Building Simple Hidden Markov Models. Classroom Notes
ERIC Educational Resources Information Center
Ching, Wai-Ki; Ng, Michael K.
2004-01-01
Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.
An introduction to hidden Markov models.
Schuster-Böckler, Benjamin; Bateman, Alex
2007-06-01
This unit introduces the concept of hidden Markov models in computational biology. It describes them using simple biological examples, requiring as little mathematical knowledge as possible. The unit also presents a brief history of hidden Markov models and an overview of their current applications before concluding with a discussion of their limitations.
Phase transitions in Hidden Markov Models
NASA Astrophysics Data System (ADS)
Bechhoefer, John; Lathouwers, Emma
In Hidden Markov Models (HMMs), a Markov process is not directly accessible. In the simplest case, a two-state Markov model ``emits'' one of two ``symbols'' at each time step. We can think of these symbols as noisy measurements of the underlying state. With some probability, the symbol implies that the system is in one state when it is actually in the other. The ability to judge which state the system is in sets the efficiency of a Maxwell demon that observes state fluctuations in order to extract heat from a coupled reservoir. The state-inference problem is to infer the underlying state from such noisy measurements at each time step. We show that there can be a phase transition in such measurements: for measurement error rates below a certain threshold, the inferred state always matches the observation. For higher error rates, there can be continuous or discontinuous transitions to situations where keeping a memory of past observations improves the state estimate. We can partly understand this behavior by mapping the HMM onto a 1d random-field Ising model at zero temperature. We also present more recent work that explores a larger parameter space and more states. Research funded by NSERC, Canada.
Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models
NASA Astrophysics Data System (ADS)
Chan, Lay Guat; Ibrahim, Adriana Irawati Nur Binti
2016-10-01
A hidden Markov model (HMM) is a mixture model which has a Markov chain with finite states as its mixing distribution. HMMs have been applied to a variety of fields, such as speech and face recognitions. The main purpose of this study is to investigate the Bayesian approach to HMMs. Using this approach, we can simulate from the parameters' posterior distribution using some Markov chain Monte Carlo (MCMC) sampling methods. HMMs seem to be useful, but there are some limitations. Therefore, by using the Mixture of Dirichlet processes Hidden Markov Model (MDPHMM) based on Yau et. al (2011), we hope to overcome these limitations. We shall conduct a simulation study using MCMC methods to investigate the performance of this model.
Estimating Neuronal Ageing with Hidden Markov Models
NASA Astrophysics Data System (ADS)
Wang, Bing; Pham, Tuan D.
2011-06-01
Neuronal degeneration is widely observed in normal ageing, meanwhile the neurode-generative disease like Alzheimer's disease effects neuronal degeneration in a faster way which is considered as faster ageing. Early intervention of such disease could benefit subjects with potentials of positive clinical outcome, therefore, early detection of disease related brain structural alteration is required. In this paper, we propose a computational approach for modelling the MRI-based structure alteration with ageing using hidden Markov model. The proposed hidden Markov model based brain structural model encodes intracortical tissue/fluid distribution using discrete wavelet transformation and vector quantization. Further, it captures gray matter volume loss, which is capable of reflecting subtle intracortical changes with ageing. Experiments were carried out on healthy subjects to validate its accuracy and robustness. Results have shown its ability of predicting the brain age with prediction error of 1.98 years without training data, which shows better result than other age predition methods.
Hidden Markov Model Analysis of Multichromophore Photobleaching
Messina, Troy C.; Kim, Hiyun; Giurleo, Jason T.; Talaga, David S.
2007-01-01
The interpretation of single-molecule measurements is greatly complicated by the presence of multiple fluorescent labels. However, many molecular systems of interest consist of multiple interacting components. We investigate this issue using multiply labeled dextran polymers that we intentionally photobleach to the background on a single-molecule basis. Hidden Markov models allow for unsupervised analysis of the data to determine the number of fluorescent subunits involved in the fluorescence intermittency of the 6-carboxy-tetramethylrhodamine labels by counting the discrete steps in fluorescence intensity. The Bayes information criterion allows us to distinguish between hidden Markov models that differ by the number of states, that is, the number of fluorescent molecules. We determine information-theoretical limits and show via Monte Carlo simulations that the hidden Markov model analysis approaches these theoretical limits. This technique has resolving power of one fluorescing unit up to as many as 30 fluorescent dyes with the appropriate choice of dye and adequate detection capability. We discuss the general utility of this method for determining aggregation-state distributions as could appear in many biologically important systems and its adaptability to general photometric experiments. PMID:16913765
Probabilistic Resilience in Hidden Markov Models
NASA Astrophysics Data System (ADS)
Panerati, Jacopo; Beltrame, Giovanni; Schwind, Nicolas; Zeltner, Stefan; Inoue, Katsumi
2016-05-01
Originally defined in the context of ecological systems and environmental sciences, resilience has grown to be a property of major interest for the design and analysis of many other complex systems: resilient networks and robotics systems other the desirable capability of absorbing disruption and transforming in response to external shocks, while still providing the services they were designed for. Starting from an existing formalization of resilience for constraint-based systems, we develop a probabilistic framework based on hidden Markov models. In doing so, we introduce two new important features: stochastic evolution and partial observability. Using our framework, we formalize a methodology for the evaluation of probabilities associated with generic properties, we describe an efficient algorithm for the computation of its essential inference step, and show that its complexity is comparable to other state-of-the-art inference algorithms.
Stochastic motif extraction using hidden Markov model
Fujiwara, Yukiko; Asogawa, Minoru; Konagaya, Akihiko
1994-12-31
In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. A stochastic protein motif represents the small segments of protein sequences that have a certain function or structure. The stochastic motif, represented by an HMM, has conditional probabilities to deal with the stochastic nature of the motif. This HMM directive reflects the characteristics of the motif, such as a protein periodical structure or grouping. In order to obtain the optimal HMM, we developed the {open_quotes}iterative duplication method{close_quotes} for HMM topology learning. It starts from a small fully-connected network and iterates the network generation and parameter optimization until it achieves sufficient discrimination accuracy. Using this method, we obtained an HMM for a leucine zipper motif. Compared to the accuracy of a symbolic pattern representation with accuracy of 14.8 percent, an HMM achieved 79.3 percent in prediction. Additionally, the method can obtain an HMM for various types of zinc finger motifs, and it might separate the mixed data. We demonstrated that this approach is applicable to the validation of the protein databases; a constructed HMM b as indicated that one protein sequence annotated as {open_quotes}lencine-zipper like sequence{close_quotes} in the database is quite different from other leucine-zipper sequences in terms of likelihood, and we found this discrimination is plausible.
Hidden Markov models in automatic speech recognition
NASA Astrophysics Data System (ADS)
Wrzoskowicz, Adam
1993-11-01
This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.
Application of Hidden Markov Models in Biomolecular Simulations.
Shukla, Saurabh; Shamsi, Zahra; Moffett, Alexander S; Selvam, Balaji; Shukla, Diwakar
2017-01-01
Hidden Markov models (HMMs) provide a framework to analyze large trajectories of biomolecular simulation datasets. HMMs decompose the conformational space of a biological molecule into finite number of states that interconvert among each other with certain rates. HMMs simplify long timescale trajectories for human comprehension, and allow comparison of simulations with experimental data. In this chapter, we provide an overview of building HMMs for analyzing bimolecular simulation datasets. We demonstrate the procedure for building a Hidden Markov model for Met-enkephalin peptide simulation dataset and compare the timescales of the process.
A HIERARCHICAL HIDDEN MARKOV DETERIORATION MODEL FOR PAVEMENT STRUCTURE
NASA Astrophysics Data System (ADS)
Kobayashi, Kiyoshi; Kaito, Kiyoyuki; Eguchi, Toshiyuki; Ohi, Akira; Okizuka, Ryosuke
The deterioration process of pavement is a complex process including the deterioration of road surface and the decrease in load bearing capacity of the entire pavement. The decrease in load bearing capacity influences the deterioration rate of road surface. The soundness of road surface can be observed by a road surface condition investigation. On the other hand, the decrease in load bearing capacity can be partially observed through the FWD testing, etc. In this study, such a deterioration process of road surface is described as a mixed Markov process that depends on the load bearing capacity of pavement. Then, the complex deterioration process, which is composed of the deterioration of road surface and the decrease in load bearing capacity of pavement, is expressed as a hierarchical hidden Markov deterioration model. Through a case study of the application into the expressway, a hierarchical hidden Markov deterioration model is estimated, and its applicability and effectiveness are empirically discussed.
Image segmentation using hidden Markov Gauss mixture models.
Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M
2007-07-01
Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.
Multivariate longitudinal data analysis with mixed effects hidden Markov models.
Raffa, Jesse D; Dubin, Joel A
2015-09-01
Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.
Quantum hidden Markov models based on transition operation matrices
NASA Astrophysics Data System (ADS)
Cholewa, Michał; Gawron, Piotr; Głomb, Przemysław; Kurzyk, Dariusz
2017-04-01
In this work, we extend the idea of quantum Markov chains (Gudder in J Math Phys 49(7):072105 [3]) in order to propose quantum hidden Markov models (QHMMs). For that, we use the notions of transition operation matrices and vector states, which are an extension of classical stochastic matrices and probability distributions. Our main result is the Mealy QHMM formulation and proofs of algorithms needed for application of this model: Forward for general case and Vitterbi for a restricted class of QHMMs. We show the relations of the proposed model to other quantum HMM propositions and present an example of application.
A Hidden Markov Approach to Modeling Interevent Earthquake Times
NASA Astrophysics Data System (ADS)
Chambers, D.; Ebel, J. E.; Kafka, A. L.; Baglivo, J.
2003-12-01
A hidden Markov process, in which the interevent time distribution is a mixture of exponential distributions with different rates, is explored as a model for seismicity that does not follow a Poisson process. In a general hidden Markov model, one assumes that a system can be in any of a finite number k of states and there is a random variable of interest whose distribution depends on the state in which the system resides. The system moves probabilistically among the states according to a Markov chain; that is, given the history of visited states up to the present, the conditional probability that the next state is a specified one depends only on the present state. Thus the transition probabilities are specified by a k by k stochastic matrix. Furthermore, it is assumed that the actual states are unobserved (hidden) and that only the values of the random variable are seen. From these values, one wishes to estimate the sequence of states, the transition probability matrix, and any parameters used in the state-specific distributions. The hidden Markov process was applied to a data set of 110 interevent times for earthquakes in New England from 1975 to 2000. Using the Baum-Welch method (Baum et al., Ann. Math. Statist. 41, 164-171), we estimate the transition probabilities, find the most likely sequence of states, and estimate the k means of the exponential distributions. Using k=2 states, we found the data were fit well by a mixture of two exponential distributions, with means of approximately 5 days and 95 days. The steady state model indicates that after approximately one fourth of the earthquakes, the waiting time until the next event had the first exponential distribution and three fourths of the time it had the second. Three and four state models were also fit to the data; the data were inconsistent with a three state model but were well fit by a four state model.
Visual Recognition of American Sign Language Using Hidden Markov Models.
1995-02-01
3.3 Previous Use of Hidden Markov Models in Gesture Recognition 19 3.4 Use of HMM’s for Recognizing Sign Language 20 4 Tracking and Modeling...Instead, computer systems may be employed to annotate certain features of sequences. A human gesture recognition system adds another dimension to...focus for many gesture recognition systems. Tracking the natural hand in real time using camera imagery is dif- ficult, but successful systems have
Probabilistic Independence Networks for Hidden Markov Probability Models
NASA Technical Reports Server (NTRS)
Smyth, Padhraic; Heckerman, Cavid; Jordan, Michael I
1996-01-01
In this paper we explore hidden Markov models(HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general enference algorithms for arbitrary PINs.
Hidden Markov Models for Fault Detection in Dynamic Systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic
1994-01-01
Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.
Bayesian restoration of ion channel records using hidden Markov models.
Rosales, R; Stark, J A; Fitzgerald, W J; Hladky, S B
2001-03-01
Hidden Markov models have been used to restore recorded signals of single ion channels buried in background noise. Parameter estimation and signal restoration are usually carried out through likelihood maximization by using variants of the Baum-Welch forward-backward procedures. This paper presents an alternative approach for dealing with this inferential task. The inferences are made by using a combination of the framework provided by Bayesian statistics and numerical methods based on Markov chain Monte Carlo stochastic simulation. The reliability of this approach is tested by using synthetic signals of known characteristics. The expectations of the model parameters estimated here are close to those calculated using the Baum-Welch algorithm, but the present methods also yield estimates of their errors. Comparisons of the results of the Bayesian Markov Chain Monte Carlo approach with those obtained by filtering and thresholding demonstrate clearly the superiority of the new methods.
A coupled hidden Markov model for disease interactions
Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike
2013-01-01
To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. PMID:24223436
A coupled hidden Markov model for disease interactions.
Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike
2013-08-01
To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis-Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites.
Hidden Markov latent variable models with multivariate longitudinal data.
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu
2017-03-01
Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.
Modeling Driver Behavior near Intersections in Hidden Markov Model.
Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei
2016-12-21
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers' behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents.
Modeling Driver Behavior near Intersections in Hidden Markov Model
Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei
2016-01-01
Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838
Behavior Detection using Confidence Intervals of Hidden Markov Models
Griffin, Christopher H
2009-01-01
Markov models are commonly used to analyze real-world problems. Their combination of discrete states and stochastic transitions is suited to applications with deterministic and stochastic components. Hidden Markov Models (HMMs) are a class of Markov model commonly used in pattern recognition. Currently, HMMs recognize patterns using a maximum likelihood approach. One major drawback with this approach is that data observations are mapped to HMMs without considering the number of data samples available. Another problem is that this approach is only useful for choosing between HMMs. It does not provide a criteria for determining whether or not a given HMM adequately matches the data stream. In this work, we recognize complex behaviors using HMMs and confidence intervals. The certainty of a data match increases with the number of data samples considered. Receiver Operating Characteristic curves are used to find the optimal threshold for either accepting or rejecting a HMM description. We present one example using a family of HMM's to show the utility of the proposed approach. A second example using models extracted from a database of consumer purchases provides additional evidence that this approach can perform better than existing techniques.
Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model
2011-01-01
have learning in them and rely on heavy processing of the data to determine the intent of the person. M . Cristani et al [1] uses non-traditional AI...Applications (IJAIA), Vol.2, No.1, January 2011 3 Template matching is performed by M . Dimitrijevic et. al. [2]. They developed a template...hidden nodes and M be the total number of observations. Let T be the total number of transitions (or time). Let state at time t is Si where 1 ≤ i
Hidden Markov model using Dirichlet process for de-identification.
Chen, Tao; Cullen, Richard M; Godwin, Marshall
2015-12-01
For the 2014 i2b2/UTHealth de-identification challenge, we introduced a new non-parametric Bayesian hidden Markov model using a Dirichlet process (HMM-DP). The model intends to reduce task-specific feature engineering and to generalize well to new data. In the challenge we developed a variational method to learn the model and an efficient approximation algorithm for prediction. To accommodate out-of-vocabulary words, we designed a number of feature functions to model such words. The results show the model is capable of understanding local context cues to make correct predictions without manual feature engineering and performs as accurately as state-of-the-art conditional random field models in a number of categories. To incorporate long-range and cross-document context cues, we developed a skip-chain conditional random field model to align the results produced by HMM-DP, which further improved the performance.
Sequence alignments and pair hidden Markov models using evolutionary history.
Knudsen, Bjarne; Miyamoto, Michael M
2003-10-17
This work presents a novel pairwise statistical alignment method based on an explicit evolutionary model of insertions and deletions (indels). Indel events of any length are possible according to a geometric distribution. The geometric distribution parameter, the indel rate, and the evolutionary time are all maximum likelihood estimated from the sequences being aligned. Probability calculations are done using a pair hidden Markov model (HMM) with transition probabilities calculated from the indel parameters. Equations for the transition probabilities make the pair HMM closely approximate the specified indel model. The method provides an optimal alignment, its likelihood, the likelihood of all possible alignments, and the reliability of individual alignment regions. Human alpha and beta-hemoglobin sequences are aligned, as an illustration of the potential utility of this pair HMM approach.
Sensitive protein comparisons with profiles and hidden Markov models.
Hofmann, K
2000-05-01
Sequence database searches have become an important tool for the life sciences in general and for gene discovery-driven biotechnology in particular. Both the functional assignment of newly found proteins and the mining of genome databases for functional candidates are equally important tasks typically addressed by database searches. Sensitivity and reliability of the search methods are of crucial importance. The overall performance of sequence alignments and database searches can be enhanced considerably, when profiles or hidden Markov models (HMMs) derived from protein families are used as query objects instead of single sequences. This review discusses the concept of profiles, generalised profiles and profile-HMMs, the methods how they are constructed and the scope of possible applications in gene discovery and gene functional assignment.
Hidden Markov models for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic J. (Inventor)
1995-01-01
The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.
Hidden Markov models for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic J. (Inventor)
1993-01-01
The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.
Data Stream Prediction Using Incremental Hidden Markov Models
NASA Astrophysics Data System (ADS)
Wakabayashi, Kei; Miura, Takao
In this paper, we propose a new technique for time-series prediction. Here we assume that time-series data occur depending on event which is unobserved directly, and we estimate future data as output from the most likely event which will happen at the time. In this investigation we model time-series based on event sequence by using Hidden Markov Model(HMM), and extract time-series patterns as trained HMM parameters. However, we can’t apply HMM approach to data stream prediction in a straightforward manner. This is because Baum-Welch algorithm, which is traditional unsupervised HMM training algorithm, requires many stored historical data and scan it many times. Here we apply incremental Baum-Welch algorithm which is an on-line HMM training method, and estimate HMM parameters dynamically to adapt new time-series patterns. And we show some experimental results to see the validity of our method.
Stylistic gait synthesis based on hidden Markov models
NASA Astrophysics Data System (ADS)
Tilmanne, Joëlle; Moinet, Alexis; Dutoit, Thierry
2012-12-01
In this work we present an expressive gait synthesis system based on hidden Markov models (HMMs), following and modifying a procedure originally developed for speaking style adaptation, in speech synthesis. A large database of neutral motion capture walk sequences was used to train an HMM of average walk. The model was then used for automatic adaptation to a particular style of walk using only a small amount of training data from the target style. The open source toolkit that we adapted for motion modeling also enabled us to take into account the dynamics of the data and to model accurately the duration of each HMM state. We also address the assessment issue and propose a procedure for qualitative user evaluation of the synthesized sequences. Our tests show that the style of these sequences can easily be recognized and look natural to the evaluators.
Understanding eye movements in face recognition using hidden Markov models.
Chuk, Tim; Chan, Antoni B; Hsiao, Janet H
2014-09-16
We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone.
pHMM-tree: phylogeny of profile hidden Markov models.
Huo, Luyang; Zhang, Han; Huo, Xueting; Yang, Yasong; Li, Xueqiong; Yin, Yanbin
2017-01-05
Protein families are often represented by profile hidden Markov models (pHMMs). Homology between two distant protein families can be determined by comparing the pHMMs. Here we explored the idea of building a phylogeny of protein families using the distance matrix of their pHMMs. We developed a new software and web server (pHMM-tree) to allow four major types of inputs: (i) multiple pHMM files, (ii) multiple aligned protein sequence files, (iii) mixture of pHMM and aligned sequence files and (iv) unaligned protein sequences in a single file. The output will be a pHMM phylogeny of different protein families delineating their relationships. We have applied pHMM-tree to build phylogenies for CAZyme (carbohydrate active enzyme) classes and Pfam clans, which attested its usefulness in the phylogenetic representation of the evolutionary relationship among distant protein families.
Hidden Markov models for evolution and comparative genomics analysis.
Bykova, Nadezda A; Favorov, Alexander V; Mironov, Andrey A
2013-01-01
The problem of reconstruction of ancestral states given a phylogeny and data from extant species arises in a wide range of biological studies. The continuous-time Markov model for the discrete states evolution is generally used for the reconstruction of ancestral states. We modify this model to account for a case when the states of the extant species are uncertain. This situation appears, for example, if the states for extant species are predicted by some program and thus are known only with some level of reliability; it is common for bioinformatics field. The main idea is formulation of the problem as a hidden Markov model on a tree (tree HMM, tHMM), where the basic continuous-time Markov model is expanded with the introduction of emission probabilities of observed data (e.g. prediction scores) for each underlying discrete state. Our tHMM decoding algorithm allows us to predict states at the ancestral nodes as well as to refine states at the leaves on the basis of quantitative comparative genomics. The test on the simulated data shows that the tHMM approach applied to the continuous variable reflecting the probabilities of the states (i.e. prediction score) appears to be more accurate then the reconstruction from the discrete states assignment defined by the best score threshold. We provide examples of applying our model to the evolutionary analysis of N-terminal signal peptides and transcription factor binding sites in bacteria. The program is freely available at http://bioinf.fbb.msu.ru/~nadya/tHMM and via web-service at http://bioinf.fbb.msu.ru/treehmmweb.
A Bayesian Hidden Markov Model-based approach for anomaly detection in electronic systems
NASA Astrophysics Data System (ADS)
Dorj, E.; Chen, C.; Pecht, M.
Early detection of anomalies in any system or component prevents impending failures and enhances performance and availability. The complex architecture of electronics, the interdependency of component functionalities, and the miniaturization of most electronic systems make it difficult to detect and analyze anomalous behaviors. A Hidden Markov Model-based classification technique determines unobservable hidden behaviors of complex and remotely inaccessible electronic systems using observable signals. This paper presents a data-driven approach for anomaly detection in electronic systems based on a Bayesian Hidden Markov Model classification technique. The posterior parameters of the Hidden Markov Models are estimated using the conjugate prior method. An application of the developed Bayesian Hidden Markov Model-based anomaly detection approach is presented for detecting anomalous behavior in Insulated Gate Bipolar Transistors using experimental data. The detection results illustrate that the developed anomaly detection approach can help detect anomalous behaviors in electronic systems, which can help prevent system downtime and catastrophic failures.
Volatility: A hidden Markov process in financial time series
NASA Astrophysics Data System (ADS)
Eisler, Zoltán; Perelló, Josep; Masoliver, Jaume
2007-11-01
Volatility characterizes the amplitude of price return fluctuations. It is a central magnitude in finance closely related to the risk of holding a certain asset. Despite its popularity on trading floors, volatility is unobservable and only the price is known. Diffusion theory has many common points with the research on volatility, the key of the analogy being that volatility is a time-dependent diffusion coefficient of the random walk for the price return. We present a formal procedure to extract volatility from price data by assuming that it is described by a hidden Markov process which together with the price forms a two-dimensional diffusion process. We derive a maximum-likelihood estimate of the volatility path valid for a wide class of two-dimensional diffusion processes. The choice of the exponential Ornstein-Uhlenbeck (expOU) stochastic volatility model performs remarkably well in inferring the hidden state of volatility. The formalism is applied to the Dow Jones index. The main results are that (i) the distribution of estimated volatility is lognormal, which is consistent with the expOU model, (ii) the estimated volatility is related to trading volume by a power law of the form σ∝V0.55 , and (iii) future returns are proportional to the current volatility, which suggests some degree of predictability for the size of future returns.
Bayesian Hidden Markov Modeling of Array CGH Data.
Guha, Subharup; Li, Yi; Neuberg, Donna
2008-06-01
Genomic alterations have been linked to the development and progression of cancer. The technique of comparative genomic hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data.We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Because the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme, and breast cancer are analyzed, and comparisons are made with some widely used algorithms to illustrate the reliability and success of the technique.
Efficient inference of hidden Markov models from large observation sequences
NASA Astrophysics Data System (ADS)
Priest, Benjamin W.; Cybenko, George
2016-05-01
The hidden Markov model (HMM) is widely used to model time series data. However, the conventional Baum- Welch algorithm is known to perform poorly when applied to long observation sequences. The literature contains several alternatives that seek to improve the memory or time complexity of the algorithm. However, for an HMM with N states and an observation sequence of length T, these alternatives require at best O(N) space and O(N2T) time. Given the preponderance of applications that increasingly deal with massive amounts of data, an alternative whose time is O(T)+poly(N) is desired. Recent research presents an alternative to the Baum-Welch algorithm that relies on nonnegative matrix factorization. This document examines the space complexity of this alternative approach and proposes further optimizations using approaches adopted from the matrix sketching literature. The result is a streaming algorithm whose space complexity is constant and time complexity is linear with respect to the size of the observation sequence. The paper also presents a batch algorithm that allow for even further improved space complexity at the expense of an additional pass over the observation sequence.
Hidden Markov models from molecular dynamics simulations on DNA.
Thayer, Kelly M; Beveridge, D L
2002-06-25
An enhanced bioinformatics tool incorporating the participation of molecular structure as well as sequence in protein DNA recognition is proposed and tested. Boltzmann probability models of sequence-dependent DNA structure from all-atom molecular dynamics simulations were obtained and incorporated into hidden Markov models (HMMs) that can recognize molecular structural signals as well as sequence in protein-DNA binding sites on a genome. The binding of catabolite activator protein (CAP) to cognate DNA sequences was used as a prototype case for implementation and testing of the method. The results indicate that even HMMs based on probabilistic roll/tilt dinucleotide models of sequence-dependent DNA structure have some capability to discriminate between known CAP binding and nonbinding sites and to predict putative CAP binding sites in unknowns. Restricting HMMs to sequence only in regions of strong consensus in which the protein makes base specific contacts with the cognate DNA further improved the discriminatory capabilities of the HMMs. Comparison of results with controls based on sequence only indicates that extending the definition of consensus from sequence to structure improves the transferability of the HMMs, and provides further supportive evidence of a role for dynamical molecular structure as well as sequence in genomic regulatory mechanisms.
Grasp Recognition by Fuzzy Modeling and Hidden Markov Models
NASA Astrophysics Data System (ADS)
Palm, Rainer; Iliev, Boyko; Kadmiry, Bourhane
Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. This chapter describes three different methods for grasp recognition for a human hand. A human operator wearing a data glove instructs the robot to perform different grasps. For a number of human grasps the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using time as input parameter and joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are compared with each other. In the first method, the test grasp is compared with model grasps using the difference between the model outputs. The second method deals with qualitative fuzzy models which used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning.
Optical character recognition of handwritten Arabic using hidden Markov models
NASA Astrophysics Data System (ADS)
Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.
2011-04-01
The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.
Optical character recognition of handwritten Arabic using hidden Markov models
Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M
2011-01-01
The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.
A new constant memory recursion for hidden Markov models.
Bartolucci, Francesco; Pandolfi, Silvia
2014-02-01
We develop the recursion for hidden Markov (HM) models proposed by Bartolucci and Besag (2002), and we show how it may be used to implement an estimation algorithm for these models that requires an amount of memory not depending on the length of the observed series of data. This recursion allows us to obtain the conditional distribution of the latent state at every occasion, given the previous state and the observed data. With respect to the estimation algorithm based on the well-known Baum-Welch recursions, which requires an amount of memory that increases with the sample size, the proposed algorithm also has the advantage of not requiring dummy renormalizations to avoid numerical problems. Moreover, it directly allows us to perform global decoding of the latent sequence of states, without the need of a Viterbi method and with a consistent reduction of the memory requirement with respect to the latter. The proposed approach is compared, in terms of computing time and memory requirement, with the algorithm based on the Baum-Welch recursions and with the so-called linear memory algorithm of Churbanov and Winters-Hilt. The comparison is also based on a series of simulations involving an HM model for continuous time-series data.
Continuous myoelectric control for powered prostheses using hidden Markov models.
Chan, Adrian D C; Englehart, Kevin B
2005-01-01
This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Nonparametric model validations for hidden Markov models with applications in financial econometrics
Zhao, Zhibiao
2011-01-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601
Efficient decoding algorithms for generalized hidden Markov model gene finders
Majoros, William H; Pertea, Mihaela; Delcher, Arthur L; Salzberg, Steven L
2005-01-01
Background The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. Results As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. Conclusions In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques. PMID:15667658
Accelerating Information Retrieval from Profile Hidden Markov Model Databases
Ashhab, Yaqoub; Tamimi, Hashem
2016-01-01
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases. PMID:27875548
Accelerating Information Retrieval from Profile Hidden Markov Model Databases.
Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem
2016-01-01
Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.
A robust hidden semi-Markov model with application to aCGH data processing.
Ding, Jiarui; Shah, Sohrab
2013-01-01
Hidden semi-Markov models are effective at modelling sequences with succession of homogenous zones by choosing appropriate state duration distributions. To compensate for model mis-specification and provide protection against outliers, we design a robust hidden semi-Markov model with Student's t mixture models as the emission distributions. The proposed approach is used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and glioblastoma multiforme data illustrate the reliability of the technique.
Wang, Hongyan; Zhou, Xiaobo
2013-04-01
By altering the electrostatic charge of histones or providing binding sites to protein recognition molecules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice.
Evidence Feed Forward Hidden Markov Models for Visual Human Action Classification (Preprint)
2011-04-12
Features for 3-D Jester Recognition,” Proceedings from IEEE Automatic Face and Gesture Recognition (AFGR), 1996, pp. 157-162. 9. Yu, C., Ballard, D...pp. 1-4, doi:10.1109/ICPR.2008.4761290. 11. Wilson, A., Bobick, A., “Parametric Hidden Markov Models for Gesture Recognition ,” IEEE Transaction on
Post processing with first- and second-order hidden Markov models
NASA Astrophysics Data System (ADS)
Taghva, Kazem; Poudel, Srijana; Malreddy, Spandana
2013-01-01
In this paper, we present the implementation and evaluation of first order and second order Hidden Markov Models to identify and correct OCR errors in the post processing of books. Our experiments show that the first order model approximately corrects 10% of the errors with 100% precision, while the second order model corrects a higher percentage of errors with much lower precision.
Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments
NASA Astrophysics Data System (ADS)
Moe, Marie E. G.; Helvik, Bjarne E.; Knapskog, Svein J.
Computational trust and reputation models are used to aid the decision-making process in complex dynamic environments, where we are unable to obtain perfect information about the interaction partners. In this paper we present a comparison of our proposed hidden Markov trust model to the Beta reputation system. The hidden Markov trust model takes the time between observations into account, it also distinguishes between system states and uses methods previously applied to intrusion detection for the prediction of which state an agent is in. We show that the hidden Markov trust model performs better when it comes to the detection of changes in behavior of agents, due to its larger richness in model features. This means that our trust model may be more realistic in dynamic environments. However, the increased model complexity also leads to bigger challenges in estimating parameter values for the model. We also show that the hidden Markov trust model can be parameterized so that it responds similarly to the Beta reputation system.
Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms
ERIC Educational Resources Information Center
Anderson, John R.
2012-01-01
Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…
Discovery of multiple hidden allosteric sites by combining Markov state models and experiments.
Bowman, Gregory R; Bolin, Eric R; Hart, Kathryn M; Maguire, Brendan C; Marqusee, Susan
2015-03-03
The discovery of drug-like molecules that bind pockets in proteins that are not present in crystallographic structures yet exert allosteric control over activity has generated great interest in designing pharmaceuticals that exploit allosteric effects. However, there have only been a small number of successes, so the therapeutic potential of these pockets--called hidden allosteric sites--remains unclear. One challenge for assessing their utility is that rational drug design approaches require foreknowledge of the target site, but most hidden allosteric sites are only discovered when a small molecule is found to stabilize them. We present a means of decoupling the identification of hidden allosteric sites from the discovery of drugs that bind them by drawing on new developments in Markov state modeling that provide unprecedented access to microsecond- to millisecond-timescale fluctuations of a protein's structure. Visualizing these fluctuations allows us to identify potential hidden allosteric sites, which we then test via thiol labeling experiments. Application of these methods reveals multiple hidden allosteric sites in an important antibiotic target--TEM-1 β-lactamase. This result supports the hypothesis that there are many as yet undiscovered hidden allosteric sites and suggests our methodology can identify such sites, providing a starting point for future drug design efforts. More generally, our results demonstrate the power of using Markov state models to guide experiments.
Wavelet-based SAR images despeckling using joint hidden Markov model
NASA Astrophysics Data System (ADS)
Li, Qiaoliang; Wang, Guoyou; Liu, Jianguo; Chen, Shaobo
2007-11-01
In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, we propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). The Expectation Maximization algorithm is used to estimate hyperparameters and specify the mixture model. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.
STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning
Kappel, David; Nessler, Bernhard; Maass, Wolfgang
2014-01-01
In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787
The Moments of Matched and Mismatched Hidden Markov Models
1987-06-11
preprocessor. Denote by hkj the probability that the observation symbol Vk is altered to symbol V. by the noise mechanism and define the m-by-m noise3...probability matrix H = [ hkj ]. It is assumed that H is independent of the state of the Markov chain and of time t. Consequently, the output of a given HMM...chain is i and that symbol j is produced, given that symbol k was the output of the given HMM. The sum over k of bik hkj gives the component b.j of the
NASA Astrophysics Data System (ADS)
Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N.
2010-07-01
Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders, we fit hidden Markov models to the time series of the sign of the tick-by-tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a significant majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transaction size distributions of these patches are fat tailed. Long patches are characterized by a large fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly dependent on the local market trend. We also compare the hidden Markov model patches with those obtained with the segmentation method used in Vaglica et al (2008 Phys. Rev. E 77 036110), and we conclude that the former ones can be interpreted as a partition of the latter ones.
The Application of Wavelet-Domain Hidden Markov Tree Model in Diabetic Retinal Image Denoising.
Cui, Dong; Liu, Minmin; Hu, Lei; Liu, Keju; Guo, Yongxin; Jiao, Qing
2015-01-01
The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and correlation of fundus angiographic images' wavelet coefficients among scales. Based on the construction of the fundus angiographic images Hidden Markov Tree Models and Gaussian Mixture Models, this paper applied expectation-maximum algorithm to estimate the wavelet coefficients of original fundus angiographic images and the Bayesian estimation to achieve the goal of fundus angiographic images denoising. As is shown in the experimental result, compared with the other algorithms as mean filter and median filter, this method effectively improved the peak signal to noise ratio of fundus angiographic images after denoising and preserved the details of vascular edge in fundus angiographic images.
A hidden Markov model for space-time precipitation
Zucchini, W. ); Guttorp, P. )
1991-08-01
Stochastic models for precipitation events in space and time over mesoscale spatial areas have important applications in hydrology, both as input to runoff models and as parts of general circulation models (GCMs) of global climate. A family of multivariate models for the occurrence/nonoccurrence of precipitation at N sites is constructed by assuming a different probability of events at the sites for each of a number of unobservable climate states. The climate process is assumed to follow a Markov chain. Simple formulae for first- and second-order parameter functions are derived, and used to find starting values for a numerical maximization of the likelihood. The method is illustrated by applying it to data for one site in Washington and to data for a network in the Great plains.
Hidden Markov models and other machine learning approaches in computational molecular biology
Baldi, P.
1995-12-31
This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.
Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models.
Wang, Xinlei; Zang, Miao; Xiao, Guanghua
2013-06-15
Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method.
NASA Astrophysics Data System (ADS)
Choi, Yeontaek; Sim, Seungwoo; Lee, Sang-Hee
2014-06-01
The locomotion behavior of Caenorhabditis elegans has been extensively studied to understand the relationship between the changes in the organism's neural activity and the biomechanics. However, so far, we have not yet achieved the understanding. This is because the worm complicatedly responds to the environmental factors, especially chemical stress. Constructing a mathematical model is helpful for the understanding the locomotion behavior in various surrounding conditions. In the present study, we built three hidden Markov models for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a polluted environment by formaldehyde, toluene, and benzene (0.1 ppm and 0.5 ppm for each case). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity entropy and classified into five groups by using the self-organizing map. To evaluate and establish the hidden Markov models, we compared correlation coefficients between the simulated behavior (i.e. temporal pattern sequence) generated by the models and the actual crawling behavior. The comparison showed that the hidden Markov models are successful to characterize the crawling behavior. In addition, we briefly discussed the possibility of using the models together with the entropy to develop bio-monitoring systems for determining water quality.
Detecting critical state before phase transition of complex systems by hidden Markov model
NASA Astrophysics Data System (ADS)
Liu, Rui; Chen, Pei; Li, Yongjun; Chen, Luonan
Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e., before-transition state, pre-transition state, and after-transition state, which can be considered as three different Markov processes. Thus, based on this dynamical feature, we present a novel computational method, i.e., hidden Markov model (HMM), to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e., the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin, and HCV-induced dysplasia and hepatocellular carcinoma.
Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes
Katahira, Kentaro; Suzuki, Kenta; Okanoya, Kazuo; Okada, Masato
2011-01-01
Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies. PMID:21915345
Complex sequencing rules of birdsong can be explained by simple hidden Markov processes.
Katahira, Kentaro; Suzuki, Kenta; Okanoya, Kazuo; Okada, Masato
2011-01-01
Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies.
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.
2016-01-01
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571
A path-independent method for barrier option pricing in hidden Markov models
NASA Astrophysics Data System (ADS)
Rashidi Ranjbar, Hedieh; Seifi, Abbas
2015-12-01
This paper presents a method for barrier option pricing under a Black-Scholes model with Markov switching. We extend the option pricing method of Buffington and Elliott to price continuously monitored barrier options under a Black-Scholes model with regime switching. We use a regime switching random Esscher transform in order to determine an equivalent martingale pricing measure, and then solve the resulting multidimensional integral for pricing barrier options. We have calculated prices for down-and-out call options under a two-state hidden Markov model using two different Monte-Carlo simulation approaches and the proposed method. A comparison of the results shows that our method is faster than Monte-Carlo simulation methods.
A nonhomogeneous hidden markov model for gene mapping based on next-generation sequencing data.
Ghavidel, Fatemeh Zamanzad; Claesen, Jürgen; Burzykowski, Tomasz
2015-02-01
The analysis of polygenetic characteristics for mapping quantitative trait loci (QTL) remains an important challenge. QTL analysis requires two or more strains of organisms that differ substantially in the (poly-)genetic trait of interest, resulting in a heterozygous offspring. The offspring with the trait of interest is selected and subsequently screened for molecular markers such as single-nucleotide polymorphisms (SNPs) with next-generation sequencing. Gene mapping relies on the co-segregation between genes and/or markers. Genes and/or markers that are linked to a QTL influencing the trait will segregate more frequently with this locus. For each identified marker, observed mismatch frequencies between the reads of the offspring and the parental reference strains can be modeled by a multinomial distribution with the probabilities depending on the state of an underlying, unobserved Markov process. The states indicate whether the SNP is located in a (vicinity of a) QTL or not. Consequently, genomic loci associated with the QTL can be discovered by analyzing hidden states along the genome. The aforementioned hidden Markov model assumes that the identified SNPs are equally distributed along the chromosome and does not take the distance between neighboring SNPs into account. The distance between the neighboring SNPs could influence the chance of co-segregation between genes and markers. To address this issue, we propose a nonhomogeneous hidden Markov model with a transition matrix that depends on a set of distance-varying observed covariates. The application of the model is illustrated on the data from a study of ethanol tolerance in yeast.
NASA Astrophysics Data System (ADS)
Dong, Ming; He, David
2007-07-01
Diagnostics and prognostics are two important aspects in a condition-based maintenance (CBM) program. However, these two tasks are often separately performed. For example, data might be collected and analysed separately for diagnosis and prognosis. This practice increases the cost and reduces the efficiency of CBM and may affect the accuracy of the diagnostic and prognostic results. In this paper, a statistical modelling methodology for performing both diagnosis and prognosis in a unified framework is presented. The methodology is developed based on segmental hidden semi-Markov models (HSMMs). An HSMM is a hidden Markov model (HMM) with temporal structures. Unlike HMM, an HSMM does not follow the unrealistic Markov chain assumption and therefore provides more powerful modelling and analysis capability for real problems. In addition, an HSMM allows modelling the time duration of the hidden states and therefore is capable of prognosis. To facilitate the computation in the proposed HSMM-based diagnostics and prognostics, new forward-backward variables are defined and a modified forward-backward algorithm is developed. The existing state duration estimation methods are inefficient because they require a huge storage and computational load. Therefore, a new approach is proposed for training HSMMs in which state duration probabilities are estimated on the lattice (or trellis) of observations and states. The model parameters are estimated through the modified forward-backward training algorithm. The estimated state duration probability distributions combined with state-changing point detection can be used to predict the useful remaining life of a system. The evaluation of the proposed methodology was carried out through a real world application: health monitoring of hydraulic pumps. In the tests, the recognition rates for all states are greater than 96%. For each individual pump, the recognition rate is increased by 29.3% in comparison with HMMs. Because of the temporal
Alignment of multiple proteins with an ensemble of Hidden Markov Models
Song, Yinglei; Qu, Junfeng; Hura, Gurdeep S.
2011-01-01
In this paper, we developed a new method that progressively construct and update a set of alignments by adding sequences in certain order to each of the existing alignments. Each of the existing alignments is modelled with a profile Hidden Markov Model (HMM) and an added sequence is aligned to each of these profile HMMs. We introduced an integer parameter for the number of profile HMMs. The profile HMMs are then updated based on the alignments with leading scores. Our experiments on BaliBASE showed that our approach could efficiently explore the alignment space and significantly improve the alignment accuracy. PMID:20376922
The hidden Markov Topic model: a probabilistic model of semantic representation.
Andrews, Mark; Vigliocco, Gabriella
2010-01-01
In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag-of-words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag-of-words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), preserving its strengths while extending its scope to incorporate more fine-grained linguistic information.
(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond
NASA Technical Reports Server (NTRS)
Baldi, Pierre
1994-01-01
We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.
Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors
NASA Astrophysics Data System (ADS)
Zhang, Yingjun; Liu, Wen; Yang, Xuefeng; Xing, Shengwei
2015-02-01
In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.
Under-reported data analysis with INAR-hidden Markov chains.
Fernández-Fontelo, Amanda; Cabaña, Alejandra; Puig, Pedro; Moriña, David
2016-11-20
In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd.
Uddin, Md; Lee, J J; Kim, T S
2008-01-01
In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.
Statistical Inference in Hidden Markov Models Using k-Segment Constraints.
Titsias, Michalis K; Holmes, Christopher C; Yau, Christopher
2016-01-02
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward-backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online.
