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Sample records for hidden variable model

  1. Quantum Discord, CHSH Inequality and Hidden Variables -- Critical reassessment of hidden-variables models

    CERN Document Server

    Fujikawa, Kazuo

    2013-01-01

    Hidden-variables models are critically reassessed. It is first examined if the quantum discord is classically described by the hidden-variable model of Bell in the Hilbert space with $d=2$. The criterion of vanishing quantum discord is related to the notion of reduction and, surprisingly, the hidden-variable model in $d=2$, which has been believed to be consistent so far, is in fact inconsistent and excluded by the analysis of conditional measurement and reduction. The description of the full contents of quantum discord by the deterministic hidden-variables models is not possible. We also re-examine CHSH inequality. It is shown that the well-known prediction of CHSH inequality $|B|\\leq 2$ for the CHSH operator $B$ introduced by Cirel'son is not unique. This non-uniqueness arises from the failure of linearity condition in the non-contextual hidden-variables model in $d=4$ used by Bell and CHSH, in agreement with Gleason's theorem which excludes $d=4$ non-contextual hidden-variables models. If one imposes the l...

  2. A Sequence of Relaxations Constraining Hidden Variable Models

    CERN Document Server

    Steeg, Greg Ver

    2011-01-01

    Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refer to any linear inequality whose violation rules out some latent variable model as a "hidden variable test" for that model. Our main contribution is to introduce a sequence of relaxations which provides progressively tighter hidden variable tests. We demonstrate applicability to mixtures of sequences of i.i.d. variables, Bell inequalities, and homophily models in social networks. For the last, we demonstrate that our method provides a test that is able to rule out latent homophily as the sole explanation for correlations on a real social network that are known to be due to influence.

  3. Optical Test of Local Hidden-Variable Model

    Institute of Scientific and Technical Information of China (English)

    WU XiaoHua; ZONG HongShi; PANG HouRong

    2001-01-01

    An inequality is deduced from local realism and a supplementary assumption. This inequality defines an experiment that can be actually performed with the present technology to test local hidden-variable models, and it is violated by quantum mechanics with a factor 1.92, while it can be simplified into a form where just two measurements are required.``

  4. Hidden variable models for quantum mechanics can have local parts

    CERN Document Server

    Larsson, Jan-Ake

    2009-01-01

    We present an explicit nonlocal nonsignaling model which has a nontrivial local part and is compatible with quantum mechanics. This model constitutes a counterexample to Colbeck and Renner's statement [Phys. Rev. Lett. 101, 050403 (2008)] that "any hidden variable model can only be compatible with quantum mechanics if its local part is trivial". Furthermore, we examine Colbeck and Renner's definition of "local part" and find that, in the case of models reproducing the quantum predictions for the singlet state, it is a restriction equivalent to the conjunction of nonsignaling and trivial local part.

  5. Hidden-variable models for the spin singlet: I. Non-local theories reproducing quantum mechanics

    CERN Document Server

    Di Lorenzo, Antonio

    2011-01-01

    A non-local hidden variable model reproducing the quantum mechanical probabilities for a spin singlet is presented. The non-locality is concentrated in the distribution of the hidden variables. The model otherwise satisfies both the hypothesis of outcome independence, made in the derivation of Bell inequality, and of compliance with Malus's law, made in the derivation of Leggett inequality. It is shown through the prescription of a protocol that the non-locality can be exploited to send information instantaneously provided that the hidden variables can be measured, even though they cannot be controlled.

  6. Algorithmic Construction of Local Hidden Variable Models for Entangled Quantum States

    Science.gov (United States)

    Hirsch, Flavien; Quintino, Marco Túlio; Vértesi, Tamás; Pusey, Matthew F.; Brunner, Nicolas

    2016-11-01

    Constructing local hidden variable (LHV) models for entangled quantum states is a fundamental problem, with implications for the foundations of quantum theory and for quantum information processing. It is, however, a challenging problem, as the model should reproduce quantum predictions for all possible local measurements. Here we present a simple method for building LHV models, applicable to any entangled state and considering continuous sets of measurements. This leads to a sequence of tests which, in the limit, fully captures the set of quantum states admitting a LHV model. Similar methods are developed for local hidden state models. We illustrate the practical relevance of these methods with several examples.

  7. Algorithmic Construction of Local Hidden Variable Models for Entangled Quantum States.

    Science.gov (United States)

    Hirsch, Flavien; Quintino, Marco Túlio; Vértesi, Tamás; Pusey, Matthew F; Brunner, Nicolas

    2016-11-04

    Constructing local hidden variable (LHV) models for entangled quantum states is a fundamental problem, with implications for the foundations of quantum theory and for quantum information processing. It is, however, a challenging problem, as the model should reproduce quantum predictions for all possible local measurements. Here we present a simple method for building LHV models, applicable to any entangled state and considering continuous sets of measurements. This leads to a sequence of tests which, in the limit, fully captures the set of quantum states admitting a LHV model. Similar methods are developed for local hidden state models. We illustrate the practical relevance of these methods with several examples.

  8. Local hidden-variable model for a recent experimental test of quantum nonlocality and local contextuality

    Science.gov (United States)

    La Cour, Brian R.

    2017-07-01

    An experiment has recently been performed to demonstrate quantum nonlocality by establishing contextuality in one of a pair of photons encoding four qubits; however, low detection efficiencies and use of the fair-sampling hypothesis leave these results open to possible criticism due to the detection loophole. In this Letter, a physically motivated local hidden-variable model is considered as a possible mechanism for explaining the experimentally observed results. The model, though not intrinsically contextual, acquires this quality upon post-selection of coincident detections.

  9. Hidden Variables or Positive Probabilities?

    CERN Document Server

    Rothman, T; Rothman, Tony

    2001-01-01

    Despite claims that Bell's inequalities are based on the Einstein locality condition, or equivalent, all derivations make an identical mathematical assumption: that local hidden-variable theories produce a set of positive-definite probabilities for detecting a particle with a given spin orientation. The standard argument is that because quantum mechanics assumes that particles are emitted in a superposition of states the theory cannot produce such a set of probabilities. We examine a paper by Eberhard who claims to show that a generalized Bell inequality, the CHSH inequality, can be derived solely on the basis of the locality condition, without recourse to hidden variables. We point out that he nonetheless assumes a set of positive-definite probabilities, which supports the claim that hidden variables or "locality" is not at issue here, positive-definite probabilities are. We demonstrate that quantum mechanics does predict a set of probabilities that violate the CHSH inequality; however these probabilities ar...

  10. On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

    Institute of Scientific and Technical Information of China (English)

    周韶园; 谢磊; 王树青

    2005-01-01

    An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

  11. Hidden variables and hidden time in quantum theory

    OpenAIRE

    Kurakin, Pavel V.

    2005-01-01

    Bell's theorem proves only that hidden variables evolving in true physical time can't exist; still the theorem's meaning is usually interpreted intolerably wide. The concept of hidden time (and, in general, hidden space-time) is introduced. Such concept provides a whole new class of physical theories, fully compatible with current knowledge, but giving new tremendous possibilities. Those theories do not violate Bell's theorem.

  12. Partially Hidden Markov Models

    DEFF Research Database (Denmark)

    Forchhammer, Søren Otto; Rissanen, Jorma

    1996-01-01

    Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression wher...

  13. Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model

    Science.gov (United States)

    Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli

    2017-07-01

    This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.

  14. Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model

    Science.gov (United States)

    Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli

    2016-04-01

    This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.

  15. Hidden Variable Theories and Quantum Nonlocality

    Science.gov (United States)

    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…

  16. Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes

    Energy Technology Data Exchange (ETDEWEB)

    Ghil, M. [Univ. of California, Los Angeles, CA (United States); Kravtsov, S. [Univ. of Wisconsin, Madison, WI (United States); Robertson, A. W. [IRI, Palisades, NY (United States); Smyth, P. [Univ. of California, Irvine, CA (United States)

    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.

  17. A survey of hidden-variables theories

    CERN Document Server

    Belinfante, F J

    1973-01-01

    A Survey of Hidden-Variables Theories is a three-part book on the hidden-variable theories, referred in this book as """"theories of the first kind"""". Part I reviews the motives in developing different types of hidden-variables theories. The quest for determinism led to theories of the first kind; the quest for theories that look like causal theories when applied to spatially separated systems that interacted in the past led to theories of the second kind. Parts II and III further describe the theories of the first kind and second kind, respectively. This book is written to make the literat

  18. Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference.

    Science.gov (United States)

    Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence

    2007-12-01

    Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.

  19. Local clustering in scale-free networks with hidden variables.

    Science.gov (United States)

    van der Hofstad, Remco; Janssen, A J E M; van Leeuwaarden, Johan S H; Stegehuis, Clara

    2017-02-01

    We investigate the presence of triangles in a class of correlated random graphs in which hidden variables determine the pairwise connections between vertices. The class rules out self-loops and multiple edges. We focus on the regime where the hidden variables follow a power law with exponent τ∈(2,3), so that the degrees have infinite variance. The natural cutoff h_{c} characterizes the largest degrees in the hidden variable models, and a structural cutoff h_{s} introduces negative degree correlations (disassortative mixing) due to the infinite-variance degrees. We show that local clustering decreases with the hidden variable (or degree). We also determine how the average clustering coefficient C scales with the network size N, as a function of h_{s} and h_{c}. For scale-free networks with exponent 2vanish only for networks as large as N=10^{9}.

  20. Coding with partially hidden Markov models

    DEFF Research Database (Denmark)

    Forchhammer, Søren; Rissanen, J.

    1995-01-01

    Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general...... 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A forward-backward reestimation of parameters with a redefined backward variable is given for these models and used for estimating the unknown parameters. Proof of convergence of this reestimation is given....... The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt...

  1. Backward causation, hidden variables and the meaning of completeness

    Indian Academy of Sciences (India)

    Huw Price

    2001-02-01

    Bell’s theorem requires the assumption that hidden variables are independent of future measurement settings. This independence assumption rests on surprisingly shaky ground. In particular, it is puzzlingly time-asymmetric. The paper begins with a summary of the case for considering hidden variable models which, in abandoning this independence assumption, allow a degree of ‘backward causation’. The remainder of the paper clarifies the physical significance of such models, in relation to the issue as to whether quantum mechanics provides a complete description of physical reality.

  2. Hidden Variables and Placebo Effects

    Science.gov (United States)

    Goradia, Shantilal

    2006-03-01

    God's response to prayers and placebo leads to a question. How does He respond deterministically? He may be controlling at least one of the two variables of the uncertainty principle by extending His invisible soul to each body particle locally. Amazingly, many Vedic verses support this answer. One describes the size of the soul as arithmetically matching the size of the nucleons as if a particle is a soul. One gives a name meaning particle soul (anu-atma), consistent with particle's indeterministic behavior like that of (soulful) bird’s flying in any directions irrespective of the direction of throw. One describes souls as eternal consistent with the conservation of baryon number. One links the souls to the omnipresent (param- atma) like Einstein Rosen bridges link particles to normal spacetime. One claims eternal coexistence of matter and soul as is inflationary universe in physics/0210040 V2. The implicit scientific consistency of such verses makes the relationship of particle source of consciousness to the omnipresent Supreme analogous to the relationship of quantum source of gravitons in my gr-qc/0507130 to normal spacetime This frees us from the postulation of quantum wormholes and quantum foam. Dr. Hooft's view in ``Does God play dice,'' Physicsword, Dec 2005 seems consistent with my progressive conference presentations in Russia, Europe, India, and USA (Hindu University) in 2004/05. I see implications for nanoscience.

  3. On Noncontextual, Non-Kolmogorovian Hidden Variable Theories

    Science.gov (United States)

    Feintzeig, Benjamin H.; Fletcher, Samuel C.

    2017-01-01

    One implication of Bell's theorem is that there cannot in general be hidden variable models for quantum mechanics that both are noncontextual and retain the structure of a classical probability space. Thus, some hidden variable programs aim to retain noncontextuality at the cost of using a generalization of the Kolmogorov probability axioms. We generalize a theorem of Feintzeig (Br J Philos Sci 66(4): 905-927, 2015) to show that such programs are committed to the existence of a finite null cover for some quantum mechanical experiments, i.e., a finite collection of probability zero events whose disjunction exhausts the space of experimental possibilities.

  4. On Noncontextual, Non-Kolmogorovian Hidden Variable Theories

    Science.gov (United States)

    Feintzeig, Benjamin H.; Fletcher, Samuel C.

    2017-02-01

    One implication of Bell's theorem is that there cannot in general be hidden variable models for quantum mechanics that both are noncontextual and retain the structure of a classical probability space. Thus, some hidden variable programs aim to retain noncontextuality at the cost of using a generalization of the Kolmogorov probability axioms. We generalize a theorem of Feintzeig (Br J Philos Sci 66(4): 905-927, 2015) to show that such programs are committed to the existence of a finite null cover for some quantum mechanical experiments, i.e., a finite collection of probability zero events whose disjunction exhausts the space of experimental possibilities.

  5. Local clustering in scale-free networks with hidden variables

    Science.gov (United States)

    van der Hofstad, Remco; Janssen, A. J. E. M.; van Leeuwaarden, Johan S. H.; Stegehuis, Clara

    2017-02-01

    We investigate the presence of triangles in a class of correlated random graphs in which hidden variables determine the pairwise connections between vertices. The class rules out self-loops and multiple edges. We focus on the regime where the hidden variables follow a power law with exponent τ ∈(2 ,3 ) , so that the degrees have infinite variance. The natural cutoff hc characterizes the largest degrees in the hidden variable models, and a structural cutoff hs introduces negative degree correlations (disassortative mixing) due to the infinite-variance degrees. We show that local clustering decreases with the hidden variable (or degree). We also determine how the average clustering coefficient C scales with the network size N , as a function of hs and hc. For scale-free networks with exponent 2 universality class at hand. We characterize the extremely slow decay of C when τ ≈2 and show that for τ =2.1 , say, clustering starts to vanish only for networks as large as N =109 .

  6. A metric measure for weight matrices of variable lengths—with applications to clustering and classification of hidden Markov models

    Science.gov (United States)

    Yu, Yi-Kuo

    2007-02-01

    We construct a metric measure among weight matrices that are commonly used in non-interacting statistical physics systems, computational biology problems, as well as in general applications such as hidden Markov models. The metric distance between two weight matrices is obtained via aligning the matrices and thus can be evaluated by dynamic programming. Capable of allowing reverse complements in distance evaluation, this metric accommodates both gapless and gapped alignments between two weight matrices. The distance statistics among random motifs is also studied. We find that the average square distance and its standard error grow with different powers of motif length, and the normalized square distance follows a Gaussian distribution for large motif lengths.

  7. Binary hidden Markov models and varieties

    CERN Document Server

    Critch, Andrew J

    2012-01-01

    The technological applications of hidden Markov models have been extremely diverse and successful, including natural language processing, gesture recognition, gene sequencing, and Kalman filtering of physical measurements. HMMs are highly non-linear statistical models, and just as linear models are amenable to linear algebraic techniques, non-linear models are amenable to commutative algebra and algebraic geometry. This paper examines closely those HMMs in which all the random variables, called nodes, are binary. Its main contributions are (1) minimal defining equations for the 4-node model, comprising 21 quadrics and 29 cubics, which were computed using Gr\\"obner bases in the cumulant coordinates of Sturmfels and Zwiernik, and (2) a birational parametrization for every binary HMM, with an explicit inverse for recovering the hidden parameters in terms of observables. The new model parameters in (2) are hence rationally identifiable in the sense of Sullivant, Garcia-Puente, and Spielvogel, and each model's Zar...

  8. Modeling Multiple Risks: Hidden Domain of Attraction

    CERN Document Server

    Mitra, Abhimanyu

    2011-01-01

    Hidden regular variation is a sub-model of multivariate regular variation and facilitates accurate estimation of joint tail probabilities. We generalize the model of hidden regular variation to what we call hidden domain of attraction. We exhibit examples that illustrate the need for a more general model and discuss detection and estimation techniques.

  9. Foundational Forces & Hidden Variables in Technology Commercialization

    Science.gov (United States)

    Barnett, Brandon

    2011-03-01

    The science of physics seems vastly different from the process of technology commercialization. Physics strives to understand our world through the experimental deduction of immutable laws and dependent variables and the resulting macro-scale phenomenon. In comparison, the~goal of business is to make a profit by addressing the needs, preferences, and whims of individuals in a market. It may seem that this environment is too dynamic to identify all the hidden variables and deduct the foundational forces that impact a business's ability to commercialize innovative technologies. One example of a business ``force'' is found in the semiconductor industry. In 1965, Intel co-founder Gordon Moore predicted that the number of transistors incorporated in a chip will approximately double every 24 months. Known as Moore's Law, this prediction has become the guiding principle for the semiconductor industry for the last 40 years. Of course, Moore's Law is not really a law of nature; rather it is the result of efforts by Intel and the entire semiconductor industry. A closer examination suggests that there are foundational principles of business that underlie the macro-scale phenomenon of Moore's Law. Principles of profitability, incentive, and strategic alignment have resulted in a coordinated influx of resources that has driven technologies to market, increasing the profitability of the semiconductor industry and optimizing the fitness of its participants. New innovations in technology are subject to these same principles. So, in addition to traditional market forces, these often unrecognized forces and variables create challenges for new technology commercialization. In this talk, I will draw from ethnographic research, complex adaptive theory, and industry data to suggest a framework with which to think about new technology commercialization. Intel's bio-silicon initiative provides a case study.

  10. A Bell-type Theorem Without Hidden Variables

    CERN Document Server

    Stapp, Henry P

    2002-01-01

    Bell's theorem rules out local hidden-variable theories. The locality condition is the demand that what an experimenter freely chooses to measure in one space-time region has no influence in a second space-time region that is spacelike separated from the first. The hidden-variable stipulation means that this demand is implemented through requirements on an assumed-to-exist substructure involving hidden variables. The question thus arises whether the locality condition itself fails, or only its implementation by means of the assumed hidden-variable structure. This paper shows that any theory that satisfies two generally accepted features of orthodox quantum theory and that yields certain predictions of quantum theory cannot satisfy the afore-mentioned locality condition. These two features are that the choices made by the experimenters can be treated as localized free variables and that such free choices do not affect outcomes that have already occurred.

  11. Hidden Markov Model for Stock Selection

    Directory of Open Access Journals (Sweden)

    Nguyet Nguyen

    2015-10-01

    Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.

  12. Estimating an Activity Driven Hidden Markov Model

    OpenAIRE

    Meyer, David A.; Shakeel, Asif

    2015-01-01

    We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent $\\textit{activity levels}$ that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone records.

  13. Nonparametric model validations for hidden Markov models with applications in financial econometrics.

    Science.gov (United States)

    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.

  14. On local-hidden-variable no-go theorems

    Science.gov (United States)

    Methot, A. A.

    2006-06-01

    The strongest attack against quantum mechanics came in 1935 in the form of a paper by Einstein, Podolsky, and Rosen. It was argued that the theory of quantum mechanics could not be called a complete theory of Nature, for every element of reality is not represented in the formalism as such. The authors then put forth a proposition: we must search for a theory where, upon knowing everything about the system, including possible hidden variables, one could make precise predictions concerning elements of reality. This project was ultimately doomed in 1964 with the work of Bell, who showed that the most general local hidden variable theory could not reproduce correlations that arise in quantum mechanics. There exist mainly three forms of no-go theorems for local hidden variable theories. Although almost every physicist knows the consequences of these no-go theorems, not every physicist is aware of the distinctions between the three or even their exact definitions. Thus, we will discuss here the three principal forms of no-go theorems for local hidden variable theories of Nature. We will define Bell theorems, Bell theorems without inequalities, and pseudo-telepathy. A discussion of the similarities and differences will follow.

  15. Fitting Hidden Markov Models to Psychological Data

    Directory of Open Access Journals (Sweden)

    Ingmar Visser

    2002-01-01

    Full Text Available Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

  16. Hidden Markov Models for Human Genes

    DEFF Research Database (Denmark)

    Baldi, Pierre; Brunak, Søren; Chauvin, Yves

    1997-01-01

    We analyse the sequential structure of human genomic DNA by hidden Markov models. We apply models of widely different design: conventional left-right constructs and models with a built-in periodic architecture. The models are trained on segments of DNA sequences extracted such that they cover...

  17. Consistent histories, quantum truth functionals, and hidden variables

    Science.gov (United States)

    Griffiths, Robert B.

    2000-01-01

    A central principle of consistent histories quantum theory, the requirement that quantum descriptions be based upon a single framework (or family), is employed to show that there is no conflict between consistent histories and a no-hidden-variables theorem of Bell, and Kochen and Specker, contrary to a recent claim by Bassi and Ghirardi. The argument makes use of `truth functionals' defined on a Boolean algebra of classical or quantum properties.

  18. Consistent histories, quantum truth functionals, and hidden variables

    CERN Document Server

    Griffiths, R B

    1999-01-01

    A central principle of consistent histories quantum theory, the requirement that quantum descriptions be based upon a single framework (or family), is employed to show that there is no conflict between consistent histories and a no-hidden-variables theorem of Bell, and Kochen and Specker, contrary to a recent claim by Bassi and Ghirardi. The argument makes use of ``truth functionals'' defined on a Boolean algebra of classical or quantum properties.

  19. Hidden blazars and emission line variability of high redshift quasars

    Directory of Open Access Journals (Sweden)

    Feng Ma

    2001-01-01

    Full Text Available We have carried out a survey to search for hidden blazars in a sample of z 2 radio{loud quasars. The idea is based on our prediction that we should be able to see large C IV line variability not associated with observed continuum variations or most other emission lines in every radio{loud quasar. Here we report the initial results including the discovery of large C IV line variations in two quasars.

  20. On local-hidden-variable no-go theorems

    CERN Document Server

    Méthot, A A

    2005-01-01

    The strongest attack against quantum mechanics came in 1935 in the form of a paper by Einstein, Podolsky and Rosen. It was argued that the theory of quantum mechanics could not be called a complete theory of Nature, for every element of reality is not represented in the formalism as such. The authors then put forth a proposition: we must search for a theory where, upon knowing everything about the system, including possible hidden variables, one could make precise predictions concerning elements of reality. This project was ultimatly doomed in 1964 with the work of Bell Bell, who showed that the most general local hidden variable theory could not reproduce correlations that arise in quantum mechanics. There exist mainly three forms of no-go theorems for local hidden variable theories. Although almost every physicist knows the consequences of these no-go theorems, not every physicist is aware of the distinctions between the three or even their exact definitions. Thus we will discuss here the three principal fo...

  1. Inferring topologies of complex networks with hidden variables.

    Science.gov (United States)

    Wu, Xiaoqun; Wang, Weihan; Zheng, Wei Xing

    2012-10-01

    Network topology plays a crucial role in determining a network's intrinsic dynamics and function, thus understanding and modeling the topology of a complex network will lead to greater knowledge of its evolutionary mechanisms and to a better understanding of its behaviors. In the past few years, topology identification of complex networks has received increasing interest and wide attention. Many approaches have been developed for this purpose, including synchronization-based identification, information-theoretic methods, and intelligent optimization algorithms. However, inferring interaction patterns from observed dynamical time series is still challenging, especially in the absence of knowledge of nodal dynamics and in the presence of system noise. The purpose of this work is to present a simple and efficient approach to inferring the topologies of such complex networks. The proposed approach is called "piecewise partial Granger causality." It measures the cause-effect connections of nonlinear time series influenced by hidden variables. One commonly used testing network, two regular networks with a few additional links, and small-world networks are used to evaluate the performance and illustrate the influence of network parameters on the proposed approach. Application to experimental data further demonstrates the validity and robustness of our method.

  2. Hidden Markov models estimation and control

    CERN Document Server

    Elliott, Robert J; Moore, John B

    1995-01-01

    As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filte

  3. Algorithmic information theory and the hidden variable question

    Science.gov (United States)

    Fuchs, Christopher

    1992-01-01

    The admissibility of certain nonlocal hidden-variable theories are explained via information theory. Consider a pair of Stern-Gerlach devices with fixed nonparallel orientations that periodically perform spin measurements on identically prepared pairs of electrons in the singlet spin state. Suppose the outcomes are recorded as binary strings l and r (with l sub n and r sub n denoting their n-length prefixes). The hidden-variable theories considered here require that there exists a recursive function which may be used to transform l sub n into r sub n for any n. This note demonstrates that such a theory cannot reproduce all the statistical predictions of quantum mechanics. Specifically, consider an ensemble of outcome pairs (l,r). From the associated probability measure, the Shannon entropies H sub n and H bar sub n for strings l sub n and pairs (l sub n, r sub n) may be formed. It is shown that such a theory requires that the absolute value of H bar sub n - H sub n be bounded - contrasting the quantum mechanical prediction that it grow with n.

  4. Finite State Transducers Approximating Hidden Markov Models

    CERN Document Server

    Kempe, A

    1999-01-01

    This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also improved. The described methods have been implemented and successfully tested on six languages.

  5. