in state-of-the-art methods. Tests conducted on a public database prove that our algorithm achieves superior performance with respect to PatchMatch (PM) and Coherence Sensitivity Hashing (CSH) algorithms in a comparable computational time.
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.
Energy Technology Data Exchange (ETDEWEB)
Pruemper, G. [Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai 980 8577 (Japan)]. E-mail: pruemper@tagen.tohoku.ac.jp; Ueda, K. [Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai 980 8577 (Japan)
2007-05-01
In molecular photofragmentation processes by soft X-rays, a number of ionic fragments can be produced, each having a different abundance and correlation with the kinetic energy of the emitted electron. For investigating these fragmentation processes, electron-ion and electron-ion-ion coincidence experiments, in which the kinetic energy of electrons are analyzed using an electrostatic analyzer while the mass of the ions is analyzed using a pulsed electric field, are very powerful. For such measurements, however, the contribution of random coincidences is substantial and affects the data in a non-trivial way. Simple intuitive subtraction methods cannot be applied. In the present paper, we describe these electron-ion and electron-ion-ion coincidence experiments together with a subtraction method for the contribution from random coincidences. We provide a comprehensive set of equations for the data treatment, including equations for the calculation of error-bars. We demonstrate the method by applying it to the fragmentation of free CF{sub 3}SF{sub 5} molecules.
A voxelation-corrected non-stationary 3D cluster-size test based on random field theory.
Li, Huanjie; Nickerson, Lisa D; Zhao, Xuna; Nichols, Thomas E; Gao, Jia-Hong
2015-09-01
Cluster-size tests (CSTs) based on random field theory (RFT) are commonly adopted to identify significant differences in brain images. However, the use of RFT in CSTs rests on the assumption of uniform smoothness (stationarity). When images are non-stationary, CSTs based on RFT will likely lead to increased false positives in smooth regions and reduced power in rough regions. An adjustment to the cluster size according to the local smoothness at each voxel has been proposed for the standard test based on RFT to address non-stationarity, however, this technique requires images with a large degree of spatial smoothing, large degrees of freedom and high intensity thresholding. Recently, we proposed a voxelation-corrected 3D CST based on Gaussian random field theory that does not place constraints on the degree of spatial smoothness. However, this approach is only applicable to stationary images, requiring further modification to enable use for non-stationary images. In this study, we present modifications of this method to develop a voxelation-corrected non-stationary 3D CST based on RFT. Both simulated and real data were used to compare the voxelation-corrected non-stationary CST to the standard cluster-size adjusted non-stationary CST based on RFT and the voxelation-corrected stationary CST. We found that voxelation-corrected stationary CST is liberal for non-stationary images and the voxelation-corrected non-stationary CST performs better than cluster-size adjusted non-stationary CST based on RFT under low smoothness, low intensity threshold and low degrees of freedom. Published by Elsevier Inc.
Krebsbach, K.; Friederichs, P.
2014-12-01
The generation of reliable precipitation products that explicitly account for spatial and temporal structures of precipitation events requires a combination of data with a variety of error structures and temporal resolutions. In-situ measurements are relatively accurate, but available only at sparse and irregularly distributed locations, whereas remote measurements cover areas but suffer from spatially and temporally inhomogeneous systematic errors. Besides gauge measurements are available on coarser spatial and temporal resolution in contrast to remote sensing measurements which are given on a fine spatial and temporal resolution. In our study we use precipitation rates from the composit of two X-band radars in Bonn and Jülich in Germany. Our aim is to formulate a statistical space-time model that aggregates and disaggregates precipitation rates from radar and gauge observations. We model a Gaussian random field as underlying process, where we face the task of dealing with a large non-Gaussian data set. To start the analysis of the unadjusted radar rainfall rates, we follow the work of D. Allcroft and C. Glasbey (2003) and transform the data to a truncated Gaussian distribution. The advantage of the latent variable approach is that it takes account of the occurence of rainfall and the intensity using a single process. We proceed by estimating the empirical correlation from these transformed values with maximum likelihood methods and fit a parametric correlation function that gives rise to a Gaussian random field. Since the transformation gives censored values to dry locations, we simulate values for this area that lie below some threshold and extend the Gaussian field to the whole domain. In order to merge gauge and radar data for precipitation, we first aggregate the data to a scale on which the comparison is reasonable and then disaggregate again back to smaller desirable scales. The disaggregation step consists of calculating the difference between radar
Random functions and turbulence
Panchev, S
1971-01-01
International Series of Monographs in Natural Philosophy, Volume 32: Random Functions and Turbulence focuses on the use of random functions as mathematical methods. The manuscript first offers information on the elements of the theory of random functions. Topics include determination of statistical moments by characteristic functions; functional transformations of random variables; multidimensional random variables with spherical symmetry; and random variables and distribution functions. The book then discusses random processes and random fields, including stationarity and ergodicity of random
Directory of Open Access Journals (Sweden)
Poeze Martijn
2011-05-01
Full Text Available Abstract Background The scaphoid bone is the most commonly fractured of the carpal bones. In the Netherlands 90% of all carpal fractures is a fracture of the scaphoid bone. The scaphoid has an essential role in functionality of the wrist, acting as a pivot. Complications in healing can result in poor functional outcome. The scaphoid fracture is a troublesome fracture and failure of treatment can result in avascular necrosis (up to 40%, non-union (5-21% and early osteo-arthritis (up to 32% which may seriously impair wrist function. Impaired consolidation of scaphoid fractures results in longer immobilization and more days lost at work with significant psychosocial and financial consequences. Initially Pulsed Electromagnetic Fields was used in the treatment of tibial pseudoarthrosis and non-union. More recently there is evidence that physical forces can also be used in the treatment of fresh fractures, showing accelerated healing by 30% and 71% reduction in nonunion within 12 weeks after initiation of therapy. Until now no double blind randomized, placebo controlled trial has been conducted to investigate the effect of this treatment on the healing of fresh fractures of the scaphoid. Methods/Design This is a multi center, prospective, double blind, placebo controlled, randomized trial. Study population consists of all patients with unilateral acute scaphoid fracture. Pregnant women, patients having a life supporting implanted electronic device, patients with additional fractures of wrist, carpal or metacarpal bones and pre-existing impairment in wrist function are excluded. The scaphoid fracture is diagnosed by a combination of physical and radiographic examination (CT-scanning. Proven scaphoid fractures are treated with cast immobilization and a small Pulsed Electromagnetic Fields bone growth stimulating device placed on the cast. Half of the devices will be disabled at random in the factory. Study parameters are clinical consolidation
Hannemann, Pascal; Göttgens, Kevin W A; van Wely, Bob J; Kolkman, Karel A; Werre, Andries J; Poeze, Martijn; Brink, Peter R G
2011-05-06
The scaphoid bone is the most commonly fractured of the carpal bones. In the Netherlands 90% of all carpal fractures is a fracture of the scaphoid bone. The scaphoid has an essential role in functionality of the wrist, acting as a pivot. Complications in healing can result in poor functional outcome. The scaphoid fracture is a troublesome fracture and failure of treatment can result in avascular necrosis (up to 40%), non-union (5-21%) and early osteo-arthritis (up to 32%) which may seriously impair wrist function. Impaired consolidation of scaphoid fractures results in longer immobilization and more days lost at work with significant psychosocial and financial consequences.Initially Pulsed Electromagnetic Fields was used in the treatment of tibial pseudoarthrosis and non-union. More recently there is evidence that physical forces can also be used in the treatment of fresh fractures, showing accelerated healing by 30% and 71% reduction in nonunion within 12 weeks after initiation of therapy. Until now no double blind randomized, placebo controlled trial has been conducted to investigate the effect of this treatment on the healing of fresh fractures of the scaphoid. This is a multi center, prospective, double blind, placebo controlled, randomized trial. Study population consists of all patients with unilateral acute scaphoid fracture. Pregnant women, patients having a life supporting implanted electronic device, patients with additional fractures of wrist, carpal or metacarpal bones and pre-existing impairment in wrist function are excluded. The scaphoid fracture is diagnosed by a combination of physical and radiographic examination (CT-scanning).Proven scaphoid fractures are treated with cast immobilization and a small Pulsed Electromagnetic Fields bone growth stimulating device placed on the cast. Half of the devices will be disabled at random in the factory.Study parameters are clinical consolidation, radiological consolidation evaluated by CT-scanning, functional
Kleeorin, N; Sokoloff, D D
2002-01-01
Magnetic fluctuations with a zero mean field in a random flow with a finite correlation time and a small yet finite magnetic diffusion are studied. Equation for the second-order correlation function of a magnetic field is derived. This equation comprises spatial derivatives of high orders due to a non-local nature of magnetic field transport in a random velocity field with a finite correlation time. For a random Gaussian velocity field with a small correlation time the equation for the second-order correlation function of the magnetic field is a third-order partial differential equation. For this velocity field and a small magnetic diffusion with large magnetic Prandtl numbers the growth rate of the second moment of magnetic field is estimated. The finite correlation time of a turbulent velocity field causes an increase of the growth rate of magnetic fluctuations. It is demonstrated that the results obtained for the cases of a small yet finite magnetic diffusion and a zero magnetic diffusion are different. As...
Tile-Level Annotation of Satellite Images Using Multi-Level Max-Margin Discriminative Random Field
Directory of Open Access Journals (Sweden)
Hong Sun
2013-05-01
Full Text Available This paper proposes a multi-level max-margin discriminative analysis (M3DA framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative topic-level features, the M3DA uses the maximum entropy discrimination latent Dirichlet Allocation (MedLDA model. Moreover, for improving the spatial coherence of visual words neglected by M3DA, conditional random field (CRF is employed to optimize the soft label field composed of multiple label posteriors. The framework of M3DA enables one to combine word-level features (generated by support vector machines and topic-level features (generated by MedLDA via the bag-of-words representation. The experimental results on high-resolution satellite images have demonstrated that, using the proposed method can not only obtain suitable semantic interpretation, but also improve the annotation performance by taking into account the multi-level semantics and the contextual information.
Le Maitre, Olivier
2015-01-07
We address model dimensionality reduction in the Bayesian inference of Gaussian fields, considering prior covariance function with unknown hyper-parameters. The Karhunen-Loeve (KL) expansion of a prior Gaussian process is traditionally derived assuming fixed covariance function with pre-assigned hyperparameter values. Thus, the modes strengths of the Karhunen-Loeve expansion inferred using available observations, as well as the resulting inferred process, dependent on the pre-assigned values for the covariance hyper-parameters. Here, we seek to infer the process and its the covariance hyper-parameters in a single Bayesian inference. To this end, the uncertainty in the hyper-parameters is treated by means of a coordinate transformation, leading to a KL-type expansion on a fixed reference basis of spatial modes, but with random coordinates conditioned on the hyper-parameters. A Polynomial Chaos (PC) expansion of the model prediction is also introduced to accelerate the Bayesian inference and the sampling of the posterior distribution with MCMC method. The PC expansion of the model prediction also rely on a coordinates transformation, enabling us to avoid expanding the dependence of the prediction with respect to the covariance hyper-parameters. We demonstrate the efficiency of the proposed method on a transient diffusion equation by inferring spatially-varying log-diffusivity fields from noisy data.
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A. Guadagnini
2012-09-01
Full Text Available We analyze the scaling behaviors of two field-scale log permeability data sets showing heavy-tailed frequency distributions in three and two spatial dimensions, respectively. One set consists of 1-m scale pneumatic packer test data from six vertical and inclined boreholes spanning a decameters scale block of unsaturated fractured tuffs near Superior, Arizona, the other of pneumatic minipermeameter data measured at a spacing of 15 cm along three horizontal transects on a 21 m long and 6 m high outcrop of the Upper Cretaceous Straight Cliffs Formation, including lower-shoreface bioturbated and cross-bedded sandstone near Escalante, Utah. Order q sample structure functions of each data set scale as a power ξ(q of separation scale or lag, s, over limited ranges of s. A procedure known as extended self-similarity (ESS extends this range to all lags and yields a nonlinear (concave functional relationship between ξ(q and q. Whereas the literature tends to associate extended and nonlinear power-law scaling with multifractals or fractional Laplace motions, we have shown elsewhere that (a ESS of data having a normal frequency distribution is theoretically consistent with (Gaussian truncated (additive, self-affine, monofractal fractional Brownian motion (tfBm, the latter being unique in predicting a breakdown in power-law scaling at small and large lags, and (b nonlinear power-law scaling of data having either normal or heavy-tailed frequency distributions is consistent with samples from sub-Gaussian random fields or processes subordinated to tfBm or truncated fractional Gaussian noise (tfGn, stemming from lack of ergodicity which causes sample moments to scale differently than do their ensemble counterparts. Here we (i demonstrate that the above two data sets are consistent with sub-Gaussian random fields subordinated to tfBm or tfGn and (ii provide maximum likelihood estimates of parameters characterizing the
Arthur, A. D.; le Roux, J. A.
2014-12-01
Observations of extreme solar energetic particle (SEP) events associated with coronal mass ejection driven shocks have detected particle energies up to a few GeV at 1 AU within the first ~10 minutes to 1 hour of shock acceleration. It is currently not well understood whether or not shock acceleration can act alone in these events or if some combination of successive shocks or solar flares is required. To investigate this, we updated our current model which has been successfully applied to the termination shock and traveling interplanetary shocks. The model solves the time-dependent Focused Transport Equation including particle preheating due to the cross shock electric field and the divergence, adiabatic compression, and acceleration of the solar wind. Particle interaction with MHD wave turbulence is modeled in terms of gyro-resonant interactions with parallel propagating Alfvén waves and diffusive shock acceleration is included via the first-order Fermi mechanism for parallel shocks. The observed onset times of the extreme SEP events place the shock in the corona when the particles escape upstream, therefore, we extended our model to include coronal conditions for the solar wind and magnetic field. Additional features were introduced to investigate two aspects of MHD wave turbulence in contributing to efficient particle acceleration at a single fast parallel shock; (1) We simulate field-line random walk on time scales much larger than a particle gyro-period to investigate how the stochastic element added to particle injection and the first-order Fermi mechanism affects the efficiency of particle acceleration. (2) Previous modeling efforts show that the ambient solar wind turbulence is too weak to quickly accelerate SEPs to GeV energies. To improve the efficiency of acceleration for a single shock, we included upstream Alfvén wave amplification due to gyro-resonant interactions with SEPs and we constrained the wave growth to not violate the Bohm limit.
DEFF Research Database (Denmark)
Jensen, J.L.
1993-01-01
Previous results on Edgeworth expansions for sums over a random field are extended to the case where the strong mixing coefficient depends not only on the distance between two sets of random variables, but also on the size of the two sets. The results are applied to the Poisson and the Strauss po...... point processes, giving rise also to local limit results. © 1993 The Institute of Statistical Mathematics....
Vuori, Jukka; Toppinen-Tanner, Salla; Mutanen, Pertti
2012-03-01
A resource-building group intervention was developed to enhance career management, mental health, and job retention in work organizations. The in-company training program provided employees with better preparedness to manage their own careers. The program activities were universally implemented using an organization-level, 2-trainer model with trainers from the human resources management and occupational health services. The study was a within-organizations, randomly assigned field experimental study; it investigated the impacts of the intervention on immediate career management preparedness and later mental health and intentions to retire early. A total of 718 eligible individuals returned a questionnaire in 17 organizations and became voluntary participants. The respondents were randomly assigned to either an intervention (N = 369) or a comparison group (N = 349). Those in the intervention group were invited to group intervention workshops, whereas those in the comparison group received printed information about career and health-related issues. The 7-month follow-up results showed that the program significantly decreased depressive symptoms and intentions to retire early and increased mental resources among the group participants compared to the others. The mediation analyses demonstrated that the increase in career management preparedness as a proximal impact of the intervention mediated the longer term mental health effects. Those who benefited most from the intervention as regards their mental health were employees with elevated levels of depression or exhaustion and younger employees, implying additional benefits of a more targeted use of the intervention. The results demonstrated the benefits of the enhancement of individual-level career management and resilience resources as career and health promotion practice in work organizations.
Directory of Open Access Journals (Sweden)
Sidi Ali Ould Abdi
2011-01-01
Full Text Available Given a stationary multidimensional spatial process (i=(i,i∈ℝ×ℝ,i∈ℤ, we investigate a kernel estimate of the spatial conditional quantile function of the response variable i given the explicative variable i. Asymptotic normality of the kernel estimate is obtained when the sample considered is an -mixing sequence.
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.
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Yisu Lu
2014-01-01
Full Text Available 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.
Energy Technology Data Exchange (ETDEWEB)
Abdulbaqi, Hayder Saad [School of Physics, Universiti Sains Malaysia, 11700, Penang (Malaysia); Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya (Iraq); Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin [School of Physics, Universiti Sains Malaysia, 11700, Penang (Malaysia); Abood, Loay Kadom [Department of Computer Science, College of Science, University of Baghdad, Baghdad (Iraq)
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.
Directory of Open Access Journals (Sweden)
David Rawlinson
2012-05-01
Full Text Available Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good infiltering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.
Zohar, Dov; Polachek, Tal
2014-01-01
The article presents a randomized field study designed to improve safety climate and resultant safety performance by modifying daily messages in supervisor-member communications. Supervisors in the experimental group received 2 individualized feedback sessions regarding the extent to which they integrated safety and productivity-related issues in daily verbal exchanges with their members; those in the control group received no feedback. Feedback data originated from 7-9 workers for each supervisor, reporting about received supervisory messages during the most recent verbal exchange. Questionnaire data collected 8 weeks before and after the 12-week intervention phase revealed significant changes for safety climate, safety behavior, subjective workload, teamwork, and (independently measured) safety audit scores for the experimental group. Data for the control group (except for safety behavior) remained unchanged. These results are explained by corresponding changes (or lack thereof in the control group) in perceived discourse messages during the 6-week period between the 1st and 2nd feedback sessions. Theoretical and practical implications for climate improvement and organizational discourse research are discussed.
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Mitchel Alioscha-Perez
2014-07-01
Full Text Available Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information among neighboring regions within images. Hierarchical conditional random fields (CRF consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results.
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Teerapong Panboonyuen
2017-07-01
Full Text Available Object segmentation of remotely-sensed aerial (or very-high resolution, VHS images and satellite (or high-resolution, HR images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs and conditional random fields (CRFs. To improve the DCNN, a modern activation function called the exponential linear unit (ELU, is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F 1 .
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Hong-Jie Dai
2015-01-01
Full Text Available Electronic health record (EHR is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactory F-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents.
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Ehwa Yang
2017-03-01
Full Text Available Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT. In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.
Dai, Hong-Jie; Syed-Abdul, Shabbir; Chen, Chih-Wei; Wu, Chieh-Chen
2015-01-01
Electronic health record (EHR) is a digital data format that collects electronic health information about an individual patient or population. To enhance the meaningful use of EHRs, information extraction techniques have been developed to recognize clinical concepts mentioned in EHRs. Nevertheless, the clinical judgment of an EHR cannot be known solely based on the recognized concepts without considering its contextual information. In order to improve the readability and accessibility of EHRs, this work developed a section heading recognition system for clinical documents. In contrast to formulating the section heading recognition task as a sentence classification problem, this work proposed a token-based formulation with the conditional random field (CRF) model. A standard section heading recognition corpus was compiled by annotators with clinical experience to evaluate the performance and compare it with sentence classification and dictionary-based approaches. The results of the experiments showed that the proposed method achieved a satisfactory F-score of 0.942, which outperformed the sentence-based approach and the best dictionary-based system by 0.087 and 0.096, respectively. One important advantage of our formulation over the sentence-based approach is that it presented an integrated solution without the need to develop additional heuristics rules for isolating the headings from the surrounding section contents. PMID:26380302
Amin, Nuhu; Pickering, Amy J.; Ram, Pavani K.; Unicomb, Leanne; Najnin, Nusrat; Homaira, Nusrat; Ashraf, Sania; Abedin, Jaynal; Islam, M. Sirajul; Luby, Stephen P.
2014-01-01
We conducted a randomized, non-inferiority field trial in urban Dhaka, Bangladesh among mothers to compare microbial efficacy of soapy water (30 g powdered detergent in 1.5 L water) with bar soap and water alone. Fieldworkers collected hand rinse samples before and after the following washing regimens: scrubbing with soapy water for 15 and 30 seconds; scrubbing with bar soap for 15 and 30 seconds; and scrubbing with water alone for 15 seconds. Soapy water and bar soap removed thermotolerant coliforms similarly after washing for 15 seconds (mean log10 reduction = 0.7 colony-forming units [CFU], P soap). Increasing scrubbing time to 30 seconds did not improve removal (P > 0.05). Scrubbing hands with water alone also reduced thermotolerant coliforms (mean log10 reduction = 0.3 CFU, P = 0.046) but was less efficacious than scrubbing hands with soapy water. Soapy water is an inexpensive and microbiologically effective cleansing agent to improve handwashing among households with vulnerable children. PMID:24914003
Liu, Zengjian; Chen, Yangxin; Tang, Buzhou; Wang, Xiaolong; Chen, Qingcai; Li, Haodi; Wang, Jingfeng; Deng, Qiwen; Zhu, Suisong
2015-12-01
De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informatics for Integrating Biology and Bedside) clinical natural language processing (NLP) challenge sets up a track for de-identification (track 1). In this study, we propose a hybrid system based on both machine learning and rule approaches for the de-identification track. In our system, PHI instances are first identified by two (token-level and character-level) conditional random fields (CRFs) and a rule-based classifier, and then are merged by some rules. Experiments conducted on the i2b2 corpus show that our system submitted for the challenge achieves the highest micro F-scores of 94.64%, 91.24% and 91.63% under the "token", "strict" and "relaxed" criteria respectively, which is among top-ranked systems of the 2014 i2b2 challenge. After integrating some refined localization dictionaries, our system is further improved with F-scores of 94.83%, 91.57% and 91.95% under the "token", "strict" and "relaxed" criteria respectively.
Institute of Scientific and Technical Information of China (English)
周良明; 郭佩芳; 王强; 杜伊
2004-01-01
Based on the maximum entropy principle, a probability density function (PDF) is derived for the distribution of wave heights in a random wave field, without any more hypothesis. The present PDF, being a non-Rayleigh form, involves two parameters: the average wave height H and the state parameter γ. The role of γ in the distribution of wave heights is examined. It is found that γ may be a certain measure of sea state. A least square method for determining γ from measured data is proposed. In virtue of the method, the values of γ are determined for three sea states from the data measured in the East China Sea. The present PDF is compared with the well known Rayleigh PDF of wave height and it is shown that it much better fits the data than the Rayleigh PDF. It is expected that the present PDF would fit some other wave variables, since its derivation is not restricted only to the wave height.
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
Li, Ming; Li, Jingyun; He, Zihuai; Lu, Qing; Witte, John S; Macleod, Stewart L; Hobbs, Charlotte A; Cleves, Mario A
2016-05-01
Family-based association studies are commonly used in genetic research because they can be robust to population stratification (PS). Recent advances in high-throughput genotyping technologies have produced a massive amount of genomic data in family-based studies. However, current family-based association tests are mainly focused on evaluating individual variants one at a time. In this article, we introduce a family-based generalized genetic random field (FB-GGRF) method to test the joint association between a set of autosomal SNPs (i.e., single-nucleotide polymorphisms) and disease phenotypes. The proposed method is a natural extension of a recently developed GGRF method for population-based case-control studies. It models offspring genotypes conditional on parental genotypes, and, thus, is robust to PS. Through simulations, we presented that under various disease scenarios the FB-GGRF has improved power over a commonly used family-based sequence kernel association test (FB-SKAT). Further, similar to GGRF, the proposed FB-GGRF method is asymptotically well-behaved, and does not require empirical adjustment of the type I error rates. We illustrate the proposed method using a study of congenital heart defects with family trios from the National Birth Defects Prevention Study (NBDPS).
Kumar, Manoj; Banerjee, Varsha; Puri, Sanjay
2017-01-01
We perform comprehensive Monte Carlo (MC) simulations to study ordering dynamics in the random field Ising model with conserved order parameter (C-RFIM) in d=2,3 . The observations from this study are: a) For a fixed value of the disorder Δ, the correlation function C(r,t;Δ) exhibits dynamical scaling. b) The scaling function is not robust with respect to Δ, i.e., super-universality (SU) is violated by C(r,t;Δ) . c) At early times, the domains follow the algebraic growth with a disorder-dependent exponent: L(t,Δ)∼ t1/\\bar{z(Δ)} . At late times, there is a crossover to logarithmic growth: L(t,Δ) ∼ (\\ln t)1/\\varphi , where φ is a disorder-independent exponent. d) The small-r behavior of the correlation function exhibits a cusp singularity: 1-C(r) ∼ rα(Δ) , where α is the cusp exponent signifying rough fractal interfaces. e) The corresponding structure factor exhibits a non-Porod tail: S(k,t;Δ)∼ k-(d+α) , and obeys a generalized Tomita sum rule \\int_0^∞ {d}p p1-α≤ft[pd+αf(p)-C\\right]=0 , where f(p) is the appropriate scaling function, and C is a constant.
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.
Sun, Jiahui; Kwan, Rachel Lai-Chu; Zheng, Yongping; Cheing, Gladys Lai-Ying
2016-07-01
Cutaneous blood flow provides nourishment that plays an essential role in maintaining skin health. We examined the effects of pulsed electromagnetic fields (PEMFs) on cutaneous circulation of dorsal feet. Twenty-two patients with diabetes mellitus (DM) and 21 healthy control subjects were randomly allocated to receive either PEMFs or sham PEMFs (0.5 mT, 12 Hz, 30 min). Blood flow velocity and diameter of the small vein were examined by using ultrasound biomicroscopy; also, microcirculation at skin over the base of the 1st metatarsal bone (Flux1) and distal 1st phalange (Flux2) was measured by laser Doppler flowmetry before and after intervention. Results indicated that PEMFs produced significantly greater changes in blood flow velocity of the smallest observable vein than did sham PEMFs (both P < 0.05) in both types of subjects. However, no significant difference was found in changes of vein diameter, nor in Flux1 and Flux2, between PEMFs and sham PEMFs groups in subjects with or without DM. We hypothesized that PEMFs would increase blood flow velocity of the smallest observable vein in people with or without DM. Bioelectromagnetics. 37:290-297, 2016. © 2016 Wiley Periodicals, Inc.
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Sean Robinson
Full Text Available 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.
Delgado Saa, Jaime F.; de Pesters, Adriana; Cetin, Mujdat
2016-06-01
Objective. In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings. Approach. The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addition, the proposed method models the dynamics of the task executed by the subject and uses information about these dynamics as prior information during the classification stage. Main results. The results show that incorporating temporal information about the executed task as well as incorporating long-range dependencies between the brain signals and the labels effectively increases the system’s classification performance compared to methods in the state of art. Significance. The method proposed in this work makes use of probabilistic graphical models to incorporate temporal information in the classification of finger movements from electrocorticographic recordings. The proposed method highlights the importance of including prior information about the task that the subjects execute. As the results show, the combination of these two features effectively produce a significant improvement of the system’s classification performance.
Large deviations of the finite-time magnetization of the Curie-Weiss random-field Ising model
Paga, Pierre; Kühn, Reimer
2017-08-01
We study the large deviations of the magnetization at some finite time in the Curie-Weiss random field Ising model with parallel updating. While relaxation dynamics in an infinite-time horizon gives rise to unique dynamical trajectories [specified by initial conditions and governed by first-order dynamics of the form mt +1=f (mt) ] , we observe that the introduction of a finite-time horizon and the specification of terminal conditions can generate a host of metastable solutions obeying second-order dynamics. We show that these solutions are governed by a Newtonian-like dynamics in discrete time which permits solutions in terms of both the first-order relaxation ("forward") dynamics and the backward dynamics mt +1=f-1(mt) . Our approach allows us to classify trajectories for a given final magnetization as stable or metastable according to the value of the rate function associated with them. We find that in analogy to the Freidlin-Wentzell description of the stochastic dynamics of escape from metastable states, the dominant trajectories may switch between the two types (forward and backward) of first-order dynamics. Additionally, we show how to compute rate functions when uncertainty in the quenched disorder is introduced.
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T. Novack
2015-03-01
Full Text Available In this work we focused on the classification of Urban Settlement Types (USTs based on two datasets from the TerraSAR-X satellite acquired at ascending and descending look directions. These data sets comprise the intensity, amplitude and coherence images from the ascending and descending datasets. In accordance to most official UST maps, the urban blocks of our study site were considered as the elements to be classified. The considered USTs classes in this paper are: Vegetated Areas, Single-Family Houses and Commercial and Residential Buildings. Three different groups of image attributes were utilized, namely: Relative Areas, Histogram of Oriented Gradients and geometrical and contextual attributes extracted from the nodes of a Max-Tree Morphological Profile. These image attributes were submitted to three powerful soft multi-class classification algorithms. In this way, each classifier output a membership value to each of the classes. This membership values were then treated as the potentials of the unary factors of a Conditional Random Fields (CRFs model. The pairwise factors of the CRFs model were parameterised with a Potts function. The reclassification performed with the CRFs model enabled a slight increase of the classification’s accuracy from 76% to 79% out of 1926 urban blocks.
Novack, T.; Stilla, U.
2015-03-01
In this work we focused on the classification of Urban Settlement Types (USTs) based on two datasets from the TerraSAR-X satellite acquired at ascending and descending look directions. These data sets comprise the intensity, amplitude and coherence images from the ascending and descending datasets. In accordance to most official UST maps, the urban blocks of our study site were considered as the elements to be classified. The considered USTs classes in this paper are: Vegetated Areas, Single-Family Houses and Commercial and Residential Buildings. Three different groups of image attributes were utilized, namely: Relative Areas, Histogram of Oriented Gradients and geometrical and contextual attributes extracted from the nodes of a Max-Tree Morphological Profile. These image attributes were submitted to three powerful soft multi-class classification algorithms. In this way, each classifier output a membership value to each of the classes. This membership values were then treated as the potentials of the unary factors of a Conditional Random Fields (CRFs) model. The pairwise factors of the CRFs model were parameterised with a Potts function. The reclassification performed with the CRFs model enabled a slight increase of the classification's accuracy from 76% to 79% out of 1926 urban blocks.
Beaulieu, Karen; Beland, Patricia; Pinard, Marilee; Handfield, Guilène; Handfield, Nicole; Goffaux, Philippe; Corriveau, Hélène; Léonard, Guillaume
2016-01-01
Previous studies suggested that pulsed electromagnetic field (PEMF) therapy can decrease pain. To date, however, it remains difficult to determine whether the analgesic effect observed in patients are attributable to a direct effect of PEMF on pain or to an indirect effect of PEMF on inflammation and healing. In the present study, we used an experimental pain paradigm to evaluate the direct effect of PEMF on pain intensity, pain unpleasantness, and temporal summation of pain. Twenty-four healthy subjects (mean age 22 ± 2 years; 9 males) participated in the experiment. Both real and sham PEMF were administered to every participant using a randomized, double-blind, cross-over design. For each visit, PEMF was applied for 10 minutes on the right forearm using a portable device. Experimental pain was evoked before (baseline) and after PEMF with a 9 cm(2) Pelletier-type thermode, applied on the right forearm (120 s stimulation; temperature individually adjusted to produce moderate baseline pain). Pain intensity and unpleasantness were evaluated using a 0-100 numerical pain rating scale. Temporal summation was evaluated by comparing pain intensity ratings obtained at the end of tonic nociceptive stimulation (120 s) with pain intensity ratings obtained after 60 s of stimulation. When compared to baseline, there was no change in pain intensity and unpleasantness following the application of real or sham PEMF. PEMF did not affect temporal summation. The present observations suggest that PEMF does not directly influence heat pain perception in healthy individuals.
Energy Technology Data Exchange (ETDEWEB)
Furtlehner, C. [Paris-6 Univ., 75 (France)
1997-09-24
This thesis deals with the two-dimensional problem of a charged particle coupled to a random magnetic field. Various situations are considered, according to the relative importance of the mean value of field and random component. The last one is conceived as a distribution of magnetic impurities (punctual vortex), having various statistical properties (local or non-local correlations, Poisson distribution, etc). The study of this system has led to two distinct situations: - the case of the charged particle feeling the influence of mean field that manifests its presence in the spectrum of broadened Landau levels; - the disordered situation in which the spectrum can be distinguished from the free one only by a low energy Lifshits behaviour. Additional properties are occurring in the limit of `strong` mean field, namely a non-conventional low energy behaviour (in contrast to Lifshits behaviour) which was interpreted in terms of localized states. (author) 78 refs.
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Khoo, V.S.; Rainford, K.; Horwich, A.; Dearnaley, D.P. [Royal Marsden NHS Trust, Sutton (United Kingdom)
1997-12-31
The purpose of this pilot study was to evaluate the acute gastrointestinal morbidity of adjuvant radiotherapy (RT) for Stage I seminoma of the testis. Ten Stage I patients receiving para-aortic and ipsilateral pelvic nodal (dog-leg) RT provided a toxicity baseline (group A). Twenty Stage I patients randomized to dog-let RT or para-aortic RT (10 per group) were further randomized to received prophylactic ondansetron or expectant therapy with metoclopramide (group B). Daily patient-completed questionnaires evaluated acute toxicity. Dog-leg RT for Stage I seminomas is associated with readily demonstrable gastrointestinal tract (GIT) toxicity. The number of patients in this study is too small to produce definitive results, but there appears to be reduced GIT toxicity with prophylactic antiemetics. The effect of reduced RT fields has been assessed further in the MRC randomized tiral of field sizes (TE10). (Author).
Karimaghaloo, Zahra; Arnold, Douglas L; Arbel, Tal
2016-01-01
Detection and segmentation of large structures in an image or within a region of interest have received great attention in the medical image processing domains. However, the problem of small pathology detection and segmentation still remains an unresolved challenge due to the small size of these pathologies, their low contrast and variable position, shape and texture. In many contexts, early detection of these pathologies is critical in diagnosis and assessing the outcome of treatment. In this paper, we propose a probabilistic Adaptive Multi-level Conditional Random Fields (AMCRF) with the incorporation of higher order cliques for detecting and segmenting such pathologies. In the first level of our graphical model, a voxel-based CRF is used to identify candidate lesions. In the second level, in order to further remove falsely detected regions, a new CRF is developed that incorporates higher order textural features, which are invariant to rotation and local intensity distortions. At this level, higher order textures are considered together with the voxel-wise cliques to refine boundaries and is therefore adaptive. The proposed algorithm is tested in the context of detecting enhancing Multiple Sclerosis (MS) lesions in brain MRI, where the problem is further complicated as many of the enhancing voxels are associated with normal structures (i.e. blood vessels) or noise in the MRI. The algorithm is trained and tested on large multi-center clinical trials from Relapsing-Remitting MS patients. The effect of several different parameter learning and inference techniques is further investigated. When tested on 120 cases, the proposed method reaches a lesion detection rate of 90%, with very few false positive lesion counts on average, ranging from 0.17 for very small (3-5 voxels) to 0 for very large (50+ voxels) regions. The proposed model is further tested on a very large clinical trial containing 2770 scans where a high sensitivity of 91% with an average false positive
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Márcia Regina Vitolo
2014-12-01
Full Text Available OBJECTIVE: To assess the impact of a child feeding training program for primary care health professionals about breastfeeding and complementary feeding practices. METHODS: Cluster-randomized field trial conducted in the city of Porto Alegre, (RS, Brazil. Twenty primary health care centers (HCC were randomized into intervention (n = 9 and control (n = 11 groups. The health professionals (n = 200 at the intervention group centers received training about healthy feeding practices. Pregnant women were enrolled at the study. Up to six months of child's age, home visits were made to obtain variables related to breastfeeding and introduction of foods. RESULTS: 619 children were evaluated: 318 from the intervention group and 301 from the control group. Exclusive breastfeeding prevalence in the first (72.3 versus 59.4%; RR = 1.21; 95%CI 1.08 - 1.38, second (62.6 versus 48.2%; RR = 1.29; 95%CI 1.10 - 1.53, and third months of life (44.0% versus 34.6%; RR = 1.27; 95%CI 1.04 - 1.56 was higher in the intervention group compared to the control group. The prevalence of children who consumed meat four or five times per week was higher in the intervention group than in the control group (36.8 versus 22.6%; RR = 1.62; 95%CI 1.32 - 2.03. The prevalence of children who had consumed soft drinks (34.9 versus 52.5%; RR = 0.66; 95%CI 0.54 - 0.80, chocolate (24.5 versus 36.7% RR = 0.66 95%CI 0.53 - 0.83, petit suisse (68.9 versus 79.7; 95%CI 0.75 - 0.98 and coffee (10.4 versus 20.1%; RR = 0.51; 95%CI 0.31 - 0.85 in their six first months of life was lower in the intervention group. CONCLUSION: The training of health professionals had a positive impact on infant feeding practices, contributing to the promotion of child health.
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Fang, W.X. [State Key Laboratory of Optoelectronic Materials and Technologies, Institute of condensed matter physics, Sun Yat-sen University, Guangzhou 510275 (China); Physics Department, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong (China); He, Z.H. [State Key Laboratory of Optoelectronic Materials and Technologies, Institute of condensed matter physics, Sun Yat-sen University, Guangzhou 510275 (China)], E-mail: stshzh@mail.sysu.edu.cn; Chen, D.H.; Shao, Y.Z. [State Key Laboratory of Optoelectronic Materials and Technologies, Institute of condensed matter physics, Sun Yat-sen University, Guangzhou 510275 (China)
2009-12-15
A model based on localized partition function and master equation was set up to calculate the zero-field-cooled (ZFC) and field-cooled (FC) curves of a non-interacting magnetic nanoparticle assembly with randomly oriented anisotropy. The peak temperature of the ZFC curve corresponds to the highest energy barrier that acts against the unblocking process, and could be described well by an equation covering the heating rate effect. The predicted H{sup 2/3} field dependence of the peak temperature is in good agreement with published results.
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Dalianis Hercules
2010-04-01
Full Text Available Abstract Background In order to perform research on the information contained in Electronic Patient Records (EPRs, access to the data itself is needed. This is often very difficult due to confidentiality regulations. The data sets need to be fully de-identified before they can be distributed to researchers. De-identification is a difficult task where the definitions of annotation classes are not self-evident. Results We present work on the creation of two refined variants of a manually annotated Gold standard for de-identification, one created automatically, and one created through discussions among the annotators. The data is a subset from the Stockholm EPR Corpus, a data set available within our research group. These are used for the training and evaluation of an automatic system based on the Conditional Random Fields algorithm. Evaluating with four-fold cross-validation on sets of around 4-6 000 annotation instances, we obtained very promising results for both Gold Standards: F-score around 0.80 for a number of experiments, with higher results for certain annotation classes. Moreover, 49 false positives that were verified true positives were found by the system but missed by the annotators. Conclusions Our intention is to make this Gold standard, The Stockholm EPR PHI Corpus, available to other research groups in the future. Despite being slightly more time-consuming we believe the manual consensus gold standard is the most valuable for further research. We also propose a set of annotation classes to be used for similar de-identification tasks.
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.
Al Otaiba, Stephanie; Connor, Carol M; Folsom, Jessica Sidler; Greulich, Luana; Meadows, Jane; Li, Zhi
2011-06-01
The purpose of this cluster-randomized control field trial was to was to examine the extent to which kindergarten teachers could learn a promising instructional strategy, wherein kindergarten reading instruction was differentiated based upon students' ongoing assessments of language and literacy skills and documented child characteristic by instruction (CXI) interactions; and to test the efficacy of this differentiated reading instruction on the reading outcomes of students from culturally diverse backgrounds. The study involved 14 schools and included 23 treatment (n = 305 students) and 21 contrast teacher (n = 251 students). Teachers in the contrast condition received only a baseline professional development that included a researcher-delivered summer day-long workshop on individualized instruction. Data sources included parent surveys, individually administered child assessments of language, cognitive, and reading skills and videotapes of classroom instruction. Using Hierarchical Multivariate Linear Modeling (HMLM), we found students in treatment classrooms outperformed students in the contrast classrooms on a latent measure of reading skills, comprised of letter-word reading, decoding, alphabetic knowledge, and phonological awareness (ES = .52). Teachers in both conditions provided small group instruction, but teachers in the treatment condition provided significantly more individualized instruction. Our findings extend research on the efficacy of teachers using Individualized Student Instruction to individualize instruction based upon students' language and literacy skills in first through third grade. Findings are discussed regarding the value of professional development related to differentiating core reading instruction and the challenges of using Response to Intervention approaches to address students' needs in the areas of reading in general education contexts.
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.
基于MRF场的SAR图像分割方法%SAR Image Segmentation Based On Markov Random Field Model
Institute of Scientific and Technical Information of China (English)
张翠; 郦苏丹; 王正志
2001-01-01
In this paper, we propose a SAR image segmentation algorithmbased on Markov Random Field Model. The model captures the image contextual information by making the assumption that the properties of a pixel are dependent upon nearby pixels and are independent of distant pixels. A two-order neighborhood system is used in this paper. The segmentation result is obtained by applying ICM（Iterative Conditional Mode）algorithm. The ICM is an iterative algorithm. During the iteration sequence, each pixel is labeled according to the MAP（maximum a posteriori）criterion. After each pixel labeling step, the parameters of each segmentation class' probability density function are re-estimated from current set of pixel labels. The influence of speckle noise is further reduced by an outlier rejection method. Using this method, outlier pixels are excluded from current pixels' neighborhood. The algorithm is applied to SAR imagery from the MSTAR（Moving and Stationary Target Acquisition and Recognition）datasets. The result shows that this algorithm can efficiently reduce the influence of speckle noise, and segment the image into three parts of target, shadow and background. The experimental results are very satisfactory.%提出了一种基于MRF（Markov Randomfield）模型的SAR（Synthetic ApertureRadar）图像分割算法。本算法利用ICM（Iterative Conditional Mode）局部优化方法，获得MAP（maximum a posteriori）准则下的图像分割结果，并引入了剔除外层数据的机制。用MSTAR（Movingand Stationary Target Acquisitionand Recognition）数据进行实验，结果表明，算法能有效减少斑点噪声的影响，将图像分割为目标、阴影、背景三部分，实验结果是令人满意的。
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Dalia M. Kamel
2017-01-01
Full Text Available The aim of this study was to compare the effects of pulsed electromagnetic field versus pulsed ultrasound in treating patients with postnatal carpal tunnel syndrome. The study was a randomized, double-blinded trial. Forty postnatal female patients with idiopathic carpal tunnel syndrome were divided randomly into two equal groups. One group received pulsed electromagnetic field, with nerve and tendon gliding exercises for the wrist, three times per week for four weeks. The other group received pulsed ultrasound and the same wrist exercises. Pain level, sensory and motor distal latencies and conduction velocities of the median nerve, functional status scale and hand grip strength were assessed pre- and post-treatment. There was a significant decrease (P 0.05. In conclusion, while the symptoms were alleviated in both groups, pulsed electromagnetic field was more effective than pulsed ultrasound in treating postnatal carpal tunnel syndrome.
Simulation of Multi-dimensional Random Fields by Stochastic Harmonic Functions%随机场的随机谐和函数表达
Institute of Scientific and Technical Information of China (English)
梁诗雪; 孙伟玲; 李杰
2012-01-01
首先证明,采取随机谐和函数表述二维随机场,则当随机波数与相位服从独立均匀分布、幅值由随机波数和目标功率谱密度共同决定时,随机谐和函数的功率谱密度精确地等于目标功率谱密度.对这类随机谐和函数的平稳性和渐进正态性进行了讨论.进而,将二维随机场的随机谐和函数表达推广至多维,给出了统一表达式.最后,通过数值算例验证了采用随机谐和函数表述随机场的正确性.%When the random phase angles and random circular frequencies are independent and uniformly distributed, the amplitude can be obtained by the target power spectral density function and the random circular frequencies specifically in the two-dimensional random field. Meanwhile, the power spectral density function is equal to the target one. Then, stochastic harmonic random field proves to be asymptotic to the normal distribution. In addition, the stochastic harmonic functions can be expanded into multi-dimensional random fields by the uniformed expression. Finally, several numerical examples are given to validate the stochastic harmonic function.
RANDOM VIBRATION OF A CURRENT CARRYING CIRCULAR PLATE IN MAGNETIC FIELD%载流圆板的磁弹性随机振动
Institute of Scientific and Technical Information of China (English)
王平; 李晓靓; 白象忠
2012-01-01
The magnetic-elastic random vibration problem of a current-carrying circular plate in a magnetic field is studied. Based on the magneto-elastic theory and the random vibration theory of a continue system with distributed parameters, the magneto-elastic random vibration equation of a current carrying circular plate in a magnetic field was derived. The stochastic response analysis of the circular plate in a magnetic field was finished. The auto-correlation function, power spectral density function and the mean function of the random displacement response of the circular plate in a magnetic field were obtained. In a specific example, the power spectral density function of the displacement response of the circular plate with stationary random current was calculated.%分析载流圆板在磁场中的磁弹性随机振动问题.根据薄板、薄壳体的磁弹性运动方程和连续体的随机振动理论,导出在磁场环境中圆板的磁弹性随机振动方程.对磁场中的载流圆板进行随机响应分析,得到圆板位移响应的自相关函数、功率谱密度函数及均值函数等数字特征.通过具体算例,对处于外加磁场中通入平稳随机电流的导电圆板,计算其位移响应的功率谱密度函数.
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Jaewook Jung
2016-12-01
Full Text Available Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is an essential facility for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS data into ten target classes representing overhead wires, movable brackets and poles, which are key objects in the electrification system. In general, the layout of the railway electrification system shows strong spatial regularity relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF, which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds are converted into a set of line segments to which the labeling process is applied. Support Vector Machine (SVM is used as a local classifier considering only node features for producing the unary potentials of the CRF model. As the short-range pairwise contextual term, the Potts model is applied to enforce a local smoothness in the short-range graph; while long-range pairwise potentials are designed to enhance the spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by the naive Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables, which represent the co-occurrence rate of spatial relations in the horizontal and vertical