Statistical Inference in Hidden Markov Models Using k-Segment Constraints
Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher
2016-01-01
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward–backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. PMID:27226674
Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)
NASA Astrophysics Data System (ADS)
Can, Ceren Eda; Ergun, Gul; Gokceoglu, Candan
2014-09-01
Earthquakes are one of the most important natural hazards to be evaluated carefully in engineering projects, due to the severely damaging effects on human-life and human-made structures. The hazard of an earthquake is defined by several approaches and consequently earthquake parameters such as peak ground acceleration occurring on the focused area can be determined. In an earthquake prone area, the identification of the seismicity patterns is an important task to assess the seismic activities and evaluate the risk of damage and loss along with an earthquake occurrence. As a powerful and flexible framework to characterize the temporal seismicity changes and reveal unexpected patterns, Poisson hidden Markov model provides a better understanding of the nature of earthquakes. In this paper, Poisson hidden Markov model is used to predict the earthquake hazard in Bilecik (NW Turkey) as a result of its important geographic location. Bilecik is in close proximity to the North Anatolian Fault Zone and situated between Ankara and Istanbul, the two biggest cites of Turkey. Consequently, there are major highways, railroads and many engineering structures are being constructed in this area. The annual frequencies of earthquakes occurred within a radius of 100 km area centered on Bilecik, from January 1900 to December 2012, with magnitudes ( M) at least 4.0 are modeled by using Poisson-HMM. The hazards for the next 35 years from 2013 to 2047 around the area are obtained from the model by forecasting the annual frequencies of M ≥ 4 earthquakes.
Prediction of earthquake hazard by hidden Markov model (around Bilecik, NW Turkey)
NASA Astrophysics Data System (ADS)
Can, Ceren; Ergun, Gul; Gokceoglu, Candan
2014-09-01
Earthquakes are one of the most important natural hazards to be evaluated carefully in engineering projects, due to the severely damaging effects on human-life and human-made structures. The hazard of an earthquake is defined by several approaches and consequently earthquake parameters such as peak ground acceleration occurring on the focused area can be determined. In an earthquake prone area, the identification of the seismicity patterns is an important task to assess the seismic activities and evaluate the risk of damage and loss along with an earthquake occurrence. As a powerful and flexible framework to characterize the temporal seismicity changes and reveal unexpected patterns, Poisson hidden Markov model provides a better understanding of the nature of earthquakes. In this paper, Poisson hidden Markov model is used to predict the earthquake hazard in Bilecik (NW Turkey) as a result of its important geographic location. Bilecik is in close proximity to the North Anatolian Fault Zone and situated between Ankara and Istanbul, the two biggest cites of Turkey. Consequently, there are major highways, railroads and many engineering structures are being constructed in this area. The annual frequencies of earthquakes occurred within a radius of 100 km area centered on Bilecik, from January 1900 to December 2012, with magnitudes (M) at least 4.0 are modeled by using Poisson-HMM. The hazards for the next 35 years from 2013 to 2047 around the area are obtained from the model by forecasting the annual frequencies of M ≥ 4 earthquakes.
Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig
2011-01-01
Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763
NASA Astrophysics Data System (ADS)
Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea; Montalto, Placido; Patanè, Domenico; Privitera, Eugenio
2016-07-01
From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
NASA Astrophysics Data System (ADS)
Romberg, Justin K.; Choi, Hyeokho; Baraniuk, Richard G.
1999-06-01
Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree model captures the key features of the joint density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training. In this paper, we prose two reduced-parameter HMT models that capture the general structure of a broad class of real-world images. In the image HMT (iHMT) model we use the fact that for a large class of images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters. In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of image estimation/denoising experiments that these two new models retain nearly all of the key structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms all other wavelet- based estimators in the current literature, both in mean- square error and visual metrics.
Sun, Shuying; Yu, Xiaoqing
2016-03-01
DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher's exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher's exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.
Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S N; Nelson, Trisalyn A; Nathoo, Farouk; Stephen, Craig
2011-01-01
Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines.
Fight deck human-automation mode confusion detection using a generalized fuzzy hidden Markov model
NASA Astrophysics Data System (ADS)
Lyu, Hao Lyu
Due to the need for aviation safety, convenience, and efficiency, the autopilot has been introduced into the cockpit. The fast development of the autopilot has brought great benefits to the aviation industry. On the human side, the flight deck has been designed to be a complex, tightly-coupled, and spatially distributed system. The problem of dysfunctional interaction between the pilot and the automation (human-automation interaction issue) has become more and more visible. Thus, detection of a mismatch between the pilot's expectation and automation's behavior in a timely manner is required. In order to solve this challenging problem, separate modeling of the pilot and the automation is necessary. In this thesis, an intent-based framework is introduced to detect the human-automation interaction issue. Under this framework, the pilot's expectation of the aircraft is modeled by pilot intent while the behavior of the automation system is modeled by automation intent. The mode confusion is detected when the automation intent differs from the pilot intent. The pilot intent is inferred by comparing the target value set by the pilot with the aircraft's current state. Meanwhile, the automation intent is inferred through the Generalized Fuzzy Hidden Markov Model (GFHMM), which is an extension of the classical Hidden Markov Model. The stochastic characteristic of the ``hidden'' intents is considered by introducing fuzzy logic. Different from the previous approaches of inferring automation intent, GFHMM does not require a probabilistic model for certain flight modes as prior knowledge. The parameters of GFHMM (initial fuzzy density of the intent, fuzzy transmission density, and fuzzy emission density) are determined through the flight data by using a machine learning technique, the Fuzzy C-Means clustering algorithm (FCM). Lastly, both the pilot's and automation's intent inference algorithms and the mode confusion detection method are validated through flight data.
Sparsely correlated hidden Markov models with application to genome-wide location studies
Choi, Hyungwon; Fermin, Damian; Nesvizhskii, Alexey I.; Ghosh, Debashis; Qin, Zhaohui S.
2013-01-01
Motivation: Multiply correlated datasets have become increasingly common in genome-wide location analysis of regulatory proteins and epigenetic modifications. Their correlation can be directly incorporated into a statistical model to capture underlying biological interactions, but such modeling quickly becomes computationally intractable. Results: We present sparsely correlated hidden Markov models (scHMM), a novel method for performing simultaneous hidden Markov model (HMM) inference for multiple genomic datasets. In scHMM, a single HMM is assumed for each series, but the transition probability in each series depends on not only its own hidden states but also the hidden states of other related series. For each series, scHMM uses penalized regression to select a subset of the other data series and estimate their effects on the odds of each transition in the given series. Following this, hidden states are inferred using a standard forward–backward algorithm, with the transition probabilities adjusted by the model at each position, which helps retain the order of computation close to fitting independent HMMs (iHMM). Hence, scHMM is a collection of inter-dependent non-homogeneous HMMs, capable of giving a close approximation to a fully multivariate HMM fit. A simulation study shows that scHMM achieves comparable sensitivity to the multivariate HMM fit at a much lower computational cost. The method was demonstrated in the joint analysis of 39 histone modifications, CTCF and RNA polymerase II in human CD4+ T cells. scHMM reported fewer high-confidence regions than iHMM in this dataset, but scHMM could recover previously characterized histone modifications in relevant genomic regions better than iHMM. In addition, the resulting combinatorial patterns from scHMM could be better mapped to the 51 states reported by the multivariate HMM method of Ernst and Kellis. Availability: The scHMM package can be freely downloaded from http://sourceforge.net/p/schmm/ and is
Alternating direction optimization for image segmentation using hidden Markov measure field models
NASA Astrophysics Data System (ADS)
Bioucas-Dias, José; Condessa, Filipe; Kovačević, Jelena
2014-02-01
Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The solution is often obtained by minimizing an objective function containing terms measuring the consistency of the candidate partition with respect to the observed image, and regularization terms promoting solutions with desired properties. This formulation ends up being an integer optimization problem that, apart from a few exceptions, is NP-hard and thus impossible to solve exactly. This roadblock has stimulated active research aimed at computing "good" approximations to the solutions of those integer optimization problems. Relevant lines of attack have focused on the representation of the regions (i.e., the partition elements) in terms of functions, instead of subsets, and on convex relaxations which can be solved in polynomial time. In this paper, inspired by the "hidden Markov measure field" introduced by Marroquin et al. in 2003, we sidestep the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with simulated and real hyperspectral and medical images.
NASA Astrophysics Data System (ADS)
Turner, Sean; Galelli, Stefano; Wilcox, Karen
2015-04-01
Water reservoir systems are often affected by recurring large-scale ocean-atmospheric anomalies, known as teleconnections, that cause prolonged periods of climatological drought. Accurate forecasts of these events -- at lead times in the order of weeks and months -- may enable reservoir operators to take more effective release decisions to improve the performance of their systems. In practice this might mean a more reliable water supply system, a more profitable hydropower plant or a more sustainable environmental release policy. To this end, climate indices, which represent the oscillation of the ocean-atmospheric system, might be gainfully employed within reservoir operating models that adapt the reservoir operation as a function of the climate condition. This study develops a Stochastic Dynamic Programming (SDP) approach that can incorporate climate indices using a Hidden Markov Model. The model simulates the climatic regime as a hidden state following a Markov chain, with the state transitions driven by variation in climatic indices, such as the Southern Oscillation Index. Time series analysis of recorded streamflow data reveals the parameters of separate autoregressive models that describe the inflow to the reservoir under three representative climate states ("normal", "wet", "dry"). These models then define inflow transition probabilities for use in a classic SDP approach. The key advantage of the Hidden Markov Model is that it allows conditioning the operating policy not only on the reservoir storage and the antecedent inflow, but also on the climate condition, thus potentially allowing adaptability to a broader range of climate conditions. In practice, the reservoir operator would effect a water release tailored to a specific climate state based on available teleconnection data and forecasts. The approach is demonstrated on the operation of a realistic, stylised water reservoir with carry-over capacity in South-East Australia. Here teleconnections relating
ERIC Educational Resources Information Center
Stifter, Cynthia A.; Rovine, Michael
2015-01-01
The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…
Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; Ta , Neslihan; Lamendella, Regina; Dvornik, Jill; Mackelprang, Rachel; Myrold, David D.; Jumpponen, Ari; Tringe, Susannah G.; Holman, Elizabeth; Mavromatis, Konstantinos; Jansson, Janet K.
2014-09-26
A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.
An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.
Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng; Wang, Hao; Jia, Wenyan; Burke, Lora E; Baranowski, Thomas; Sun, Mingui
2015-05-01
Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.
NASA Astrophysics Data System (ADS)
Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Hongchao; Yuan, Haodong
2016-01-01
Due to the important role rolling element bearings play in rotating machines, condition monitoring and fault diagnosis system should be established to avoid abrupt breakage during operation. Various features from time, frequency and time-frequency domain are usually used for bearing or machinery condition monitoring. In this study, NCA-based feature extraction (FE) approach is proposed to reduce the dimensionality of original feature set and avoid the "curse of dimensionality". Furthermore, coupled hidden Markov model (CHMM) based on multichannel data acquisition is applied to diagnose bearing or machinery fault. Two case studies are presented to validate the proposed approach both in bearing fault diagnosis and fault severity classification. The experiment results show that the proposed NCA-CHMM can remove redundant information, fuse data from different channels and improve the diagnosis results.
Medical Inpatient Journey Modeling and Clustering: A Bayesian Hidden Markov Model Based Approach
Huang, Zhengxing; Dong, Wei; Wang, Fei; Duan, Huilong
2015-01-01
Modeling and clustering medical inpatient journeys is useful to healthcare organizations for a number of reasons including inpatient journey reorganization in a more convenient way for understanding and browsing, etc. In this study, we present a probabilistic model-based approach to model and cluster medical inpatient journeys. Specifically, we exploit a Bayesian Hidden Markov Model based approach to transform medical inpatient journeys into a probabilistic space, which can be seen as a richer representation of inpatient journeys to be clustered. Then, using hierarchical clustering on the matrix of similarities, inpatient journeys can be clustered into different categories w.r.t their clinical and temporal characteristics. We evaluated the proposed approach on a real clinical data set pertaining to the unstable angina treatment process. The experimental results reveal that our method can identify and model latent treatment topics underlying in personalized inpatient journeys, and yield impressive clustering quality. PMID:26958200
On-line monitoring of pharmaceutical production processes using Hidden Markov Model.
Zhang, Hui; Jiang, Zhuangde; Pi, J Y; Xu, H K; Du, R
2009-04-01
This article presents a new method for on-line monitoring of pharmaceutical production process, especially the powder blending process. The new method consists of two parts: extracting features from the Near Infrared (NIR) spectroscopy signals and recognizing patterns from the features. Features are extracted from spectra by using Partial Least Squares method (PLS). The pattern recognition is done by using Hidden Markov Model (HMM). A series of experiments are conducted to evaluate the effectiveness of this new method. In the experiments, wheat powder and corn powder are blended together at a set concentration. The proposed method can effectively detect the blending uniformity (the success rate is 99.6%). In comparison to the conventional Moving Block of Standard Deviation (MBSD), the proposed method has a number of advantages, including higher reliability, higher robustness and more transparent decision making. It can be used for effective on-line monitoring of pharmaceutical production processes.
Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods
Liao, T. W.; Hua, G; Qu, Jun; Blau, Peter Julian
2006-01-01
Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.
Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
Wiedenhoeft, John; Brugel, Eric; Schliep, Alexander
2016-01-01
By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings. PMID:27177143
Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model
NASA Astrophysics Data System (ADS)
Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao
2014-11-01
A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television
Post processing of optically recognized text via second order hidden Markov model
NASA Astrophysics Data System (ADS)
Poudel, Srijana
In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.
Damage evaluation by a guided wave-hidden Markov model based method
NASA Astrophysics Data System (ADS)
Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin
2016-02-01
Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.
Extended hidden Markov model for optimized segmentation of breast thermography images
NASA Astrophysics Data System (ADS)
Mahmoudzadeh, E.; Montazeri, M. A.; Zekri, M.; Sadri, S.
2015-09-01
Breast cancer is the most commonly diagnosed form of cancer in women. Thermography has been shown to provide an efficient screening modality for detecting breast cancer as it is able to detect small tumors and hence can lead to earlier diagnosis. This paper presents a novel extended hidden Markov model (EHMM), for optimized segmentation of breast thermogram for more effective image interpretation and easier analysis of Infrared (IR) thermal patterns. Competitive advantage of EHMM method refers to handling random sampling of the breast IR images with re-estimation of the model parameters. The performance of the algorithm is illustrated by applying EHMM segmentation method on the images of IUT_OPTIC database and compared with previously related methods. Simulation results indicate the remarkable capabilities of the proposed approach. It is worth noting that the presented algorithm is able to map semi hot regions into distinct areas and extract the regions of breast thermal images significantly, while the execution time is reduced.
Estimating the completeness of volcanic eruption records using hidden Markov models
NASA Astrophysics Data System (ADS)
Wang, Ting; Bebbington, Mark
2016-04-01
Despite ongoing efforts worldwide to compile different databases for volcanic eruptions, eruption records are pervasively incomplete, a problem that is exacerbated when dealing with catalogs derived from geologic records. When using statistical models to analyze this type of records, missing data can strongly influence the parameter estimates, which are usually obtained by maximizing the log likelihood function, and hence affect the future hazard estimate. This work explores a hidden Markov model framework to handle missing data in volcanic eruption records. This framework will enable us to estimate the completeness level of the records, and offers a means of determining where in the record the missing observations are likely to be found. We apply this method to different volcanic eruption records with the aim to estimate the completeness of the records over time and to obtain more robust estimates of the future hazard.
Ficz, Gabriella; Wolf, Verena; Walter, Jörn
2016-01-01
DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation by replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of 5mC. Several alternative mechanisms have been proposed suggesting that 5hmC influences either replication dependent maintenance of DNA methylation or replication independent processes of active demethylation. Using high resolution hairpin oxidative bisulfite sequencing data, we precisely determine the amount of 5mC and 5hmC and model the contribution of 5hmC to processes of demethylation in mouse ESCs. We develop an extended hidden Markov model capable of accurately describing the regional contribution of 5hmC to demethylation dynamics. Our analysis shows that 5hmC has a strong impact on replication dependent demethylation, mainly by impairing methylation maintenance. PMID:27224554
Hidden Markov model analysis of force/torque information in telemanipulation
NASA Technical Reports Server (NTRS)
Hannaford, Blake; Lee, Paul
1991-01-01
A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.
Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting
NASA Astrophysics Data System (ADS)
Talib Bon, Abdul; Isah, Nuhu
2016-11-01
In light of the importance of crude oil to the world's economy, it is not surprising that economists have devoted great efforts towards developing methods to forecast price and volatility levels. Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile such as economic, political and social. Hence, forecasting the crude oil prices is essential to avoid unforeseen circumstances towards economic activity. In this study, daily crude oil prices data was obtained from WTI dated 2nd January to 29th May 2015. We used Hidden Markov Model (HMM) and Forward-Backward Algorithm to forecasting the crude oil prices. In this study, the analyses were done using Maple software. Based on the study, we concluded that model (0 3 0) is able to produce accurate forecast based on a description of history patterns in crude oil prices.
Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models
Riber Marklund, A.; Prakash, V.; Rajan, K.K.
2015-07-01
Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)
Detection of selective cationic amphipatic antibacterial peptides by Hidden Markov models.
Polanco, Carlos; Samaniego, Jose L
2009-01-01
Antibacterial peptides are researched mainly for the potential benefit they have in a variety of socially relevant diseases, used by the host to protect itself from different types of pathogenic bacteria. We used the mathematical-computational method known as Hidden Markov models (HMMs) in targeting a subset of antibacterial peptides named Selective Cationic Amphipatic Antibacterial Peptides (SCAAPs). The main difference in the implementation of HMMs was focused on the detection of SCAAP using principally five physical-chemical properties for each candidate SCAAPs, instead of using the statistical information about the amino acids which form a peptide. By this method a cluster of antibacterial peptides was detected and as a result the following were found: 9 SCAAPs, 6 synthetic antibacterial peptides that belong to a subregion of Cecropin A and Magainin 2, and 19 peptides from the Cecropin A family. A scoring function was developed using HMMs as its core, uniquely employing information accessible from the databases.
NASA Astrophysics Data System (ADS)
Attaluri, Pavan K.; Chen, Zhengxin; Weerakoon, Aruna M.; Lu, Guoqing
Multiple criteria decision making (MCDM) has significant impact in bioinformatics. In the research reported here, we explore the integration of decision tree (DT) and Hidden Markov Model (HMM) for subtype prediction of human influenza A virus. Infection with influenza viruses continues to be an important public health problem. Viral strains of subtype H3N2 and H1N1 circulates in humans at least twice annually. The subtype detection depends mainly on the antigenic assay, which is time-consuming and not fully accurate. We have developed a Web system for accurate subtype detection of human influenza virus sequences. The preliminary experiment showed that this system is easy-to-use and powerful in identifying human influenza subtypes. Our next step is to examine the informative positions at the protein level and extend its current functionality to detect more subtypes. The web functions can be accessed at http://glee.ist.unomaha.edu/.
Speaker-independent isolated-digit recognition using neural networks and hidden Markov model
NASA Astrophysics Data System (ADS)
McGuire, Michael; Ingraham, Diane; Cuperman, Vladimir
A speaker independent isolated-digit recognition system is described which incorporates neural networks for vector quantization (VQ) and postprocessing of VQ and hidden Markov model (HMM) decision information. Vector quantization was performed using word-based parameter specific codebooks generated with a frequency sensitive competitive learning (FSCL) training algorithm. A single-layer perceptron neural network was used to increase recognition performance by postprocessing VQ distortion and HMM output probabilistic information. Experiments using both studio and telephone-line recorded databases demonstrate the FSCL VQ-HMM system without postprocessing achieves a recognition performance increase when compared to a previously developed VQ-HMM digit recognition system. With the addition of postprocessing to the FSCL VQ-HMM system, recognition performance was further increased.
A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning
NASA Astrophysics Data System (ADS)
Roth, John; Tummala, Murali; McEachen, John
2016-09-01
This paper presents a computationally efficient approach for mobile subscriber position estimation in wireless networks. A method of data scaling assisted by timing adjust is introduced in fingerprint-based location estimation under a framework which allows for minimising computational cost. The proposed method maintains a comparable level of accuracy to the traditional case where no data scaling is used and is evaluated in a simulated environment under varying channel conditions. The proposed scheme is studied when it is augmented by a hidden-Markov model to match the internal parameters to the channel conditions that present, thus minimising computational cost while maximising accuracy. Furthermore, the timing adjust quantity, available in modern wireless signalling messages, is shown to be able to further reduce computational cost and increase accuracy when available. The results may be seen as a significant step towards integrating advanced position-based modelling with power-sensitive mobile devices.
Autoregressive hidden Markov models for the early detection of neonatal sepsis.
Stanculescu, Ioan; Williams, Christopher K I; Freer, Yvonne
2014-09-01
Late onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patient's monitoring traces could be used for the early detection of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM). Both learning and inference carefully use domain knowledge to extract the baby's true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit at the Royal Infirmary of Edinburgh.
NASA Astrophysics Data System (ADS)
Jiang, Huiming; Chen, Jin; Dong, Guangming
2016-05-01
Hidden Markov model (HMM) has been widely applied in bearing performance degradation assessment. As a machine learning-based model, its accuracy, subsequently, is dependent on the sensitivity of the features used to estimate the degradation performance of bearings. It's a big challenge to extract effective features which are not influenced by other qualities or attributes uncorrelated with the bearing degradation condition. In this paper, a bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. NAP can filter out the effect of nuisance attributes in feature space through projection. The new feature space projected by NAP is more sensitive to bearing health changes and barely influenced by other interferences occurring in operation condition. To verify the effectiveness of the proposed method, two different experimental databases are utilized. The results show that the combination of HMM and NAP can effectively improve the accuracy and robustness of the bearing performance degradation assessment system.
Development of the hidden Markov models based Lithuanian speech recognition system
NASA Astrophysics Data System (ADS)
Ringeliene, Z.; Lipeika, A.
2010-09-01
The paper presents a prototype of the speaker-independent Lithuanian isolated word recognition system. The system is based on the hidden Markov models, a powerful statistical method for modeling speech signals. The prototype system can be used for Lithuanian words recognition investigations and is a good starting point for the development of a more sophisticated recognition system. The system graphical user interface is easy to control. Visualization of the entire recognition process is useful for analyzing of the recognition results. Based on this recognizer, a system for Web browser control by voice was developed. The program, which implements control by voice commands, was integrated in the speech recognition system. The system performance was evaluated by using different sets of acoustic models and vocabularies.
Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.
Huang, Hsien-Da; Lee, Tzong-Yi; Tzeng, Shih-Wei; Wu, Li-Cheng; Horng, Jorng-Tzong; Tsou, Ann-Ping; Huang, Kuan-Tsae
2005-07-30
Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.
The discovery of processing stages: analyzing EEG data with hidden semi-Markov models.
Borst, Jelmer P; Anderson, John R
2015-03-01
In this paper we propose a new method for identifying processing stages in human information processing. Since the 1860s scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used hidden semi-Markov models (HSMMs) to analyze EEG data. This HSMM-EEG methodology can identify stages of processing and how they vary with experimental condition. By combining this information with the brain signatures of the identified stages one can infer their function, and deduce underlying cognitive processes. To demonstrate the method we applied it to an associative recognition task. The stage-discovery method indicated that three major processes play a role in associative recognition: a familiarity process, an associative retrieval process, and a decision process. We conclude that the new stage-discovery method can provide valuable insight into human information processing.
Hidden Markov models and neural networks for fault detection in dynamic systems
NASA Technical Reports Server (NTRS)
Smyth, Padhraic
1994-01-01
Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.
NASA Astrophysics Data System (ADS)
Liu, Qinming; Dong, Ming; Lv, Wenyuan; Geng, Xiuli; Li, Yupeng
2015-12-01
Health prognosis for equipment is considered as a key process of the condition-based maintenance strategy. This paper presents an integrated framework for multi-sensor equipment diagnosis and prognosis based on adaptive hidden semi-Markov model (AHSMM). Unlike hidden semi-Markov model (HSMM), the basic algorithms in an AHSMM are first modified in order for decreasing computation and space complexity. Then, the maximum likelihood linear regression transformations method is used to train the output and duration distributions to re-estimate all unknown parameters. The AHSMM is used to identify the hidden degradation state and obtain the transition probabilities among health states and durations. Finally, through the proposed hazard rate equations, one can predict the useful remaining life of equipment with multi-sensor information. Our main results are verified in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the proposed methods are more effective for multi-sensor monitoring equipment health prognosis.
Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model
NASA Astrophysics Data System (ADS)
Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.
2009-04-01
The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.
Hatt, M; Lamare, F; Boussion, N; Turzo, A; Collet, C; Salzenstein, F; Roux, C; Jarritt, P; Carson, K; Cheze-Le Rest, C; Visvikis, D
2007-06-21
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Syed, Sheyum; Müllner, Fiona E; Selvin, Paul R; Sigworth, Fred J
2010-12-01
Unbiased interpretation of noisy single molecular motor recordings remains a challenging task. To address this issue, we have developed robust algorithms based on hidden Markov models (HMMs) of motor proteins. The basic algorithm, called variable-stepsize HMM (VS-HMM), was introduced in the previous article. It improves on currently available Markov-model based techniques by allowing for arbitrary distributions of step sizes, and shows excellent convergence properties for the characterization of staircase motor timecourses in the presence of large measurement noise. In this article, we extend the VS-HMM framework for better performance with experimental data. The extended algorithm, variable-stepsize integrating-detector HMM (VSI-HMM) better models the data-acquisition process, and accounts for random baseline drifts. Further, as an extension, maximum a posteriori estimation is provided. When used as a blind step detector, the VSI-HMM outperforms conventional step detectors. The fidelity of the VSI-HMM is tested with simulations and is applied to in vitro myosin V data where a small 10 nm population of steps is identified. It is also applied to an in vivo recording of melanosome motion, where strong evidence is found for repeated, bidirectional steps smaller than 8 nm in size, implying that multiple motors simultaneously carry the cargo.
Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models.
Trifa, Vlad M; Kirschel, Alexander N G; Taylor, Charles E; Vallejo, Edgar E
2008-04-01
Behavioral and ecological studies would benefit from the ability to automatically identify species from acoustic recordings. The work presented in this article explores the ability of hidden Markov models to distinguish songs from five species of antbirds that share the same territory in a rainforest environment in Mexico. When only clean recordings were used, species recognition was nearly perfect, 99.5%. With noisy recordings, performance was lower but generally exceeding 90%. Besides the quality of the recordings, performance has been found to be heavily influenced by a multitude of factors, such as the size of the training set, the feature extraction method used, and number of states in the Markov model. In general, training with noisier data also improved recognition in test recordings, because of an increased ability to generalize. Considerations for improving performance, including beamforming with sensor arrays and design of preprocessing methods particularly suited for bird songs, are discussed. Combining sensor network technology with effective event detection and species identification algorithms will enable observation of species interactions at a spatial and temporal resolution that is simply impossible with current tools. Analysis of animal behavior through real-time tracking of individuals and recording of large amounts of data with embedded devices in remote locations is thus a realistic goal.
Detecting Gait Phases from RGB-D Images Based on Hidden Markov Model
Heravi, Hamed; Ebrahimi, Afshin; Olyaee, Ehsan
2016-01-01
Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60–40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision. PMID:27563572
Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks
NASA Astrophysics Data System (ADS)
Zhu, Shijia; Wang, Yadong
2015-12-01
Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.
A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data
Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing
2015-01-01
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing. PMID:26710073
Detecting Gait Phases from RGB-D Images Based on Hidden Markov Model.
Heravi, Hamed; Ebrahimi, Afshin; Olyaee, Ehsan
2016-01-01
Gait contains important information about the status of the human body and physiological signs. In many medical applications, it is important to monitor and accurately analyze the gait of the patient. Since walking shows the reproducibility signs in several phases, separating these phases can be used for the gait analysis. In this study, a method based on image processing for extracting phases of human gait from RGB-Depth images is presented. The sequence of depth images from the front view has been processed to extract the lower body depth profile and distance features. Feature vector extracted from image is the same as observation vector of hidden Markov model, and the phases of gait are considered as hidden states of the model. After training the model using the images which are randomly selected as training samples, the phase estimation of gait becomes possible using the model. The results confirm the rate of 60-40% of two major phases of the gait and also the mid-stance phase is recognized with 85% precision.
A language independent acronym extraction from biomedical texts with hidden Markov models.
Osiek, Bruno Adam; Xexeo, Gexéo; Vidal de Carvalho, Luis Alfredo
2010-11-01
This paper proposes to model the extraction of acronyms and their meaning from unstructured text as a stochastic process using Hidden Markov Models (HMM). The underlying, or hidden, chain is derived from the acronym where the states in the chain are made by the acronyms characters. The transition between two states happens when the origin state emits a signal. Signals recognizable by the HMM are tokens extracted from text. Observations are sequence of tokens also extracted from text. Given a set of observations, the acronym definition will be the observation with the highest probability to emerge from the HMM. Modelling this extraction probabilistically allows us to deal with two difficult aspects of this process: ambiguity and noise. We characterize ambiguity when there is no unique alignment between a character in the acronym with a token in the expansion while the feature characterizing noise is the absence of such alignment. Our experiments have proven that this approach has high precision (93.50%) and recall (85.50%) rates in an environment where acronym coinage is ambiguous and noisy such as the biomedical domain. Processing and comparing the HMM approach with different ones, showed ours to reach the highest F1 score (89.40%) on the same corpus.
A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.
Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing
2015-01-01
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.
Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model
NASA Astrophysics Data System (ADS)
Nawaz, Muhammad Atif; Curtis, Andrew
2017-02-01
Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of 2-D spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2-D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P- and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell-facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations - an assumption referred to as `localized likelihoods'. The hidden state (facies) at a location cannot be determined solely by the observation at that
Bayesian Inversion of Seismic Attributes for Geological Facies using a Hidden Markov Model
NASA Astrophysics Data System (ADS)
Nawaz, Muhammad Atif; Curtis, Andrew
2016-11-01
Markov chain Monte-Carlo (McMC) sampling generates correlated random samples such that their distribution would converge to the true distribution only as the number of samples tends to infinity. In practice, McMC is found to be slow to converge, convergence is not guaranteed to be achieved in finite time, and detection of convergence requires the use of subjective criteria. Although McMC has been used for decades as the algorithm of choice for inference in complex probability distributions, there is a need to seek alternative approaches, particularly in high dimensional problems. Walker & Curtis (2014) developed a method for Bayesian inversion of two-dimensional spatial data using an exact sampling alternative to McMC which always draws independent samples of the target distribution. Their method thus obviates the need for convergence and removes the concomitant bias exhibited by finite sample sets. Their algorithm is nevertheless computationally intensive and requires large memory. We propose a more efficient method for Bayesian inversion of categorical variables, such as geological facies that requires no sampling at all. The method is based on a 2D Hidden Markov Model (2D-HMM) over a grid of cells where observations represent localized data constraining each cell. The data in our example application are seismic attributes such as P- and S-wave impedances and rock density; our categorical variables are the hidden states and represent the geological rock types in each cell - facies of distinct subsets of lithology and fluid combinations such as shale, brine-sand and gas-sand. The observations at each location are assumed to be generated from a random function of the hidden state (facies) at that location, and to be distributed according to a certain probability distribution that is independent of hidden states at other locations - an assumption referred to as localized likelihoods. The hidden state (facies) at a location cannot be determined solely by the
NASA Astrophysics Data System (ADS)
Andriyas, S.; McKee, M.
2014-12-01
Anticipating farmers' irrigation decisions can provide the possibility of improving the efficiency of canal operations in on-demand irrigation systems. Although multiple factors are considered during irrigation decision making, for any given farmer there might be one factor playing a major role. Identification of that biophysical factor which led to a farmer deciding to irrigate is difficult because of high variability of those factors during the growing season. Analysis of the irrigation decisions of a group of farmers for a single crop can help to simplify the problem. We developed a hidden Markov model (HMM) to analyze irrigation decisions and explore the factor and level at which the majority of farmers decide to irrigate. The model requires observed variables as inputs and the hidden states. The chosen model inputs were relatively easily measured, or estimated, biophysical data, including such factors (i.e., those variables which are believed to affect irrigation decision-making) as cumulative evapotranspiration, soil moisture depletion, soil stress coefficient, and canal flows. Irrigation decision series were the hidden states for the model. The data for the work comes from the Canal B region of the Lower Sevier River Basin, near Delta, Utah. The main crops of the region are alfalfa, barley, and corn. A portion of the data was used to build and test the model capability to explore that factor and the level at which the farmer takes the decision to irrigate for future irrigation events. Both group and individual level behavior can be studied using HMMs. The study showed that the farmers cannot be classified into certain classes based on their irrigation decisions, but vary in their behavior from irrigation-to-irrigation across all years and crops. HMMs can be used to analyze what factor and, subsequently, what level of that factor on which the farmer most likely based the irrigation decision. The study shows that the HMM is a capable tool to study a process
Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET
Hatt, Mathieu; Lamare, Frédéric; Boussion, Nicolas; Roux, Christian; Turzo, Alexandre; Cheze-Lerest, Catherine; Jarritt, Peter; Carson, Kathryn; Salzenstein, Fabien; Collet, Christophe; Visvikis, Dimitris
2007-01-01
Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the Fuzzy Hidden Markov Chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical Hidden Markov Chain (HMC) algorithm, FHMC takes into account noise, voxel’s intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the “fuzzy” nature of the object on interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8mm3 and 64mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both
Zhang, Qiang; Snow Jones, Alison; Rijmen, Frank; Ip, Edward H.
2016-01-01
Many studies in the social and behavioral sciences involve multivariate discrete measurements, which are often characterized by the presence of an underlying individual trait, the existence of clusters such as domains of measurements, and the availability of multiple waves of cohort data. Motivated by an application in child development, we propose a class of extended multivariate discrete hidden Markov models for analyzing domain-based measurements of cognition and behavior. A random effects model is used to capture the long-term trait. Additionally, we develop a model selection criterion based on the Bayes factor for the extended hidden Markov model. The National Longitudinal Survey of Youth (NLSY) is used to illustrate the methods. Supplementary technical details and computer codes are available online. PMID:28066134
Hidden Markov Model Analysis of Maternal Behavior Patterns in Inbred and Reciprocal Hybrid Mice
Carola, Valeria; Mirabeau, Olivier; Gross, Cornelius T.
2011-01-01
Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM), to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females) were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to highlight them
Hidden Markov model analysis of maternal behavior patterns in inbred and reciprocal hybrid mice.
Carola, Valeria; Mirabeau, Olivier; Gross, Cornelius T
2011-03-08
Individual variation in maternal care in mammals shows a significant heritable component, with the maternal behavior of daughters resembling that of their mothers. In laboratory mice, genetically distinct inbred strains show stable differences in maternal care during the first postnatal week. Moreover, cross fostering and reciprocal breeding studies demonstrate that differences in maternal care between inbred strains persist in the absence of genetic differences, demonstrating a non-genetic or epigenetic contribution to maternal behavior. In this study we applied a mathematical tool, called hidden Markov model (HMM), to analyze the behavior of female mice in the presence of their young. The frequency of several maternal behaviors in mice has been previously described, including nursing/grooming pups and tending to the nest. However, the ordering, clustering, and transitions between these behaviors have not been systematically described and thus a global description of maternal behavior is lacking. Here we used HMM to describe maternal behavior patterns in two genetically distinct mouse strains, C57BL/6 and BALB/c, and their genetically identical reciprocal hybrid female offspring. HMM analysis is a powerful tool to identify patterns of events that cluster in time and to determine transitions between these clusters, or hidden states. For the HMM analysis we defined seven states: arched-backed nursing, blanket nursing, licking/grooming pups, grooming, activity, eating, and sleeping. By quantifying the frequency, duration, composition, and transition probabilities of these states we were able to describe the pattern of maternal behavior in mouse and identify aspects of these patterns that are under genetic and nongenetic inheritance. Differences in these patterns observed in the experimental groups (inbred and hybrid females) were detected only after the application of HMM analysis whereas classical statistical methods and analyses were not able to highlight them.
Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; ...
2014-09-26
A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associatedmore » functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.« less
Rotation-invariant image retrieval using hidden Markov tree for remote sensing data
NASA Astrophysics Data System (ADS)
Miao, Congcong; Zhao, Yindi
2014-11-01
The rapid increase in quantity of available remote sensing data brought an urgent need for intelligent retrieval techniques for remote sensing images. As one of the basic visual characteristics and important information sources of remote sensing images, texture is widely used in the scheme of remote sensing image retrieval. Since many images or regions with identical texture features usually show the diversity of direction, the consideration of rotation-invariance in the description of texture features is of significance both theoretically and practically. To address these issues, we develop a rotation-invariant image retrieval method based on the texture features of remote sensing images. We use the steerable pyramid transform to get the multi-scale and multi-orientation representation of texture images. Then we employ the hidden Markov tree (HMT) model, which provides a good tool to describe texture feature, to capture the dependencies across scales and orientations, by which the statistical properties of the transform domain coefficients can be obtained. Utilizing the inherent tree structure of the HMT and its fast training and likelihood computation algorithms, we can extract the rotation-invariant features of texture images. Similarity between the query image and each candidate image in the database can be measured by computing the Kullback-Leibler distance between the corresponding models. We evaluate the retrieval effectiveness of the algorithm with Brodatz texture database and remote sensing images. The experimental results show that this method has satisfactory performance in image retrieval and less sensitivity to texture rotation.
NASA Astrophysics Data System (ADS)
Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran
2016-05-01
Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.
Extracting duration information in a picture category decoding task using hidden Markov Models
NASA Astrophysics Data System (ADS)
Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg
2016-04-01
Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.
Identifying bubble collapse in a hydrothermal system using hidden Markov models
Dawson, P.B.; Benitez, M.C.; Lowenstern, J. B.; Chouet, B.A.
2012-01-01
Beginning in July 2003 and lasting through September 2003, the Norris Geyser Basin in Yellowstone National Park exhibited an unusual increase in ground temperature and hydrothermal activity. Using hidden Markov model theory, we identify over five million high-frequency (>15Hz) seismic events observed at a temporary seismic station deployed in the basin in response to the increase in hydrothermal activity. The source of these seismic events is constrained to within ???100 m of the station, and produced ???3500-5500 events per hour with mean durations of ???0.35-0.45s. The seismic event rate, air temperature, hydrologic temperatures, and surficial water flow of the geyser basin exhibited a marked diurnal pattern that was closely associated with solar thermal radiance. We interpret the source of the seismicity to be due to the collapse of small steam bubbles in the hydrothermal system, with the rate of collapse being controlled by surficial temperatures and daytime evaporation rates. copyright 2012 by the American Geophysical Union.
NASA Astrophysics Data System (ADS)
Wissel, Tobias; Pfeiffer, Tim; Frysch, Robert; Knight, Robert T.; Chang, Edward F.; Hinrichs, Hermann; Rieger, Jochem W.; Rose, Georg
2013-10-01
Objective. Support vector machines (SVM) have developed into a gold standard for accurate classification in brain-computer interfaces (BCI). The choice of the most appropriate classifier for a particular application depends on several characteristics in addition to decoding accuracy. Here we investigate the implementation of hidden Markov models (HMM) for online BCIs and discuss strategies to improve their performance. Approach. We compare the SVM, serving as a reference, and HMMs for classifying discrete finger movements obtained from electrocorticograms of four subjects performing a finger tapping experiment. The classifier decisions are based on a subset of low-frequency time domain and high gamma oscillation features. Main results. We show that decoding optimization between the two approaches is due to the way features are extracted and selected and less dependent on the classifier. An additional gain in HMM performance of up to 6% was obtained by introducing model constraints. Comparable accuracies of up to 90% were achieved with both SVM and HMM with the high gamma cortical response providing the most important decoding information for both techniques. Significance. We discuss technical HMM characteristics and adaptations in the context of the presented data as well as for general BCI applications. Our findings suggest that HMMs and their characteristics are promising for efficient online BCIs.
Classifying movement behaviour in relation to environmental conditions using hidden Markov models.
Patterson, Toby A; Basson, Marinelle; Bravington, Mark V; Gunn, John S
2009-11-01
1. Linking the movement and behaviour of animals to their environment is a central problem in ecology. Through the use of electronic tagging and tracking (ETT), collection of in situ data from free-roaming animals is now commonplace, yet statistical approaches enabling direct relation of movement observations to environmental conditions are still in development. 2. In this study, we examine the hidden Markov model (HMM) for behavioural analysis of tracking data. HMMs allow for prediction of latent behavioural states while directly accounting for the serial dependence prevalent in ETT data. Updating the probability of behavioural switches with tag or remote-sensing data provides a statistical method that links environmental data to behaviour in a direct and integrated manner. 3. It is important to assess the reliability of state categorization over the range of time-series lengths typically collected from field instruments and when movement behaviours are similar between movement states. Simulation with varying lengths of times series data and contrast between average movements within each state was used to test the HMMs ability to estimate movement parameters. 4. To demonstrate the methods in a realistic setting, the HMMs were used to categorize resident and migratory phases and the relationship between movement behaviour and ocean temperature using electronic tagging data from southern bluefin tuna (Thunnus maccoyii). Diagnostic tools to evaluate the suitability of different models and inferential methods for investigating differences in behaviour between individuals are also demonstrated.
Karchin, Rachel; Cline, Melissa; Mandel-Gutfreund, Yael; Karplus, Kevin
2003-06-01
An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE.
A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading
NASA Astrophysics Data System (ADS)
Dong, Liang; Foo, Say Wei; Lian, Yong
2005-12-01
Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM) operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.
Griffin, William A.; Li, Xun
2016-01-01
Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects—some good and some bad—on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes. PMID:27187319
Hidden Markov modeling for single channel kinetics with filtering and correlated noise.
Qin, F; Auerbach, A; Sachs, F
2000-01-01
Hidden Markov modeling (HMM) can be applied to extract single channel kinetics at signal-to-noise ratios that are too low for conventional analysis. There are two general HMM approaches: traditional Baum's reestimation and direct optimization. The optimization approach has the advantage that it optimizes the rate constants directly. This allows setting constraints on the rate constants, fitting multiple data sets across different experimental conditions, and handling nonstationary channels where the starting probability of the channel depends on the unknown kinetics. We present here an extension of this approach that addresses the additional issues of low-pass filtering and correlated noise. The filtering is modeled using a finite impulse response (FIR) filter applied to the underlying signal, and the noise correlation is accounted for using an autoregressive (AR) process. In addition to correlated background noise, the algorithm allows for excess open channel noise that can be white or correlated. To maximize the efficiency of the algorithm, we derive the analytical derivatives of the likelihood function with respect to all unknown model parameters. The search of the likelihood space is performed using a variable metric method. Extension of the algorithm to data containing multiple channels is described. Examples are presented that demonstrate the applicability and effectiveness of the algorithm. Practical issues such as the selection of appropriate noise AR orders are also discussed through examples. PMID:11023898
A hidden Markov model that finds genes in E. coli DNA.
Krogh, A; Mian, I S; Haussler, D
1994-01-01
A hidden Markov model (HMM) has been developed to find protein coding genes in E. coli DNA using E. coli genome DNA sequence from the EcoSeq6 database maintained by Kenn Rudd. This HMM includes states that model the codons and their frequencies in E. coli genes, as well as the patterns found in the intergenic region, including repetitive extragenic palindromic sequences and the Shine-Delgarno motif. To account for potential sequencing errors and or frameshifts in raw genomic DNA sequence, it allows for the (very unlikely) possibility of insertions and deletions of individual nucleotides within a codon. The parameters of the HMM are estimated using approximately one million nucleotides of annotated DNA in EcoSeq6 and the model tested on a disjoint set of contigs containing about 325,000 nucleotides. The HMM finds the exact locations of about 80% of the known E. coli genes, and approximate locations for about 10%. It also finds several potentially new genes, and locates several places were insertion or deletion errors/and or frameshifts may be present in the contigs. PMID:7984429
Häme, Yrjö; Angelini, Elsa D.; Hoffman, Eric A.; Barr, R. Graham; Laine, Andrew F.
2014-01-01
The extent of pulmonary emphysema is commonly estimated from CT images by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions in the lung and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the present model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was used to quantify emphysema on a cohort of 87 subjects, with repeated CT scans acquired over a time period of 8 years using different imaging protocols. The scans were acquired approximately annually, and the data set included a total of 365 scans. The results show that the emphysema estimates produced by the proposed method have very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. In addition, the generated emphysema delineations promise great advantages for regional analysis of emphysema extent and progression, possibly advancing disease subtyping. PMID:24759984
NASA Astrophysics Data System (ADS)
Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant
2015-07-01
Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.
Extracting duration information in a picture category decoding task using hidden Markov Models
Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y; Schoenfeld, Mircea A; Knight, Robert T; Rose, Georg
2016-01-01
Objective Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain–computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations. PMID:26859831
Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales
Quick, Nicola J.; Isojunno, Saana; Sadykova, Dina; Bowers, Matthew; Nowacek, Douglas P.; Read, Andrew J.
2017-01-01
Diving behaviour of short-finned pilot whales is often described by two states; deep foraging and shallow, non-foraging dives. However, this simple classification system ignores much of the variation that occurs during subsurface periods. We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the transitions between states in short-finned pilot whales. We used three parameters (number of buzzes, maximum dive depth and duration) measured in 259 dives by digital acoustic recording tags (DTAGs) deployed on 20 individual whales off Cape Hatteras, North Carolina, USA. The HMM identified a four-state model as the best descriptor of diving behaviour. The state-dependent distributions for the diving parameters showed variation between states, indicative of different diving behaviours. Transition probabilities were considerably higher for state persistence than state switching, indicating that dive types occurred in bouts. Our results indicate that subsurface behaviour in short-finned pilot whales is more complex than a simple dichotomy of deep and shallow diving states, and labelling all subsurface behaviour as deep dives or shallow dives discounts a significant amount of important variation. We discuss potential drivers of these patterns, including variation in foraging success, prey availability and selection, bathymetry, physiological constraints and socially mediated behaviour. PMID:28361954
Reverse engineering a social agent-based hidden markov model--visage.
Chen, Hung-Ching Justin; Goldberg, Mark; Magdon-Ismail, Malik; Wallace, William A
2008-12-01
We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.
Characterization of the crawling activity of Caenorhabditis elegans using a Hidden Markov model.
Lee, Sang-Hee; Kang, Seung-Ho
2015-12-01
The locomotion behavior of Caenorhabditis elegans has been studied extensively to understand the respective roles of neural control and biomechanics as well as the interaction between them. Constructing a mathematical model is helpful to understand the locomotion behavior in various surrounding conditions that are difficult to realize in experiments. In this study, we built three hidden Markov models (HMMs) for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 and 0.5 ppm). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into four groups using the self-organizing map (SOM). Comparison of the simulated behavior generated by HMMs and the actual crawling behavior demonstrated that the HMM coupled with the SOM was successful in characterizing the crawling behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems to determine water quality.
Martinez-Murcia, Francisco J; Górriz, Juan M; Ramírez, Javier; Ortiz, Andres
2016-11-01
The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI).
Constructing a Hidden Markov Model based earthquake detector: application to induced seismicity
NASA Astrophysics Data System (ADS)
Beyreuther, Moritz; Hammer, Conny; Wassermann, Joachim; Ohrnberger, Matthias; Megies, Tobias
2012-04-01
The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia.
Hame, Yrjo; Angelini, Elsa D; Hoffman, Eric A; Barr, R Graham; Laine, Andrew F
2014-07-01
The extent of pulmonary emphysema is commonly estimated from CT scans by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols, and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the presented model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was applied on a longitudinal data set with 87 subjects and a total of 365 scans acquired with varying imaging protocols. The resulting emphysema estimates had very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. The generated emphysema delineations promise advantages for regional analysis of emphysema extent and progression.
Sim, SeungWoo; Kang, Seung-Ho; Lee, Sang-Hee
2015-02-01
Subterranean termites live underground and build tunnel networks to obtain food and nesting space. After obtaining food, termites return to their nests to transfer it. The efficiency of termite movement through the tunnels is directly connected to their survival. Tunnels should therefore be optimized to ensure highly efficient returns. An optimization factor that strongly affects movement efficiency is tunnel curvature. In the present study, we investigated traveling behavior in tunnels with different curvatures. We then characterized traveling behavior at the level of the individual using hidden Markov models (HMMs) constructed from the experimental data. To observe traveling behavior, we designed 5-cm long artificial tunnels that had different curvatures. The tunnels had widths (W) of 2, 3, or 4mm, and the linear distances between the two ends of the tunnels were (D) 20, 30, 40, or 50mm. High values of D indicate low curvature. We systematically observed the traveling behavior of Coptotermes formosanus shiraki and Reticulitermes speratus kyushuensis and measured the time (τ) required for a termite to pass through the tunnel. Using HMM models, we calculated τ for different tunnels and compared the results with the τ of real termites. We characterized the traveling behavior in terms of transition probability matrices (TPM) and emission probability matrices (EPM) of HMMs. We briefly discussed the construction of a sinusoidal-like tunnels in relation to the energy required for termites to pass through tunnels and provided suggestions for the development of more sophisticated HMMs to better understand termite foraging behavior.
Identifying spatiotemporal migration patterns of non-volcanic tremors using hidden Markov models
NASA Astrophysics Data System (ADS)
Zhuang, J.; Wang, T.; Obara, K.; Tsuruoka, H.
2015-12-01
Tremor activity has been recently detected in various tectonic areas worldwide, and is spatially segmented and temporally recurrent. We design a type of hidden Markov models (HMMs) to investigate this phenomenon, where each state represents a distinct segment of tremor sources. We systematically analyze the tremor data from the Tokai region in southwest Japan using this model and find that tremors in this region concentrate around several distinct centers. We find: (1) The system is classified into three classes, background (quiescent), quasi-quiescent, and active states; (2) The region can be separated into two subsystems, the southwest and northeast parts, with most of the active transitions being among the states in each subsystem and the other transitions mainly to the quiescent/quasi-quiescent states; and (3) Tremor activity lasts longer in the northeastern part than in the southwest part. The success of this analysis indicates the power of HMMs in revealing the underlying physical process that drives non-volcanic tremors. Figure： The migration pattern for the HMM with 8 states. Top panel: Observed distances with the center μi of each state overlayed as the red line and ±σi on the left-hand side of the panel in green lines; Middle panel: the tracked most likely state sequence of the 8-state HMM; Bottom panel: the estimated probability of the data being in each state, with blank representing the probability of being in State 1 (the null state).
Identification of novel peptide hormones in the human proteome by hidden Markov model screening.
Mirabeau, Olivier; Perlas, Emerald; Severini, Cinzia; Audero, Enrica; Gascuel, Olivier; Possenti, Roberta; Birney, Ewan; Rosenthal, Nadia; Gross, Cornelius
2007-03-01
Peptide hormones are small, processed, and secreted peptides that signal via membrane receptors and play critical roles in normal and pathological physiology. The search for novel peptide hormones has been hampered by their small size, low or restricted expression, and lack of sequence similarity. To overcome these difficulties, we developed a bioinformatics search tool based on the hidden Markov model formalism that uses several peptide hormone sequence features to estimate the likelihood that a protein contains a processed and secreted peptide of this class. Application of this tool to an alignment of mammalian proteomes ranked 90% of known peptide hormones among the top 300 proteins. An analysis of the top scoring hypothetical and poorly annotated human proteins identified two novel candidate peptide hormones. Biochemical analysis of the two candidates, which we called spexin and augurin, showed that both were localized to secretory granules in a transfected pancreatic cell line and were recovered from the cell supernatant. Spexin was expressed in the submucosal layer of the mouse esophagus and stomach, and a predicted peptide from the spexin precursor induced muscle contraction in a rat stomach explant assay. Augurin was specifically expressed in mouse endocrine tissues, including pituitary and adrenal gland, choroid plexus, and the atrio-ventricular node of the heart. Our findings demonstrate the utility of a bioinformatics approach to identify novel biologically active peptides. Peptide hormones and their receptors are important diagnostic and therapeutic targets, and our results suggest that spexin and augurin are novel peptide hormones likely to be involved in physiological homeostasis.
Hidden semi-Markov models reveal multiphasic movement of the endangered Florida panther.
van de Kerk, Madelon; Onorato, David P; Criffield, Marc A; Bolker, Benjamin M; Augustine, Ben C; McKinley, Scott A; Oli, Madan K
2015-03-01
Animals must move to find food and mates, and to avoid predators; movement thus influences survival and reproduction, and ultimately determines fitness. Precise description of movement and understanding of spatial and temporal patterns as well as relationships with intrinsic and extrinsic factors is important both for theoretical and applied reasons. We applied hidden semi-Markov models (HSMM) to hourly geographic positioning system (GPS) location data to understand movement patterns of the endangered Florida panther (Puma concolor coryi) and to discern factors influencing these patterns. Three distinct movement modes were identified: (1) Resting mode, characterized by short step lengths and turning angles around 180(o); (2) Moderately active (or intermediate) mode characterized by intermediate step lengths and variable turning angles, and (3) Traveling mode, characterized by long step lengths and turning angles around 0(o). Males and females, and females with and without kittens, exhibited distinctly different movement patterns. Using the Viterbi algorithm, we show that differences in movement patterns of male and female Florida panthers were a consequence of sex-specific differences in diurnal patterns of state occupancy and sex-specific differences in state-specific movement parameters, whereas the differences between females with and without dependent kittens were caused solely by variation in state occupancy. Our study demonstrates the use of HSMM methodology to precisely describe movement and to dissect differences in movement patterns according to sex, and reproductive status.
Detection of unusual optical flow patterns by multilevel hidden Markov models
NASA Astrophysics Data System (ADS)
Utasi, Ákos; Czúni, László
2010-01-01
The analysis of motion information is one of the main tools for the understanding of complex behaviors in video. However, due to the quality of the optical flow of low-cost surveillance camera systems and the complexity of motion, new robust image-processing methods are required to generate reliable higher-level information. In our novel approach there is no need for tracking objects (vehicles, pedestrians) in order to recognize anomalous motion, but dense optical flow information is used to construct mixtures of Gaussians, which are analyzed temporally. We create a multilevel model, where low-level states of non-overlapping image regions are modeled by continuous hidden Markov models (HMMs). From low-level HMMs we compose high-level HMMs to analyze the occurrence of the low-level states. The processing of large numbers of data in traditional HMMs can result in a precision problem due to the multiplication of low probability values. Thus, besides introducing new motion models, we incorporate a scaling technique into the mathematical model of HMMs to avoid precision problems and to get an effective tool for the analysis of large numbers of motion vectors. We illustrate the use of our models with real-life traffic videos.
Shihab, Hashem A; Gough, Julian; Cooper, David N; Stenson, Peter D; Barker, Gary L A; Edwards, Keith J; Day, Ian N M; Gaunt, Tom R
2013-01-01
The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.
Newton, Richard; Hinds, Jason; Wernisch, Lorenz
2006-01-01
Whole genome DNA microarray genomotyping experiments compare the gene content of different species or strains of bacteria. A statistical approach to analysing the results of these experiments was developed, based on a Hidden Markov model (HMM), which takes adjacency of genes along the genome into account when calling genes present or absent. The model was implemented in the statistical language R and applied to three datasets. The method is numerically stable with good convergence properties. Error rates are reduced compared with approaches that ignore spatial information. Moreover, the HMM circumvents a problem encountered in a conventional analysis: determining the cut-off value to use to classify a gene as absent. An Apache Struts web interface for the R script was created for the benefit of users unfamiliar with R. The application may be found at http://hmmgd.cryst.bbk.ac.uk/hmmgd. The source code illustrating how to run R scripts from an Apache Struts-based web application is available from the corresponding author on request. The application is also available for local installation if required.
A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model
Lu, Yi; Wei, Dongyan; Lai, Qifeng; Li, Wen; Yuan, Hong
2016-01-01
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. PMID:27916922
Vakanski, A; Mantegh, I; Irish, A; Janabi-Sharifi, F
2012-08-01
The main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity.
On the application of mixed hidden Markov models to multiple behavioural time series
Schliehe-Diecks, S.; Kappeler, P. M.; Langrock, R.
2012-01-01
Analysing behavioural sequences and quantifying the likelihood of occurrences of different behaviours is a difficult task as motivational states are not observable. Furthermore, it is ecologically highly relevant and yet more complicated to scale an appropriate model for one individual up to the population level. In this manuscript (mixed) hidden Markov models (HMMs) are used to model the feeding behaviour of 54 subadult grey mouse lemurs (Microcebus murinus), small nocturnal primates endemic to Madagascar that forage solitarily. Our primary aim is to introduce ecologists and other users to various HMM methods, many of which have been developed only recently, and which in this form have not previously been synthesized in the ecological literature. Our specific application of mixed HMMs aims at gaining a better understanding of mouse lemur behaviour, in particular concerning sex-specific differences. The model we consider incorporates random effects for accommodating heterogeneity across animals, i.e. accounts for different personalities of the animals. Additional subject- and time-specific covariates in the model describe the influence of sex, body mass and time of night. PMID:23565332
Lapuyade-Lahorgue, Jerome; Xue, Jing-Hao; Ruan, Su
2017-03-21
Nowadays, multi-source image acquisition attracts an increasing interest in many fields such as multi-modal medical image segmentation. Such acquisition aims at considering complementary information to perform image segmentation since the same scene has been observed by various types of images. However, strong dependency often exists between multi-source images. This dependency should be taken into account when we try to extract joint information for precisely making a decision. In order to statistically model this dependency between multiple sources, we propose a novel multi-source fusion method based on the Gaussian copula. The proposed fusion model is integrated in a statistical framework with the hidden Markov field inference in order to delineate a target volume from multi-source images. Estimation of parameters of the models and segmentation of the images are jointly performed by an iterative algorithm based on Gibbs sampling. Experiments are performed on multi-sequence MRI to segment tumors. The results show that the proposed method based on the Gaussian copula is effective to accomplish multi-source image segmentation.
Hidden Markov model for analyzing time-series health checkup data.
Kawamoto, Ryouhei; Nazir, Alwis; Kameyama, Atsuyuki; Ichinomiya, Takashi; Yamamoto, Keiko; Tamura, Satoshi; Yamamoto, Mayumi; Hayamizu, Satoru; Kinosada, Yasutomi
2013-01-01
In this paper, we apply a Hidden Markov Model (HMM) to analyze time-series personal health checkup data. HMM is widely used for data having continuation and extensibility such as time-series health checkup data. Therefore, using HMM as probabilistic model to model the health checkup data is considered to be suitable, and HMM can express the process of health condition changes of a person. In this paper, a HMM with six states placed in a 2×3 matrix was prepared. We collected training features including the time-series health checkup data. Each feature consists of eight inspection parameters such as BMI, SBP, and TG. The HMM was then built using the training features. In the experiments, we built five HMMs for different gender and age conditions (e.g. male 50's) using thousands of training feature vectors, respectively. Investigating the HMMs we found that the HMMs can model three health risk levels. The models can also represent health transitions or changes, indicating the possibility of estimating the risk of lifestyle-related diseases.
Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model
Kwon, Namyeon; Shin, Yongwook; Ryo, Chuh Yeop; Park, Jonghun
2014-01-01
With the advance of military technology, the number of unmanned combat aerial vehicles (UCAVs) has rapidly increased. However, it has been reported that the accident rate of UCAVs is much higher than that of manned combat aerial vehicles. One of the main reasons for the high accident rate of UCAVs is the hypovigilance problem which refers to the decrease in vigilance levels of UCAV operators while maneuvering. In this paper, we propose hypovigilance detection models for UCAV operators based on EEG signal to minimize the number of occurrences of hypovigilance. To enable detection, we have applied hidden Markov models (HMMs), two of which are used to indicate the operators' dual states, normal vigilance and hypovigilance, and, for each operator, the HMMs are trained as a detection model. To evaluate the efficacy and effectiveness of the proposed models, we conducted two experiments on the real-world data obtained by using EEG-signal acquisition devices, and they yielded satisfactory results. By utilizing the proposed detection models, the problem of hypovigilance of UCAV operators and the problem of high accident rate of UCAVs can be addressed. PMID:24963338
A hidden Markov model for decoding and the analysis of replay in spike trains.
Box, Marc; Jones, Matt W; Whiteley, Nick
2016-12-01
We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.
Adaptive hidden Markov model with anomaly States for price manipulation detection.
Cao, Yi; Li, Yuhua; Coleman, Sonya; Belatreche, Ammar; McGinnity, Thomas Martin
2015-02-01
Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models.
Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric
2016-01-01
Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and
Lin, Yen-Jen; Chen, Yu-Tin; Hsu, Shu-Ni; Peng, Chien-Hua; Tang, Chuan-Yi; Yen, Tzu-Chen; Hsieh, Wen-Ping
2014-01-01
Copy number variation (CNV) has been reported to be associated with disease and various cancers. Hence, identifying the accurate position and the type of CNV is currently a critical issue. There are many tools targeting on detecting CNV regions, constructing haplotype phases on CNV regions, or estimating the numerical copy numbers. However, none of them can do all of the three tasks at the same time. This paper presents a method based on Hidden Markov Model to detect parent specific copy number change on both chromosomes with signals from SNP arrays. A haplotype tree is constructed with dynamic branch merging to model the transition of the copy number status of the two alleles assessed at each SNP locus. The emission models are constructed for the genotypes formed with the two haplotypes. The proposed method can provide the segmentation points of the CNV regions as well as the haplotype phasing for the allelic status on each chromosome. The estimated copy numbers are provided as fractional numbers, which can accommodate the somatic mutation in cancer specimens that usually consist of heterogeneous cell populations. The algorithm is evaluated on simulated data and the previously published regions of CNV of the 270 HapMap individuals. The results were compared with five popular methods: PennCNV, genoCN, COKGEN, QuantiSNP and cnvHap. The application on oral cancer samples demonstrates how the proposed method can facilitate clinical association studies. The proposed algorithm exhibits comparable sensitivity of the CNV regions to the best algorithm in our genome-wide study and demonstrates the highest detection rate in SNP dense regions. In addition, we provide better haplotype phasing accuracy than similar approaches. The clinical association carried out with our fractional estimate of copy numbers in the cancer samples provides better detection power than that with integer copy number states.
NASA Astrophysics Data System (ADS)
Suvorova, S.; Sun, L.; Melatos, A.; Moran, W.; Evans, R. J.
2016-06-01
Gravitational wave searches for continuous-wave signals from neutron stars are especially challenging when the star's spin frequency is unknown a priori from electromagnetic observations and wanders stochastically under the action of internal (e.g., superfluid or magnetospheric) or external (e.g., accretion) torques. It is shown that frequency tracking by hidden Markov model (HMM) methods can be combined with existing maximum likelihood coherent matched filters like the F -statistic to surmount some of the challenges raised by spin wandering. Specifically, it is found that, for an isolated, biaxial rotor whose spin frequency walks randomly, HMM tracking of the F -statistic output from coherent segments with duration Tdrift=10 d over a total observation time of Tobs=1 yr can detect signals with wave strains h0>2 ×10-26 at a noise level characteristic of the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). For a biaxial rotor with randomly walking spin in a binary orbit, whose orbital period and semimajor axis are known approximately from electromagnetic observations, HMM tracking of the Bessel-weighted F -statistic output can detect signals with h0>8 ×10-26. An efficient, recursive, HMM solver based on the Viterbi algorithm is demonstrated, which requires ˜103 CPU hours for a typical, broadband (0.5-kHz) search for the low-mass x-ray binary Scorpius X-1, including generation of the relevant F -statistic input. In a "realistic" observational scenario, Viterbi tracking successfully detects 41 out of 50 synthetic signals without spin wandering in stage I of the Scorpius X-1 Mock Data Challenge convened by the LIGO Scientific Collaboration down to a wave strain of h0=1.1 ×10-25, recovering the frequency with a root-mean-square accuracy of ≤4.3 ×10-3 Hz .
Multi-stream continuous hidden Markov models with application to landmine detection
NASA Astrophysics Data System (ADS)
Missaoui, Oualid; Frigui, Hichem; Gader, Paul
2013-12-01
We propose a multi-stream continuous hidden Markov model (MSCHMM) framework that can learn from multiple modalities. We assume that the feature space is partitioned into subspaces generated by different sources of information. In order to fuse the different modalities, the proposed MSCHMM introduces stream relevance weights. First, we modify the probability density function (pdf) that characterizes the standard continuous HMM to include state and component dependent stream relevance weights. The resulting pdf approximate is a linear combination of pdfs characterizing multiple modalities. Second, we formulate the CHMM objective function to allow for the simultaneous optimization of all model parameters including the relevance weights. Third, we generalize the maximum likelihood based Baum-Welch algorithm and the minimum classification error/gradient probabilistic descent (MCE/GPD) learning algorithms to include stream relevance weights. We propose two versions of the MSCHMM. The first one introduces the relevance weights at the state level while the second one introduces the weights at the component level. We illustrate the performance of the proposed MSCHMM structures using synthetic data sets. We also apply them to the problem of landmine detection using ground penetrating radar. We show that when the multiple sources of information are equally relevant across all training data, the performance of the proposed MSCHMM is comparable to the baseline CHMM. However, when the relevance of the sources varies, the MSCHMM outperforms the baseline CHMM because it can learn the optimal relevance weights. We also show that our approach outperforms existing multi-stream HMM because the latter one cannot optimize all model parameters simultaneously.
2013-01-01
Background Fungal pathogens cause devastating losses in economically important cereal crops by utilising pathogen proteins to infect host plants. Secreted pathogen proteins are referred to as effectors and have thus far been identified by selecting small, cysteine-rich peptides from the secretome despite increasing evidence that not all effectors share these attributes. Results We take advantage of the availability of sequenced fungal genomes and present an unbiased method for finding putative pathogen proteins and secreted effectors in a query genome via comparative hidden Markov model analyses followed by unsupervised protein clustering. Our method returns experimentally validated fungal effectors in Stagonospora nodorum and Fusarium oxysporum as well as the N-terminal Y/F/WxC-motif from the barley powdery mildew pathogen. Application to the cereal pathogen Fusarium graminearum reveals a secreted phosphorylcholine phosphatase that is characteristic of hemibiotrophic and necrotrophic cereal pathogens and shares an ancient selection process with bacterial plant pathogens. Three F. graminearum protein clusters are found with an enriched secretion signal. One of these putative effector clusters contains proteins that share a [SG]-P-C-[KR]-P sequence motif in the N-terminal and show features not commonly associated with fungal effectors. This motif is conserved in secreted pathogenic Fusarium proteins and a prime candidate for functional testing. Conclusions Our pipeline has successfully uncovered conservation patterns, putative effectors and motifs of fungal pathogens that would have been overlooked by existing approaches that identify effectors as small, secreted, cysteine-rich peptides. It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms. PMID:24252298
Modeling strategic use of human computer interfaces with novel hidden Markov models
Mariano, Laura J.; Poore, Joshua C.; Krum, David M.; Schwartz, Jana L.; Coskren, William D.; Jones, Eric M.
2015-01-01
Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit. PMID
Automated Detection and Classification of Rockfall Induced Seismic Signals with Hidden-Markov-Models
NASA Astrophysics Data System (ADS)
Zeckra, M.; Hovius, N.; Burtin, A.; Hammer, C.
2015-12-01
Originally introduced in speech recognition, Hidden Markov Models are applied in different research fields of pattern recognition. In seismology, this technique has recently been introduced to improve common detection algorithms, like STA/LTA ratio or cross-correlation methods. Mainly used for the monitoring of volcanic activity, this study is one of the first applications to seismic signals induced by geomorphologic processes. With an array of eight broadband seismometers deployed around the steep Illgraben catchment (Switzerland) with high-level erosion, we studied a sequence of landslides triggered over a period of several days in winter. A preliminary manual classification led us to identify three main seismic signal classes that were used as a start for the HMM automated detection and classification: (1) rockslide signal, including a failure source and the debris mobilization along the slope, (2) rockfall signal from the remobilization of debris along the unstable slope, and (3) single cracking signal from the affected cliff observed before the rockslide events. Besides the ability to classify the whole dataset automatically, the HMM approach reflects the origin and the interactions of the three signal classes, which helps us to understand this geomorphic crisis and the possible triggering mechanisms for slope processes. The temporal distribution of crack events (duration > 5s, frequency band [2-8] Hz) follows an inverse Omori law, leading to the catastrophic behaviour of the failure mechanisms and the interest for warning purposes in rockslide risk assessment. Thanks to a dense seismic array and independent weather observations in the landslide area, this dataset also provides information about the triggering mechanisms, which exhibit a tight link between rainfall and freezing level fluctuations.
A transition-constrained discrete hidden Markov model for automatic sleep staging
2012-01-01
Background Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. Method The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. Results Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%. Conclusion The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies. PMID:22908930
Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric
2016-01-01
Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and
A robust hidden Markov Gauss mixture vector quantizer for a noisy source.
Pyun, Kyungsuk Peter; Lim, Johan; Gray, Robert M
2009-07-01
Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.
A Bayesian hierarchical nonhomogeneous hidden Markov model for multisite streamflow reconstructions
NASA Astrophysics Data System (ADS)
Bracken, C.; Rajagopalan, B.; Woodhouse, C.
2016-10-01
In many complex water supply systems, the next generation of water resources planning models will require simultaneous probabilistic streamflow inputs at multiple locations on an interconnected network. To make use of the valuable multicentury records provided by tree-ring data, reconstruction models must be able to produce appropriate multisite inputs. Existing streamflow reconstruction models typically focus on one site at a time, not addressing intersite dependencies and potentially misrepresenting uncertainty. To this end, we develop a model for multisite streamflow reconstruction with the ability to capture intersite correlations. The proposed model is a hierarchical Bayesian nonhomogeneous hidden Markov model (NHMM). A NHMM is fit to contemporary streamflow at each location using lognormal component distributions. Leading principal components of tree rings are used as covariates to model nonstationary transition probabilities and the parameters of the lognormal component distributions. Spatial dependence between sites is captured with a Gaussian elliptical copula. Parameters of the model are estimated in a fully Bayesian framework, in that marginal posterior distributions of all the parameters are obtained. The model is applied to reconstruct flows at 20 sites in the Upper Colorado River Basin (UCRB) from 1473 to 1906. Many previous reconstructions are available for this basin, making it ideal for testing this new method. The results show some improvements over regression-based methods in terms of validation statistics. Key advantages of the Bayesian NHMM over traditional approaches are a dynamic representation of uncertainty and the ability to make long multisite simulations that capture at-site statistics and spatial correlations between sites.
Efficient view based 3-D object retrieval using Hidden Markov Model
NASA Astrophysics Data System (ADS)
Jain, Yogendra Kumar; Singh, Roshan Kumar
2013-12-01
Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.
Identification of novel peptide hormones in the human proteome by hidden Markov model screening
Mirabeau, Olivier; Perlas, Emerald; Severini, Cinzia; Audero, Enrica; Gascuel, Olivier; Possenti, Roberta; Birney, Ewan; Rosenthal, Nadia; Gross, Cornelius
2007-01-01
Peptide hormones are small, processed, and secreted peptides that signal via membrane receptors and play critical roles in normal and pathological physiology. The search for novel peptide hormones has been hampered by their small size, low or restricted expression, and lack of sequence similarity. To overcome these difficulties, we developed a bioinformatics search tool based on the hidden Markov model formalism that uses several peptide hormone sequence features to estimate the likelihood that a protein contains a processed and secreted peptide of this class. Application of this tool to an alignment of mammalian proteomes ranked 90% of known peptide hormones among the top 300 proteins. An analysis of the top scoring hypothetical and poorly annotated human proteins identified two novel candidate peptide hormones. Biochemical analysis of the two candidates, which we called spexin and augurin, showed that both were localized to secretory granules in a transfected pancreatic cell line and were recovered from the cell supernatant. Spexin was expressed in the submucosal layer of the mouse esophagus and stomach, and a predicted peptide from the spexin precursor induced muscle contraction in a rat stomach explant assay. Augurin was specifically expressed in mouse endocrine tissues, including pituitary and adrenal gland, choroid plexus, and the atrio-ventricular node of the heart. Our findings demonstrate the utility of a bioinformatics approach to identify novel biologically active peptides. Peptide hormones and their receptors are important diagnostic and therapeutic targets, and our results suggest that spexin and augurin are novel peptide hormones likely to be involved in physiological homeostasis. PMID:17284679
A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction
NASA Astrophysics Data System (ADS)
Peng, Ying; Dong, Ming
2011-01-01
Health monitoring and prognostics of equipment is a basic requirement for condition-based maintenance (CBM) in many application domains. This paper presents an age-dependent hidden semi-Markov model (HSMM) based prognosis method to predict equipment health. By using hazard function (h.f.), CBM is based on a failure rate which is a function of both the equipment age and the equipment conditions. The state values of the equipment condition considered in CBM, however, are limited to those stochastically increasing over time and those having non-decreasing effect on the hazard rate. The previous HSMM based prognosis algorithm assumed that the transition probabilities are only state-dependent, which means that the probability of making transition to a less healthy state does not increase with the age. In the proposed method, in order to characterize the deterioration of equipment, three types of aging factors that discount the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states are integrated into the HSMM. With an iteration algorithm, the original transition matrix obtained from the HSMM can be renewed with aging factors. To predict the remaining useful life (RUL) of the equipment, hazard rate is introduced to combine with the health-state transition matrix. With the classification information obtained from the HSMM, which provides the current health state of the equipment, the new RUL computation algorithm could be applied for the equipment prognostics. The performances of the HSMMs with aging factors are compared by using historical data colleted from hydraulic pumps through a case study.
Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models
NASA Astrophysics Data System (ADS)
Dammeier, Franziska; Moore, Jeffrey R.; Hammer, Conny; Haslinger, Florian; Loew, Simon
2016-02-01
Data from continuously recording permanent seismic networks can contain information about rockslide occurrence and timing complementary to eyewitness observations and thus aid in construction of robust event catalogs. However, detecting infrequent rockslide signals within large volumes of continuous seismic waveform data remains challenging and often requires demanding manual intervention. We adapted an automatic classification method using hidden Markov models to detect rockslide signals in seismic data from two stations in central Switzerland. We first processed 21 known rockslides, with event volumes spanning 3 orders of magnitude and station event distances varying by 1 order of magnitude, which resulted in 13 and 19 successfully classified events at the two stations. Retraining the models to incorporate seismic noise from the day of the event improved the respective results to 16 and 19 successful classifications. The missed events generally had low signal-to-noise ratio and small to medium volumes. We then processed nearly 14 years of continuous seismic data from the same two stations to detect previously unknown events. After postprocessing, we classified 30 new events as rockslides, of which we could verify three through independent observation. In particular, the largest new event, with estimated volume of 500,000 m3, was not generally known within the Swiss landslide community, highlighting the importance of regional seismic data analysis even in densely populated mountainous regions. Our method can be easily implemented as part of existing earthquake monitoring systems, and with an average event detection rate of about two per month, manual verification would not significantly increase operational workload.
NASA Astrophysics Data System (ADS)
Yoo, Jiyoung; Kwon, Hyun-Han; So, Byung-Jin; Rajagopalan, Balaji; Kim, Tae-Woong
2015-04-01
This study proposed a hidden Markov chain model-based drought analysis (HMM-DA) tool to understand the beginning and ending of meteorological drought and to further characterize typhoon-induced drought busters (TDB) by exploring spatiotemporal drought patterns in South Korea. It was found that typhoons have played a dominant role in ending drought events (EDE) during the typhoon season (July-September) over the last four decades (1974-2013). The percentage of EDEs terminated by TDBs was about 43-90% mainly along coastal regions in South Korea. Furthermore, the TDBs, mainly during summer, have a positive role in managing extreme droughts during the subsequent autumn and spring seasons. The HMM-DA models the temporal dependencies between drought states using Markov chain, consequently capturing the dependencies between droughts and typhoons well, thus, enabling a better performance in modeling spatiotemporal drought attributes compared to traditional methods.
Guenterberg, Eric; Yang, Allen Y; Ghasemzadeh, Hassan; Jafari, Roozbeh; Bajcsy, Ruzena; Sastry, S Shankar
2009-11-01
Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes.
Ghil, M.; Kravtsov, S.; Robertson, A. W.; Smyth, P.
2008-10-14
This project was a continuation of previous work under DOE CCPP funding, in which we had developed a twin approach of probabilistic network (PN) models (sometimes called dynamic Bayesian networks) and intermediate-complexity coupled ocean-atmosphere models (ICMs) to identify the predictable modes of climate variability and to investigate their impacts on the regional scale. We had developed a family of PNs (similar to Hidden Markov Models) to simulate historical records of daily rainfall, and used them to downscale GCM seasonal predictions. Using an idealized atmospheric model, we had established a novel mechanism through which ocean-induced sea-surface temperature (SST) anomalies might influence large-scale atmospheric circulation patterns on interannual and longer time scales; we had found similar patterns in a hybrid coupled ocean-atmosphere-sea-ice model. The goal of the this continuation project was to build on these ICM results and PN model development to address prediction of rainfall and temperature statistics at the local scale, associated with global climate variability and change, and to investigate the impact of the latter on coupled ocean-atmosphere modes. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling together with the development of associated software; new intermediate coupled models; a new methodology of inverse modeling for linking ICMs with observations and GCM results; and, observational studies of decadal and multi-decadal natural climate results, informed by ICM results.
Yang, Sejung; Lee, Byung-Uk
2015-01-01
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach.
NASA Astrophysics Data System (ADS)
Young, Dylan
Particle tracking offers significant insight into the molecular mechanics that govern the behavior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures such as the cell cytoskeleton, membrane or extracellular matrix. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks, however robust methods for failure detection are required when HMMs are applied to individual particle tracks and limited data sets. Here, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used for both the optimization of likelihood models, and also to generate custom failure tests based on a modified Bayesian Information Criterion. In the first instance, these failure tests can be applied to assess the quality of the HMM results. In addition, they provide critical information for the successful design of particle tracking experiments where trajectories containing multiple mobile states are expected.
Benoit, Julia S; Chan, Wenyaw; Luo, Sheng; Yeh, Hung-Wen; Doody, Rachelle
2016-04-30
Understanding the dynamic disease process is vital in early detection, diagnosis, and measuring progression. Continuous-time Markov chain (CTMC) methods have been used to estimate state-change intensities but challenges arise when stages are potentially misclassified. We present an analytical likelihood approach where the hidden state is modeled as a three-state CTMC model allowing for some observed states to be possibly misclassified. Covariate effects of the hidden process and misclassification probabilities of the hidden state are estimated without information from a 'gold standard' as comparison. Parameter estimates are obtained using a modified expectation-maximization (EM) algorithm, and identifiability of CTMC estimation is addressed. Simulation studies and an application studying Alzheimer's disease caregiver stress-levels are presented. The method was highly sensitive to detecting true misclassification and did not falsely identify error in the absence of misclassification. In conclusion, we have developed a robust longitudinal method for analyzing categorical outcome data when classification of disease severity stage is uncertain and the purpose is to study the process' transition behavior without a gold standard.
Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin
2014-01-01
Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.
Slaboda, Jill C; Boston, J Robert; Rudy, Thomas E
2006-01-01
Two hidden Markov models (HMMs) were designed to identify sub-groups of chronic lower back pain (CLBP) subjects based on time series of lifting parameters obtained during a repetitive lifting task. Two simulation studies were conducted to determine the reliability of this approach, using data from the repetitive lifting study. The first simulation verifies that control and CLBP HMMs based on these data can reliably identify sequences that were generated from that model. The second simulation determines whether the HMMs can reliably identify sequences that are intentionally misclassified (CLBP lifting sequences included in the control group and visa versa). The kappa statistic is used to quantify reliability. The simulation results show that the HMMs provide a reliable technique to analyze time series of lifting patterns and can be used to identify misclassified subjects as a subgroup.
A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.
Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang
2015-10-01
In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.
NASA Astrophysics Data System (ADS)
Ito, Sosuke
2016-11-01
The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics.
Yau, C.; Papaspiliopoulos, O.; Roberts, G. O.; Holmes, C.
2011-01-01
We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation. PMID:21687778
NASA Astrophysics Data System (ADS)
Lisiecki, L. E.; Ahn, S.; Khider, D.; Lawrence, C.
2015-12-01
Stratigraphic alignment is the primary way in which long marine climate records are placed on a common age model. We previously presented a probabilistic pairwise alignment algorithm, HMM-Match, which uses hidden Markov models to estimate alignment uncertainty and apply it to the alignment of benthic δ18O records to the "LR04" global benthic stack of Lisiecki and Raymo (2005) (Lin et al., 2014). However, since the LR04 stack is deterministic, the algorithm does not account for uncertainty in the stack. Here we address this limitation by developing a probabilistic stack, HMM-Stack. In this model the stack is a probabilistic inhomogeneous hidden Markov model, a.k.a. profile HMM. The HMM-stack is represented by a probabilistic model that "emits" each of the input records (Durbin et al., 1998). The unknown parameters of this model are learned from a set of input records using the expectation maximization (EM) algorithm. Because the multiple alignment of these records is unknown and uncertain, the expected contribution of each input point to each point in the stack is determined probabilistically. For each time step in the HMM-stack, δ18O values are described by a Gaussian probability distribution. Available δ18O records (N=180) are employed to estimate the mean and variance of δ18O at each time point. The mean of HMM-Stack follows the predicted pattern of glacial cycles with increased amplitude after the Pliocene-Pleistocene boundary and also larger and longer cycles after the mid-Pleistocene transition. Furthermore, the δ18O variance increases with age, producing a substantial loss in the signal-to-noise ratio. Not surprisingly, uncertainty in alignment and thus estimated age also increase substantially in the older portion of the stack.
Zheng, Jie; Vankataramanan, Lalitha; Sigworth, Fred J.
2001-01-01
Cooperativity among the four subunits helps give rise to the remarkable voltage sensitivity of Shaker potassium channels, whose open probability changes tenfold for a 5-mV change in membrane potential. The cooperativity in these channels is thought to arise from a concerted structural transition as the final step in opening the channel. Recordings of single-channel ionic currents from certain other channel types, as well as our previous recordings from T442S mutant Shaker channels, however, display intermediate conductance levels in addition to the fully open and closed states. These sublevels might represent stepwise, rather than concerted, transitions in the final steps of channel activation. Here, we report a similar fine structure in the closing transitions of Shaker channels lacking the mutation. Describing the deactivation time course with hidden Markov models, we find that two subconductance levels are rapidly traversed during most closing transitions of chimeric, high conductance Shaker channels. The lifetimes of these levels are voltage-dependent, with maximal values of 52 and 22 μs at −100 mV, and the voltage dependences of transitions among these states suggest that they arise from equivalent conformational changes occurring in individual subunits. At least one subconductance level is found to be traversed in normal conductance Shaker channels. We speculate that voltage-dependent conformational changes in the subunits give rise to changes in a “pore gate” associated with the selectivity filter region of the channel, producing the subconductance states. As a control for the hidden Markov analysis, we applied the same procedures to recordings of the recovery from N-type inactivation in Shaker channels. These transitions are found to be instantaneous in comparison. PMID:11696611
Fieberg, John R; Conn, Paul B
2014-01-01
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their
Gelfond, Jonathan A L; Gupta, Mayetri; Ibrahim, Joseph G
2009-12-01
We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP-enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity.
Gelfond, Jonathan A. L.; Gupta, Mayetri; Ibrahim, Joseph G.
2009-01-01
SUMMARY We propose a unified framework for the analysis of Chromatin (Ch) Immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov Chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity. PMID:19210737
NASA Astrophysics Data System (ADS)
Yu, Jianbo
2017-01-01
This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.
Introduction to Hidden Markov Models and Their Applications to Classification Problems
1999-09-01
34 model (the model is called ergotic when backward transitions are allowed, as illustrated in Fig 2.2). 5 We define the probability aij as the...Figure 2.2 A Markov Process (Chain), ergotic model 7 For the discrete-time case, a system governed by known or predictable dynamics can be modeled...Total number of positions=N*T :Possible movement / Most likely path Figure 2.4a Trellis Diagram for T=4, and N=3, ergotic model 17 tl t2 t3 t4 $1
Garcia-Gomez, Juan Miguel; Benedi, Jose Miguel; Vicente, Javier; Robles, Montserrat
2005-01-01
In this paper, a new method for modelling tRNA secondary structures is presented. This method is based on the combination of stochastic context-free grammars (SCFG) and Hidden Markov Models (HMM). HMM are used to capture the local relations in the loops of the molecule (nonstructured regions) and SCFG are used to capture the long term relations between nucleotides of the arms (structured regions). Given annotated public databases, the HMM and SCFG models are learned by means of automatic inductive learning methods. Two SCFG learning methods have been explored. Both of them take advantage of the structural information associated with the training sequences: one of them is based on a stochastic version of the Sakakibara algorithm and the other one is based on a Corpus based algorithm. A final model is then obtained by merging of the HMM of the nonstructured regions and the SCFG of the structured regions. Finally, the performed experiments on the tRNA sequence corpus and the non-tRNA sequence corpus give significant results. Comparative experiments with another published method are also presented.
Taborri, Juri; Scalona, Emilia; Palermo, Eduardo; Rossi, Stefano; Cappa, Paolo
2015-01-01
Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to avoid the intra-subject one in two, four and six gait-phase models in pediatric subjects. The inter-subject procedure consists in the identification of a standardized parameter set to adapt the model to measurements. We tested the inter-subject procedure both on scalar and distributed classifiers. Ten healthy children and ten hemiplegic children, each equipped with two Inertial Measurement Units placed on shank and foot, were recruited. The sagittal component of angular velocity was recorded by gyroscopes while subjects performed four walking trials on a treadmill. The goodness of classifiers was evaluated with the Receiver Operating Characteristic. The results provided a goodness from good to optimum for all examined classifiers (0 < G < 0.6), with the best performance for the distributed classifier in two-phase recognition (G = 0.02). Differences were found among gait partitioning models, while no differences were found between training procedures with the exception of the shank classifier. Our results raise the possibility of avoiding subject-specific training in HMM for gait-phase recognition and its implementation to control exoskeletons for the pediatric population. PMID:26404309
NASA Astrophysics Data System (ADS)
Joshi, J. C.; Kumar, Tankeshwar; Srivastava, Sunita; Sachdeva, Divya
2017-02-01
Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum-Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012-13, 2013-14). The HMM for maximum temperature has shown a 4-12% and 17-19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6-38% and 5-12% improvement for day-1 and day-2, respectively.
Kogan, J A; Margoliash, D
1998-04-01
The performance of two techniques is compared for automated recognition of bird song units from continuous recordings. The advantages and limitations of dynamic time warping (DTW) and hidden Markov models (HMMs) are evaluated on a large database of male songs of zebra finches (Taeniopygia guttata) and indigo buntings (Passerina cyanea), which have different types of vocalizations and have been recorded under different laboratory conditions. Depending on the quality of recordings and complexity of song, the DTW-based technique gives excellent to satisfactory performance. Under challenging conditions such as noisy recordings or presence of confusing short-duration calls, good performance of the DTW-based technique requires careful selection of templates that may demand expert knowledge. Because HMMs are trained, equivalent or even better performance of HMMs can be achieved based only on segmentation and labeling of constituent vocalizations, albeit with many more training examples than DTW templates. One weakness in HMM performance is the misclassification of short-duration vocalizations or song units with more variable structure (e.g., some calls, and syllables of plastic songs). To address these and other limitations, new approaches for analyzing bird vocalizations are discussed.
Narasimhan, Vagheesh; Danecek, Petr; Scally, Aylwyn; Xue, Yali; Tyler-Smith, Chris; Durbin, Richard
2016-01-01
Summary: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity. Availability and implementation: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools. Contact: vn2@sanger.ac.uk or pd3@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26826718
Ito, Sosuke
2016-01-01
The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics. PMID:27833120
Eldar, Eran; Morris, Genela; Niv, Yael
2011-09-30
A central goal of neuroscience is to understand how neural dynamics bring about the dynamics of behavior. However, neural and behavioral measures are noisy, requiring averaging over trials and subjects. Unfortunately, averaging can obscure the very dynamics that we are interested in, masking abrupt changes and artificially creating gradual processes. We develop a hidden semi-Markov model for precisely characterizing dynamic processes and their alteration due to experimental manipulations. This method takes advantage of multiple trials and subjects without compromising the information available in individual events within a trial. We apply our model to studying the effects of motivation on response rates, analyzing data from hungry and sated rats trained to press a lever to obtain food rewards on a free-operant schedule. Our method can accurately account for punctate changes in the rate of responding and for sequential dependencies between responses. It is ideal for inferring the statistics of underlying response rates and the probability of switching from one response rate to another. Using the model, we show that hungry rats have more distinct behavioral states that are characterized by high rates of responding and they spend more time in these high-press-rate states. Moreover, hungry rats spend less time in, and have fewer distinct states that are characterized by a lack of responding (Waiting/Eating states). These results demonstrate the utility of our analysis method, and provide a precise quantification of the effects of motivation on response rates.
Ghosh, Tarini Shankar; Gajjalla, Purnachander; Mohammed, Monzoorul Haque; Mande, Sharmila S
2012-04-01
Recent advances in high throughput sequencing technologies and concurrent refinements in 16S rDNA isolation techniques have facilitated the rapid extraction and sequencing of 16S rDNA content of microbial communities. The taxonomic affiliation of these 16S rDNA fragments is subsequently obtained using either BLAST-based or word frequency based approaches. However, the classification accuracy of such methods is observed to be limited in typical metagenomic scenarios, wherein a majority of organisms are hitherto unknown. In this study, we present a 16S rDNA classification algorithm, called C16S, that uses genus-specific Hidden Markov Models for taxonomic classification of 16S rDNA sequences. Results obtained using C16S have been compared with the widely used RDP classifier. The performance of C16S algorithm was observed to be consistently higher than the RDP classifier. In some scenarios, this increase in accuracy is as high as 34%. A web-server for the C16S algorithm is available at http://metagenomics.atc.tcs.com/C16S/.
NASA Astrophysics Data System (ADS)
Bhatti, Sohail Masood; Khan, Muhammad Salman; Wuth, Jorge; Huenupan, Fernando; Curilem, Millaray; Franco, Luis; Yoma, Nestor Becerra
2016-09-01
In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.
A novel seizure detection algorithm informed by hidden Markov model event states
NASA Astrophysics Data System (ADS)
Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian
2016-06-01
Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.
Phillips, Joe Scutt; Patterson, Toby A; Leroy, Bruno; Pilling, Graham M; Nicol, Simon J
2015-07-01
Analysis of complex time-series data from ecological system study requires quantitative tools for objective description and classification. These tools must take into account largely ignored problems of bias in manual classification, autocorrelation, and noise. Here we describe a method using existing estimation techniques for multivariate-normal hidden Markov models (HMMs) to develop such a classification. We use high-resolution behavioral data from bio-loggers attached to free-roaming pelagic tuna as an example. Observed patterns are assumed to be generated by an unseen Markov process that switches between several multivariate-normal distributions. Our approach is assessed in two parts. The first uses simulation experiments, from which the ability of the HMM to estimate known parameter values is examined using artificial time series of data consistent with hypotheses about pelagic predator foraging ecology. The second is the application to time series of continuous vertical movement data from yellowfin and bigeye tuna taken from tuna tagging experiments. These data were compressed into summary metrics capturing the variation of patterns in diving behavior and formed into a multivariate time series used to estimate a HMM. Each observation was associated with covariate information incorporating the effect of day and night on behavioral switching. Known parameter values were well recovered by the HMMs in our simulation experiments, resulting in mean correct classification rates of 90-97%, although some variance-covariance parameters were estimated less accurately. HMMs with two distinct behavioral states were selected for every time series of real tuna data, predicting a shallow warm state, which was similar across all individuals, and a deep colder state, which was more variable. Marked diurnal behavioral switching was predicted, consistent with many previous empirical studies on tuna. HMMs provide easily interpretable models for the objective classification of
Hogden, J.
1996-11-05
The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation may decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.
Zaidel, Adam; Spivak, Alexander; Shpigelman, Lavi; Bergman, Hagai; Israel, Zvi
2009-09-15
Positive therapeutic response without adverse side effects to subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) depends to a large extent on electrode location within the STN. The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. Thus, using an HMM in conjunction with RMS and PSD measures of intraoperative MER can provide improved refinement of STN entry and exit in comparison with previously reported automatic methods, and introduces a novel (intra-STN) detection of a distinct DLOR-ventral boundary.
2014-01-01
Background In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known. Results To deal with small samples for particular subfamilies of HIV-1, we introduce a novel model-based information sharing protocol. It estimates the emission probabilities of the pHMM modeling a particular subfamily not only based on the nucleotide frequencies of the respective subfamily but also incorporating the nucleotide frequencies of all available subfamilies. To this end, the underlying probabilistic model mimics the pattern of commonality and variation between the subtypes with regards to the biological characteristics of HI viruses. In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine. Conclusions We apply the modified algorithm to classify HIV-1 sequence data in the form of partial HIV-1 sequences and semi-artificial recombinants. Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique. Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning. PMID:24946781
NASA Astrophysics Data System (ADS)
Williams, R. M.; Ray, L. E.
2012-12-01
This paper presents methods to automatically classify ground penetrating radar (GPR) images of crevasses on ice sheets for use with a completely autonomous robotic system. We use a combination of support vector machines (SVM) and hidden Markov models (HMM) with appropriate un-biased processing that is suitable for real-time analysis and detection. We tested and evaluated three processing schemes on 96 examples of Antarctic GPR imagery from 2010 and 104 examples of Greenland imagery from 2011, collected by our robot and a Pisten Bully tractor. The Antarctic and Greenland data were collected in the shear zone near McMurdo Station and between Thule Air Base and Summit Station, respectively. Using a modified cross validation technique, we correctly classified 86 of the Antarctic examples and 90 of the Greenland examples with a radial basis kernel SVM trained and evaluated on down-sampled and texture-mapped GPR images of crevasses, compared to 60% classification rate using raw data. In order to reduce false positives, we use the SVM classification results as pre-screener flags that mark locations in the GPR files to evaluate with two gaussian HMMs, and evaluate our results with a similar modified cross validation technique. The combined SVM pre-screen-HMM confirm method retains all the correct classifications by the SVM, and reduces the false positive rate to 4%. This method also reduces the computational burden in classifying GPR traces because the HMM is only being evaluated on select pre-screened traces. Our experiments demonstrate the promise, robustness and reliability of real-time crevasse detection and classification with robotic GPR surveys.
Mannini, Andrea; Sabatini, Angelo Maria
2012-09-01
In this paper we present a classifier based on a hidden Markov model (HMM) that was applied to a gait treadmill dataset for gait phase detection and walking/jogging discrimination. The gait events foot strike, foot flat, heel off, toe off were detected using a uni-axial gyroscope that measured the foot instep angular velocity in the sagittal plane. Walking/jogging activities were discriminated by processing gyroscope data from each detected stride. Supervised learning of the classifier was undertaken using reference data from an optical motion analysis system. Remarkably good generalization properties were achieved across tested subjects and gait speeds. Sensitivity and specificity of gait phase detection exceeded 94% and 98%, respectively, with timing errors that were less than 20 ms, on average; the accuracy of walking/jogging discrimination was approximately 99%.
Ko, Albert Hung-Ren; Cavalin, Paulo Rodrigo; Sabourin, Robert; de Souza Britto, Alceu
2009-12-01
Hidden Markov Models (HMMs) have been shown to be useful in handwritten pattern recognition. However, owing to their fundamental structure, they have little resistance to unexpected noise among observation sequences. In other words, unexpected noise in a sequence might "break" the normal transmission of states for this sequence, making it unrecognizable to trained models. To resolve this problem, we propose a leave-one-out-training strategy, which will make the models more robust. We also propose a leave-one-out-testing method, which will compensate for some of the negative effects of this noise. The latter is actually an example of a system with a single classifier and multiple classifications. Compared with the 98.00 percent accuracy of the benchmark HMMs, the new system achieves a 98.88 percent accuracy rate on handwritten digits.
Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina
2015-01-01
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies.
Borgy, Benjamin; Reboud, Xavier; Peyrard, Nathalie; Sabbadin, Régis; Gaba, Sabrina
2015-01-01
Predicting the population dynamics of annual plants is a challenge due to their hidden seed banks in the field. However, such predictions are highly valuable for determining management strategies, specifically in agricultural landscapes. In agroecosystems, most weed seeds survive during unfavourable seasons and persist for several years in the seed bank. This causes difficulties in making accurate predictions of weed population dynamics and life history traits (LHT). Consequently, it is very difficult to identify management strategies that limit both weed populations and species diversity. In this article, we present a method of assessing weed population dynamics from both standing plant time series data and an unknown seed bank. We use a Hidden Markov Model (HMM) to obtain estimates of over 3,080 botanical records for three major LHT: seed survival in the soil, plant establishment (including post-emergence mortality), and seed production of 18 common weed species. Maximum likelihood and Bayesian approaches were complementarily used to estimate LHT values. The results showed that the LHT provided by the HMM enabled fairly accurate estimates of weed populations in different crops. There was a positive correlation between estimated germination rates and an index of the specialisation to the crop type (IndVal). The relationships between estimated LHTs and that between the estimated LHTs and the ecological characteristics of weeds provided insights into weed strategies. For example, a common strategy to cope with agricultural practices in several weeds was to produce less seeds and increase germination rates. This knowledge, especially of LHT for each type of crop, should provide valuable information for developing sustainable weed management strategies. PMID:26427023
Uğuz, Harun; Güraksın, Gür Emre; Ergün, Uçman; Saraçoğlu, Rıdvan
2011-07-01
When the maximum likelihood approach (ML) is used during the calculation of the Discrete Hidden Markov Model (DHMM) parameters, DHMM parameters of the each class are only calculated using the training samples (positive training samples) of the same class. The training samples (negative training samples) not belonging to that class are not used in the calculation of DHMM model parameters. With the aim of supplying that deficiency, by involving the training samples of all classes in calculating processes, a Rocchio algorithm based approach is suggested. During the calculation period, in order to determine the most appropriate values of parameters for adjusting the relative effect of the positive and negative training samples, a Genetic algorithm is used as an optimization technique. The purposed method is used to classify the internal carotid artery Doppler signals recorded from 136 patients as well as of 55 healthy people. Our proposed method reached 97.38% classification accuracy with fivefold cross-validation (CV) technique. The classification results showed that the proposed method was effective for the classification of internal carotid artery Doppler signals.
Wang, Kai; Li, Mingyao; Hadley, Dexter; Liu, Rui; Glessner, Joseph; Grant, Struan F.A.; Hakonarson, Hakon; Bucan, Maja
2007-01-01
Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected ∼27 CNVs for each individual with a median size of ∼12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping. PMID:17921354
NASA Astrophysics Data System (ADS)
Jeon, Wonju; Kang, Seung-Ho; Leem, Joo-Baek; Lee, Sang-Hee
2013-05-01
Fish that swim in schools benefit from increased vigilance, and improved predator recognition and assessment. Fish school size varies according to species and environmental conditions. In this study, we present a Hidden Markov Model (HMM) that we use to characterize fish schooling behavior in different sized schools, and explore how school size affects schooling behavior. We recorded the schooling behavior of Medaka (Oryzias latipes) and goldfish (Carassius auratus) using different numbers of individual fish (10-40), in a circular aquarium. Eight to ten 3 s video clips were extracted from the recordings for each group size. Schooling behavior was characterized by three variables: linear speed, angular speed, and Pearson coefficient. The values of the variables were categorized into two events each for linear and angular speed (high and low), and three events for the Pearson coefficient (high, medium, and low). Schooling behavior was then described as a sequence of 12 events (2×2×3), which was input to an HMM as data for training the model. Comparisons of model output with observations of actual schooling behavior demonstrated that the HMM was successful in characterizing fish schooling behavior. We briefly discuss possible applications of the HMM for recognition of fish species in a school, and for developing bio-monitoring systems to determine water quality.
Chen, Tianwen; Kochalka, John; Padmanabhan, Aarthi; Menon, Vinod
2016-01-01
Little is currently known about dynamic brain networks involved in high-level cognition and their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to investigate dynamic temporal properties of interactions between salience (SN), default mode (DMN), and central executive (CEN) networks—three brain systems that play a critical role in human cognition. In contrast to conventional models, VB-HMM revealed multiple short-lived states characterized by rapid switching and transient connectivity between SN, CEN, and DMN. Furthermore, the three “static” networks occurred in a segregated state only intermittently. Findings were replicated in two adult cohorts from the Human Connectome Project. VB-HMM further revealed immature dynamic interactions between SN, CEN, and DMN in children, characterized by higher mean lifetimes in individual states, reduced switching probability between states and less differentiated connectivity across states. Our computational techniques provide new insights into human brain network dynamics and its maturation with development. PMID:27959921
Dean, Ben; Freeman, Robin; Kirk, Holly; Leonard, Kerry; Phillips, Richard A.; Perrins, Chris M.; Guilford, Tim
2013-01-01
The use of miniature data loggers is rapidly increasing our understanding of the movements and habitat preferences of pelagic seabirds. However, objectively interpreting behavioural information from the large volumes of highly detailed data collected by such devices can be challenging. We combined three biologging technologies—global positioning system (GPS), saltwater immersion and time–depth recorders—to build a detailed picture of the at-sea behaviour of the Manx shearwater (Puffinus puffinus) during the breeding season. We used a hidden Markov model to explore discrete states within the combined GPS and immersion data, and found that behaviour could be organized into three principal activities representing (i) sustained direct flight, (ii) sitting on the sea surface, and (iii) foraging, comprising tortuous flight interspersed with periods of immersion. The additional logger data verified that the foraging activity corresponded well to the occurrence of diving. Applying this approach to a large tracking dataset revealed that birds from two different colonies foraged in local waters that were exclusive, but overlapped in one key area: the Irish Sea Front (ISF). We show that the allocation of time to each activity differed between colonies, with birds breeding furthest from the ISF spending the greatest proportion of time engaged in direct flight and the smallest proportion of time engaged in foraging activity. This type of analysis has considerable potential for application in future biologging studies and in other taxa. PMID:23034356
NASA Astrophysics Data System (ADS)
Power, Sarah D.; Falk, Tiago H.; Chau, Tom
2010-04-01
Near-infrared spectroscopy (NIRS) has recently been investigated as a non-invasive brain-computer interface (BCI). In particular, previous research has shown that NIRS signals recorded from the motor cortex during left- and right-hand imagery can be distinguished, providing a basis for a two-choice NIRS-BCI. In this study, we investigated the feasibility of an alternative two-choice NIRS-BCI paradigm based on the classification of prefrontal activity due to two cognitive tasks, specifically mental arithmetic and music imagery. Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations (International 10-20 System) while ten able-bodied adults performed mental arithmetic and music imagery within a synchronous shape-matching paradigm. With the 18 filtered AC signals, we created task- and subject-specific maximum likelihood classifiers using hidden Markov models. Mental arithmetic and music imagery were classified with an average accuracy of 77.2% ± 7.0 across participants, with all participants significantly exceeding chance accuracies. The results suggest the potential of a two-choice NIRS-BCI based on cognitive rather than motor tasks.
Yamamoto, Toshiyuki; Shimojima, Keiko; Ondo, Yumiko; Imai, Katsumi; Chong, Pin Fee; Kira, Ryutaro; Amemiya, Mitsuhiro; Saito, Akira; Okamoto, Nobuhiko
2016-01-01
Next-generation sequencing (NGS) is widely used for the detection of disease-causing nucleotide variants. The challenges associated with detecting copy number variants (CNVs) using NGS analysis have been reported previously. Disease-related exome panels such as Illumina TruSight One are more cost-effective than whole-exome sequencing (WES) because of their selective target regions (~21% of the WES). In this study, CNVs were analyzed using data extracted through a disease-related exome panel analysis and the eXome Hidden Markov Model (XHMM). Samples from 61 patients with undiagnosed developmental delays and 52 healthy parents were included in this study. In the preliminary study to validate the constructed XHMM system (microarray-first approach), 34 patients who had previously been analyzed by chromosomal microarray testing were used. Among the five CNVs larger than 200 kb that were considered as non-pathogenic CNVs and were used as positive controls, four CNVs was successfully detected. The system was subsequently used to analyze different samples from 27 patients (NGS-first approach); 2 of these patients were successfully diagnosed as having pathogenic CNVs (an unbalanced translocation der(5)t(5;14) and a 16p11.2 duplication). These diagnoses were re-confirmed by chromosomal microarray testing and/or fluorescence in situ hybridization. The NGS-first approach generated no false-negative or false-positive results for pathogenic CNVs, indicating its high sensitivity and specificity in detecting pathogenic CNVs. The results of this study show the possible clinical utility of pathogenic CNV screening using disease-related exome panel analysis and XHMM. PMID:27579173
Khayatt, Barzan I; Overmars, Lex; Siezen, Roland J; Francke, Christof
2013-01-01
There is a growing interest in the Non-ribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) of microbes, fungi and plants because they can produce bioactive peptides such as antibiotics. The ability to identify the substrate specificity of the enzyme's adenylation (A) and acyl-transferase (AT) domains is essential to rationally deduce or engineer new products. We here report on a Hidden Markov Model (HMM)-based ensemble method to predict the substrate specificity at high quality. We collected a new reference set of experimentally validated sequences. An initial classification based on alignment and Neighbor Joining was performed in line with most of the previously published prediction methods. We then created and tested single substrate specific HMMs and found that their use improved the correct identification significantly for A as well as for AT domains. A major advantage of the use of HMMs is that it abolishes the dependency on multiple sequence alignment and residue selection that is hampering the alignment-based clustering methods. Using our models we obtained a high prediction quality for the substrate specificity of the A domains similar to two recently published tools that make use of HMMs or Support Vector Machines (NRPSsp and NRPS predictor2, respectively). Moreover, replacement of the single substrate specific HMMs by ensembles of models caused a clear increase in prediction quality. We argue that the superiority of the ensemble over the single model is caused by the way substrate specificity evolves for the studied systems. It is likely that this also holds true for other protein domains. The ensemble predictor has been implemented in a simple web-based tool that is available at http://www.cmbi.ru.nl/NRPS-PKS-substrate-predictor/.
Nielsen, Rasmus
2017-01-01
Admixture—the mixing of genomes from divergent populations—is increasingly appreciated as a central process in evolution. To characterize and quantify patterns of admixture across the genome, a number of methods have been developed for local ancestry inference. However, existing approaches have a number of shortcomings. First, all local ancestry inference methods require some prior assumption about the expected ancestry tract lengths. Second, existing methods generally require genotypes, which is not feasible to obtain for many next-generation sequencing projects. Third, many methods assume samples are diploid, however a wide variety of sequencing applications will fail to meet this assumption. To address these issues, we introduce a novel hidden Markov model for estimating local ancestry that models the read pileup data, rather than genotypes, is generalized to arbitrary ploidy, and can estimate the time since admixture during local ancestry inference. We demonstrate that our method can simultaneously estimate the time since admixture and local ancestry with good accuracy, and that it performs well on samples of high ploidy—i.e. 100 or more chromosomes. As this method is very general, we expect it will be useful for local ancestry inference in a wider variety of populations than what previously has been possible. We then applied our method to pooled sequencing data derived from populations of Drosophila melanogaster on an ancestry cline on the east coast of North America. We find that regions of local recombination rates are negatively correlated with the proportion of African ancestry, suggesting that selection against foreign ancestry is the least efficient in low recombination regions. Finally we show that clinal outlier loci are enriched for genes associated with gene regulatory functions, consistent with a role of regulatory evolution in ecological adaptation of admixed D. melanogaster populations. Our results illustrate the potential of local ancestry
Ambrosini, Pierre; Smal, Ihor; Ruijters, Daniel; Niessen, Wiro; Moelker, Adriaan; van Walsum, Theo
2016-11-07
In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.
Srivastava, Prashant K; Desai, Dhwani K; Nandi, Soumyadeep; Lynn, Andrew M
2007-01-01
Background Profile Hidden Markov Models (HMM) are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives). Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. Results The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors sub-classified with
Deviney, Frank A.; Rice, Karen; Brown, Donald E.
2012-01-01
Natural resource managers require information concerning the frequency, duration, and long-term probability of occurrence of water-quality indicator (WQI) violations of defined thresholds. The timing of these threshold crossings often is hidden from the observer, who is restricted to relatively infrequent observations. Here, a model for the hidden process is linked with a model for the observations, and the parameters describing duration, return period, and long-term probability of occurrence are estimated using Bayesian methods. A simulation experiment is performed to evaluate the approach under scenarios based on the equivalent of a total monitoring period of 5-30 years and an observation frequency of 1-50 observations per year. Given constant threshold crossing rate, accuracy and precision of parameter estimates increased with longer total monitoring period and more-frequent observations. Given fixed monitoring period and observation frequency, accuracy and precision of parameter estimates increased with longer times between threshold crossings. For most cases where the long-term probability of being in violation is greater than 0.10, it was determined that at least 600 observations are needed to achieve precise estimates. An application of the approach is presented using 22 years of quasi-weekly observations of acid-neutralizing capacity from Deep Run, a stream in Shenandoah National Park, Virginia. The time series also was sub-sampled to simulate monthly and semi-monthly sampling protocols. Estimates of the long-term probability of violation were unbiased despite sampling frequency; however, the expected duration and return period were over-estimated using the sub-sampled time series with respect to the full quasi-weekly time series.
NASA Astrophysics Data System (ADS)
Chen, Jinsong; Hubbard, Susan S.; Williams, Kenneth H.
2013-10-01
Although mechanistic reaction networks have been developed to quantify the biogeochemical evolution of subsurface systems associated with bioremediation, it is difficult in practice to quantify the onset and distribution of these transitions at the field scale using commonly collected wellbore datasets. As an alternative approach to the mechanistic methods, we develop a data-driven, statistical model to identify biogeochemical transitions using various time-lapse aqueous geochemical data (e.g., Fe(II), sulfate, sulfide, acetate, and uranium concentrations) and induced polarization (IP) data. We assume that the biogeochemical transitions can be classified as several dominant states that correspond to redox transitions and test the method at a uranium-contaminated site. The relationships between the geophysical observations and geochemical time series vary depending upon the unknown underlying redox status, which is modeled as a hidden Markov random field. We estimate unknown parameters by maximizing the joint likelihood function using the maximization-expectation algorithm. The case study results show that when considered together aqueous geochemical data and IP imaginary conductivity provide a key diagnostic signature of biogeochemical stages. The developed method provides useful information for evaluating the effectiveness of bioremediation, such as the probability of being in specific redox stages following biostimulation where desirable pathways (e.g., uranium removal) are more highly favored. The use of geophysical data in the approach advances the possibility of using noninvasive methods to monitor critical biogeochemical system stages and transitions remotely and over field relevant scales (e.g., from square meters to several hectares).
Stochastic thermodynamics of hidden pumps
NASA Astrophysics Data System (ADS)
Esposito, Massimiliano; Parrondo, Juan M. R.
2015-05-01
We show that a reversible pumping mechanism operating between two states of a kinetic network can give rise to Poisson transitions between these two states. An external observer, for whom the pumping mechanism is not accessible, will observe a Markov chain satisfying local detailed balance with an emerging effective force induced by the hidden pump. Due to the reversibility of the pump, the actual entropy production turns out to be lower than the coarse-grained entropy production estimated from the flows and affinities of the resulting Markov chain. Moreover, in presence of a large time scale separation between the fast-pumping dynamics and the slow-network dynamics, a finite current with zero dissipation may be produced. We make use of these general results to build a synthetase-like kinetic scheme able to reversibly produce high free-energy molecules at a finite rate and a rotatory motor achieving 100% efficiency at finite speed.
Stochastic thermodynamics of hidden pumps.
Esposito, Massimiliano; Parrondo, Juan M R
2015-05-01
We show that a reversible pumping mechanism operating between two states of a kinetic network can give rise to Poisson transitions between these two states. An external observer, for whom the pumping mechanism is not accessible, will observe a Markov chain satisfying local detailed balance with an emerging effective force induced by the hidden pump. Due to the reversibility of the pump, the actual entropy production turns out to be lower than the coarse-grained entropy production estimated from the flows and affinities of the resulting Markov chain. Moreover, in presence of a large time scale separation between the fast-pumping dynamics and the slow-network dynamics, a finite current with zero dissipation may be produced. We make use of these general results to build a synthetase-like kinetic scheme able to reversibly produce high free-energy molecules at a finite rate and a rotatory motor achieving 100% efficiency at finite speed.
Markov stochasticity coordinates
NASA Astrophysics Data System (ADS)
Eliazar, Iddo
2017-01-01
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method-termed Markov Stochasticity Coordinates-is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
Using Evidence Feed-Forward Hidden Markov Models
2010-05-11
M . Cristani et al [1] uses non-traditional AI methods by taking in both audio and visual data to determine simple events in an office. First...Prescribed by ANSI Std Z39-18 2 Template matching is performed by M . Dimitrijevic et. al. [2]. They developed a template database of actions based...observation Ot = Vk given you are in state j and 0 ≤ k ≤ M (total number of possible observations is M ); C is a 3D matrix holding ci(h,k) = probability
Hidden Markov models for estimating animal mortality from anthropogenic hazards
Carcasses searches are a common method for studying the risk of anthropogenic hazards to wildlife, including non-target poisoning and collisions with anthropogenic structures. Typically, numbers of carcasses found must be corrected for scavenging rates and imperfect detection. ...
Hidden Markov Model Classification of Myoelectric Signals in Speech
2007-11-02
Biomedical Engineering, University of New Brunswick, Fredericton , Canada 2Department of Electrical and Computer Engineering, University of New Brunswick... Fredericton , Canada Proceedings – 23rd Annual Conference – IEEE/EMBS Oct.25-28, 2001, Istanbul, TURKEY 0-7803-7211-5/01$10.00©2001 IEEE Report...Performing Organization Name(s) and Address(es) Institute of Bioemdical Engineering University of New Brunswick Fredericton , Canada Performing Organization
Detection and Classification of Network Intrusions Using Hidden Markov Models
2002-01-01
system was able to detect buffer overflows, ftp-write attack, warez attack, guess telnet, guest and HTTPtunnel attacks. They claim that no false...already known attack that has been altered or a completely new attack. 95 Bibliography [1] Stuart McClure et. al “ Hacking Exposed: Network Security
Speaker Recognition by Hidden Markov Models and Neural Networks
1996-12-01
of the model itself, a single - layer perceptron is added to perform neural post-processing. The second solution is the novel application of an...the Equal Error Rate from 3.38% to 1.56%. In addition, a new method of cohort selection is implemented based on the structure of the single layer perceptron . Feasibility
Spatio-Temporal Pattern Recognition Using Hidden Markov Models
1994-06-01
motion. Bulpitt and Allinson have a method that uses a neural network to interpret the motion in MLDs (12). A measure of the relative position of each...Report RC-4788, IBM Thomas J. Watson Research Center, April 1974. 4. Dana H. Ballard and Christopher M. Brown. Computer Vision. Prentice-Hall, New...1987. 12. A. J. Bulpitt and N. M. Allinson . Motion perception and recognition using moving light displays. In Second International Conference on
Generalized Hidden Filter Markov Models Applied to Speaker Recognition
1996-03-01
Automatic Speech Recognition. PhD thesis, Dept of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, May 1987. CMU-CS-87-125. 17. Joseph P...Campbell, Jr. Testing with the YOHO CD-ROM voice verification corpus. In Proc. of the 1995 ICASSP, pages 541-545, 1995. 18. Joseph Paul Campbell, Jr...Processing Standards Publication 190, September 1994. 87. Joseph P. Olive, Alice Greenwood, and John Coleman. Acoustics of American En- glish Speech: A
Modelling proteins' hidden conformations to predict antibiotic resistance
NASA Astrophysics Data System (ADS)
Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.
2016-10-01
TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.
NASA Technical Reports Server (NTRS)
Smith, R. M.
1991-01-01
Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.
Discovering the Hidden Person.
ERIC Educational Resources Information Center
Zener, Rita; Ezcurdia, Laura Noriega
1997-01-01
Working from normalization theory, uses a graphical metaphor to illustrate the liberation of the "hidden self." Explains the layers of the metaphor, the "false person," the "intelligent, rational person," and the "hidden person," and offers several ways educators can work to uncover the layers surrounding…
Generator estimation of Markov jump processes
NASA Astrophysics Data System (ADS)
Metzner, P.; Dittmer, E.; Jahnke, T.; Schütte, Ch.
2007-11-01
Estimating the generator of a continuous-time Markov jump process based on incomplete data is a problem which arises in various applications ranging from machine learning to molecular dynamics. Several methods have been devised for this purpose: a quadratic programming approach (cf. [D.T. Crommelin, E. Vanden-Eijnden, Fitting timeseries by continuous-time Markov chains: a quadratic programming approach, J. Comp. Phys. 217 (2006) 782-805]), a resolvent method (cf. [T. Müller, Modellierung von Proteinevolution, PhD thesis, Heidelberg, 2001]), and various implementations of an expectation-maximization algorithm ([S. Asmussen, O. Nerman, M. Olsson, Fitting phase-type distributions via the EM algorithm, Scand. J. Stat. 23 (1996) 419-441; I. Holmes, G.M. Rubin, An expectation maximization algorithm for training hidden substitution models, J. Mol. Biol. 317 (2002) 753-764; U. Nodelman, C.R. Shelton, D. Koller, Expectation maximization and complex duration distributions for continuous time Bayesian networks, in: Proceedings of the twenty-first conference on uncertainty in AI (UAI), 2005, pp. 421-430; M. Bladt, M. Sørensen, Statistical inference for discretely observed Markov jump processes, J.R. Statist. Soc. B 67 (2005) 395-410]). Some of these methods, however, seem to be known only in a particular research community, and have later been reinvented in a different context. The purpose of this paper is to compile a catalogue of existing approaches, to compare the strengths and weaknesses, and to test their performance in a series of numerical examples. These examples include carefully chosen model problems and an application to a time series from molecular dynamics.
Abstraction Augmented Markov Models.
Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina; Honavar, Vasant
2010-12-13
High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of overfitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric parameters of k(th) order MMs by successively grouping strings of length k (i.e., k-grams) into abstraction hierarchies. We evaluate AAMMs on three protein subcellular localization prediction tasks. The results of our experiments show that abstraction makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) as compared to MMs. AAMMs are competitive with and, in some cases, significantly outperform MMs. Moreover, the results show that AAMMs often perform significantly better than variable order Markov models, such as decomposed context tree weighting, prediction by partial match, and probabilistic suffix trees.
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.
Constructing 1/ωα noise from reversible Markov chains
NASA Astrophysics Data System (ADS)
Erland, Sveinung; Greenwood, Priscilla E.
2007-09-01
This paper gives sufficient conditions for the output of 1/ωα noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/ωα condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/ω noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/ωα noise which also has a long memory.
Nonlocal order parameters for the 1D Hubbard model.
Montorsi, Arianna; Roncaglia, Marco
2012-12-07
We characterize the Mott-insulator and Luther-Emery phases of the 1D Hubbard model through correlators that measure the parity of spin and charge strings along the chain. These nonlocal quantities order in the corresponding gapped phases and vanish at the critical point U(c)=0, thus configuring as hidden order parameters. The Mott insulator consists of bound doublon-holon pairs, which in the Luther-Emery phase turn into electron pairs with opposite spins, both unbinding at U(c). The behavior of the parity correlators is captured by an effective free spinless fermion model.
Nonlocal Order Parameters for the 1D Hubbard Model
NASA Astrophysics Data System (ADS)
Montorsi, Arianna; Roncaglia, Marco
2012-12-01
We characterize the Mott-insulator and Luther-Emery phases of the 1D Hubbard model through correlators that measure the parity of spin and charge strings along the chain. These nonlocal quantities order in the corresponding gapped phases and vanish at the critical point Uc=0, thus configuring as hidden order parameters. The Mott insulator consists of bound doublon-holon pairs, which in the Luther-Emery phase turn into electron pairs with opposite spins, both unbinding at Uc. The behavior of the parity correlators is captured by an effective free spinless fermion model.
NASA Astrophysics Data System (ADS)
Volchenkov, Dima; Dawin, Jean René
A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.
Infinite hidden 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), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.
Hidden circuits and argumentation
NASA Astrophysics Data System (ADS)
Leinonen, Risto; Kesonen, Mikko H. P.; Hirvonen, Pekka E.
2016-11-01
Despite the relevance of DC circuits in everyday life and schools, they have been shown to cause numerous learning difficulties at various school levels. In the course of this article, we present a flexible method for teaching DC circuits at lower secondary level. The method is labelled as hidden circuits, and the essential idea underlying hidden circuits is in hiding the actual wiring of DC circuits, but to make their behaviour evident for pupils. Pupils are expected to find out the wiring of the circuit which should enhance their learning of DC circuits. We present two possible ways to utilise hidden circuits in a classroom. First, they can be used to test and enhance pupils’ conceptual understanding when pupils are expected to find out which one of the offered circuit diagram options corresponds to the actual circuit shown. This method aims to get pupils to evaluate the circuits holistically rather than locally, and as a part of that aim this method highlights any learning difficulties of pupils. Second, hidden circuits can be used to enhance pupils’ argumentation skills with the aid of argumentation sheet that illustrates the main elements of an argument. Based on the findings from our co-operating teachers and our own experiences, hidden circuits offer a flexible and motivating way to supplement teaching of DC circuits.
ERIC Educational Resources Information Center
Donley, H. Edward; George, Elizabeth Ann
1993-01-01
Demonstrates how to construct rational, exponential, and sinusoidal functions that appear normal on one scale but exhibit interesting hidden behavior when viewed on another scale. By exploring these examples, students learn the importance of scale, window size, and resolution effects in computer and calculator graphing. (MAZ)
Metrics for Labeled Markov Systems
NASA Technical Reports Server (NTRS)
Desharnais, Josee; Jagadeesan, Radha; Gupta, Vineet; Panangaden, Prakash
1999-01-01
Partial Labeled Markov Chains are simultaneously generalizations of process algebra and of traditional Markov chains. They provide a foundation for interacting discrete probabilistic systems, the interaction being synchronization on labels as in process algebra. Existing notions of process equivalence are too sensitive to the exact probabilities of various transitions. This paper addresses contextual reasoning principles for reasoning about more robust notions of "approximate" equivalence between concurrent interacting probabilistic systems. The present results indicate that:We develop a family of metrics between partial labeled Markov chains to formalize the notion of distance between processes. We show that processes at distance zero are bisimilar. We describe a decision procedure to compute the distance between two processes. We show that reasoning about approximate equivalence can be done compositionally by showing that process combinators do not increase distance. We introduce an asymptotic metric to capture asymptotic properties of Markov chains; and show that parallel composition does not increase asymptotic distance.
Raberto, Marco; Rapallo, Fabio; Scalas, Enrico
2011-01-01
In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first ingredient is a Markov chain on the space of possible graphs. The second ingredient is a semi-Markov counting process of renewal type. The model consists in subordinating the Markov chain to the semi-Markov counting process. In simple words, this means that the chain transitions occur at random time instants called epochs. The model is quite rich and its possible connections with algebraic geometry are briefly discussed. Moreover, for the sake of simplicity, we focus on the space of undirected graphs with a fixed number of nodes. However, in an example, we present an interbank market model where it is meaningful to use directed graphs or even weighted graphs. PMID:21887245
Markov Chains and Chemical Processes
ERIC Educational Resources Information Center
Miller, P. J.
1972-01-01
Views as important the relating of abstract ideas of modern mathematics now being taught in the schools to situations encountered in the sciences. Describes use of matrices and Markov chains to study first-order processes. (Author/DF)
2016-01-01
Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods. PMID:27891146
Addressing hidden financial risk.
Kruger, Jan; Kruger, Jan
2014-02-01
Managing low-dollar, high-volume claim denials associated with outpatient procedures is a challenge for many hospitals because of the expense involved in manually reviewing such denials. These denials often are the source of "hidden loss" for hospitals. For some hospitals, the most practical, cost-effective approach for managing low-dollar, high-volume claim denials will include the use of automated systems to monitor and highlight denials and expose trends.
2006-01-01
Syverson Naval Research Laboratory syverson@itd.nrl.navy.mil Abstract Hidden services were deployed on the Tor anonymous communication network in 2004. An...services over Tor, our results apply to any client us- ing a variety of anonymity networks. In fact, these are the first actual intersection attacks...we have demonstrated. They have been implemented. 1 Introduction Tor is a distributed low-latency anonymous communication network developed by the
Detecting Hidden Communications Protocols
2013-02-11
Release The work funded by the grant is structured in three parts: We analyzed the vulnerability of the current generation anonymity tools to...traffic analysis attacks. We specifically concentrate on SSH security and The Onion Router (Tor) anonymity tools. Our analysis used deterministic hidden...grant is structured in three parts: I. We analyzed the vulnerability of the current generation anonymity tools to traffic analysis attacks. We
On Markov parameters in system identification
NASA Technical Reports Server (NTRS)
Phan, Minh; Juang, Jer-Nan; Longman, Richard W.
1991-01-01
A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.
Markov Analysis of Sleep Dynamics
NASA Astrophysics Data System (ADS)
Kim, J. W.; Lee, J.-S.; Robinson, P. A.; Jeong, D.-U.
2009-05-01
A new approach, based on a Markov transition matrix, is proposed to explain frequent sleep and wake transitions during sleep. The matrix is determined by analyzing hypnograms of 113 obstructive sleep apnea patients. Our approach shows that the statistics of sleep can be constructed via a single Markov process and that durations of all states have modified exponential distributions, in contrast to recent reports of a scale-free form for the wake stage and an exponential form for the sleep stage. Hypnograms of the same subjects, but treated with Continuous Positive Airway Pressure, are analyzed and compared quantitatively with the pretreatment ones, suggesting potential clinical applications.
Thermal relics in hidden sectors
Feng, Jonathan L; Tu, Huitzu; Yu, Hai-Bo E-mail: huitzut@uci.edu
2008-10-15
Dark matter may be hidden, with no standard model gauge interactions. At the same time, in WIMPless models (WIMP: weakly interacting massive particles) with hidden matter masses proportional to hidden gauge couplings squared, the hidden dark matter's thermal relic density may naturally be in the right range, preserving the key quantitative virtue of WIMPs. We consider this possibility in detail. We first determine model-independent constraints on hidden sectors from big bang nucleosynthesis and the cosmic microwave background. Contrary to conventional wisdom, large hidden sectors are easily accommodated. A flavour-free version of the standard model is allowed if the hidden sector is just 30% colder than the observable sector after reheating. Alternatively, if the hidden sector contains a one-generation version of the standard model with characteristic mass scale below 1 MeV, even identical reheating temperatures are allowed. We then analyse hidden sector freeze-out in detail for a concrete model, solving the Boltzmann equation numerically and explaining the results from both observable and hidden sector points of view. We find that WIMPless dark matter does indeed obtain the correct relic density for masses in the range keV{approx}
On a Result for Finite Markov Chains
ERIC Educational Resources Information Center
Kulathinal, Sangita; Ghosh, Lagnojita
2006-01-01
In an undergraduate course on stochastic processes, Markov chains are discussed in great detail. Textbooks on stochastic processes provide interesting properties of finite Markov chains. This note discusses one such property regarding the number of steps in which a state is reachable or accessible from another state in a finite Markov chain with M…
Rodriguez, Rechell G; Mai, Derek
2012-09-01
The Uniformed Services University of the Health Sciences Internal Medicine Third Year Clerkship Program recently instituted an academic exercise to be completed by medical students during the first 6 weeks of their 12 weeks of Internal Medicine. The academic exercise involves reflecting on professional values through art and being exposed to the hidden curriculum of professionalism. Students are instructed at the beginning of their clerkship to observe the professional activities of their teachers, peers, ancillary staff, and of themselves. Students are provided a selection of art pieces to choose from. They select one which best exemplifies the professional activity they observed and are then to write a structured, reflective article.
Benchmarks and models for 1-D radiation transport in stochastic participating media
Miller, David Scott
2000-08-01
Benchmark calculations for radiation transport coupled to a material temperature equation in a 1-D slab and 1-D spherical geometry binary random media are presented. The mixing statistics are taken to be homogeneous Markov statistics in the 1-D slab but only approximately Markov statistics in the 1-D sphere. The material chunk sizes are described by Poisson distribution functions. The material opacities are first taken to be constant and then allowed to vary as a strong function of material temperature. Benchmark values and variances for time evolution of the ensemble average of material temperature energy density and radiation transmission are computed via a Monte Carlo type method. These benchmarks are used as a basis for comparison with three other approximate methods of solution. One of these approximate methods is simple atomic mix. The second approximate model is an adaptation of what is commonly called the Levermore-Pomraning model and which is referred to here as the standard model. It is shown that recasting the temperature coupling as a type of effective scattering can be useful in formulating the third approximate model, an adaptation of a model due to Su and Pomraning which attempts to account for the effects of scattering in a stochastic context. This last adaptation shows consistent improvement over both the atomic mix and standard models when used in the 1-D slab geometry but shows limited improvement in the 1-D spherical geometry. Benchmark values are also computed for radiation transmission from the 1-D sphere without material heating present. This is to evaluate the performance of the standard model on this geometry--something which has never been done before. All of the various tests demonstrate the importance of stochastic structure on the solution. Also demonstrated are the range of usefulness and limitations of a simple atomic mix formulation.
Benchmarks and models for 1-D radiation transport in stochastic participating media
NASA Astrophysics Data System (ADS)
Miller, David Scott
Benchmark calculations for radiation transport coupled to a material temperature equation in a 1-D slab and 1-D spherical geometry binary random media are presented. The mixing statistics are taken to be homogeneous Markov statistics in the 1-D slab but only approximately Markov statistics in the 1-D sphere. The material chunk sizes are described by Poisson distribution functions. The material opacities are first taken to be constant and then allowed to vary as a strong function of material temperature. Benchmark values and variances for time evolution of the ensemble average of material temperature energy density and radiation transmission are computed via a Monte Carlo type method. These benchmarks are used as a basis for comparison with three other approximate methods of solution. One of these approximate methods is simple atomic mix. The second approximate model is an adaptation of what is commonly called the Levermore-Pomraning model and which is referred to here as the standard model. It is shown that recasting the temperature coupling as a type of effective scattering can be useful in formulating the third approximate model, an adaptation of a model due to Su and Pomraning which attempts to account for the effects of scattering in a stochastic context. This last adaptation shows consistent improvement over both the atomic mix and standard models when used in the 1-D slab geometry but shows limited improvement in the 1-D spherical geometry. Benchmark values are also computed for radiation transmission from the 1-D sphere without material heating present. This is to evaluate the performance of the standard model on this geometry-something which has never been done before. All of the various tests demonstrate the importance of stochastic structure on the solution. Also demonstrated are the range of usefulness and limitations of a simple atomic mix formulation.
Drifting Markov models with polynomial drift and applications to DNA sequences.
Vergne, Nicolas
2008-01-01
In this article, we introduce the drifting Markov models (DMMs) which are inhomogeneous Markov models designed for modeling the heterogeneities of sequences (in our case DNA or protein sequences) in a more flexible way than homogeneous Markov chains or even hidden Markov models (HMMs). We focus here on the polynomial drift: the transition matrix varies in a polynomial way. To show the reliability of our models on DNA, we exhibit high similarities between the probability distributions of nucleotides obtained by our models and the frequencies of these nucleotides computed by using a sliding window. In a further step, these DMMs can be used as the states of an HMM: on each of its segments, the observed process can be modeled by a drifting Markov model. Search of rare words in DNA sequences remains possible with DMMs and according to the fits provided, DMMs turn out to be a powerful tool for this purpose. The software is available on request from the author. It will soon be integrated on seq++ library (http://stat.genopole.cnrs.fr/seqpp/).
NASA Astrophysics Data System (ADS)
Hassan, Kazi; Allen, Deonie; Haynes, Heather
2016-04-01
This paper considers 1D hydraulic model data on the effect of high flow clusters and sequencing on sediment transport. Using observed flow gauge data from the River Caldew, England, a novel stochastic modelling approach was developed in order to create alternative 50 year flow sequences. Whilst the observed probability density of gauge data was preserved in all sequences, the order in which those flows occurred was varied using the output from a Hidden Markov Model (HMM) with generalised Pareto distribution (GP). In total, one hundred 50 year synthetic flow series were generated and used as the inflow boundary conditions for individual flow series model runs using the 1D sediment transport model HEC-RAS. The model routed graded sediment through the case study river reach to define the long-term morphological changes. Comparison of individual simulations provided a detailed understanding of the sensitivity of channel capacity to flow sequence. Specifically, each 50 year synthetic flow sequence was analysed using a 3-month, 6-month or 12-month rolling window approach and classified for clusters in peak discharge. As a cluster is described as a temporal grouping of flow events above a specified threshold, the threshold condition used herein is considered as a morphologically active channel forming discharge event. Thus, clusters were identified for peak discharges in excess of 10%, 20%, 50%, 100% and 150% of the 1 year Return Period (RP) event. The window of above-peak flows also required cluster definition and was tested for timeframes 1, 2, 10 and 30 days. Subsequently, clusters could be described in terms of the number of events, maximum peak flow discharge, cumulative flow discharge and skewness (i.e. a description of the flow sequence). The model output for each cluster was analysed for the cumulative flow volume and cumulative sediment transport (mass). This was then compared to the total sediment transport of a single flow event of equivalent flow volume
Cover estimation and payload location using Markov random fields
NASA Astrophysics Data System (ADS)
Quach, Tu-Thach
2014-02-01
Payload location is an approach to find the message bits hidden in steganographic images, but not necessarily their logical order. Its success relies primarily on the accuracy of the underlying cover estimators and can be improved if more estimators are used. This paper presents an approach based on Markov random field to estimate the cover image given a stego image. It uses pairwise constraints to capture the natural two-dimensional statistics of cover images and forms a basis for more sophisticated models. Experimental results show that it is competitive against current state-of-the-art estimators and can locate payload embedded by simple LSB steganography and group-parity steganography. Furthermore, when combined with existing estimators, payload location accuracy improves significantly.
Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition
NASA Astrophysics Data System (ADS)
Fujii, Yasuhisa; Yamamoto, Kazumasa; Nakagawa, Seiichi
In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.
State-space dimensionality in short-memory hidden-variable theories
Montina, Alberto
2011-03-15
Recently we have presented a hidden-variable model of measurements for a qubit where the hidden-variable state-space dimension is one-half the quantum-state manifold dimension. The absence of a short memory (Markov) dynamics is the price paid for this dimensional reduction. The conflict between having the Markov property and achieving the dimensional reduction was proved by Montina [A. Montina, Phys. Rev. A 77, 022104 (2008)] using an additional hypothesis of trajectory relaxation. Here we analyze in more detail this hypothesis introducing the concept of invertible process and report a proof that makes clearer the role played by the topology of the hidden-variable space. This is accomplished by requiring suitable properties of regularity of the conditional probability governing the dynamics. In the case of minimal dimension the set of continuous hidden variables is identified with an object living an N-dimensional Hilbert space whose dynamics is described by the Schroedinger equation. A method for generating the economical non-Markovian model for the qubit is also presented.
NASA Astrophysics Data System (ADS)
Cai, Wen-jing; Chi, Xue-fen; Zhao, Lin-lin
2016-11-01
Due to the directionality of light, the hidden device problem and the obstruction cannot be ignored for carrier sense multiple access with collision avoidance (CSMA/CA)-based uplink visible light communication (VLC). In this paper, we introduce multipacket reception (MPR) to handle the hidden device problem in VLC system. We model the traffic of the device with on/off Markov source. With the unsaturated traffic, we formulate a two dimensional (2D) Markov chain to model the CSMA/CA-based slotted random access procedure to evaluate the effects of hidden devices and obstructions on the performance of MPR-aided VLC system, which are mapped into the transition probabilities of the Markov chain. Then, we analyze the throughput and the reception power efficiency (RE) of MPR-aided VLC system with the obstructed optical channel. Numerical results show that the effect is negative when hidden devices or obstructions appear solely. But when they appear simultaneously, they will interact with each other to mitigate the negative effects.
Markov Tracking for Agent Coordination
NASA Technical Reports Server (NTRS)
Washington, Richard; Lau, Sonie (Technical Monitor)
1998-01-01
Partially observable Markov decision processes (POMDPs) axe an attractive representation for representing agent behavior, since they capture uncertainty in both the agent's state and its actions. However, finding an optimal policy for POMDPs in general is computationally difficult. In this paper we present Markov Tracking, a restricted problem of coordinating actions with an agent or process represented as a POMDP Because the actions coordinate with the agent rather than influence its behavior, the optimal solution to this problem can be computed locally and quickly. We also demonstrate the use of the technique on sequential POMDPs, which can be used to model a behavior that follows a linear, acyclic trajectory through a series of states. By imposing a "windowing" restriction that restricts the number of possible alternatives considered at any moment to a fixed size, a coordinating action can be calculated in constant time, making this amenable to coordination with complex agents.
Hidden attractors in dynamical systems
NASA Astrophysics Data System (ADS)
Dudkowski, Dawid; Jafari, Sajad; Kapitaniak, Tomasz; Kuznetsov, Nikolay V.; Leonov, Gennady A.; Prasad, Awadhesh
2016-06-01
Complex dynamical systems, ranging from the climate, ecosystems to financial markets and engineering applications typically have many coexisting attractors. This property of the system is called multistability. The final state, i.e., the attractor on which the multistable system evolves strongly depends on the initial conditions. Additionally, such systems are very sensitive towards noise and system parameters so a sudden shift to a contrasting regime may occur. To understand the dynamics of these systems one has to identify all possible attractors and their basins of attraction. Recently, it has been shown that multistability is connected with the occurrence of unpredictable attractors which have been called hidden attractors. The basins of attraction of the hidden attractors do not touch unstable fixed points (if exists) and are located far away from such points. Numerical localization of the hidden attractors is not straightforward since there are no transient processes leading to them from the neighborhoods of unstable fixed points and one has to use the special analytical-numerical procedures. From the viewpoint of applications, the identification of hidden attractors is the major issue. The knowledge about the emergence and properties of hidden attractors can increase the likelihood that the system will remain on the most desirable attractor and reduce the risk of the sudden jump to undesired behavior. We review the most representative examples of hidden attractors, discuss their theoretical properties and experimental observations. We also describe numerical methods which allow identification of the hidden attractors.
Hidden variables in bipartite networks.
Kitsak, Maksim; Krioukov, Dmitri
2011-08-01
We introduce and study random bipartite networks with hidden variables. Nodes in these networks are characterized by hidden variables that control the appearance of links between node pairs. We derive analytic expressions for the degree distribution, degree correlations, the distribution of the number of common neighbors, and the bipartite clustering coefficient in these networks. We also establish the relationship between degrees of nodes in original bipartite networks and in their unipartite projections. We further demonstrate how hidden variable formalism can be applied to analyze topological properties of networks in certain bipartite network models, and verify our analytical results in numerical simulations.
Hidden Magnetic Portals Around Earth
A NASA-sponsored researcher at the University of Iowa has developed a way for spacecraft to hunt down hidden magnetic portals in the vicinity of Earth. These gateways link the magnetic field of our...
Hidden Statistics of Schroedinger Equation
NASA Technical Reports Server (NTRS)
Zak, Michail
2011-01-01
Work was carried out in determination of the mathematical origin of randomness in quantum mechanics and creating a hidden statistics of Schr dinger equation; i.e., to expose the transitional stochastic process as a "bridge" to the quantum world. The governing equations of hidden statistics would preserve such properties of quantum physics as superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods.
Markov and semi-Markov processes as a failure rate
NASA Astrophysics Data System (ADS)
Grabski, Franciszek
2016-06-01
In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.
Aldridge, David F.
2016-07-06
Program EMODEL_1D is an electromagnetic earth model construction utility designed to generate a three-dimensional (3D) uniformly-gridded representation of one-dimensional (1D) layered earth model. Each layer is characterized by the isotropic EM properties electric permittivity ?, magnetic permeability ?, and current conductivity ?. Moreover, individual layers of the model may possess a linear increase/decrease of any or all of these properties with depth.
NASA Astrophysics Data System (ADS)
Nguyen, Tuyen Van; Liu, Yuedan; Jung, Il-Hyo; Chon, Tae-Soo; Lee, Sang-Hee
Revealing biological responses of organisms in responding to environmental stressors is the critical issue in contemporary ecological sciences. Markov processes in behavioral data were unraveled by utilizing the hidden Markov model (HMM). Individual organisms of daphnia (Daphnia magna) and zebrafish (Danio rerio) were exposed to diazinon at low concentrations. The transition probability matrix (TPM) and the emission probability matrix (EPM) were accordingly estimated by training with the HMM and were verified before and after the treatments with 10-6 tolerance in 103 iterations. Structured property in behavioral changes was accordingly revealed to characterize dynamic processes in movement patterns. Parameters and sequences produced through the HMM training could be a suitable means of monitoring toxic chemicals in environment.
Using Games to Teach Markov Chains
ERIC Educational Resources Information Center
Johnson, Roger W.
2003-01-01
Games are promoted as examples for classroom discussion of stationary Markov chains. In a game context Markov chain terminology and results are made concrete, interesting, and entertaining. Game length for several-player games such as "Hi Ho! Cherry-O" and "Chutes and Ladders" is investigated and new, simple formulas are given. Slight…
Semi-Markov Unreliability-Range Evaluator
NASA Technical Reports Server (NTRS)
Butler, Ricky W.
1988-01-01
Reconfigurable, fault-tolerant systems modeled. Semi-Markov unreliability-range evaluator (SURE) computer program is software tool for analysis of reliability of reconfigurable, fault-tolerant systems. Based on new method for computing death-state probabilities of semi-Markov model. Computes accurate upper and lower bounds on probability of failure of system. Written in PASCAL.
Heat Capacity of 1D Molecular Chains
NASA Astrophysics Data System (ADS)
Bagatskii, M. I.; Barabashko, M. S.; Sumarokov, V. V.; Jeżowski, A.; Stachowiak, P.
2017-04-01
The heat capacity of 1D chains of nitrogen and methane molecules (adsorbed in the outer grooves of bundles of closed-cap single-walled carbon nanotubes) has been studied in the temperature ranges 2-40 and 2-60 K, respectively. The temperature dependence of the heat capacity of 1D chains of nitrogen molecules below 3 K is close to a linear. It was found that the rotational heat capacity of methane molecules is a significant part of the total heat capacity of the chains throughout the whole investigated temperature range, whereas in the case of nitrogen, the librations are significant only above 15 K. The dependence of the heat capacity for methane below 10 K indicates the presence of a Schottky anomaly caused by the tunneling between the lowest energy levels of the CH4 molecule rotational spectra. Characteristic features observed in the temperature dependence of the heat capacity of 1D methane crystals are also discussed.
NASA Astrophysics Data System (ADS)
Sawada, Hiroyuki
Recently, engineering design environment of Japan is changing variously. Manufacturing companies are being challenged to design and bring out products that meet the diverse demands of customers and are competitive against those produced by rising countries(1). In order to keep and strengthen the competitiveness of Japanese companies, it is necessary to create new added values as well as conventional ones. It is well known that design at the early stages has a great influence on the final design solution. Therefore, design support tools for the upstream design is necessary for creating new added values. We have established a research society for 1D-CAE (1 Dimensional Computer Aided Engineering)(2), which is a general term for idea, methodology and tools applicable for the upstream design support, and discuss the concept and definition of 1D-CAE. This paper reports our discussion about 1D-CAE.
Hidden photons in connection to dark matter
NASA Astrophysics Data System (ADS)
Andreas, Sarah; Goodsell, Mark D.; Ringwald, Andreas
2013-11-01
Light extra U(1) gauge bosons, so called hidden photons, which reside in a hidden sector have attracted much attention since they are a well motivated feature of many scenarios beyond the Standard Model and furthermore could mediate the interaction with hidden sector dark matter. We review limits on hidden photons from past electron beam dump experiments including two new limits from such experiments at KEK and Orsay. In addition, we study the possibility of having dark matter in the hidden sector. A simple toy model and different supersymmetric realisations are shown to provide viable dark matter candidates in the hidden sector that are in agreement with recent direct detection limits.
Helical Floquet Channels in 1D Lattices
NASA Astrophysics Data System (ADS)
Budich, Jan Carl; Hu, Ying; Zoller, Peter
2017-03-01
We show how dispersionless channels exhibiting perfect spin-momentum locking can arise in a 1D lattice model. While such spectra are forbidden by fermion doubling in static 1D systems, here we demonstrate their appearance in the stroboscopic dynamics of a periodically driven system. Remarkably, this phenomenon does not rely on any adiabatic assumptions, in contrast to the well known Thouless pump and related models of adiabatic spin pumps. The proposed setup is shown to be experimentally feasible with state-of-the-art techniques used to control ultracold alkaline earth atoms in optical lattices.
Investment strategies and hidden variables
NASA Astrophysics Data System (ADS)
Petroni, F.; Serva, M.
2006-06-01
The present study shows how the information on `hidden' market variables effects optimal investment strategies. We take the point of view of two investors, one who has access to the hidden variables and one who only knows the quotes of a given asset. Following Kelly's theory on investment strategies, the Shannon information and the doubling investment rate are quantified for both investors. Thanks to his privileged knowledge, the first investor can follow a better investment strategy. Nevertheless, the second investor can extract some of the hidden information looking at the past history of the asset variable. Unfortunately, due to the complexity of his strategy, this investor will have computational difficulties when he tries to apply it. He will than follow a simplified strategy, based only on the average sign of the last l quotes of the asset. This results have been tested with some Monte Carlo simulations.
Localization of hidden Chua's attractors
NASA Astrophysics Data System (ADS)
Leonov, G. A.; Kuznetsov, N. V.; Vagaitsev, V. I.
2011-06-01
The classical attractors of Lorenz, Rossler, Chua, Chen, and other widely-known attractors are those excited from unstable equilibria. From computational point of view this allows one to use numerical method, in which after transient process a trajectory, started from a point of unstable manifold in the neighborhood of equilibrium, reaches an attractor and identifies it. However there are attractors of another type: hidden attractors, a basin of attraction of which does not contain neighborhoods of equilibria. In the present Letter for localization of hidden attractors of Chua's circuit it is suggested to use a special analytical-numerical algorithm.
Hidden temporal order unveiled in stock market volatility variance
NASA Astrophysics Data System (ADS)
Shapira, Y.; Kenett, D. Y.; Raviv, Ohad; Ben-Jacob, E.
2011-06-01
When analyzed by standard statistical methods, the time series of the daily return of financial indices appear to behave as Markov random series with no apparent temporal order or memory. This empirical result seems to be counter intuitive since investor are influenced by both short and long term past market behaviors. Consequently much effort has been devoted to unveil hidden temporal order in the market dynamics. Here we show that temporal order is hidden in the series of the variance of the stocks volatility. First we show that the correlation between the variances of the daily returns and means of segments of these time series is very large and thus cannot be the output of random series, unless it has some temporal order in it. Next we show that while the temporal order does not show in the series of the daily return, rather in the variation of the corresponding volatility series. More specifically, we found that the behavior of the shuffled time series is equivalent to that of a random time series, while that of the original time series have large deviations from the expected random behavior, which is the result of temporal structure. We found the same generic behavior in 10 different stock markets from 7 different countries. We also present analysis of specially constructed sequences in order to better understand the origin of the observed temporal order in the market sequences. Each sequence was constructed from segments with equal number of elements taken from algebraic distributions of three different slopes.
Calibration of a 1D/1D urban flood model using 1D/2D model results in the absence of field data.
Leandro, J; Djordjević, S; Chen, A S; Savić, D A; Stanić, M
2011-01-01
Recently increased flood events have been prompting researchers to improve existing coupled flood-models such as one-dimensional (1D)/1D and 1D/two-dimensional (2D) models. While 1D/1D models simulate sewer and surface networks using a one-dimensional approach, 1D/2D models represent the surface network by a two-dimensional surface grid. However their application raises two issues to urban flood modellers: (1) stormwater systems planning/emergency or risk analysis demands for fast models, and the 1D/2D computational time is prohibitive, (2) and the recognized lack of field data (e.g. Hunter et al. (2008)) causes difficulties for the calibration/validation of 1D/1D models. In this paper we propose to overcome these issues by calibrating a 1D/1D model with the results of a 1D/2D model. The flood-inundation results show that: (1) 1D/2D results can be used to calibrate faster 1D/1D models, (2) the 1D/1D model is able to map the 1D/2D flood maximum extent well, and the flooding limits satisfactorily in each time-step, (3) the 1D/1D model major differences are the instantaneous flow propagation and overestimation of the flood-depths within surface-ponds, (4) the agreement in the volume surcharged by both models is a necessary condition for the 1D surface-network validation and (5) the agreement of the manholes discharge shapes measures the fitness of the calibrated 1D surface-network.
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.
Preschoolers Search for Hidden Objects
ERIC Educational Resources Information Center
Haddad, Jeffrey M.; Chen, Yuping; Keen, Rachel
2011-01-01
The issue of whether young children use spatio-temporal information (e.g., movement of objects through time and space) and/or contact-mechanical information (e.g., interaction between objects) to search for a hidden object was investigated. To determine whether one cue can have priority over the other, a dynamic event that put these cues into…
Hidden Rules of the Superintendency.
ERIC Educational Resources Information Center
Caloss, Ronald
1999-01-01
Effective superintendents recognize three key management precepts and their hidden rules. Administrators should avoid mixing emotion and logic, attending to detractors' emotional needs before presenting a differing viewpoint. They should be graceful under pressure, expect the unexpected, and build coalitions gradually, mindful of all community…
Lu, Ji; Pan, Junhao; Zhang, Qiang; Dubé, Laurette; Ip, Edward H.
2015-01-01
With intensively collected longitudinal data, recent advances in Experience Sampling Method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well-equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet & Dube, 2011) that observed 160 participants’ food consumption and momentary emotions six times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal healthiness decision, the proposed Reciprocal Markov Model (RMM) can accommodate both hidden (“general” emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent to the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters. PMID:26717120
Glass-based 1-D dielectric microcavities
NASA Astrophysics Data System (ADS)
Chiasera, Alessandro; Scotognella, Francesco; Valligatla, Sreeramulu; Varas, Stefano; Jasieniak, Jacek; Criante, Luigino; Lukowiak, Anna; Ristic, Davor; Gonçalves, Rogeria Rocha; Taccheo, Stefano; Ivanda, Mile; Righini, Giancarlo C.; Ramponi, Roberta; Martucci, Alessandro; Ferrari, Maurizio
2016-11-01
We have developed a reliable RF sputtering techniques allowing to fabricate glass-based one dimensional microcavities, with high quality factor. This property is strongly related to the modification of the density of states due to the confinement of the gain medium in a photonic band gap structure. In this short review we present some of the more recent results obtained by our team exploiting these 1D microcavities. In particular we present: (1) Er3+ luminescence enhancement of the 4I13/2 → 4I15/2 transition; (2) broad band filters based on disordered 1-D photonic structures; (3) threshold defect-mode lasing action in a hybrid structure.
Centrosome Positioning in 1D Cell Migration
NASA Astrophysics Data System (ADS)
Adlerz, Katrina; Aranda-Espinoza, Helim
During cell migration, the positioning of the centrosome and nucleus define a cell's polarity. For a cell migrating on a two-dimensional substrate the centrosome is positioned in front of the nucleus. Under one-dimensional confinement, however, the centrosome is positioned behind the nucleus in 60% of cells. It is known that the centrosome is positioned by CDC42 and dynein for cells moving on a 2D substrate in a wound-healing assay. It is currently unknown, however, if this is also true for cells moving under 1D confinement, where the centrosome position is often reversed. Therefore, centrosome positioning was studied in cells migrating under 1D confinement, which mimics cells migrating through 3D matrices. 3 to 5 μm fibronectin lines were stamped onto a glass substrate and cells with fluorescently labeled nuclei and centrosomes migrated on the lines. Our results show that when a cell changes directions the centrosome position is maintained. That is, when the centrosome is between the nucleus and the cell's trailing edge and the cell changes direction, the centrosome will be translocated across the nucleus to the back of the cell again. A dynein inhibitor did have an influence on centrosome positioning in 1D migration and change of directions.
Observation uncertainty in reversible Markov chains.
Metzner, Philipp; Weber, Marcus; Schütte, Christof
2010-09-01
In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .
Application of Markov Graphs in Marketing
NASA Astrophysics Data System (ADS)
Bešić, C.; Sajfert, Z.; Đorđević, D.; Sajfert, V.
2007-04-01
The applications of Markov's processes theory in marketing are discussed. It was turned out that Markov's processes have wide field of applications. The advancement of marketing by the use of convolution of stationary Markov's distributions is analysed. It turned out that convolution distribution gives average net profit that is two times higher than the one obtained by usual Markov's distribution. It can be achieved if one selling chain is divided onto two parts with different ratios of output and input frequencies. The stability of marketing system was examined by the use of conforming coefficients. It was shown, by means of Jensen inequality that system remains stable if initial capital is higher than averaged losses.
Semi-Markov Unreliability Range Evaluator
NASA Technical Reports Server (NTRS)
Butler, Ricky W.; Boerschlein, David P.
1993-01-01
Semi-Markov Unreliability Range Evaluator, SURE, computer program is software tool for analysis of reconfigurable, fault-tolerant systems. Traditional reliability analyses based on aggregates of fault-handling and fault-occurrence models. SURE provides efficient means for calculating accurate upper and lower bounds for probabilities of death states for large class of semi-Markov mathematical models, and not merely those reduced to critical-pair architectures.
Exact significance test for Markov order
NASA Astrophysics Data System (ADS)
Pethel, S. D.; Hahs, D. W.
2014-02-01
We describe an exact significance test of the null hypothesis that a Markov chain is nth order. The procedure utilizes surrogate data to yield an exact test statistic distribution valid for any sample size. Surrogate data are generated using a novel algorithm that guarantees, per shot, a uniform sampling from the set of sequences that exactly match the nth order properties of the observed data. Using the test, the Markov order of Tel Aviv rainfall data is examined.
Isolated Word Recognition From In-Ear Microphone Data Using Hidden Markov Models (HMM)
2006-03-01
to more complicated and intrusive alternatives. The microphone earpiece contains a small passive 10 sensor, which is commercially available off-the...noisy surroundings and allow for a relatively simple speech recognition approach, an unconventional recording device, an earpiece microphone placed
ERIC Educational Resources Information Center
Li, Dingcheng
2011-01-01
Coreference resolution (CR) and entity relation detection (ERD) aim at finding predefined relations between pairs of entities in text. CR focuses on resolving identity relations while ERD focuses on detecting non-identity relations. Both CR and ERD are important as they can potentially improve other natural language processing (NLP) related tasks…
New seismic events identified in the Apollo lunar data by application of a Hidden Markov Model
NASA Astrophysics Data System (ADS)
Knapmeyer-Endrun, B.; Hammer, C.
2015-10-01
The Apollo astronauts installed seismic stations on the Moon during Apollo missions 11, 12, 14, 15 and 16. The stations consisted of a three-component long- period seismometer (eigenperiod 15 s) and a vertical short-period sensor (eigenperiod 1 s). Until today, the Apollo seismic network provides the only confirmed recordings of seismic events from any extrater-restrial. The recorded event waveforms differ significantly from what had been expected based on Earth data, mainly by their long duration body wave codas caused by strong near-surface scattering and weak attenuation due to lack of fluids. The main lunar event types are deep moonquakes, impacts, and the rare shallow moonquakes.
ERIC Educational Resources Information Center
Boyer, Kristy Elizabeth; Phillips, Robert; Ingram, Amy; Ha, Eun Young; Wallis, Michael; Vouk, Mladen; Lester, James
2011-01-01
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning…
Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine
NASA Technical Reports Server (NTRS)
Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)
2002-01-01
The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.
Marino, Maria Francesca; Tzavidis, Nikos; Alfò, Marco
2016-01-01
Quantile regression provides a detailed and robust picture of the distribution of a response variable, conditional on a set of observed covariates. Recently, it has be been extended to the analysis of longitudinal continuous outcomes using either time-constant or time-varying random parameters. However, in real-life data, we frequently observe both temporal shocks in the overall trend and individual-specific heterogeneity in model parameters. A benchmark dataset on HIV progression gives a clear example. Here, the evolution of the CD4 log counts exhibits both sudden temporal changes in the overall trend and heterogeneity in the effect of the time since seroconversion on the response dynamics. To accommodate such situations, we propose a quantile regression model, where time-varying and time-constant random coefficients are jointly considered. Since observed data may be incomplete due to early drop-out, we also extend the proposed model in a pattern mixture perspective. We assess the performance of the proposals via a large-scale simulation study and the analysis of the CD4 count data.
Multi-Observation Continuous Density Hidden Markov Models for Anomaly Detection in Full Motion Video
2012-06-01
relaxed [6]. Civilian use of video surveillance systems has allowed increased security and better forensic analysis of criminal activity , but its...points of greatest interest. For example, if intelligence ties adversary activity to a particular building, then observed movement from that building to...another may demonstrate adversary activity at the second building [5]. Such movement may indicate need for force application (or more intelligence
An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device
Technology Transfer Automated Retrieval System (TEKTRAN)
Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...
The Characterization of Phonetic Variation in American English Schwa Using Hidden Markov Models
ERIC Educational Resources Information Center
Lilley, Jason
2012-01-01
The discovery and characterization of a phonetic segment's variants and the prediction of their distribution are two of the chief goals of phonology. In this dissertation, I develop a new, mostly automatic technique for discovering and classifying contextual variation. The focus is on a set of sounds in English that undergoes considerable…
GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs.
Shigeta, Ron; Cline, Melissa; Liu, Guoying; Siani-Rose, Michael A
2003-03-22
GPCR-GRAPA-LIB is a library of HMMs describing G protein coupled receptor families. These families are initially defined by class of receptor ligand, with divergent families divided into subfamilies using phylogenic analysis and knowledge of GPCR function. Protein sequences are applied to the models with the GRAPA curve-based selection criteria. RefSeq sequences for Homo sapiens, Drosophila melanogaster, and Caenorhabditis elegans have been annotated using this approach.
Advancing the Accuracy of Protein Fold Recognition by Utilizing Profiles From Hidden Markov Models.
Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Yang, Yuedong; Zhou, Yaoqi; Sharma, Alok; Paliwal, Kuldip
2015-10-01
Protein fold recognition is an important step towards solving protein function and tertiary structure prediction problems. Among a wide range of approaches proposed to solve this problem, pattern recognition based techniques have achieved the best results. The most effective pattern recognition-based techniques for solving this problem have been based on extracting evolutionary-based features. Most studies have relied on the Position Specific Scoring Matrix (PSSM) to extract these features. However it is known that profile-profile sequence alignment techniques can identify more remote homologs than sequence-profile approaches like PSIBLAST. In this study we use a profile-profile sequence alignment technique, namely HHblits, to extract HMM profiles. We will show that unlike previous studies, using the HMM profile to extract evolutionary information can significantly enhance the protein fold prediction accuracy. We develop a new pattern recognition based system called HMMFold which extracts HMM based evolutionary information and captures remote homology information better than previous studies. Using HMMFold we achieve up to 93.8% and 86.0% prediction accuracies when the sequential similarity rates are less than 40% and 25%, respectively. These results are up to 10% better than previously reported results for this task. Our results show significant enhancement especially for benchmarks with sequential similarity as low as 25% which highlights the effectiveness of HMMFold to address this problem and its superiority over previously proposed approaches found in the literature. The HMMFold is available online at: http://sparks-lab.org/pmwiki/download/index.php?Download =HMMFold.tar.bz2.
Spatial-Temporal Clustering of Neural Data Using Linked-Mixtures of Hidden Markov Models
NASA Astrophysics Data System (ADS)
Darmanjian, Shalom; Principe, Jose
2010-12-01
This paper builds upon the previous Brain Machine Interface (BMI) signal processing models that require apriori knowledge about the patient's arm kinematics. Specifically, we propose an unsupervised hierarchical clustering model that attempts to discover both the interdependencies between neural channels and the self-organized clusters represented in the spatial-temporal neural data. Results from both synthetic data generated with a realistic neural model and real BMI data are used to quantify the performance of the proposed methodology. Since BMIs must work with disabled patients who lack arm kinematic information, the clustering work described within this paper is relevant for future BMIs.
Algorithms for Discovery of Multiple Markov Boundaries
Statnikov, Alexander; Lytkin, Nikita I.; Lemeire, Jan; Aliferis, Constantin F.
2013-01-01
Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have been proposed to identify a single Markov boundary of the response variable. Even though faithful distributions and, more broadly, distributions that satisfy the intersection property always have a single Markov boundary, other distributions/data sets may have multiple Markov boundaries of the response variable. The latter distributions/data sets are common in practical data-analytic applications, and there are several reasons why it is important to induce multiple Markov boundaries from such data. However, there are currently no sound and efficient algorithms that can accomplish this task. This paper describes a family of algorithms TIE* that can discover all Markov boundaries in a distribution. The broad applicability as well as efficiency of the new algorithmic family is demonstrated in an extensive benchmarking study that involved comparison with 26 state-of-the-art algorithms/variants in 15 data sets from a diversity of application domains. PMID:25285052
Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars
Poon, Art F. Y.; Brouwer, Kimberly C.; Strathdee, Steffanie A.; Firestone-Cruz, Michelle; Lozada, Remedios M.; Kosakovsky Pond, Sergei L.; Heckathorn, Douglas D.; Frost, Simon D. W.
2009-01-01
Background Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Methodology/Principal Findings Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of
Harnessing graphical structure in Markov chain Monte Carlo learning
Stolorz, P.E.; Chew P.C.
1996-12-31
The Monte Carlo method is recognized as a useful tool in learning and probabilistic inference methods common to many datamining problems. Generalized Hidden Markov Models and Bayes nets are especially popular applications. However, the presence of multiple modes in many relevant integrands and summands often renders the method slow and cumbersome. Recent mean field alternatives designed to speed things up have been inspired by experience gleaned from physics. The current work adopts an approach very similar to this in spirit, but focusses instead upon dynamic programming notions as a basis for producing systematic Monte Carlo improvements. The idea is to approximate a given model by a dynamic programming-style decomposition, which then forms a scaffold upon which to build successively more accurate Monte Carlo approximations. Dynamic programming ideas alone fail to account for non-local structure, while standard Monte Carlo methods essentially ignore all structure. However, suitably-crafted hybrids can successfully exploit the strengths of each method, resulting in algorithms that combine speed with accuracy. The approach relies on the presence of significant {open_quotes}local{close_quotes} information in the problem at hand. This turns out to be a plausible assumption for many important applications. Example calculations are presented, and the overall strengths and weaknesses of the approach are discussed.
A 1-D dusty plasma photonic crystal
Mitu, M. L.; Ticoş, C. M.; Toader, D.; Banu, N.; Scurtu, A.
2013-09-21
It is demonstrated numerically that a 1-D plasma crystal made of micron size cylindrical dust particles can, in principle, work as a photonic crystal for terahertz waves. The dust rods are parallel to each other and arranged in a linear string forming a periodic structure of dielectric-plasma regions. The dispersion equation is found by solving the waves equation with the boundary conditions at the dust-plasma interface and taking into account the dielectric permittivity of the dust material and plasma. The wavelength of the electromagnetic waves is in the range of a few hundred microns, close to the interparticle separation distance. The band gaps of the 1-D plasma crystal are numerically found for different types of dust materials, separation distances between the dust rods and rod diameters. The distance between levitated dust rods forming a string in rf plasma is shown experimentally to vary over a relatively wide range, from 650 μm to about 1350 μm, depending on the rf power fed into the discharge.
Semi-Markov adjunction to the Computer-Aided Markov Evaluator (CAME)
NASA Technical Reports Server (NTRS)
Rosch, Gene; Hutchins, Monica A.; Leong, Frank J.; Babcock, Philip S., IV
1988-01-01
The rule-based Computer-Aided Markov Evaluator (CAME) program was expanded in its ability to incorporate the effect of fault-handling processes into the construction of a reliability model. The fault-handling processes are modeled as semi-Markov events and CAME constructs and appropriate semi-Markov model. To solve the model, the program outputs it in a form which can be directly solved with the Semi-Markov Unreliability Range Evaluator (SURE) program. As a means of evaluating the alterations made to the CAME program, the program is used to model the reliability of portions of the Integrated Airframe/Propulsion Control System Architecture (IAPSA 2) reference configuration. The reliability predictions are compared with a previous analysis. The results bear out the feasibility of utilizing CAME to generate appropriate semi-Markov models to model fault-handling processes.
1D-VAR Retrieval Using Superchannels
NASA Technical Reports Server (NTRS)
Liu, Xu; Zhou, Daniel; Larar, Allen; Smith, William L.; Schluessel, Peter; Mango, Stephen; SaintGermain, Karen
2008-01-01
Since modern ultra-spectral remote sensors have thousands of channels, it is difficult to include all of them in a 1D-var retrieval system. We will describe a physical inversion algorithm, which includes all available channels for the atmospheric temperature, moisture, cloud, and surface parameter retrievals. Both the forward model and the inversion algorithm compress the channel radiances into super channels. These super channels are obtained by projecting the radiance spectra onto a set of pre-calculated eigenvectors. The forward model provides both super channel properties and jacobian in EOF space directly. For ultra-spectral sensors such as Infrared Atmospheric Sounding Interferometer (IASI) and the NPOESS Airborne Sounder Testbed Interferometer (NAST), a compression ratio of more than 80 can be achieved, leading to a significant reduction in computations involved in an inversion process. Results will be shown applying the algorithm to real IASI and NAST data.
Handling target obscuration through Markov chain observations
NASA Astrophysics Data System (ADS)
Kouritzin, Michael A.; Wu, Biao
2008-04-01
Target Obscuration, including foliage or building obscuration of ground targets and landscape or horizon obscuration of airborne targets, plagues many real world filtering problems. In particular, ground moving target identification Doppler radar, mounted on a surveillance aircraft or unattended airborne vehicle, is used to detect motion consistent with targets of interest. However, these targets try to obscure themselves (at least partially) by, for example, traveling along the edge of a forest or around buildings. This has the effect of creating random blockages in the Doppler radar image that move dynamically and somewhat randomly through this image. Herein, we address tracking problems with target obscuration by building memory into the observations, eschewing the usual corrupted, distorted partial measurement assumptions of filtering in favor of dynamic Markov chain assumptions. In particular, we assume the observations are a Markov chain whose transition probabilities depend upon the signal. The state of the observation Markov chain attempts to depict the current obscuration and the Markov chain dynamics are used to handle the evolution of the partially obscured radar image. Modifications of the classical filtering equations that allow observation memory (in the form of a Markov chain) are given. We use particle filters to estimate the position of the moving targets. Moreover, positive proof-of-concept simulations are included.
[Decision analysis in radiology using Markov models].
Golder, W
2000-01-01
Markov models (Multistate transition models) are mathematical tools to simulate a cohort of individuals followed over time to assess the prognosis resulting from different strategies. They are applied on the assumption that persons are in one of a finite number of states of health (Markov states). Each condition is given a transition probability as well as an incremental value. Probabilities may be chosen constant or varying over time due to predefined rules. Time horizon is divided into equal increments (Markov cycles). The model calculates quality-adjusted life expectancy employing real-life units and values and summing up the length of time spent in each health state adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common decision trees. Markov models can be evaluated by matrix algebra, probabilistic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic and therapeutic procedures in radiology. More interventions should be submitted to Markov analyses in order to elucidate their cost-effectiveness.
75 FR 27411 - Airworthiness Directives; Turbomeca Arriel 1B, 1D, 1D1, and 1S1 Turboshaft Engines
Federal Register 2010, 2011, 2012, 2013, 2014
2010-05-17
... (that incorporate Turbomeca Modification (mod) TU 148), Arriel 1D, 1D1, and 1S1 turboshaft engines that do not incorporate mod TU 347. That AD also requires initial and repetitive replacements of 2nd stage... incorporate mod TU 148), 1D, 1D1, and 1S1 turboshaft engines that do not incorporate mod TU 347. We...
Markov chains for testing redundant software
NASA Technical Reports Server (NTRS)
White, Allan L.; Sjogren, Jon A.
1988-01-01
A preliminary design for a validation experiment has been developed that addresses several problems unique to assuring the extremely high quality of multiple-version programs in process-control software. The procedure uses Markov chains to model the error states of the multiple version programs. The programs are observed during simulated process-control testing, and estimates are obtained for the transition probabilities between the states of the Markov chain. The experimental Markov chain model is then expanded into a reliability model that takes into account the inertia of the system being controlled. The reliability of the multiple version software is computed from this reliability model at a given confidence level using confidence intervals obtained for the transition probabilities during the experiment. An example demonstrating the method is provided.
Solving the "Hidden Line" Problem
NASA Technical Reports Server (NTRS)
1984-01-01
David Hedgley Jr., a mathematician at Dryden Flight Research Center, has developed an accurate computer program that considers whether a line in a graphic model of a three dimensional object should or should not be visible. The Hidden Line Computer Code, program automatically removes superfluous lines and permits the computer to display an object from specific viewpoints, just as the human eye would see it. Users include Rowland Institute for Science in Cambridge, MA, several departments of Lockheed Georgia Co., and Nebraska Public Power District (NPPD).
Protein family classification using sparse Markov transducers.
Eskin, E; Grundy, W N; Singer, Y
2000-01-01
In this paper we present a method for classifying proteins into families using sparse Markov transducers (SMTs). Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Because substitutions of amino acids are common in protein families, incorporating wildcards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. We also present efficient data structures to improve the memory usage of the models. We evaluate SMTs by building protein family classifiers using the Pfam database and compare our results to previously published results.
Entropy production fluctuations of finite Markov chains
NASA Astrophysics Data System (ADS)
Jiang, Da-Quan; Qian, Min; Zhang, Fu-Xi
2003-09-01
For almost every trajectory segment over a finite time span of a finite Markov chain with any given initial distribution, the logarithm of the ratio of its probability to that of its time-reversal converges exponentially to the entropy production rate of the Markov chain. The large deviation rate function has a symmetry of Gallavotti-Cohen type, which is called the fluctuation theorem. Moreover, similar symmetries also hold for the rate functions of the joint distributions of general observables and the logarithmic probability ratio.
Parallel Markov chain Monte Carlo simulations.
Ren, Ruichao; Orkoulas, G
2007-06-07
With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.
Parallel Markov chain Monte Carlo simulations
NASA Astrophysics Data System (ADS)
Ren, Ruichao; Orkoulas, G.
2007-06-01
With strict detailed balance, parallel Monte Carlo simulation through domain decomposition cannot be validated with conventional Markov chain theory, which describes an intrinsically serial stochastic process. In this work, the parallel version of Markov chain theory and its role in accelerating Monte Carlo simulations via cluster computing is explored. It is shown that sequential updating is the key to improving efficiency in parallel simulations through domain decomposition. A parallel scheme is proposed to reduce interprocessor communication or synchronization, which slows down parallel simulation with increasing number of processors. Parallel simulation results for the two-dimensional lattice gas model show substantial reduction of simulation time for systems of moderate and large size.
Dissolved gas - the hidden saboteur
Magorien, V.G.
1993-12-31
Almost all hydraulic power components, to properly perform their tasks, rely on one basic, physical property, i.e., the incompressibility of the working fluid. Unfortunately, a frequently overlooked fluid property which frustrates this requirement is its ability to absorb, i.e., dissolve, store and give off gas. The gas is, most often but not always, air. This property is a complex one because it is a function not only of the fluid`s chemical make-up but temperature, pressure, exposed area, depth and time. In its relationshiop to aircraft landing-gear, where energy is absorbed hydraulically, this multi-faceted fluid property can be detrimental in two ways: dynamically, i.e., loss of energy absorption ability and statically, i.e., improper aircraft attitude on the ground. The pupose of this paper is to bring an awareness to this property by presenting: (1) examples of these manifestations with some empirical and practical solutions to them, (2) illustrations of this normally `hidden saboteur` at work, (3) Henry`s Dissolved Gas Law, (4) room-temperature, saturated values of dissolved gas for a number of different working fluids, (5) a description of the instrument used to obtain them, (6) some `missing elements` of the Dissolved Gas Law pertaining to absoption, (7) how static and dynamic conditions effect gas absorption and (8) some recommended solutions to prevent becoming a victim of this `hidden saboteur`
Hidden Variable Theories and Quantum Nonlocality
ERIC Educational Resources Information Center
Boozer, A. D.
2009-01-01
We clarify the meaning of Bell's theorem and its implications for the construction of hidden variable theories by considering an example system consisting of two entangled spin-1/2 particles. Using this example, we present a simplified version of Bell's theorem and describe several hidden variable theories that agree with the predictions of…
Finite Markov Chains and Random Discrete Structures
1994-07-26
arrays with fixed margins 4. Persi Diaconis and Susan Holmes, Three Examples of Monte- Carlo Markov Chains: at the Interface between Statistical Computing...solutions for a math- ematical model of thermomechanical phase transitions in shape memory materials with Landau- Ginzburg free energy 1168 Angelo Favini
Semi-Markov Unreliability Range Evaluator (SURE)
NASA Technical Reports Server (NTRS)
Butler, R. W.
1989-01-01
Analysis tool for reconfigurable, fault-tolerant systems, SURE provides efficient way to calculate accurate upper and lower bounds for death state probabilities for large class of semi-Markov models. Calculated bounds close enough for use in reliability studies of ultrareliable computer systems. Written in PASCAL for interactive execution and runs on DEC VAX computer under VMS.
Evaluation of Usability Utilizing Markov Models
ERIC Educational Resources Information Center
Penedo, Janaina Rodrigues; Diniz, Morganna; Ferreira, Simone Bacellar Leal; Silveira, Denis S.; Capra, Eliane
2012-01-01
Purpose: The purpose of this paper is to analyze the usability of a remote learning system in its initial development phase, using a quantitative usability evaluation method through Markov models. Design/methodology/approach: The paper opted for an exploratory study. The data of interest of the research correspond to the possible accesses of users…
Markov Chain Estimation of Avian Seasonal Fecundity
To explore the consequences of modeling decisions on inference about avian seasonal fecundity we generalize previous Markov chain (MC) models of avian nest success to formulate two different MC models of avian seasonal fecundity that represent two different ways to model renestin...
Multiscale Representations of Markov Random Fields
1992-09-08
modeling a wide variety of biological, chelmical, electrical, mechanical and economic phenomena, [10]. Moreover, the Markov structure makes the models...Transactions on Informlation Theory, 18:232-240, March 1972. [65] J. WOODS AND C. RADEWAN, "Kalman Filtering in Two Dimensions," IEEE Trans- actions on
Heating up the Galaxy with hidden photons
Dubovsky, Sergei; Hernández-Chifflet, Guzmán
2015-12-29
We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the way down to the hidden photon masses of order 10{sup −20} eV.
Heating up the Galaxy with hidden photons
Dubovsky, Sergei; Hernández-Chifflet, Guzmán E-mail: ghc236@nyu.edu
2015-12-01
We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the way down to the hidden photon masses of order 10{sup −20} eV.
1D-1D Coulomb drag in a 6 Million Mobility Bi-layer Heterostructure
NASA Astrophysics Data System (ADS)
Bilodeau, Simon; Laroche, Dominique; Xia, Jian-Sheng; Lilly, Mike; Reno, John; Pfeiffer, Loren; West, Ken; Gervais, Guillaume
We report Coulomb drag measurements in vertically-coupled quantum wires. The wires are fabricated in GaAs/AlGaAs bilayer heterostructures grown from two different MBE chambers: one at Sandia National Laboratories (1.2M mobility), and the other at Princeton University (6M mobility). The previously observed positive and negative drag signals are seen in both types of devices, demonstrating the robustness of the result. However, attempts to determine the temperature dependence of the drag signal in the 1D regime proved challenging in the higher mobility heterostructure (Princeton), in part because of difficulties in aligning the wires within the same transverse subband configuration. Nevertheless, this work, performed at the Microkelvin laboratory of the University of Florida, is an important proof-of-concept for future investigations of the temperature dependence of the 1D-1D drag signal down to a few mK. Such an experiment could confirm the Luttinger charge density wave interlocking predicted to occur in the wires. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL8500.
Jung, Minsoo
2015-01-01
When there is no sampling frame within a certain group or the group is concerned that making its population public would bring social stigma, we say the population is hidden. It is difficult to approach this kind of population survey-methodologically because the response rate is low and its members are not quite honest with their responses when probability sampling is used. The only alternative known to address the problems caused by previous methods such as snowball sampling is respondent-driven sampling (RDS), which was developed by Heckathorn and his colleagues. RDS is based on a Markov chain, and uses the social network information of the respondent. This characteristic allows for probability sampling when we survey a hidden population. We verified through computer simulation whether RDS can be used on a hidden population of cancer survivors. According to the simulation results of this thesis, the chain-referral sampling of RDS tends to minimize as the sample gets bigger, and it becomes stabilized as the wave progresses. Therefore, it shows that the final sample information can be completely independent from the initial seeds if a certain level of sample size is secured even if the initial seeds were selected through convenient sampling. Thus, RDS can be considered as an alternative which can improve upon both key informant sampling and ethnographic surveys, and it needs to be utilized for various cases domestically as well.
Smooth non-extremal D1-D5-P solutions as charged gravitational instantons
NASA Astrophysics Data System (ADS)
Chakrabarty, Bidisha; Rocha, Jorge V.; Virmani, Amitabh
2016-08-01
We present an alternative and more direct construction of the non-super-symmetric D1-D5-P supergravity solutions found by Jejjala, Madden, Ross and Titchener. We show that these solutions — with all three charges and both rotations turned on — can be viewed as a charged version of the Myers-Perry instanton. We present an inverse scattering construction of the Myers-Perry instanton metric in Euclidean five-dimensional gravity. The angular momentum bounds in this construction turn out to be precisely the ones necessary for the smooth microstate geometries. We add charges on the Myers-Perry instanton using appropriate SO(4, 4) hidden symmetry transformations. The full construc-tion can be viewed as an extension and simplification of a previous work by Katsimpouri, Kleinschmidt and Virmani.
Identifying bubble collapse in a hydrothermal system using hiddden Markov models
Dawson, Phillip B.; Benitez, M.C.; Lowenstern, Jacob B.; Chouet, Bernard A.
2012-01-01
Beginning in July 2003 and lasting through September 2003, the Norris Geyser Basin in Yellowstone National Park exhibited an unusual increase in ground temperature and hydrothermal activity. Using hidden Markov model theory, we identify over five million high-frequency (>15 Hz) seismic events observed at a temporary seismic station deployed in the basin in response to the increase in hydrothermal activity. The source of these seismic events is constrained to within ~100 m of the station, and produced ~3500–5500 events per hour with mean durations of ~0.35–0.45 s. The seismic event rate, air temperature, hydrologic temperatures, and surficial water flow of the geyser basin exhibited a marked diurnal pattern that was closely associated with solar thermal radiance. We interpret the source of the seismicity to be due to the collapse of small steam bubbles in the hydrothermal system, with the rate of collapse being controlled by surficial temperatures and daytime evaporation rates.
Markov random field and Gaussian mixture for segmented MRI-based partial volume correction in PET
NASA Astrophysics Data System (ADS)
Bousse, Alexandre; Pedemonte, Stefano; Thomas, Benjamin A.; Erlandsson, Kjell; Ourselin, Sébastien; Arridge, Simon; Hutton, Brian F.
2012-10-01
In this paper we propose a segmented magnetic resonance imaging (MRI) prior-based maximum penalized likelihood deconvolution technique for positron emission tomography (PET) images. The model assumes the existence of activity classes that behave like a hidden Markov random field (MRF) driven by the segmented MRI. We utilize a mean field approximation to compute the likelihood of the MRF. We tested our method on both simulated and clinical data (brain PET) and compared our results with PET images corrected with the re-blurred Van Cittert (VC) algorithm, the simplified Guven (SG) algorithm and the region-based voxel-wise (RBV) technique. We demonstrated our algorithm outperforms the VC algorithm and outperforms SG and RBV corrections when the segmented MRI is inconsistent (e.g. mis-segmentation, lesions, etc) with the PET image.
Markov-switching model for nonstationary runoff conditioned on El Niño information
NASA Astrophysics Data System (ADS)
Gelati, E.; Madsen, H.; Rosbjerg, D.
2010-02-01
We define a Markov-modulated autoregressive model with exogenous input (MARX) to generate runoff scenarios using climatic information. Runoff parameterization is assumed to be conditioned on a hidden climate state following a Markov chain, where state transition probabilities are functions of the climatic input. MARX allows stochastic modeling of nonstationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We apply MARX to inflow time series of the Daule Peripa reservoir (Ecuador). El Niño-Southern Oscillation (ENSO) information is used to condition runoff parameterization. Among the investigated ENSO indexes, the NINO 1+2 sea surface temperature anomalies and the trans-Niño index perform best as predictors. In the perspective of reservoir optimization at various time scales, MARX produces realistic long-term scenarios and short-term forecasts, especially when intense El Niño events occur. Low predictive ability is found for negative runoff anomalies, as no climatic index correlating properly with negative inflow anomalies has yet been identified.
Stochastic Dynamics through Hierarchically Embedded Markov Chains
NASA Astrophysics Data System (ADS)
Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.
2017-02-01
Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.
Stochastic Dynamics through Hierarchically Embedded Markov Chains.
Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M
2017-02-03
Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.
A critical appraisal of Markov state models
NASA Astrophysics Data System (ADS)
Schütte, Ch.; Sarich, M.
2015-09-01
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a molecular system in equilibrium has gained a lot of attention recently. The last 10 years have seen an ever increasing publication activity on how to construct Markov State Models (MSMs) for very different molecular systems ranging from peptides to proteins, from RNA to DNA, and via molecular sensors to molecular aggregation. Simultaneously the accompanying theory behind MSM building and approximation quality has been developed well beyond the concepts and ideas used in practical applications. This article reviews the main theoretical results, provides links to crucial new developments, outlines the full power of MSM building today, and discusses the essential limitations still to overcome.
The cutoff phenomenon in finite Markov chains.
Diaconis, P
1996-01-01
Natural mixing processes modeled by Markov chains often show a sharp cutoff in their convergence to long-time behavior. This paper presents problems where the cutoff can be proved (card shuffling, the Ehrenfests' urn). It shows that chains with polynomial growth (drunkard's walk) do not show cutoffs. The best general understanding of such cutoffs (high multiplicity of second eigenvalues due to symmetry) is explored. Examples are given where the symmetry is broken but the cutoff phenomenon persists. PMID:11607633
Numerical methods in Markov chain modeling
NASA Technical Reports Server (NTRS)
Philippe, Bernard; Saad, Youcef; Stewart, William J.
1989-01-01
Several methods for computing stationary probability distributions of Markov chains are described and compared. The main linear algebra problem consists of computing an eigenvector of a sparse, usually nonsymmetric, matrix associated with a known eigenvalue. It can also be cast as a problem of solving a homogeneous singular linear system. Several methods based on combinations of Krylov subspace techniques are presented. The performance of these methods on some realistic problems are compared.
On Measures Driven by Markov Chains
NASA Astrophysics Data System (ADS)
Heurteaux, Yanick; Stos, Andrzej
2014-12-01
We study measures on which are driven by a finite Markov chain and which generalize the famous Bernoulli products.We propose a hands-on approach to determine the structure function and to prove that the multifractal formalism is satisfied. Formulas for the dimension of the measures and for the Hausdorff dimension of their supports are also provided. Finally, we identify the measures with maximal dimension.
Fibroid Tumors in Women: A Hidden Epidemic?
... Home Current Issue Past Issues Fibroid Tumors in Women: A Hidden Epidemic? Past Issues / Spring 2007 Table ... turn Javascript on. Dr. Cynthia Morton is seeking women who have fibroid tumors for a "sister study" ...
Perspective: Disclosing hidden sources of funding.
Resnik, David B
2009-09-01
In this article, the author discusses ethical and policy issues related to the disclosure of hidden sources of funding in research. The author argues that authors have an ethical obligation to disclose hidden sources of funding and that journals should adopt policies to enforce this obligation. Journal policies should require disclosure of hidden sources of funding that authors know about and that have a direct relation to their research. To stimulate this discussion, the author describes a recent case: investigators who conducted a lung cancer screening study had received funding from a private foundation that was supported by a tobacco company, but they did not disclose this relationship to the journal. Investigators and journal editors must be prepared to deal with these issues in a manner that promotes honesty, transparency, fairness, and accountability in research. The development of well-defined, reasonable policies pertaining to hidden sources of funding can be a step in this direction.
Hidden figures are ever present.
Mens, L H; Leeuwenberg, E L
1988-11-01
Preference judgments about alternative interpretations of unambiguous patterns can be explained in terms of a rivalry between a preferred and a second-best interpretation (cf. Leeuwenberg & Buffart, 1983). We tested whether this second-best interpretation corresponds to a suppressed but concurrently present interpretation or whether it merely reflects an alternative view that happens to be preferred less often. Two patterns were present immediately following each other with a very short onset asynchrony: a complete pattern and one out of three possible subpatterns of it, corresponding to the best, the second best, or an odd interpretation of the complete pattern. Subjects indicated which subpattern was presented by choosing among the three subpatterns shown after each trial. The scores, corrected for response-bias effects, indicated a relative facilitation of the second-best interpretation, in agreement with its predicted "hidden" presence. This result is more in line with theories that capitalize on the quality of the finally selected representation than with processing models aimed at reaching one single solution as fast and as economically as possible.
Hidden local symmetry and beyond
NASA Astrophysics Data System (ADS)
Yamawaki, Koichi
Gerry Brown was a godfather of our hidden local symmetry (HLS) for the vector meson from the birth of the theory throughout his life. The HLS is originated from very nature of the nonlinear realization of the symmetry G based on the manifold G/H, and thus is universal to any physics based on the nonlinear realization. Here, I focus on the Higgs Lagrangian of the Standard Model (SM), which is shown to be equivalent to the nonlinear sigma model based on G/H = SU(2)L ×SU(2)R/SU(2)V with additional symmetry, the nonlinearly-realized scale symmetry. Then, the SM does have a dynamical gauge boson of the SU(2)V HLS, “SM ρ meson”, in addition to the Higgs as a pseudo-dilaton as well as the NG bosons to be absorbed in to the W and Z. Based on the recent work done with Matsuzaki and Ohki, I discuss a novel possibility that the SM ρ meson acquires kinetic term by the SM dynamics itself, which then stabilizes the skyrmion dormant in the SM as a viable candidate for the dark matter, what we call “dark SM skyrmion (DSMS)”.
Constructing Dynamic Event Trees from Markov Models
Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood
2006-05-01
In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: a) partitioning the process variable state space into magnitude intervals (cells), b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank
Markov chains and semi-Markov models in time-to-event analysis
Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.
2014-01-01
A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062
Dynamic Bandwidth Provisioning Using Markov Chain Based on RSVP
2013-09-01
Cambridge University Press,2008. [20] P. Bremaud, Markov Chains : Gibbs Fields, Monte Carlo Simulation and Queues, New York, NY, Springer Science...is successful. Qualnet, a simulation platform for the wireless environment is used to simulate the algorithm (integration of Markov chain ...in Qualnet, the simulation platform used. 16 THIS PAGE INTENTIONALLY LEFT BLANK 17 III. GENERAL DISCUSSION OF MARKOV CHAIN ALGORITHM AND RSVP
NASA Technical Reports Server (NTRS)
English, Thomas
2005-01-01
A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.
NASA Astrophysics Data System (ADS)
Xiao, C. W.; Ozpineci, A.; Oset, E.
2015-10-01
Using a coupled channel unitary approach, combining the heavy quark spin symmetry and the dynamics of the local hidden gauge, we investigate the meson-meson interaction with hidden beauty. We obtain several new states of isospin I = 0: six bound states, and weakly bound six more possible states which depend on the influence of the coupled channel effects.
Brady 1D seismic velocity model ambient noise prelim
Mellors, Robert J.
2013-10-25
Preliminary 1D seismic velocity model derived from ambient noise correlation. 28 Green's functions filtered between 4-10 Hz for Vp, Vs, and Qs were calculated. 1D model estimated for each path. The final model is a median of the individual models. Resolution is best for the top 1 km. Poorly constrained with increasing depth.
Algorithms for the Markov entropy decomposition
NASA Astrophysics Data System (ADS)
Ferris, Andrew J.; Poulin, David
2013-05-01
The Markov entropy decomposition (MED) is a recently proposed, cluster-based simulation method for finite temperature quantum systems with arbitrary geometry. In this paper, we detail numerical algorithms for performing the required steps of the MED, principally solving a minimization problem with a preconditioned Newton's algorithm, as well as how to extract global susceptibilities and thermal responses. We demonstrate the power of the method with the spin-1/2 XXZ model on the 2D square lattice, including the extraction of critical points and details of each phase. Although the method shares some qualitative similarities with exact diagonalization, we show that the MED is both more accurate and significantly more flexible.
Markov chain Monte Carlo without likelihoods.
Marjoram, Paul; Molitor, John; Plagnol, Vincent; Tavare, Simon
2003-12-23
Many stochastic simulation approaches for generating observations from a posterior distribution depend on knowing a likelihood function. However, for many complex probability models, such likelihoods are either impossible or computationally prohibitive to obtain. Here we present a Markov chain Monte Carlo method for generating observations from a posterior distribution without the use of likelihoods. It can also be used in frequentist applications, in particular for maximum-likelihood estimation. The approach is illustrated by an example of ancestral inference in population genetics. A number of open problems are highlighted in the discussion.
Spectral Design in Markov Random Fields
NASA Astrophysics Data System (ADS)
Wang, Jiao; Thibault, Jean-Baptiste; Yu, Zhou; Sauer, Ken; Bouman, Charles
2011-03-01
Markov random fields (MRFs) have been shown to be a powerful and relatively compact stochastic model for imagery in the context of Bayesian estimation. The simplicity of their conventional embodiment implies local computation in iterative processes and relatively noncommittal statistical descriptions of image ensembles, resulting in stable estimators, particularly under models with strictly convex potential functions. This simplicity may be a liability, however, when the inherent bias of minimum mean-squared error or maximum a posteriori probability (MAP) estimators attenuate all but the lowest spatial frequencies. In this paper we explore generalization of MRFs by considering frequency-domain design of weighting coefficients which describe strengths of interconnections between clique members.
Hybrid Discrete-Continuous Markov Decision Processes
NASA Technical Reports Server (NTRS)
Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich
2003-01-01
This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.
Markov Chain Analysis of Musical Dice Games
NASA Astrophysics Data System (ADS)
Volchenkov, D.; Dawin, J. R.
2012-07-01
A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.
Show Me the Invisible: Visualizing Hidden Content
Geymayer, Thomas; Steinberger, Markus; Lex, Alexander; Streit, Marc; Schmalstieg, Dieter
2014-01-01
Content on computer screens is often inaccessible to users because it is hidden, e.g., occluded by other windows, outside the viewport, or overlooked. In search tasks, the efficient retrieval of sought content is important. Current software, however, only provides limited support to visualize hidden occurrences and rarely supports search synchronization crossing application boundaries. To remedy this situation, we introduce two novel visualization methods to guide users to hidden content. Our first method generates awareness for occluded or out-of-viewport content using see-through visualization. For content that is either outside the screen’s viewport or for data sources not opened at all, our second method shows off-screen indicators and an on-demand smart preview. To reduce the chances of overlooking content, we use visual links, i.e., visible edges, to connect the visible content or the visible representations of the hidden content. We show the validity of our methods in a user study, which demonstrates that our technique enables a faster localization of hidden content compared to traditional search functionality and thereby assists users in information retrieval tasks. PMID:25325078
Popovic, Marta; Zaja, Roko; Fent, Karl; Smital, Tvrtko
2014-10-01
Polyspecific transporters from the organic anion transporting polypeptide (OATP/Oatp) superfamily mediate the uptake of a wide range of compounds. In zebrafish, Oatp1d1 transports conjugated steroid hormones and cortisol. It is predominantly expressed in the liver, brain and testes. In this study we have characterized the transport of xenobiotics by the zebrafish Oatp1d1 transporter. We developed a novel assay for assessing Oatp1d1 interactors using the fluorescent probe Lucifer yellow and transient transfection in HEK293 cells. Our data showed that numerous environmental contaminants interact with zebrafish Oatp1d1. Oatp1d1 mediated the transport of diclofenac with very high affinity, followed by high affinity towards perfluorooctanesulfonic acid (PFOS), nonylphenol, gemfibrozil and 17α-ethinylestradiol; moderate affinity towards carbaryl, diazinon and caffeine; and low affinity towards metolachlor. Importantly, many environmental chemicals acted as strong inhibitors of Oatp1d1. A strong inhibition of Oatp1d1 transport activity was found by perfluorooctanoic acid (PFOA), chlorpyrifos-methyl, estrone (E1) and 17β-estradiol (E2), followed by moderate to low inhibition by diethyl phthalate, bisphenol A, 7-acetyl-1,1,3,4,4,6-hexamethyl-1,2,3,4 tetrahydronapthalene and clofibrate. In this study we identified Oatp1d1 as a first Solute Carrier (SLC) transporter involved in the transport of a wide range of xenobiotics in fish. Considering that Oatps in zebrafish have not been characterized before, our work on zebrafish Oatp1d1 offers important new insights on the understanding of uptake processes of environmental contaminants, and contributes to the better characterization of zebrafish as a model species. - Highlights: • We optimized a novel assay for determination of Oatp1d1 interactors • Oatp1d1 is the first SLC characterized fish xenobiotic transporter • PFOS, nonylphenol, diclofenac, EE2, caffeine are high affinity Oatp1d1substrates • PFOA, chlorpyrifos
Black hole portal into hidden valleys
NASA Astrophysics Data System (ADS)
Dubovsky, Sergei; Gorbenko, Victor
2011-05-01
Superradiant instability turns rotating astrophysical black holes into unique probes of light axions. We consider what happens when a light axion is coupled to a strongly coupled hidden gauge sector. In this case superradiance results in an adiabatic increase of a hidden sector CP-violating θ parameter in a near horizon region. This may trigger a first order phase transition in the gauge sector. As a result a significant fraction of a black hole mass is released as a cloud of hidden mesons and can be later converted into electromagnetic radiation. This results in a violent electromagnetic burst. The characteristic frequency of such bursts may range from ˜100eV to ˜100MeV.
Metagenomic Classification Using an Abstraction Augmented Markov Model
Zhu, Xiujun (Sylvia)
2016-01-01
Abstract The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification. PMID:26618474
Lifting—A nonreversible Markov chain Monte Carlo algorithm
NASA Astrophysics Data System (ADS)
Vucelja, Marija
2016-12-01
Markov chain Monte Carlo algorithms are invaluable tools for exploring stationary properties of physical systems, especially in situations where direct sampling is unfeasible. Common implementations of Monte Carlo algorithms employ reversible Markov chains. Reversible chains obey detailed balance and thus ensure that the system will eventually relax to equilibrium, though detailed balance is not necessary for convergence to equilibrium. We review nonreversible Markov chains, which violate detailed balance and yet still relax to a given target stationary distribution. In particular cases, nonreversible Markov chains are substantially better at sampling than the conventional reversible Markov chains with up to a square root improvement in the convergence time to the steady state. One kind of nonreversible Markov chain is constructed from the reversible ones by enlarging the state space and by modifying and adding extra transition rates to create non-reversible moves. Because of the augmentation of the state space, such chains are often referred to as lifted Markov Chains. We illustrate the use of lifted Markov chains for efficient sampling on several examples. The examples include sampling on a ring, sampling on a torus, the Ising model on a complete graph, and the one-dimensional Ising model. We also provide a pseudocode implementation, review related work, and discuss the applicability of such methods.
Limit measures for affine cellular automata on topological Markov subgroups
NASA Astrophysics Data System (ADS)
Maass, Alejandro; Martínez, Servet; Sobottka, Marcelo
2006-09-01
Consider a topological Markov subgroup which is ps-torsion (with p prime) and an affine cellular automaton defined on it. We show that the Cesàro mean of the iterates, by the automaton of a probability measure with complete connections and summable memory decay that is compatible with the topological Markov subgroup, converges to the Haar measure.
Protein family classification using sparse markov transducers.
Eskin, Eleazar; Noble, William Stafford; Singer, Yoram
2003-01-01
We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.
Unmixing hyperspectral images using Markov random fields
Eches, Olivier; Dobigeon, Nicolas; Tourneret, Jean-Yves
2011-03-14
This paper proposes a new spectral unmixing strategy based on the normal compositional model that exploits the spatial correlations between the image pixels. The pure materials (referred to as endmembers) contained in the image are assumed to be available (they can be obtained by using an appropriate endmember extraction algorithm), while the corresponding fractions (referred to as abundances) are estimated by the proposed algorithm. Due to physical constraints, the abundances have to satisfy positivity and sum-to-one constraints. The image is divided into homogeneous distinct regions having the same statistical properties for the abundance coefficients. The spatial dependencies within each class are modeled thanks to Potts-Markov random fields. Within a Bayesian framework, prior distributions for the abundances and the associated hyperparameters are introduced. A reparametrization of the abundance coefficients is proposed to handle the physical constraints (positivity and sum-to-one) inherent to hyperspectral imagery. The parameters (abundances), hyperparameters (abundance mean and variance for each class) and the classification map indicating the classes of all pixels in the image are inferred from the resulting joint posterior distribution. To overcome the complexity of the joint posterior distribution, Markov chain Monte Carlo methods are used to generate samples asymptotically distributed according to the joint posterior of interest. Simulations conducted on synthetic and real data are presented to illustrate the performance of the proposed algorithm.
Non-Markov effects in intersecting sprays
NASA Astrophysics Data System (ADS)
Panchagnula, Mahesh; Kumaran, Dhivyaraja; Deevi, Sri Vallabha; Tangirala, Arun
2016-11-01
Sprays have been assumed to follow a Markov process. In this study, we revisit that assumption relying on experimental data from intersecting and non-intersecting sprays. A phase Doppler Particle Analyzer (PDPA) is used to measure particle diameter and velocity at various axial locations in the intersection region of two sprays. Measurements of single sprays, with one nozzle turned off alternatively are also obtained at the same locations. This data, treated as an unstructured time series is classified into three bins each for diameter (small, medium, large) and velocity (slow, medium, fast). Conditional probability analysis on this binned data showed a higher static correlation between droplet velocities, while diameter correlation is significantly alleviated (reduced) in intersecting sprays, compared to single sprays. Further analysis using serial correlation measures: auto-correlation function (ACF) and partial auto-correlation function (PACF) shows that the lagged correlations in droplet velocity are enhanced while those in the droplet diameter are significantly debilitated in intersecting sprays. We show that sprays are not necessarily Markov processes and that memory persists, even though curtailed to fewer lags in case of size, and enhanced in case of droplet velocity.
Equilibrium Control Policies for Markov Chains
Malikopoulos, Andreas
2011-01-01
The average cost criterion has held great intuitive appeal and has attracted considerable attention. It is widely employed when controlling dynamic systems that evolve stochastically over time by means of formulating an optimization problem to achieve long-term goals efficiently. The average cost criterion is especially appealing when the decision-making process is long compared to other timescales involved, and there is no compelling motivation to select short-term optimization. This paper addresses the problem of controlling a Markov chain so as to minimize the average cost per unit time. Our approach treats the problem as a dual constrained optimization problem. We derive conditions guaranteeing that a saddle point exists for the new dual problem and we show that this saddle point is an equilibrium control policy for each state of the Markov chain. For practical situations with constraints consistent to those we study here, our results imply that recognition of such saddle points may be of value in deriving in real time an optimal control policy.
Sunspots and ENSO relationship using Markov method
NASA Astrophysics Data System (ADS)
Hassan, Danish; Iqbal, Asif; Ahmad Hassan, Syed; Abbas, Shaheen; Ansari, Muhammad Rashid Kamal
2016-01-01
The various techniques have been used to confer the existence of significant relations between the number of Sunspots and different terrestrial climate parameters such as rainfall, temperature, dewdrops, aerosol and ENSO etc. Improved understanding and modelling of Sunspots variations can explore the information about the related variables. This study uses a Markov chain method to find the relations between monthly Sunspots and ENSO data of two epochs (1996-2009 and 1950-2014). Corresponding transition matrices of both data sets appear similar and it is qualitatively evaluated by high values of 2-dimensional correlation found between transition matrices of ENSO and Sunspots. The associated transition diagrams show that each state communicates with the others. Presence of stronger self-communication (between same states) confirms periodic behaviour among the states. Moreover, closeness found in the expected number of visits from one state to the other show the existence of a possible relation between Sunspots and ENSO data. Moreover, perfect validation of dependency and stationary tests endorses the applicability of the Markov chain analyses on Sunspots and ENSO data. This shows that a significant relation between Sunspots and ENSO data exists. Improved understanding and modelling of Sunspots variations can help to explore the information about the related variables. This study can be useful to explore the influence of ENSO related local climatic variability.
Markov Chain Monte Carlo and Irreversibility
NASA Astrophysics Data System (ADS)
Ottobre, Michela
2016-06-01
Markov Chain Monte Carlo (MCMC) methods are statistical methods designed to sample from a given measure π by constructing a Markov chain that has π as invariant measure and that converges to π. Most MCMC algorithms make use of chains that satisfy the detailed balance condition with respect to π; such chains are therefore reversible. On the other hand, recent work [18, 21, 28, 29] has stressed several advantages of using irreversible processes for sampling. Roughly speaking, irreversible diffusions converge to equilibrium faster (and lead to smaller asymptotic variance as well). In this paper we discuss some of the recent progress in the study of nonreversible MCMC methods. In particular: i) we explain some of the difficulties that arise in the analysis of nonreversible processes and we discuss some analytical methods to approach the study of continuous-time irreversible diffusions; ii) most of the rigorous results on irreversible diffusions are available for continuous-time processes; however, for computational purposes one needs to discretize such dynamics. It is well known that the resulting discretized chain will not, in general, retain all the good properties of the process that it is obtained from. In particular, if we want to preserve the invariance of the target measure, the chain might no longer be reversible. Therefore iii) we conclude by presenting an MCMC algorithm, the SOL-HMC algorithm [23], which results from a nonreversible discretization of a nonreversible dynamics.
Stochastic seismic tomography by interacting Markov chains
NASA Astrophysics Data System (ADS)
Bottero, Alexis; Gesret, Alexandrine; Romary, Thomas; Noble, Mark; Maisons, Christophe
2016-10-01
Markov chain Monte Carlo sampling methods are widely used for non-linear Bayesian inversion where no analytical expression for the forward relation between data and model parameters is available. Contrary to the linear(ized) approaches, they naturally allow to evaluate the uncertainties on the model found. Nevertheless their use is problematic in high-dimensional model spaces especially when the computational cost of the forward problem is significant and/or the a posteriori distribution is multimodal. In this case, the chain can stay stuck in one of the modes and hence not provide an exhaustive sampling of the distribution of interest. We present here a still relatively unknown algorithm that allows interaction between several Markov chains at different temperatures. These interactions (based on importance resampling) ensure a robust sampling of any posterior distribution and thus provide a way to efficiently tackle complex fully non-linear inverse problems. The algorithm is easy to implement and is well adapted to run on parallel supercomputers. In this paper, the algorithm is first introduced and applied to a synthetic multimodal distribution in order to demonstrate its robustness and efficiency compared to a simulated annealing method. It is then applied in the framework of first arrival traveltime seismic tomography on real data recorded in the context of hydraulic fracturing. To carry out this study a wavelet-based adaptive model parametrization has been used. This allows to integrate the a priori information provided by sonic logs and to reduce optimally the dimension of the problem.
Neyman, Markov processes and survival analysis.
Yang, Grace
2013-07-01
J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.
D1/D5 dopamine receptors modulate spatial memory formation.
da Silva, Weber C N; Köhler, Cristiano C; Radiske, Andressa; Cammarota, Martín
2012-02-01
We investigated the effect of the intra-CA1 administration of the D1/D5 receptor antagonist SCH23390 and the D1/D5 receptor agonist SKF38393 on spatial memory in the water maze. When given immediately, but not 3h after training, SCH23390 hindered long-term spatial memory formation without affecting non-spatial memory or the normal functionality of the hippocampus. On the contrary, post-training infusion of SKF38393 enhanced retention and facilitated the spontaneous recovery of the original spatial preference after reversal learning. Our findings demonstrate that hippocampal D1/D5 receptors play an essential role in spatial memory processing.
Hidden treasures - 50 km points of interests
NASA Astrophysics Data System (ADS)
Lommi, Matias; Kortelainen, Jaana
2015-04-01
Tampere is third largest city in Finland and a regional centre. During 70's there occurred several communal mergers. Nowadays this local area has both strong and diversed identity - from wilderness and agricultural fields to high density city living. Outside the city center there are interesting geological points unknown for modern city settlers. There is even a local proverb, "Go abroad to Teisko!". That is the area the Hidden Treasures -student project is focused on. Our school Tammerkoski Upper Secondary School (or Gymnasium) has emphasis on visual arts. We are going to offer our art students scientific and artistic experiences and knowledge about the hidden treasures of Teisko area and involve the Teisko inhabitants into this project. Hidden treasures - Precambrian subduction zone and a volcanism belt with dense bed of gold (Au) and arsenic (As), operating goldmines and quarries of minerals and metamorphic slates. - North of subduction zone a homogenic precambrian magmastone area with quarries, products known as Kuru Grey. - Former ashores of post-glasial Lake Näsijärvi and it's sediments enabled the developing agriculture and sustained settlement. Nowadays these ashores have both scenery and biodiversity values. - Old cattle sheds and dairy buildings made of local granite stones related to cultural stonebuilding inheritance. - Local active community of Kapee, about 100 inhabitants. Students will discover information of these "hidden" phenomena, and rendering this information trough Enviromental Art Method. Final form of this project will be published in several artistic and informative geocaches. These caches are achieved by a GPS-based special Hidden Treasures Cycling Route and by a website guiding people to find these hidden points of interests.
Test to determine the Markov order of a time series.
Racca, E; Laio, F; Poggi, D; Ridolfi, L
2007-01-01
The Markov order of a time series is an important measure of the "memory" of a process, and its knowledge is fundamental for the correct simulation of the characteristics of the process. For this reason, several techniques have been proposed in the past for its estimation. However, most of this methods are rather complex, and often can be applied only in the case of Markov chains. Here we propose a simple and robust test to evaluate the Markov order of a time series. Only the first-order moment of the conditional probability density function characterizing the process is used to evaluate the memory of the process itself. This measure is called the "expected value Markov (EVM) order." We show that there is good agreement between the EVM order and the known Markov order of some synthetic time series.
Severe Hypertriglyceridemia in Glut1D on Ketogenic Diet.
Klepper, Joerg; Leiendecker, Baerbel; Heussinger, Nicole; Lausch, Ekkehart; Bosch, Friedrich
2016-04-01
High-fat ketogenic diets are the only treatment available for Glut1 deficiency (Glut1D). Here, we describe an 8-year-old girl with classical Glut1D responsive to a 3:1 ketogenic diet and ethosuximide. After 3 years on the diet a gradual increase of blood lipids was followed by rapid, severe asymptomatic hypertriglyceridemia (1,910 mg/dL). Serum lipid apheresis was required to determine liver, renal, and pancreatic function. A combination of medium chain triglyceride-oil and a reduction of the ketogenic diet to 1:1 ratio normalized triglyceride levels within days but triggered severe myoclonic seizures requiring comedication with sultiam. Severe hypertriglyceridemia in children with Glut1D on ketogenic diets may be underdiagnosed and harmful. In contrast to congenital hypertriglyceridemias, children with Glut1D may be treated effectively by dietary adjustments alone.
TBC1D24 genotype–phenotype correlation
Balestrini, Simona; Milh, Mathieu; Castiglioni, Claudia; Lüthy, Kevin; Finelli, Mattea J.; Verstreken, Patrik; Cardon, Aaron; Stražišar, Barbara Gnidovec; Holder, J. Lloyd; Lesca, Gaetan; Mancardi, Maria M.; Poulat, Anne L.; Repetto, Gabriela M.; Banka, Siddharth; Bilo, Leonilda; Birkeland, Laura E.; Bosch, Friedrich; Brockmann, Knut; Cross, J. Helen; Doummar, Diane; Félix, Temis M.; Giuliano, Fabienne; Hori, Mutsuki; Hüning, Irina; Kayserili, Hulia; Kini, Usha; Lees, Melissa M.; Meenakshi, Girish; Mewasingh, Leena; Pagnamenta, Alistair T.; Peluso, Silvio; Mey, Antje; Rice, Gregory M.; Rosenfeld, Jill A.; Taylor, Jenny C.; Troester, Matthew M.; Stanley, Christine M.; Ville, Dorothee; Walkiewicz, Magdalena; Falace, Antonio; Fassio, Anna; Lemke, Johannes R.; Biskup, Saskia; Tardif, Jessica; Ajeawung, Norbert F.; Tolun, Aslihan; Corbett, Mark; Gecz, Jozef; Afawi, Zaid; Howell, Katherine B.; Oliver, Karen L.; Berkovic, Samuel F.; Scheffer, Ingrid E.; de Falco, Fabrizio A.; Oliver, Peter L.; Striano, Pasquale; Zara, Federico
2016-01-01
Objective: To evaluate the phenotypic spectrum associated with mutations in TBC1D24. Methods: We acquired new clinical, EEG, and neuroimaging data of 11 previously unreported and 37 published patients. TBC1D24 mutations, identified through various sequencing methods, can be found online (http://lovd.nl/TBC1D24). Results: Forty-eight patients were included (28 men, 20 women, average age 21 years) from 30 independent families. Eighteen patients (38%) had myoclonic epilepsies. The other patients carried diagnoses of focal (25%), multifocal (2%), generalized (4%), and unclassified epilepsy (6%), and early-onset epileptic encephalopathy (25%). Most patients had drug-resistant epilepsy. We detail EEG, neuroimaging, developmental, and cognitive features, treatment responsiveness, and physical examination. In silico evaluation revealed 7 different highly conserved motifs, with the most common pathogenic mutation located in the first. Neuronal outgrowth assays showed that some TBC1D24 mutations, associated with the most severe TBC1D24-associated disorders, are not necessarily the most disruptive to this gene function. Conclusions: TBC1D24-related epilepsy syndromes show marked phenotypic pleiotropy, with multisystem involvement and severity spectrum ranging from isolated deafness (not studied here), benign myoclonic epilepsy restricted to childhood with complete seizure control and normal intellect, to early-onset epileptic encephalopathy with severe developmental delay and early death. There is no distinct correlation with mutation type or location yet, but patterns are emerging. Given the phenotypic breadth observed, TBC1D24 mutation screening is indicated in a wide variety of epilepsies. A TBC1D24 consortium was formed to develop further research on this gene and its associated phenotypes. PMID:27281533
Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation.
Stathopoulos, Vassilios; Girolami, Mark A
2013-02-13
Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.
Markov and non-Markov processes in complex systems by the dynamical information entropy
NASA Astrophysics Data System (ADS)
Yulmetyev, R. M.; Gafarov, F. M.
1999-12-01
We consider the Markov and non-Markov processes in complex systems by the dynamical information Shannon entropy (DISE) method. The influence and important role of the two mutually dependent channels of entropy alternation (creation or generation of correlation) and anti-correlation (destroying or annihilation of correlation) have been discussed. The developed method has been used for the analysis of the complex systems of various natures: slow neutron scattering in liquid cesium, psychology (short-time numeral and pattern human memory and effect of stress on the dynamical taping-test), random dynamics of RR-intervals in human ECG (problem of diagnosis of various disease of the human cardio-vascular systems), chaotic dynamics of the parameters of financial markets and ecological systems.
Subtleties of Hidden Quantifiers in Implication
ERIC Educational Resources Information Center
Shipman, Barbara A.
2016-01-01
Mathematical conjectures and theorems are most often of the form P(x) ? Q(x), meaning ?x,P(x) ? Q(x). The hidden quantifier ?x is crucial in understanding the implication as a statement with a truth value. Here P(x) and Q(x) alone are only predicates, without truth values, since they contain unquantified variables. But standard textbook…
Computerized Testing: The Hidden Figures Test.
ERIC Educational Resources Information Center
Jacobs, Ronald L.; And Others
1985-01-01
This study adapted the Hidden Figures Test for use on PLATO and determined the reliability of the computerized version compared to the paper and pencil version. Results indicate the test was successfully adapted with some modifications, and it was judged reliable although it may be measuring additional constructs. (MBR)
Expose hidden failures to prevent cascading outages
Phadke, A.G.; Thorp, J.S.
1996-07-01
This article describes how to identify lines and buses that present the greatest hazard to system reliability, and add digital equipment to supervise and control hidden failures of the associated relays. Major blackouts are rare events, but their impact can be catastrophic. A study of significant disturbances reported by NERC in the period from 1984 through 1988 indicates that protective relays are involved in one way or another in 75% of major disturbances. A common scenario is that the relay has an undetected (hidden) defect that was exposed due to the conditions created by other disturbances. For example, near-by faults, overloads, or reverse power flows expose the defective relay and cause a false trip, which exacerbates the situation. Given the importance of hidden failure modes in traditional relaying systems, intervention by computer-based rational control schemes is proposed in this article. Relays with high-vulnerability indices can be identified, and their vulnerable functions and failure modes identified. Countermeasures to reduce or eliminate the likelihood of the hidden failure of key relays can be provided.
Registration of 'Hidden Valley' meadow fescue
Technology Transfer Automated Retrieval System (TEKTRAN)
'Hidden Valley' (Reg. No. CV-xxxx, PI xxxxxx) meadow fescue [Schedonorus pratensis (Huds.) P. Beauv.; syn. Festuca pratensis Huds.; syn. Lolium pratense (Huds.) Darbysh.] is a synthetic population originating from 561 parental genotypes. The original germplasm is of unknown central or northern Europ...
Observation of vector solitons with hidden vorticity.
Izdebskaya, Yana V; Rebling, Johannes; Desyatnikov, Anton S; Kivshar, Yuri S
2012-03-01
This letter reports the first experimental observation, to our knowledge, of optical vector solitons composed of two incoherently coupled vortex components. We employ nematic liquid crystal to generate stable vector solitons with counterrotating vortices and hidden vorticity. In contrast, the solitons with explicit vorticity and corotating vortex components show azimuthal splitting.
Hidden supersymmetry in quantum bosonic systems
Correa, Francisco Plyushchay, Mikhail S.
2007-10-15
We show that some simple well-studied quantum mechanical systems without fermion (spin) degrees of freedom display, surprisingly, a hidden supersymmetry. The list includes the bound state Aharonov-Bohm, the Dirac delta and the Poeschl-Teller potential problems, in which the unbroken and broken N = 2 supersymmetry of linear and nonlinear (polynomial) forms is revealed.
Discovering Hidden Treasures with GPS Technology
ERIC Educational Resources Information Center
Nagel, Paul; Palmer, Roger
2014-01-01
"I found it!" Addison proudly proclaimed, as she used an iPhone and Global Positioning System (GPS) software to find the hidden geocache along the riverbank. Others in Lisa Bostick's fourth grade class were jealous, but there would be other geocaches to find. With the excitement of movies like "Pirates of the Caribbean" and…
Hidden Messages: Instructional Materials for Investigating Culture.
ERIC Educational Resources Information Center
Finkelstein, Barbara, Ed.; Eder, Elizabeth K., Ed.
This book, intended to be used in the middle and high school classroom, provides teachers with unique ideas and lesson plans for exploring culture and adding a multicultural perspective to diverse subjects. "Hidden messages" are the messages of culture that are entwined in everyday lives, but which are seldom recognized or appreciated…
A Hidden Minority Amidst White Privilege
ERIC Educational Resources Information Center
Singer, Miriam J.
2008-01-01
It seems rather amusing to say that the author belongs to a minority, no less a hidden minority. After all, at first glance, she appears to be just another white girl (or woman). She grew up in the mid-west in a predominantly white community, middle class, and well educated. The paradox comes in their definition of minority. Today, as they seek to…
The Hidden Civic Lessons of Public and Private Schools
ERIC Educational Resources Information Center
Sikkink, David
2004-01-01
Curriculum theory has long acknowledged the presence of a hidden curriculum in schools. Whereas the formal curriculum is explicit and documented, the hidden curriculum involves those attitudes, experiences, and learnings that are largely implicit and unintended. This article compares the hidden civic lessons found in public and private schools.…
Rab28 is a TBC1D1/TBC1D4 substrate involved in GLUT4 trafficking.
Zhou, Zhou; Menzel, Franziska; Benninghoff, Tim; Chadt, Alexandra; Du, Chen; Holman, Geoffrey D; Al-Hasani, Hadi
2017-01-01
The Rab-GTPase-activating proteins (GAPs) TBC1D1 and TBC1D4 play important roles in the insulin-stimulated translocation of the glucose transporter GLUT4 from intracellular vesicles to the plasma membrane in muscle cells and adipocytes. We identified Rab28 as a substrate for the GAP domains of both TBC1D1 and TBC1D4 in vitro. Rab28 is expressed in adipose cells and skeletal muscle, and its GTP-binding state is acutely regulated by insulin. We found that in intact isolated mouse skeletal muscle, siRNA-mediated knockdown of Rab28 decreases basal glucose uptake. Conversely, in primary rat adipose cells, overexpression of Rab28-Q72L, a constitutively active mutant, increases basal cell surface levels of an epitope-tagged HA-GLUT4. Our results indicate that Rab28 is a novel GTPase involved in the intracellular retention of GLUT4 in insulin target cells.
Transition-Independent Decentralized Markov Decision Processes
NASA Technical Reports Server (NTRS)
Becker, Raphen; Silberstein, Shlomo; Lesser, Victor; Goldman, Claudia V.; Morris, Robert (Technical Monitor)
2003-01-01
There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied up by a global reward function that depends on both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.
Markov state models of biomolecular conformational dynamics
Chodera, John D.; Noé, Frank
2014-01-01
It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges. PMID:24836551
Markov state models and molecular alchemy
NASA Astrophysics Data System (ADS)
Schütte, Christof; Nielsen, Adam; Weber, Marcus
2015-01-01
In recent years, Markov state models (MSMs) have attracted a considerable amount of attention with regard to modelling conformation changes and associated function of biomolecular systems. They have been used successfully, e.g. for peptides including time-resolved spectroscopic experiments, protein function and protein folding , DNA and RNA, and ligand-receptor interaction in drug design and more complicated multivalent scenarios. In this article, a novel reweighting scheme is introduced that allows to construct an MSM for certain molecular system out of an MSM for a similar system. This permits studying how molecular properties on long timescales differ between similar molecular systems without performing full molecular dynamics simulations for each system under consideration. The performance of the reweighting scheme is illustrated for simple test cases, including one where the main wells of the respective energy landscapes are located differently and an alchemical transformation of butane to pentane where the dimension of the state space is changed.
Multivariate Markov chain modeling for stock markets
NASA Astrophysics Data System (ADS)
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Growth and Dissolution of Macromolecular Markov Chains
NASA Astrophysics Data System (ADS)
Gaspard, Pierre
2016-07-01
The kinetics and thermodynamics of free living copolymerization are studied for processes with rates depending on k monomeric units of the macromolecular chain behind the unit that is attached or detached. In this case, the sequence of monomeric units in the growing copolymer is a kth-order Markov chain. In the regime of steady growth, the statistical properties of the sequence are determined analytically in terms of the attachment and detachment rates. In this way, the mean growth velocity as well as the thermodynamic entropy production and the sequence disorder can be calculated systematically. These different properties are also investigated in the regime of depolymerization where the macromolecular chain is dissolved by the surrounding solution. In this regime, the entropy production is shown to satisfy Landauer's principle.
Anatomy Ontology Matching Using Markov Logic Networks
Li, Chunhua; Zhao, Pengpeng; Wu, Jian; Cui, Zhiming
2016-01-01
The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. We combine several different matching strategies through first-order logic formulas according to the structure of anatomy ontologies. Experiments on the adult mouse anatomy and the human anatomy have demonstrated the effectiveness of proposed approach in terms of the quality of result alignment. PMID:27382498
Estimation and uncertainty of reversible Markov models
NASA Astrophysics Data System (ADS)
Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank
2015-11-01
Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software — http://pyemma.org — as of version 2.0.
Hidden vorticity in binary Bose-Einstein condensates
Brtka, Marijana; Gammal, Arnaldo; Malomed, Boris A.
2010-11-15
We consider a binary Bose-Einstein condensate (BEC) described by a system of two-dimensional (2D) Gross-Pitaevskii equations with the harmonic-oscillator trapping potential. The intraspecies interactions are attractive, while the interaction between the species may have either sign. The same model applies to the copropagation of bimodal beams in photonic-crystal fibers. We consider a family of trapped hidden-vorticity (HV) modes in the form of bound states of two components with opposite vorticities S{sub 1,2}={+-}1, the total angular momentum being zero. A challenging problem is the stability of the HV modes. By means of a linear-stability analysis and direct simulations, stability domains are identified in a relevant parameter plane. In direct simulations, stable HV modes feature robustness against large perturbations, while unstable ones split into fragments whose number is identical to the azimuthal index of the fastest growing perturbation eigenmode. Conditions allowing for the creation of the HV modes in the experiment are discussed too. For comparison, a similar but simpler problem is studied in an analytical form, viz., the modulational instability of an HV state in a one-dimensional (1D) system with periodic boundary conditions (this system models a counterflow in a binary BEC mixture loaded into a toroidal trap or a bimodal optical beam coupled into a cylindrical shell). We demonstrate that the stabilization of the 1D HV modes is impossible, which stresses the significance of the stabilization of the HV modes in the 2D setting.
Benchmark solutions for transport in d-dimensional Markov binary mixtures
NASA Astrophysics Data System (ADS)
Larmier, Coline; Hugot, François-Xavier; Malvagi, Fausto; Mazzolo, Alain; Zoia, Andrea
2017-03-01
Linear particle transport in stochastic media is key to such relevant applications as neutron diffusion in randomly mixed immiscible materials, light propagation through engineered optical materials, and inertial confinement fusion, only to name a few. We extend the pioneering work by Adams, Larsen and Pomraning [1] (recently revisited by Brantley [2]) by considering a series of benchmark configurations for mono-energetic and isotropic transport through Markov binary mixtures in dimension d. The stochastic media are generated by resorting to Poisson random tessellations in 1 d slab, 2 d extruded, and full 3 d geometry. For each realization, particle transport is performed by resorting to the Monte Carlo simulation. The distributions of the transmission and reflection coefficients on the free surfaces of the geometry are subsequently estimated, and the average values over the ensemble of realizations are computed. Reference solutions for the benchmark have never been provided before for two- and three-dimensional Poisson tessellations, and the results presented in this paper might thus be useful in order to validate fast but approximated models for particle transport in Markov stochastic media, such as the celebrated Chord Length Sampling algorithm.
Performability analysis using semi-Markov reward processes
NASA Technical Reports Server (NTRS)
Ciardo, Gianfranco; Marie, Raymond A.; Sericola, Bruno; Trivedi, Kishor S.
1990-01-01
Beaudry (1978) proposed a simple method of computing the distribution of performability in a Markov reward process. Two extensions of Beaudry's approach are presented. The method is generalized to a semi-Markov reward process by removing the restriction requiring the association of zero reward to absorbing states only. The algorithm proceeds by replacing zero-reward nonabsorbing states by a probabilistic switch; it is therefore related to the elimination of vanishing states from the reachability graph of a generalized stochastic Petri net and to the elimination of fast transient states in a decomposition approach to stiff Markov chains. The use of the approach is illustrated with three applications.
Polar discontinuities and 1D interfaces in monolayered materials
NASA Astrophysics Data System (ADS)
Martinez-Gordillo, Rafael; Pruneda, Miguel
2015-12-01
Interfaces are the birthplace of a multitude of fascinating discoveries in fundamental science, and have enabled modern electronic devices, from transistors, to lasers, capacitors or solar cells. These interfaces between bulk materials are always bi-dimensional (2D) 'surfaces'. However the advent of graphene and other 2D crystals opened up a world of possibilities, as in this case the interfaces become one-dimensional (1D) lines. Although the properties of 1D nanoribbons have been extensively discussed in the last few years, 1D interfaces within infinite 2D systems had remained mostly unexplored until very recently. These include grain boundaries in polycrystalline samples, or interfaces in hybrid 2D sheets composed by segregated domains of different materials (as for example graphene/BN hybrids, or chemically different transition metal dichalcogenides). As for their 2D counterparts, some of these 1D interfaces exhibit polar characteristics, and can give rise to fascinating new physical properties. Here, recent experimental discoveries and theoretical predictions on the polar discontinuities that arise at these 1D interfaces will be reviewed, and the perspectives of this new research topic, discussed.
Ion-sensing properties of 1D vanadium pentoxide nanostructures
2012-01-01
The application of one-dimensional (1D) V2O5·nH2O nanostructures as pH sensing material was evaluated. 1D V2O5·nH2O nanostructures were obtained by a hydrothermal method with systematic control of morphology forming different nanostructures: nanoribbons, nanowires and nanorods. Deposited onto Au-covered substrates, 1D V2O5·nH2O nanostructures were employed as gate material in pH sensors based on separative extended gate FET as an alternative to provide FET isolation from the chemical environment. 1D V2O5·nH2O nanostructures showed pH sensitivity around the expected theoretical value. Due to high pH sensing properties, flexibility and low cost, further applications of 1D V2O5·nH2O nanostructures comprise enzyme FET-based biosensors using immobilized enzymes. PMID:22709724
An Overview of Markov Chain Methods for the Study of Stage-Sequential Developmental Processes
ERIC Educational Resources Information Center
Kapland, David
2008-01-01
This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model.…
Synthetic gene design with a large number of hidden stops.
Phan, Vinhthuy; Saha, Sudip; Pandey, Ashutosh; Wong, Tit-Yee
2010-01-01
Hidden stops are nucleotide triples TAA, TAG and TGA that appear on the second and third reading frames of a protein coding gene. Recent studies suggested the important role of hidden stops in preventing misread of mRNA. We study the problem of designing protein-encoding genes with large number of hidden stops under several biological constraints. With simple constraints, redesigned genes have provable maximal number of hidden stops. With more complex constraints, redesigned genes still have many more hidden stops than wild-type genes. We showed that redesigned genes have a distinct positional advantage in assisting early termination of frame-shifts.
Pitch-based pattern splitting for 1D layout
NASA Astrophysics Data System (ADS)
Nakayama, Ryo; Ishii, Hiroyuki; Mikami, Koji; Tsujita, Koichiro; Yaegashi, Hidetami; Oyama, Kenichi; Smayling, Michael C.; Axelrad, Valery
2015-07-01
The pattern splitting algorithm for 1D Gridded-Design-Rules layout (1D layout) for sub-10 nm node logic devices is shown. It is performed with integer linear programming (ILP) based on the conflict graph created from a grid map for each designated pitch. The relation between the number of times for patterning and the minimum pitch is shown systematically with a sample pattern of contact layer for each node. From the result, the number of times for patterning for 1D layout is fewer than that for conventional 2D layout. Moreover, an experimental result including SMO and total integrated process with hole repair technique is presented with the sample pattern of contact layer whose pattern density is relatively high among critical layers (fin, gate, local interconnect, contact, and metal).
Flexible Photodetectors Based on 1D Inorganic Nanostructures
Lou, Zheng
2015-01-01
Flexible photodetectors with excellent flexibility, high mechanical stability and good detectivity, have attracted great research interest in recent years. 1D inorganic nanostructures provide a number of opportunities and capabilities for use in flexible photodetectors as they have unique geometry, good transparency, outstanding mechanical flexibility, and excellent electronic/optoelectronic properties. This article offers a comprehensive review of several types of flexible photodetectors based on 1D nanostructures from the past ten years, including flexible ultraviolet, visible, and infrared photodetectors. High‐performance organic‐inorganic hybrid photodetectors, as well as devices with 1D nanowire (NW) arrays, are also reviewed. Finally, new concepts of flexible photodetectors including piezophototronic, stretchable and self‐powered photodetectors are examined to showcase the future research in this exciting field. PMID:27774404
PC-1D installation manual and user's guide
Basore, P.A.
1991-05-01
PC-1D is a software package for personal computers that uses finite-element analysis to solve the fully-coupled two-carrier semiconductor transport equations in one dimension. This program is particularly useful for analyzing the performance of optoelectronic devices such as solar cells, but can be applied to any bipolar device whose carrier flows are primarily one-dimensional. This User's Guide provides the information necessary to install PC-1D, define a problem for solution, solve the problem, and examine the results. Example problems are presented which illustrate these steps. The physical models and numerical methods utilized are presented in detail. This document supports version 3.1 of PC-1D, which incorporates faster numerical algorithms with better convergence properties than previous versions of the program. 51 refs., 17 figs., 5 tabs.
GIS-BASED 1-D DIFFUSIVE WAVE OVERLAND FLOW MODEL
KALYANAPU, ALFRED; MCPHERSON, TIMOTHY N.; BURIAN, STEVEN J.
2007-01-17
This paper presents a GIS-based 1-d distributed overland flow model and summarizes an application to simulate a flood event. The model estimates infiltration using the Green-Ampt approach and routes excess rainfall using the 1-d diffusive wave approximation. The model was designed to use readily available topographic, soils, and land use/land cover data and rainfall predictions from a meteorological model. An assessment of model performance was performed for a small catchment and a large watershed, both in urban environments. Simulated runoff hydrographs were compared to observations for a selected set of validation events. Results confirmed the model provides reasonable predictions in a short period of time.
Slator, Paddy J.; Cairo, Christopher W.; Burroughs, Nigel J.
2015-01-01
We develop a Bayesian analysis framework to detect heterogeneity in the diffusive behaviour of single particle trajectories on cells, implementing model selection to classify trajectories as either consistent with Brownian motion or with a two-state (diffusion coefficient) switching model. The incorporation of localisation accuracy is essential, as otherwise false detection of switching within a trajectory was observed and diffusion coefficient estimates were inflated. Since our analysis is on a single trajectory basis, we are able to examine heterogeneity between trajectories in a quantitative manner. Applying our method to the lymphocyte function-associated antigen 1 (LFA-1) receptor tagged with latex beads (4 s trajectories at 1000 frames s−1), both intra- and inter-trajectory heterogeneity were detected; 12–26% of trajectories display clear switching between diffusive states dependent on condition, whilst the inter-trajectory variability is highly structured with the diffusion coefficients being related by D1 = 0.68D0 − 1.5 × 104 nm2 s−1, suggestive that on these time scales we are detecting switching due to a single process. Further, the inter-trajectory variability of the diffusion coefficient estimates (1.6 × 102 − 2.6 × 105 nm2 s−1) is very much larger than the measurement uncertainty within trajectories, suggesting that LFA-1 aggregation and cytoskeletal interactions are significantly affecting mobility, whilst the timescales of these processes are distinctly different giving rise to inter- and intra-trajectory variability. There is also an ‘immobile’ state (defined as D < 3.0 × 103 nm2 s−1) that is rarely involved in switching, immobility occurring with the highest frequency (47%) under T cell activation (phorbol-12-myristate-13-acetate (PMA) treatment) with enhanced cytoskeletal attachment (calpain inhibition). Such ‘immobile’ states frequently display slow linear drift, potentially reflecting binding to a dynamic actin cortex. Our methods allow significantly more information to be extracted from individual trajectories (ultimately limited by time resolution and time-series length), and allow statistical comparisons between trajectories thereby quantifying inter-trajectory heterogeneity. Such methods will be highly informative for the construction and fitting of molecule mobility models within membranes incorporating aggregation, binding to the cytoskeleton, or traversing membrane microdomains. PMID:26473352
Tae, Hongseok; Sohng, Jae Kyung; Park, Kiejung
2009-02-01
MAPSI (Management and Analysis for Polyketide Synthase Type I) has been developed to offer computational analysis methods to detect type I PKS (polyketide synthase) gene clusters in genome sequences. MAPSI provides a genome analysis component, which detects PKS gene clusters by identifying domains in proteins of a genome. MAPSI also contains databases on polyketides and genome annotation data, as well as analytic components such as new PKS assembly and domain analysis. The polyketide data and analysis component are accessible through Web interfaces and are displayed with diverse information. MAPSI, which was developed to aid researchers studying type I polyketides, provides diverse components to access and analyze polyketide information and should become a very powerful computational tool for polyketide research. The system can be extended through further studies of factors related to the biological activities of polyketides.
Hidden SU(N) glueball dark matter
Soni, Amarjit; Zhang, Yue
2016-06-21
Here we investigate the possibility that the dark matter candidate is from a pure non-abelian gauge theory of the hidden sector, motivated in large part by its elegance and simplicity. The dark matter is the lightest bound state made of the confined gauge fields, the hidden glueball. We point out this simple setup is capable of providing rich and novel phenomena in the dark sector, especially in the parameter space of large N. They include self-interacting and warm dark matter scenarios, Bose-Einstein condensation leading to massive dark stars possibly millions of times heavier than our sun giving rise to gravitationalmore » lensing effects, and indirect detections through higher dimensional operators as well as interesting collider signatures.« less
Hidden geometric correlations in real multiplex networks
NASA Astrophysics Data System (ADS)
Kleineberg, Kaj-Kolja; Boguñá, Marián; Ángeles Serrano, M.; Papadopoulos, Fragkiskos
2016-11-01
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Biofortification for combating 'hidden hunger' for iron.
Murgia, Irene; Arosio, Paolo; Tarantino, Delia; Soave, Carlo
2012-01-01
Micronutrient deficiencies are responsible for so-called 'hidden undernutrition'. In particular, iron (Fe) deficiency adversely affects growth, immune function and can cause anaemia. However, supplementation of iron can exacerbate infectious diseases and current policies of iron therapy carefully evaluate the risks and benefits of these interventions. Here we review the approaches of biofortification of valuable crops for reducing 'hidden undernutrition' of iron in the light of the latest nutritional and medical advances. The increase of iron and prebiotics in edible parts of plants is expected to improve health, whereas the reduction of phytic acid concentration, in crops valuable for human diet, might be less beneficial for the developed countries, or for the developing countries exposed to endemic infections.
Hidden variables and nonlocality in quantum mechanics
NASA Astrophysics Data System (ADS)
Hemmick, Douglas Lloyd
1997-05-01
Most physicists hold a skeptical attitude toward a 'hidden variables' interpretation of quantum theory, despite David Bohm's successful construction of such a theory and John S. Bell's strong arguments in favor of the idea. The first reason for doubt concerns certain mathematical theorems (von Neumann's, Gleason's, Kochen and Specker's, and Bell's) which can be applied to the hidden variables issue. These theorems are often credited with proving that hidden variables are indeed 'impossible', in the sense that they cannot replicate the predictions of quantum mechanics. Many who do not draw such a strong conclusion nevertheless accept that hidden variables have been shown to exhibit prohibitively complicated features. The second concern is that the most sophisticated example of a hidden variables theory-that of David Bohm-exhibits non-locality, i.e., consequences of events at one place can propagate to other places instantaneously. However, neither the mathematical theorems in question nor the attribute of nonlocality detract from the importance of a hidden variables interpretation of quantum theory. Nonlocality is present in quantum mechanics itself, and is a required characteristic of any theory that agrees with the quantum mechanical predictions. We first discuss the earliest analysis of hidden variables-that of von Neumann's theorem-and review John S. Bell's refutation of von Neumann's 'impossibility proof'. We recall and elaborate on Bell's arguments regarding the theorems of Gleason, and Kochen and Specker. According to Bell, these latter theorems do not imply that hidden variables interpretations are untenable, but instead that such theories must exhibit contextuality, i.e., they must allow for the dependence of measurement results on the characteristics of both measured system and measuring apparatus. We demonstrate a new way to understand the implications of both Gleason's theorem and Kochen and Specker's theorem by noting that they prove a result we call
Hidden quasars in ultraluminous infared galaxies
Brotherton, M S; Stanford, S A; Tran, H; van Breugel, W
1998-08-27
Abstract. Many ultraluminous infrared galaxies (ULIRGS) are pow- ered by quasars hidden in the center, but many are also powered by starbursts. A simply diagnostic diagram is proposed that can iden- tify obscured quasars in ULIRGs by their high-ionization emission lines ([O III]λ5007/Hβ ≳ 5), and "warm" IR color (ƒ_{25}/ƒ_{60} ≳ 0.25).
"Hidden" social networks in behavior change interventions.
Hunter, Ruth F; McAneney, Helen; Davis, Michael; Tully, Mark A; Valente, Thomas W; Kee, Frank
2015-03-01
We investigated whether "hidden" (or unobserved) social networks were evident in a 2011 physical activity behavior change intervention in Belfast, Northern Ireland. Results showed evidence of unobserved social networks in the intervention and illustrated how the network evolved over short periods and affected behavior. Behavior change interventions should account for the interaction among participants (i.e., social networks) and how such interactions affect intervention outcome.
Nonlinear realization and hidden local symmetries
NASA Astrophysics Data System (ADS)
Bando, Masako; Kugo, Taichiro; Yamawaki, Koichi
1988-07-01
The idea of dynamical gauge bosons of hidden local symmetries in nonlinear sigma models is reviewed. Starting with a fresh look at the Goldstone theorem and low energy theorems, we present a modern review of the general theory of nonlinear realization both in nonsupersymmetric and supersymmetric cases. We then show that any nonlinear sigma model based on the manifold G/ H is gauge equivalent to a “linear” model possessing a Gglobal × Hlocal symmetry, Hlocal being a hidden local symmetry. The corresponding supersymmetric formulation is also presented. The above gauge equivalence can be extended to a model having a larger symmetry Gglobal × Glocal. Also reviewed are dynamical calculatio ns showing that in some two-, three- and four-dimensional models, the gauge bosons of the hidden local symmetries acquire the kinetic terms via quantum effects, thus becoming “dynamical”. We suggest that such a dynamical gauge boson may be a rather common phenomenon realized in Nature. As a realistic example, we examine the QCD case where we identify the vector mesons (ϱ,ω,ф,K ∗) with the dynamical gauge bosons of the hidden U(3) v local symmetry in the U(3) L × U(3) R/U(3) V nonlinear sigma model. The totality of the vector meson phenomenology seems to support our basic idea. The axial-vector mesons are also incorporated into our framework. Also given is a brief sketch of some applications of this formalism to unified models beyond the standard model, such as technicolor, composite W/Z boson and supergravity models.
The Hidden Gifts of Quiet Kids
ERIC Educational Resources Information Center
Trierweiler, Hannah
2006-01-01
The author relates that she was an introvert child. It has always taken her time and energy to find her place in a group. As a grown-up, she still needed quiet time to regroup during a busy day. In this article, the author presents an interview with Marti Olsen Laney, author of "The Hidden Gifts of the Introverted Child." During the interview,…
Non-cooperative Brownian donkeys: A solvable 1D model
NASA Astrophysics Data System (ADS)
Jiménez de Cisneros, B.; Reimann, P.; Parrondo, J. M. R.
2003-12-01
A paradigmatic 1D model for Brownian motion in a spatially symmetric, periodic system is tackled analytically. Upon application of an external static force F the system's response is an average current which is positive for F < 0 and negative for F > 0 (absolute negative mobility). Under suitable conditions, the system approaches 100% efficiency when working against the external force F.
NonMarkov Ito Processes with 1- state memory
NASA Astrophysics Data System (ADS)
McCauley, Joseph L.
2010-08-01
A Markov process, by definition, cannot depend on any previous state other than the last observed state. An Ito process implies the Fokker-Planck and Kolmogorov backward time partial differential eqns. for transition densities, which in turn imply the Chapman-Kolmogorov eqn., but without requiring the Markov condition. We present a class of Ito process superficially resembling Markov processes, but with 1-state memory. In finance, such processes would obey the efficient market hypothesis up through the level of pair correlations. These stochastic processes have been mislabeled in recent literature as 'nonlinear Markov processes'. Inspired by Doob and Feller, who pointed out that the ChapmanKolmogorov eqn. is not restricted to Markov processes, we exhibit a Gaussian Ito transition density with 1-state memory in the drift coefficient that satisfies both of Kolmogorov's partial differential eqns. and also the Chapman-Kolmogorov eqn. In addition, we show that three of the examples from McKean's seminal 1966 paper are also nonMarkov Ito processes. Last, we show that the transition density of the generalized Black-Scholes type partial differential eqn. describes a martingale, and satisfies the ChapmanKolmogorov eqn. This leads to the shortest-known proof that the Green function of the Black-Scholes eqn. with variable diffusion coefficient provides the so-called martingale measure of option pricing.
Extracting hidden messages in steganographic images
Quach, Tu-Thach
2014-07-17
The eventual goal of steganalytic forensic is to extract the hidden messages embedded in steganographic images. A promising technique that addresses this problem partially is steganographic payload location, an approach to reveal the message bits, but not their logical order. It works by finding modified pixels, or residuals, as an artifact of the embedding process. This technique is successful against simple least-significant bit steganography and group-parity steganography. The actual messages, however, remain hidden as no logical order can be inferred from the located payload. This paper establishes an important result addressing this shortcoming: we show that the expected mean residualsmore » contain enough information to logically order the located payload provided that the size of the payload in each stego image is not fixed. The located payload can be ordered as prescribed by the mean residuals to obtain the hidden messages without knowledge of the embedding key, exposing the vulnerability of these embedding algorithms. We provide experimental results to support our analysis.« less
Extracting hidden messages in steganographic images
Quach, Tu-Thach
2014-07-17
The eventual goal of steganalytic forensic is to extract the hidden messages embedded in steganographic images. A promising technique that addresses this problem partially is steganographic payload location, an approach to reveal the message bits, but not their logical order. It works by finding modified pixels, or residuals, as an artifact of the embedding process. This technique is successful against simple least-significant bit steganography and group-parity steganography. The actual messages, however, remain hidden as no logical order can be inferred from the located payload. This paper establishes an important result addressing this shortcoming: we show that the expected mean residuals contain enough information to logically order the located payload provided that the size of the payload in each stego image is not fixed. The located payload can be ordered as prescribed by the mean residuals to obtain the hidden messages without knowledge of the embedding key, exposing the vulnerability of these embedding algorithms. We provide experimental results to support our analysis.
Markov state modeling of sliding friction.
Pellegrini, F; Landes, François P; Laio, A; Prestipino, S; Tosatti, E
2016-11-01
Markov state modeling (MSM) has recently emerged as one of the key techniques for the discovery of collective variables and the analysis of rare events in molecular simulations. In particular in biochemistry this approach is successfully exploited to find the metastable states of complex systems and their evolution in thermal equilibrium, including rare events, such as a protein undergoing folding. The physics of sliding friction and its atomistic simulations under external forces constitute a nonequilibrium field where relevant variables are in principle unknown and where a proper theory describing violent and rare events such as stick slip is still lacking. Here we show that MSM can be extended to the study of nonequilibrium phenomena and in particular friction. The approach is benchmarked on the Frenkel-Kontorova model, used here as a test system whose properties are well established. We demonstrate that the method allows the least prejudiced identification of a minimal basis of natural microscopic variables necessary for the description of the forced dynamics of sliding, through their probabilistic evolution. The steps necessary for the application to realistic frictional systems are highlighted.
Markov state modeling of sliding friction
NASA Astrophysics Data System (ADS)
Pellegrini, F.; Landes, François P.; Laio, A.; Prestipino, S.; Tosatti, E.
2016-11-01
Markov state modeling (MSM) has recently emerged as one of the key techniques for the discovery of collective variables and the analysis of rare events in molecular simulations. In particular in biochemistry this approach is successfully exploited to find the metastable states of complex systems and their evolution in thermal equilibrium, including rare events, such as a protein undergoing folding. The physics of sliding friction and its atomistic simulations under external forces constitute a nonequilibrium field where relevant variables are in principle unknown and where a proper theory describing violent and rare events such as stick slip is still lacking. Here we show that MSM can be extended to the study of nonequilibrium phenomena and in particular friction. The approach is benchmarked on the Frenkel-Kontorova model, used here as a test system whose properties are well established. We demonstrate that the method allows the least prejudiced identification of a minimal basis of natural microscopic variables necessary for the description of the forced dynamics of sliding, through their probabilistic evolution. The steps necessary for the application to realistic frictional systems are highlighted.
Markov source model for printed music decoding
NASA Astrophysics Data System (ADS)
Kopec, Gary E.; Chou, Philip A.; Maltz, David A.
1995-03-01
This paper describes a Markov source model for a simple subset of printed music notation. The model is based on the Adobe Sonata music symbol set and a message language of our own design. Chord imaging is the most complex part of the model. Much of the complexity follows from a rule of music typography that requires the noteheads for adjacent pitches to be placed on opposite sides of the chord stem. This rule leads to a proliferation of cases for other typographic details such as dot placement. We describe the language of message strings accepted by the model and discuss some of the imaging issues associated with various aspects of the message language. We also point out some aspects of music notation that appear problematic for a finite-state representation. Development of the model was greatly facilitated by the duality between image synthesis and image decoding. Although our ultimate objective was a music image model for use in decoding, most of the development proceeded by using the evolving model for image synthesis, since it is computationally far less costly to image a message than to decode an image.
Manpower planning using Markov Chain model
NASA Astrophysics Data System (ADS)
Saad, Syafawati Ab; Adnan, Farah Adibah; Ibrahim, Haslinda; Rahim, Rahela
2014-07-01
Manpower planning is a planning model which understands the flow of manpower based on the policies changes. For such purpose, numerous attempts have been made by researchers to develop a model to investigate the track of movements of lecturers for various universities. As huge number of lecturers in a university, it is difficult to track the movement of lecturers and also there is no quantitative way used in tracking the movement of lecturers. This research is aimed to determine the appropriate manpower model to understand the flow of lecturers in a university in Malaysia by determine the probability and mean time of lecturers remain in the same status rank. In addition, this research also intended to estimate the number of lecturers in different status rank (lecturer, senior lecturer and associate professor). From the previous studies, there are several methods applied in manpower planning model and appropriate method used in this research is Markov Chain model. Results obtained from this study indicate that the appropriate manpower planning model used is validated by compare to the actual data. The smaller margin of error gives a better result which means that the projection is closer to actual data. These results would give some suggestions for the university to plan the hiring lecturers and budgetary for university in future.
Clustering metagenomic sequences with interpolated Markov models
2010-01-01
Background Sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects. Results We present SCIMM (Sequence Clustering with Interpolated Markov Models), an unsupervised sequence clustering method. SCIMM achieves greater clustering accuracy than previous unsupervised approaches. We examine the limitations of unsupervised learning on complex datasets, and suggest a hybrid of SCIMM and supervised learning method Phymm called PHYSCIMM that performs better when evolutionarily close training genomes are available. Conclusions SCIMM and PHYSCIMM are highly accurate methods to cluster metagenomic sequences. SCIMM operates entirely unsupervised, making it ideal for environments containing mostly novel microbes. PHYSCIMM uses supervised learning to improve clustering in environments containing microbial strains from well-characterized genera. SCIMM and PHYSCIMM are available open source from http://www.cbcb.umd.edu/software/scimm. PMID:21044341
Relativized hierarchical decomposition of Markov decision processes.
Ravindran, B
2013-01-01
Reinforcement Learning (RL) is a popular paradigm for sequential decision making under uncertainty. A typical RL algorithm operates with only limited knowledge of the environment and with limited feedback on the quality of the decisions. To operate effectively in complex environments, learning agents require the ability to form useful abstractions, that is, the ability to selectively ignore irrelevant details. It is difficult to derive a single representation that is useful for a large problem setting. In this chapter, we describe a hierarchical RL framework that incorporates an algebraic framework for modeling task-specific abstraction. The basic notion that we will explore is that of a homomorphism of a Markov Decision Process (MDP). We mention various extensions of the basic MDP homomorphism framework in order to accommodate different commonly understood notions of abstraction, namely, aspects of selective attention. Parts of the work described in this chapter have been reported earlier in several papers (Narayanmurthy and Ravindran, 2007, 2008; Ravindran and Barto, 2002, 2003a,b; Ravindran et al., 2007).
Fitting complex population models by combining particle filters with Markov chain Monte Carlo.
Knape, Jonas; de Valpine, Perry
2012-02-01
We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.
Prediction of feather damage in laying hens using optical flows and Markov models.
Lee, Hyoung-joo; Roberts, Stephen J; Drake, Kelly A; Dawkins, Marian Stamp
2011-04-06
Feather pecking in laying hens is a major welfare and production problem for commercial egg producers, resulting in mortality, loss of production as well as welfare issues for the damaged birds. Damaging outbreaks of feather pecking are currently impossible to control, despite a number of proposed interventions. However, the ability to predict feather damage in advance would be a valuable research tool for identifying which management or environmental factors could be the most effective interventions at different ages. This paper proposes a framework for forecasting the damage caused by injurious pecking based on automated image processing and statistical analysis. By frame-by-frame analysis of video recordings of laying hen flocks, optical flow measures are calculated as indicators of the movement of the birds. From the optical flow datasets, measures of disturbance are extracted using hidden Markov models. Based on these disturbance measures and age-related variables, the levels of feather damage in flocks in future weeks is predicted. Applying the proposed method to real-world datasets, it is shown that the disturbance measures offer improved predictive values for feather damage thus enabling an identification of flocks with probable prevalence of damage and injury later in lay.
Plasmonic Excitations of 1D Metal-Dielectric Interfaces in 2D Systems: 1D Surface Plasmon Polaritons
NASA Astrophysics Data System (ADS)
Mason, Daniel R.; Menabde, Sergey G.; Yu, Sunkyu; Park, Namkyoo
2014-04-01
Surface plasmon-polariton (SPP) excitations of metal-dielectric interfaces are a fundamental light-matter interaction which has attracted interest as a route to spatial confinement of light far beyond that offered by conventional dielectric optical devices. Conventionally, SPPs have been studied in noble-metal structures, where the SPPs are intrinsically bound to a 2D metal-dielectric interface. Meanwhile, recent advances in the growth of hybrid 2D crystals, which comprise laterally connected domains of distinct atomically thin materials, provide the first realistic platform on which a 2D metal-dielectric system with a truly 1D metal-dielectric interface can be achieved. Here we show for the first time that 1D metal-dielectric interfaces support a fundamental 1D plasmonic mode (1DSPP) which exhibits cutoff behavior that provides dramatically improved light confinement in 2D systems. The 1DSPP constitutes a new basic category of plasmon as the missing 1D member of the plasmon family: 3D bulk plasmon, 2DSPP, 1DSPP, and 0D localized SP.
Microwave background constraints on mixing of photons with hidden photons
Mirizzi, Alessandro; Redondo, Javier; Sigl, Guenter E-mail: javier.redondo@desy.de
2009-03-15
Various extensions of the Standard Model predict the existence of hidden photons kinetically mixing with the ordinary photon. This mixing leads to oscillations between photons and hidden photons, analogous to the observed oscillations between different neutrino flavors. In this context, we derive new bounds on the photon-hidden photon mixing parameters using the high precision cosmic microwave background spectral data collected by the Far Infrared Absolute Spectrophotometer instrument on board of the Cosmic Background Explorer. Requiring the distortions of the CMB induced by the photon-hidden photon mixing to be smaller than experimental upper limits, this leads to a bound on the mixing angle {chi}{sub 0} {approx}< 10{sup -7}-10{sup -5} for hidden photon masses between 10{sup -14} eV and 10{sup -7} eV. This low-mass and low-mixing region of the hidden photon parameter space was previously unconstrained.
NASA Astrophysics Data System (ADS)
Lauer, J. Wesley; Viparelli, Enrica; Piégay, Hervé
2016-07-01
Bed material transported in geomorphically active gravel bed rivers often has a local source at nearby eroding banks and ends up sequestered in bars not far downstream. However, most 1-D numerical models for gravel transport assume that gravel originates from and deposits on the channel bed. In this paper, we present a 1-D framework for simulating morphodynamic evolution of bed elevation and size distribution in a gravel-bed river that actively exchanges sediment with its floodplain, which is represented as an off-channel sediment reservoir. The model is based on the idea that sediment enters the channel at eroding banks whose elevation depends on total floodplain sediment storage and on the average elevation of the floodplain relative to the channel bed. Lateral erosion of these banks occurs at a specified rate that can represent either net channel migration or channel widening. Transfer of material out of the channel depends on a typical bar thickness and a specified lateral exchange rate due either to net channel migration or narrowing. The model is implemented using an object oriented framework that allows users to explore relationships between bank supply, bed structure, and lateral change rates. It is applied to a ∼50-km reach of the Ain River, France, that experienced significant reduction in sediment supply due to dam construction during the 20th century. Results are strongly sensitive to lateral exchange rates, showing that in this reach, the supply of sand and gravel at eroding banks and the sequestration of gravel in point bars can have strong influence on overall reach-scale sediment budgets.
1D Josephson quantum interference grids: diffraction patterns and dynamics
NASA Astrophysics Data System (ADS)
Lucci, M.; Badoni, D.; Corato, V.; Merlo, V.; Ottaviani, I.; Salina, G.; Cirillo, M.; Ustinov, A. V.; Winkler, D.
2016-02-01
We investigate the magnetic response of transmission lines with embedded Josephson junctions and thus generating a 1D underdamped array. The measured multi-junction interference patterns are compared with the theoretical predictions for Josephson supercurrent modulations when an external magnetic field couples both to the inter-junction loops and to the junctions themselves. The results provide a striking example of the analogy between Josephson phase modulation and 1D optical diffraction grid. The Fiske resonances in the current-voltage characteristics with voltage spacing {Φ0}≤ft(\\frac{{\\bar{c}}}{2L}\\right) , where L is the total physical length of the array, {Φ0} the magnetic flux quantum and \\bar{c} the speed of light in the transmission line, demonstrate that the discrete line supports stable dynamic patterns generated by the ac Josephson effect interacting with the cavity modes of the line.
1-D Numerical Analysis of ABCC Engine Performance
NASA Technical Reports Server (NTRS)
Holden, Richard
1999-01-01
ABCC engine combines air breathing and rocket engine into a single engine to increase the specific impulse over an entire flight trajectory. Except for the heat source, the basic operation of the ABCC is similar to the basic operation of the RBCC engine. The ABCC is intended to have a higher specific impulse than the RBCC for single stage Earth to orbit vehicle. Computational fluid dynamics (CFD) is a useful tool for the analysis of complex transport processes in various components in ABCC propulsion system. The objective of the present research was to develop a transient 1-D numerical model using conservation of mass, linear momentum, and energy equations that could be used to predict flow behavior throughout a generic ABCC engine following a flight path. At specific points during the development of the 1-D numerical model a myriad of tests were performed to prove the program produced consistent, realistic numbers that follow compressible flow theory for various inlet conditions.
Ultrahigh-Q nanocavity with 1D photonic gap.
Notomi, M; Kuramochi, E; Taniyama, H
2008-07-21
Recently, various wavelength-sized cavities with theoretical Q values of approximately 10(8) have been reported, however, they all employ 2D or 3D photonic band gaps to realize strong light confinement. Here we numerically demonstrate that ultrahigh-Q (2.0x10(8)) and wavelength-sized (V(eff) approximately 1.4(lambda/n)3) cavities can be achieved by employing only 1D periodicity.
Nonreciprocity of edge modes in 1D magnonic crystal
NASA Astrophysics Data System (ADS)
Lisenkov, I.; Kalyabin, D.; Osokin, S.; Klos, J. W.; Krawczyk, M.; Nikitov, S.
2015-03-01
Spin waves propagation in 1D magnonic crystals is investigated theoretically. Mathematical model based on plane wave expansion method is applied to different types of magnonic crystals, namely bi-component magnonic crystal with symmetric/asymmetric boundaries and ferromagnetic film with periodically corrugated top surface. It is shown that edge modes in magnonic crystals may exhibit nonreciprocal behaviour at much lower frequencies than in homogeneous films.
Multiple Detector Optimization for Hidden Radiation Source Detection
2015-03-26
copyright protection in the United States. AFIT-ENP-MS-15-M-082 OPTIMIZATION OF DETECTOR PLACEMENT FOR HIDDEN RADIATION SOURCE DETECTION...AFIT-ENP-MS-15-M-082 OPTIMIZATION OF DETECTOR PLACEMENT FOR HIDDEN RADIATION SOURCE DETECTION Michael E. Morrison, BS Major, USA Committee...process of hidden source detection significantly. The model focused on detection of the full energy peak of a radiation source. Methods to optimize
The stability of 1-D soliton in transverse direction
NASA Astrophysics Data System (ADS)
Verma, Deepa; Bera, Ratan Kumar; Das, Amita; Kaw, Predhiman
2016-12-01
The complete characterization of the exact 1-D solitary wave solutions (both stationary and propagating) for light plasma coupled system have been studied extensively in the parameter space of light frequency and the group speed [Poornakala et al., Phys. Plasmas 9(5), 1820 (2002)]. It has been shown in 1-D that solutions with single light wave peak and paired structures are stable and hence long lived. However, solutions having multiple peaks of light wave are unstable due to Raman scattering instability [Saxena et al., Phys. Plasmas 14, 072307 (2007)]. Here, we have shown with the help of 2-D fluid simulation that single peak and paired solutions too get destabilized by the transverse filamentation instability. The numerical growth rates obtained from simulations is seen to compare well with the analytical values. It is also shown that multiple peaks solitons first undergo the regular 1-D forward Raman scattering instability. Subsequently, they undergo a distinct second phase of destabilization through transverse filamentation instability. This is evident from the structure as well as the plot of the perturbed energy which shows a second phase of growth after saturating initially. The growth rate of the filamentation instability being comparatively slower than the forward Raman instability this phase comes quite late and is clearly distinguishable.
Examining Prebiotic Chemistry Using O(^1D) Insertion Reactions
NASA Astrophysics Data System (ADS)
Hays, Brian M.; Laas, Jacob C.; Weaver, Susanna L. Widicus
2013-06-01
Aminomethanol, methanediol, and methoxymethanol are all prebiotic molecules expected to form via photo-driven grain surface chemistry in the interstellar medium (ISM). These molecules are expected to be precursors for larger, biologically-relevant molecules in the ISM such as sugars and amino acids. These three molecules have not yet been detected in the ISM because of the lack of available rotational spectra. A high resolution (sub)millimeter spectrometer coupled to a molecular source is being used to study these molecules using O(^1D) insertion reactions. The O(^1D) chemistry is initiated using an excimer laser, and the products of the insertion reactions are adiabatically cooled using a supersonic expansion. Experimental parameters are being optimized by examination of methanol formed from O(^1D) insertion into methane. Theoretical studies of the structure and reaction energies for aminomethanol, methanediol, and methoxymethanol have been conducted to guide the laboratory studies once the methanol experiment has been optimized. The results of the calculations and initial experimental results will be presented.
Development of 1D Liner Compression Code for IDL
NASA Astrophysics Data System (ADS)
Shimazu, Akihisa; Slough, John; Pancotti, Anthony
2015-11-01
A 1D liner compression code is developed to model liner implosion dynamics in the Inductively Driven Liner Experiment (IDL) where FRC plasmoid is compressed via inductively-driven metal liners. The driver circuit, magnetic field, joule heating, and liner dynamics calculations are performed at each time step in sequence to couple these effects in the code. To obtain more realistic magnetic field results for a given drive coil geometry, 2D and 3D effects are incorporated into the 1D field calculation through use of correction factor table lookup approach. Commercial low-frequency electromagnetic fields solver, ANSYS Maxwell 3D, is used to solve the magnetic field profile for static liner condition at various liner radius in order to derive correction factors for the 1D field calculation in the code. The liner dynamics results from the code is verified to be in good agreement with the results from commercial explicit dynamics solver, ANSYS Explicit Dynamics, and previous liner experiment. The developed code is used to optimize the capacitor bank and driver coil design for better energy transfer and coupling. FRC gain calculations are also performed using the liner compression data from the code for the conceptual design of the reactor sized system for fusion energy gains.
Enhancing Solar Cell Efficiencies through 1-D Nanostructures
2009-01-01
The current global energy problem can be attributed to insufficient fossil fuel supplies and excessive greenhouse gas emissions resulting from increasing fossil fuel consumption. The huge demand for clean energy potentially can be met by solar-to-electricity conversions. The large-scale use of solar energy is not occurring due to the high cost and inadequate efficiencies of existing solar cells. Nanostructured materials have offered new opportunities to design more efficient solar cells, particularly one-dimensional (1-D) nanomaterials for enhancing solar cell efficiencies. These 1-D nanostructures, including nanotubes, nanowires, and nanorods, offer significant opportunities to improve efficiencies of solar cells by facilitating photon absorption, electron transport, and electron collection; however, tremendous challenges must be conquered before the large-scale commercialization of such cells. This review specifically focuses on the use of 1-D nanostructures for enhancing solar cell efficiencies. Other nanostructured solar cells or solar cells based on bulk materials are not covered in this review. Major topics addressed include dye-sensitized solar cells, quantum-dot-sensitized solar cells, and p-n junction solar cells.
Maximally reliable Markov chains under energy constraints.
Escola, Sean; Eisele, Michael; Miller, Kenneth; Paninski, Liam
2009-07-01
Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that the reliability of a signal generated by a multistate system with no memory (i.e., a Markov chain) is maximal if and only if the system topology is such that the process steps irreversibly through each state, with transition rates chosen such that an equal fraction of the total signal is generated in each state. Furthermore, our result indicates that by increasing the number of states, it is possible to arbitrarily increase the reliability of the system. In a physical system, however, an energy cost is associated with maintaining irreversible transitions, and this cost increases with the number of such transitions (i.e., the number of states). Thus, an infinite-length chain, which would be perfectly reliable, is infeasible. To model the effects of energy demands on the maximally reliable solution, we numerically optimize the topology under two distinct energy functions that penalize either irreversible transitions or incommunicability between states, respectively. In both cases, the solutions are essentially irreversible linear chains, but with upper bounds on the number of states set by the amount of available energy. We therefore conclude that a physical system for which signal reliability is important should employ a linear architecture, with the number of states (and thus the reliability) determined by the intrinsic energy constraints of the system.
On Markov Earth Mover’s Distance
Wei, Jie
2015-01-01
In statistics, pattern recognition and signal processing, it is of utmost importance to have an effective and efficient distance to measure the similarity between two distributions and sequences. In statistics this is referred to as goodness-of-fit problem. Two leading goodness of fit methods are chi-square and Kolmogorov–Smirnov distances. The strictly localized nature of these two measures hinders their practical utilities in patterns and signals where the sample size is usually small. In view of this problem Rubner and colleagues developed the earth mover’s distance (EMD) to allow for cross-bin moves in evaluating the distance between two patterns, which find a broad spectrum of applications. EMD-L1 was later proposed to reduce the time complexity of EMD from super-cubic by one order of magnitude by exploiting the special L1 metric. EMD-hat was developed to turn the global EMD to a localized one by discarding long-distance earth movements. In this work, we introduce a Markov EMD (MEMD) by treating the source and destination nodes absolutely symmetrically. In MEMD, like hat-EMD, the earth is only moved locally as dictated by the degree d of neighborhood system. Nodes that cannot be matched locally is handled by dummy source and destination nodes. By use of this localized network structure, a greedy algorithm that is linear to the degree d and number of nodes is then developed to evaluate the MEMD. Empirical studies on the use of MEMD on deterministic and statistical synthetic sequences and SIFT-based image retrieval suggested encouraging performances. PMID:25983362
Hidden photons in Aharonov-Bohm-type experiments
NASA Astrophysics Data System (ADS)
Arias, Paola; Diaz, Christian; Diaz, Marco Aurelio; Jaeckel, Joerg; Koch, Benjamin; Redondo, Javier
2016-07-01
We discuss the Aharonov-Bohm effect in the presence of hidden photons kinetically mixed with the ordinary electromagnetic photons. The hidden photon field causes a slight phase shift in the observable interference pattern. It is then shown how the limited sensitivity of this experiment can be largely improved. The key observation is that the hidden photon field causes a leakage of the ordinary magnetic field into the supposedly field-free region. The direct measurement of this magnetic field can provide a sensitive experiment with a good discovery potential, particularly below the ˜meV mass range for hidden photons.
Tracking Human Pose Using Max-Margin Markov Models.
Zhao, Lin; Gao, Xinbo; Tao, Dacheng; Li, Xuelong
2015-12-01
We present a new method for tracking human pose by employing max-margin Markov models. Representing a human body by part-based models, such as pictorial structure, the problem of pose tracking can be modeled by a discrete Markov random field. Considering max-margin Markov networks provide an efficient way to deal with both structured data and strong generalization guarantees, it is thus natural to learn the model parameters using the max-margin technique. Since tracking human pose needs to couple limbs in adjacent frames, the model will introduce loops and will be intractable for learning and inference. Previous work has resorted to pose estimation methods, which discard temporal information by parsing frames individually. Alternatively, approximate inference strategies have been used, which can overfit to statistics of a particular data set. Thus, the performance and generalization of these methods are limited. In this paper, we approximate the full model by introducing an ensemble of two tree-structured sub-models, Markov networks for spatial parsing and Markov chains for temporal parsing. Both models can be trained jointly using the max-margin technique, and an iterative parsing process is proposed to achieve the ensemble inference. We apply our model on three challengeable data sets, which contains highly varied and articulated poses. Comprehensive experimental results demonstrate the superior performance of our method over the state-of-the-art approaches.
Dual technicolor with hidden local symmetry
Belitsky, A. V.
2010-08-15
We consider a dual description of the technicolor-like gauge theory within the D4/D8-brane configuration with varying confinement and electroweak symmetry breaking scales. Constructing an effective truncated model valid below a certain cutoff, we identify the particle spectrum with Kaluza-Klein modes of the model in a manner consistent with the hidden local symmetry. Integrating out heavy states, we find that the low-energy action receives nontrivial corrections stemming from the mixing between standard model and heavy gauge bosons, which results in reduction of oblique parameters.
Costigliola, Lorenzo; Schrøder, Thomas B; Dyre, Jeppe C
2016-06-21
The recent theoretical prediction by Maimbourg and Kurchan [e-print arXiv:1603.05023 (2016)] that for regular pair-potential systems the virial potential-energy correlation coefficient increases towards unity as the dimension d goes to infinity is investigated for the standard 12-6 Lennard-Jones fluid. This is done by computer simulations for d = 2, 3, 4 going from the critical point along the critical isotherm/isochore to higher density/temperature. In both cases the virial potential-energy correlation coefficient increases significantly. For a given density and temperature relative to the critical point, with increasing number of dimension the Lennard-Jones system conforms better to the hidden-scale-invariance property characterized by high virial potential-energy correlations (a property that leads to the existence of isomorphs in the thermodynamic phase diagram, implying that it becomes effectively one-dimensional in regard to structure and dynamics). The present paper also gives the first numerical demonstration of isomorph invariance of structure and dynamics in four dimensions. Our findings emphasize the need for a universally applicable 1/d expansion in liquid-state theory; we conjecture that the systems known to obey hidden scale invariance in three dimensions are those for which the yet-to-be-developed 1/d expansion converges rapidly.
MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes
Williams, B.K.
1988-01-01
Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.
Influence of credit scoring on the dynamics of Markov chain
NASA Astrophysics Data System (ADS)
Galina, Timofeeva
2015-11-01
Markov processes are widely used to model the dynamics of a credit portfolio and forecast the portfolio risk and profitability. In the Markov chain model the loan portfolio is divided into several groups with different quality, which determined by presence of indebtedness and its terms. It is proposed that dynamics of portfolio shares is described by a multistage controlled system. The article outlines mathematical formalization of controls which reflect the actions of the bank's management in order to improve the loan portfolio quality. The most important control is the organization of approval procedure of loan applications. The credit scoring is studied as a control affecting to the dynamic system. Different formalizations of "good" and "bad" consumers are proposed in connection with the Markov chain model.
Dynamical Systems Based Non Equilibrium Statistical Mechanics for Markov Chains
NASA Astrophysics Data System (ADS)
Prevost, Mireille
We introduce an abstract framework concerning non-equilibrium statistical mechanics in the specific context of Markov chains. This framework encompasses both the Evans-Searles and the Gallavotti-Cohen fluctuation theorems. To support and expand on these concepts, several results are proven, among which a central limit theorem and a large deviation principle. The interest for Markov chains is twofold. First, they model a great variety of physical systems. Secondly, their simplicity allows for an easy introduction to an otherwise complicated field encompassing the statistical mechanics of Anosov and Axiom A diffeomorphisms. We give two examples relating the present framework to physical cases modelled by Markov chains. One of these concerns chemical reactions and links key concepts from the framework to their well known physical counterpart.
Markov chain solution of photon multiple scattering through turbid slabs.
Lin, Ying; Northrop, William F; Li, Xuesong
2016-11-14
This work introduces a Markov Chain solution to model photon multiple scattering through turbid slabs via anisotropic scattering process, i.e., Mie scattering. Results show that the proposed Markov Chain model agree with commonly used Monte Carlo simulation for various mediums such as medium with non-uniform phase functions and absorbing medium. The proposed Markov Chain solution method successfully converts the complex multiple scattering problem with practical phase functions into a matrix form and solves transmitted/reflected photon angular distributions by matrix multiplications. Such characteristics would potentially allow practical inversions by matrix manipulation or stochastic algorithms where widely applied stochastic methods such as Monte Carlo simulations usually fail, and thus enable practical diagnostics reconstructions such as medical diagnosis, spray analysis, and atmosphere sciences.
Markov sequential pattern recognition : dependency and the unknown class.
Malone, Kevin Thomas; Haschke, Greg Benjamin; Koch, Mark William
2004-10-01
The sequential probability ratio test (SPRT) minimizes the expected number of observations to a decision and can solve problems in sequential pattern recognition. Some problems have dependencies between the observations, and Markov chains can model dependencies where the state occupancy probability is geometric. For a non-geometric process we show how to use the effective amount of independent information to modify the decision process, so that we can account for the remaining dependencies. Along with dependencies between observations, a successful system needs to handle the unknown class in unconstrained environments. For example, in an acoustic pattern recognition problem any sound source not belonging to the target set is in the unknown class. We show how to incorporate goodness of fit (GOF) classifiers into the Markov SPRT, and determine the worse case nontarget model. We also develop a multiclass Markov SPRT using the GOF concept.
Extended-Range Ultrarefractive 1D Photonic Crystal Prisms
NASA Technical Reports Server (NTRS)
Ting, David Z.
2007-01-01
A proposal has been made to exploit the special wavelength-dispersive characteristics of devices of the type described in One-Dimensional Photonic Crystal Superprisms (NPO-30232) NASA Tech Briefs, Vol. 29, No. 4 (April 2005), page 10a. A photonic crystal is an optical component that has a periodic structure comprising two dielectric materials with high dielectric contrast (e.g., a semiconductor and air), with geometrical feature sizes comparable to or smaller than light wavelengths of interest. Experimental superprisms have been realized as photonic crystals having three-dimensional (3D) structures comprising regions of amorphous Si alternating with regions of SiO2, fabricated in a complex process that included sputtering. A photonic crystal of the type to be exploited according to the present proposal is said to be one-dimensional (1D) because its contrasting dielectric materials would be stacked in parallel planar layers; in other words, there would be spatial periodicity in one dimension only. The processes of designing and fabricating 1D photonic crystal superprisms would be simpler and, hence, would cost less than do those for 3D photonic crystal superprisms. As in 3D structures, 1D photonic crystals may be used in applications such as wavelength-division multiplexing. In the extended-range configuration, it is also suitable for spectrometry applications. As an engineered structure or artificially engineered material, a photonic crystal can exhibit optical properties not commonly found in natural substances. Prior research had revealed several classes of photonic crystal structures for which the propagation of electromagnetic radiation is forbidden in certain frequency ranges, denoted photonic bandgaps. It had also been found that in narrow frequency bands just outside the photonic bandgaps, the angular wavelength dispersion of electromagnetic waves propagating in photonic crystal superprisms is much stronger than is the angular wavelength dispersion obtained
Atlas of solar hidden photon emission
Redondo, Javier
2015-07-01
Hidden photons, gauge bosons of a U(1) symmetry of a hidden sector, can constitute the dark matter of the universe and a smoking gun for large volume compactifications of string theory. In the sub-eV mass range, a possible discovery experiment consists on searching the copious flux of these particles emitted from the Sun in a helioscope setup à la Sikivie. In this paper, we compute in great detail the flux of HPs from the Sun, a necessary ingredient for interpreting such experiments. We provide a detailed exposition of transverse photon-HP oscillations in inhomogenous media, with special focus on resonance oscillations, which play a leading role in many cases. The region of the Sun emitting HPs resonantly is a thin spherical shell for which we justify an averaged-emission formula and which implies a distinctive morphology of the angular distribution of HPs on Earth in many cases. Low mass HPs with energies in the visible and IR have resonances very close to the photosphere where the solar plasma is not fully ionised and requires building a detailed model of solar refraction and absorption. We present results for a broad range of HP masses (from 0–1 keV) and energies (from the IR to the X-ray range), the most complete atlas of solar HP emission to date.
Atlas of solar hidden photon emission
Redondo, Javier
2015-07-20
Hidden photons, gauge bosons of a U(1) symmetry of a hidden sector, can constitute the dark matter of the universe and a smoking gun for large volume compactifications of string theory. In the sub-eV mass range, a possible discovery experiment consists on searching the copious flux of these particles emitted from the Sun in a helioscope setup à la Sikivie. In this paper, we compute in great detail the flux of HPs from the Sun, a necessary ingredient for interpreting such experiments. We provide a detailed exposition of transverse photon-HP oscillations in inhomogenous media, with special focus on resonance oscillations, which play a leading role in many cases. The region of the Sun emitting HPs resonantly is a thin spherical shell for which we justify an averaged-emission formula and which implies a distinctive morphology of the angular distribution of HPs on Earth in many cases. Low mass HPs with energies in the visible and IR have resonances very close to the photosphere where the solar plasma is not fully ionised and requires building a detailed model of solar refraction and absorption. We present results for a broad range of HP masses (from 0–1 keV) and energies (from the IR to the X-ray range), the most complete atlas of solar HP emission to date.
Women's hidden transcripts about abortion in Brazil.
Nations, M K; Misago, C; Fonseca, W; Correia, L L; Campbell, O M
1997-06-01
Two folk medical conditions, "delayed" (atrasada) and "suspended" (suspendida) menstruation, are described as perceived by poor Brazilian women in Northeast Brazil. Culturally prescribed methods to "regulate" these conditions and provoke menstrual bleeding are also described, including ingesting herbal remedies, patent drugs, and modern pharmaceuticals. The ingestion of such self-administered remedies is facilitated by the cognitive ambiguity, euphemisms, folklore, etc., which surround conception and gestation. The authors argue that the ethnomedical conditions of "delayed" and "suspended" menstruation and subsequent menstrual regulation are part of the "hidden reproductive transcript" of poor and powerless Brazilian women. Through popular culture, they voice their collective dissent to the official, public opinion about the illegality and immorality of induced abortion and the chronic lack of family planning services in Northeast Brazil. While many health professionals consider women's explanations of menstrual regulation as a "cover-up" for self-induced abortions, such popular justifications may represent either an unconscious or artful manipulation of hegemonic, anti-abortion ideology expressed in prudent, unobtrusive and veiled ways. The development of safer abortion alternatives should consider women's hidden reproductive transcripts.
ESO's Hidden Treasures Brought to Light
NASA Astrophysics Data System (ADS)
2011-01-01
ESO's Hidden Treasures 2010 astrophotography competition attracted nearly 100 entries, and ESO is delighted to announce the winners. Hidden Treasures gave amateur astronomers the opportunity to search ESO's vast archives of astronomical data for a well-hidden cosmic gem. Astronomy enthusiast Igor Chekalin from Russia won the first prize in this difficult but rewarding challenge - the trip of a lifetime to ESO's Very Large Telescope at Paranal, Chile. The pictures of the Universe that can be seen in ESO's releases are impressive. However, many hours of skilful work are required to assemble the raw greyscale data captured by the telescopes into these colourful images, correcting them for distortions and unwanted signatures of the instrument, and enhancing them so as to bring out the details contained in the astronomical data. ESO has a team of professional image processors, but for the ESO's Hidden Treasures 2010 competition, the experts decided to give astronomy and photography enthusiasts the opportunity to show the world what they could do with the mammoth amount of data contained in ESO's archives. The enthusiasts who responded to the call submitted nearly 100 entries in total - far exceeding initial expectations, given the difficult nature of the challenge. "We were completely taken aback both by the quantity and the quality of the images that were submitted. This was not a challenge for the faint-hearted, requiring both an advanced knowledge of data processing and an artistic eye. We are thrilled to have discovered so many talented people," said Lars Lindberg Christensen, Head of ESO's education and Public Outreach Department. Digging through many terabytes of professional astronomical data, the entrants had to identify a series of greyscale images of a celestial object that would reveal the hidden beauty of our Universe. The chance of a great reward for the lucky winner was enough to spur on the competitors; the first prize being a trip to ESO's Very Large
ESO science data product standard for 1D spectral products
NASA Astrophysics Data System (ADS)
Micol, Alberto; Arnaboldi, Magda; Delmotte, Nausicaa A. R.; Mascetti, Laura; Retzlaff, Joerg
2016-07-01
The ESO Phase 3 process allows the upload, validation, storage, and publication of reduced data through the ESO Science Archive Facility. Since its introduction, 2 million data products have been archived and published; 80% of them are one-dimensional extracted and calibrated spectra. Central to Phase3 is the ESO science data product standard that defines metadata and data format of any product. This contribution describes the ESO data standard for 1d-spectra, its adoption by the reduction pipelines of selected instrument modes for in-house generation of reduced spectra, the enhanced archive legacy value. Archive usage statistics are provided.
Deconvolution/identification techniques for 1-D transient signals
Goodman, D.M.
1990-10-01
This paper discusses a variety of nonparametric deconvolution and identification techniques that we have developed for application to 1-D transient signal problems. These methods are time-domain techniques that use direct methods for matrix inversion. Therefore, they are not appropriate for large data'' problems. These techniques involve various regularization methods and permit the use of certain kinds of a priori information in estimating the unknown. These techniques have been implemented in a package using standard FORTRAN that should make the package readily transportable to most computers. This paper is also meant to be an instruction manual for the package. 25 refs., 17 figs., 1 tab.
Breakdown of 1D water wires inside charged carbon nanotubes
NASA Astrophysics Data System (ADS)
Pant, Shashank
2016-11-01
Using molecular dynamics approach we investigated the structure and dynamics of water confined inside pristine and charged 6,6 carbon nanotubes (CNTs). This study reports the breakdown of 1D water wires and the emergence of triangular faced water on incorporating charges in 6,6 CNTs. Incorporation of charges results in high potential barriers to flipping of water molecules due to the formation of large number of hydrogen bonds. The PMF analyses show the presence of ∼2 kcal/mol barrier for the movement of water inside pristine CNT and almost negligible barrier in charged CNTs.
Spatial coherence of polaritons in a 1D channel
Savenko, I. G.; Iorsh, I. V.; Kaliteevski, M. A.; Shelykh, I. A.
2013-01-15
We analyze time evolution of spatial coherence of a polariton ensemble in a quantum wire (1D channel) under constant uniform resonant pumping. Using the theoretical approach based on the Lindblad equation for a one-particle density matrix, which takes into account the polariton-phonon and excitonexciton interactions, we study the behavior of the first-order coherence function g{sup 1} for various pump intensities and temperatures in the range of 1-20 K. Bistability and hysteresis in the dependence of the first-order coherence function on the pump intensity is demonstrated.
Nanofluidic sustainable energy conversion using a 1D nanofluidic network.
Kim, Sang Hui; Kwak, Seungmin; Han, Sung Il; Chun, Dong Won; Lee, Kyu Hyoung; Kim, Jinseok; Lee, Jeong Hoon
2014-05-01
We propose a 1-dimensional (1D) nanofluidic energy conversion device by implementing a surface-patterned Nafion membrane for the direct energy conversion of the pressure to electrical power. By implementing a -200-nm-thick nano-bridge with a 5-nm pore size between two microfluidic channels, we acquired an effective streaming potential of 307 mV and output power of 94 pW with 0.1 mM KCI under pressure difference of 45 MPa. The experimental results show both the effects of applied pressure differences and buffer concentrations on the effective streaming potential, and are consistent with the analytical prediction.
1-D blood flow modelling in a running human body.
Szabó, Viktor; Halász, Gábor
2017-04-10
In this paper an attempt was made to simulate blood flow in a mobile human arterial network, specifically, in a running human subject. In order to simulate the effect of motion, a previously published immobile 1-D model was modified by including an inertial force term into the momentum equation. To calculate inertial force, gait analysis was performed at different levels of speed. Our results show that motion has a significant effect on the amplitudes of the blood pressure and flow rate but the average values are not effected significantly.
Assessing significance in a Markov chain without mixing.
Chikina, Maria; Frieze, Alan; Pegden, Wesley
2017-03-14
We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a [Formula: see text] value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a [Formula: see text] outlier compared with the sampled ranks (its rank is in the bottom [Formula: see text] of sampled ranks), then this observation should correspond to a [Formula: see text] value of [Formula: see text] This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an [Formula: see text]-outlier on the walk is significant at [Formula: see text] under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at [Formula: see text] is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting.
Time operator of Markov chains and mixing times. Applications to financial data
NASA Astrophysics Data System (ADS)
Gialampoukidis, I.; Gustafson, K.; Antoniou, I.
2014-12-01
We extend the notion of Time Operator from Kolmogorov Dynamical Systems and Bernoulli processes to Markov processes. The general methodology is presented and illustrated in the simple case of binary processes. We present a method to compute the eigenfunctions of the Time Operator. Internal Ages are related to other characteristic times of Markov chains, namely the Kemeny time, the convergence rate and Goodman’s intrinsic time. We clarified the concept of mixing time by providing analytic formulas for two-state Markov chains. Explicit formulas for mixing times are presented for any two-state regular Markov chain. The mixing time of a Markov chain is determined also by the Time Operator of the Markov chain, within its Age computation. We illustrate these results in terms of two realistic examples: A Markov chain from US GNP data and a Markov chain from Dow Jones closing prices. We propose moreover a representation for the Kemeny constant, in terms of internal Ages.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
NASA Astrophysics Data System (ADS)
Sheehan, D. P.; Wright, J. H.
2006-10-01
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
Harmonic Oscillator Model for Radin's Markov-Chain Experiments
Sheehan, D. P.; Wright, J. H.
2006-10-16
The conscious observer stands as a central figure in the measurement problem of quantum mechanics. Recent experiments by Radin involving linear Markov chains driven by random number generators illuminate the role and temporal dynamics of observers interacting with quantum mechanically labile systems. In this paper a Lagrangian interpretation of these experiments indicates that the evolution of Markov chain probabilities can be modeled as damped harmonic oscillators. The results are best interpreted in terms of symmetric equicausal determinism rather than strict retrocausation, as posited by Radin. Based on the present analysis, suggestions are made for more advanced experiments.
Markov bases and toric ideals for some contingency tables
NASA Astrophysics Data System (ADS)
Mohammed, N. F.; Rakhimov, I. S.; Shitan, M.
2016-06-01
The main objective of this work is to study Markov bases and toric ideals for p/(v -1 )(p -v ) 2 v ×v ×p/v - contingency tables that has fixed two-dimensional marginal when p is a multiple of v and greater than or equal to 2v. Moreover, the connected bipartite graph is also constructed by using elements of Markov basis. This work is an extension on results, that has been found by Hadi and Salman in 2014.
Markov chain Monte Carlo linkage analysis of complex quantitative phenotypes.
Hinrichs, A; Reich, T
2001-01-01
We report a Markov chain Monte Carlo analysis of the five simulated quantitative traits in Genetic Analysis Workshop 12 using the Loki software. Our objectives were to determine the efficacy of the Markov chain Monte Carlo method and to test a new scoring technique. Our initial blind analysis, on replicate 42 (the "best replicate") successfully detected four out of the five disease loci and found no false positives. A power analysis shows that the software could usually detect 4 of the 10 trait/gene combinations at an empirical point-wise p-value of 1.5 x 10(-4).
a Markov-Process Inspired CA Model of Highway Traffic
NASA Astrophysics Data System (ADS)
Wang, Fa; Li, Li; Hu, Jian-Ming; Ji, Yan; Ma, Rui; Jiang, Rui
To provide a more accurate description of the driving behaviors especially in car-following, namely a Markov-Gap cellular automata model is proposed in this paper. It views the variation of the gap between two consequent vehicles as a Markov process whose stationary distribution corresponds to the observed gap distribution. This new model provides a microscopic simulation explanation for the governing interaction forces (potentials) between the queuing vehicles, which cannot be directly measurable for traffic flow applications. The agreement between empirical observations and simulation results suggests the soundness of this new approach.
A semi-Markov model with memory for price changes
NASA Astrophysics Data System (ADS)
D'Amico, Guglielmo; Petroni, Filippo
2011-12-01
We study the high-frequency price dynamics of traded stocks by means of a model of returns using a semi-Markov approach. More precisely we assume that the intraday returns are described by a discrete time homogeneous semi-Markov model which depends also on a memory index. The index is introduced to take into account periods of high and low volatility in the market. First of all we derive the equations governing the process and then theoretical results are compared with empirical findings from real data. In particular we analyzed high-frequency data from the Italian stock market from 1 January 2007 until the end of December 2010.
Efficient maximum likelihood parameterization of continuous-time Markov processes
McGibbon, Robert T.; Pande, Vijay S.
2015-01-01
Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce a maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is dramatically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance. We demonstrate and discuss the advantages of these models over existing discrete-time Markov models for the analysis of molecular dynamics simulations. PMID:26203016
Seuss's Butter Battle Book: Is There Hidden Harm?
ERIC Educational Resources Information Center
Van Cleaf, David W.; Martin, Rita J.
1986-01-01
Examines whether elementary school children relate to the "harmful hidden message" about nuclear war in Dr. Seuss's THE BUTTER BATTLE BOOK. After ascertaining the children's cognitive level, they participated in activities to find hidden meanings in stories, including Seuss's book. Students failed to identify the nuclear war message in…
Hidden Curriculum as One of Current Issue of Curriculum
ERIC Educational Resources Information Center
Alsubaie, Merfat Ayesh
2015-01-01
There are several issues in the education system, especially in the curriculum field that affect education. Hidden curriculum is one of current controversial curriculum issues. Many hidden curricular issues are the result of assumptions and expectations that are not formally communicated, established, or conveyed within the learning environment.…
Hidden attractor in the Rabinovich system, Chua circuits and PLL
NASA Astrophysics Data System (ADS)
Kuznetsov, N. V.; Leonov, G. A.; Mokaev, T. N.; Seledzhi, S. M.
2016-06-01
In this report the existence of hidden attractors in Rabinovich system, phase-locked loop and coupled Chua circuits is considered. It is shown that the existence of hidden attractors may complicate the analysis of the systems and significantly affect the synchronization.
Secret Codes: The Hidden Curriculum of Semantic Web Technologies
ERIC Educational Resources Information Center
Edwards, Richard; Carmichael, Patrick
2012-01-01
There is a long tradition in education of examination of the hidden curriculum, those elements which are implicit or tacit to the formal goals of education. This article draws upon that tradition to open up for investigation the hidden curriculum and assumptions about students and knowledge that are embedded in the coding undertaken to facilitate…
NASA Astrophysics Data System (ADS)
Zhu, Yanzheng; Zhang, Lixian; Sreeram, Victor; Shammakh, Wafa; Ahmad, Bashir
2016-10-01
In this paper, the resilient model approximation problem for a class of discrete-time Markov jump time-delay systems with input sector-bounded nonlinearities is investigated. A linearised reduced-order model is determined with mode changes subject to domination by a hierarchical Markov chain containing two different nonhomogeneous Markov chains. Hence, the reduced-order model obtained not only reflects the dependence of the original systems but also model external influence that is related to the mode changes of the original system. Sufficient conditions formulated in terms of bilinear matrix inequalities for the existence of such models are established, such that the resulting error system is stochastically stable and has a guaranteed l2-l∞ error performance. A linear matrix inequalities optimisation coupled with line search is exploited to solve for the corresponding reduced-order systems. The potential and effectiveness of the developed theoretical results are demonstrated via a numerical example.
Blood flow quantification using 1D CFD parameter identification
NASA Astrophysics Data System (ADS)
Brosig, Richard; Kowarschik, Markus; Maday, Peter; Katouzian, Amin; Demirci, Stefanie; Navab, Nassir
2014-03-01
Patient-specific measurements of cerebral blood flow provide valuable diagnostic information concerning cerebrovascular diseases rather than visually driven qualitative evaluation. In this paper, we present a quantitative method to estimate blood flow parameters with high temporal resolution from digital subtraction angiography (DSA) image sequences. Using a 3D DSA dataset and a 2D+t DSA sequence, the proposed algorithm employs a 1D Computational Fluid Dynamics (CFD) model for estimation of time-dependent flow values along a cerebral vessel, combined with an additional Advection Diffusion Equation (ADE) for contrast agent propagation. The CFD system, followed by the ADE, is solved with a finite volume approximation, which ensures the conservation of mass. Instead of defining a new imaging protocol to obtain relevant data, our cost function optimizes the bolus arrival time (BAT) of the contrast agent in 2D+t DSA sequences. The visual determination of BAT is common clinical practice and can be easily derived from and be compared to values, generated by a 1D-CFD simulation. Using this strategy, we ensure that our proposed method fits best to clinical practice and does not require any changes to the medical work flow. Synthetic experiments show that the recovered flow estimates match the ground truth values with less than 12% error in the mean flow rates.
Tunability and Sensing Properties of Plasmonic/1D Photonic Crystal
NASA Astrophysics Data System (ADS)
Shaban, Mohamed; Ahmed, Ashour M.; Abdel-Rahman, Ehab; Hamdy, Hany
2017-02-01
Gold/one-dimensional photonic crystal (Au/1D-PC) is fabricated and applied for sensitive sensing of glucose and different chemical molecules of various refractive indices. The Au layer thickness is optimized to produce surface plasmon resonance (SPR) at the right edge of the photonic band gap (PBG). As the Au deposition time increased to 60 sec, the PBG width is increased from 46 to 86 nm in correlation with the behavior of the SPR. The selectivity of the optimized Au/1D-PC sensor is tested upon the increase of the environmental refractive index of the detected molecules. The resonance wavelength and the PBG edges increased linearly and the transmitted intensity increased nonlinearly as the environment refractive index increased. The SPR splits to two modes during the detection of chloroform molecules based on the localized capacitive coupling of Au particles. Also, this structure shows high sensitivity at different glucose concentrations. The PBG and SPR are shifted to longer wavelengths, and PBG width is decreased linearly with a rate of 16.04 Å/(μg/mm3) as the glucose concentration increased. The proposed structure merits; operation at room temperature, compact size, and easy fabrication; suggest that the proposed structure can be efficiently used for the biomedical and chemical application.
Tunability and Sensing Properties of Plasmonic/1D Photonic Crystal
Shaban, Mohamed; Ahmed, Ashour M.; Abdel-Rahman, Ehab; Hamdy, Hany
2017-01-01
Gold/one-dimensional photonic crystal (Au/1D-PC) is fabricated and applied for sensitive sensing of glucose and different chemical molecules of various refractive indices. The Au layer thickness is optimized to produce surface plasmon resonance (SPR) at the right edge of the photonic band gap (PBG). As the Au deposition time increased to 60 sec, the PBG width is increased from 46 to 86 nm in correlation with the behavior of the SPR. The selectivity of the optimized Au/1D-PC sensor is tested upon the increase of the environmental refractive index of the detected molecules. The resonance wavelength and the PBG edges increased linearly and the transmitted intensity increased nonlinearly as the environment refractive index increased. The SPR splits to two modes during the detection of chloroform molecules based on the localized capacitive coupling of Au particles. Also, this structure shows high sensitivity at different glucose concentrations. The PBG and SPR are shifted to longer wavelengths, and PBG width is decreased linearly with a rate of 16.04 Å/(μg/mm3) as the glucose concentration increased. The proposed structure merits; operation at room temperature, compact size, and easy fabrication; suggest that the proposed structure can be efficiently used for the biomedical and chemical application. PMID:28176799
Engineered atom-light interactions in 1D photonic crystals
NASA Astrophysics Data System (ADS)
Martin, Michael J.; Hung, Chen-Lung; Yu, Su-Peng; Goban, Akihisa; Muniz, Juan A.; Hood, Jonathan D.; Norte, Richard; McClung, Andrew C.; Meenehan, Sean M.; Cohen, Justin D.; Lee, Jae Hoon; Peng, Lucas; Painter, Oskar; Kimble, H. Jeff
2014-05-01
Nano- and microscale optical systems offer efficient and scalable quantum interfaces through enhanced atom-field coupling in both resonators and continuous waveguides. Beyond these conventional topologies, new opportunities emerge from the integration of ultracold atomic systems with nanoscale photonic crystals. One-dimensional photonic crystal waveguides can be engineered for both stable trapping configurations and strong atom-photon interactions, enabling novel cavity QED and quantum many-body systems, as well as distributed quantum networks. We present the experimental realization of such a nanophotonic quantum interface based on a nanoscale photonic crystal waveguide, demonstrating a fractional waveguide coupling of Γ1 D /Γ' of 0 . 32 +/- 0 . 08 , where Γ1 D (Γ') is the atomic emission rate into the guided (all other) mode(s). We also discuss progress towards intra-waveguide trapping of ultracold Cs. This work was supported by the IQIM, an NSF Physics Frontiers Center with support from the Moore Foundation, the DARPA ORCHID program, the AFOSR QuMPASS MURI, the DoD NSSEFF program, NSF, and the Kavli Nanoscience Institute (KNI) at Caltech.
Constitutive modeling and control of 1D smart composite structures
NASA Astrophysics Data System (ADS)
Briggs, Jonathan P.; Ostrowski, James P.; Ponte-Castaneda, Pedro
1998-07-01
Homogenization techniques for determining effective properties of composite materials may provide advantages for control of stiffness and strain in systems using hysteretic smart actuators embedded in a soft matrix. In this paper, a homogenized model of a 1D composite structure comprised of shape memory alloys and a rubber-like matrix is presented. With proportional and proportional/integral feedback, using current as the input state and global strain as an error state, implementation scenarios include the use of tractions on the boundaries and a nonlinear constitutive law for the matrix. The result is a simple model which captures the nonlinear behavior of the smart composite material system and is amenable to experiments with various control paradigms. The success of this approach in the context of the 1D model suggests that the homogenization method may prove useful in investigating control of more general smart structures. Applications of such materials could include active rehabilitation aids, e.g. wrist braces, as well as swimming/undulating robots, or adaptive molds for manufacturing processes.
Tunability and Sensing Properties of Plasmonic/1D Photonic Crystal.
Shaban, Mohamed; Ahmed, Ashour M; Abdel-Rahman, Ehab; Hamdy, Hany
2017-02-08
Gold/one-dimensional photonic crystal (Au/1D-PC) is fabricated and applied for sensitive sensing of glucose and different chemical molecules of various refractive indices. The Au layer thickness is optimized to produce surface plasmon resonance (SPR) at the right edge of the photonic band gap (PBG). As the Au deposition time increased to 60 sec, the PBG width is increased from 46 to 86 nm in correlation with the behavior of the SPR. The selectivity of the optimized Au/1D-PC sensor is tested upon the increase of the environmental refractive index of the detected molecules. The resonance wavelength and the PBG edges increased linearly and the transmitted intensity increased nonlinearly as the environment refractive index increased. The SPR splits to two modes during the detection of chloroform molecules based on the localized capacitive coupling of Au particles. Also, this structure shows high sensitivity at different glucose concentrations. The PBG and SPR are shifted to longer wavelengths, and PBG width is decreased linearly with a rate of 16.04 Å/(μg/mm(3)) as the glucose concentration increased. The proposed structure merits; operation at room temperature, compact size, and easy fabrication; suggest that the proposed structure can be efficiently used for the biomedical and chemical application.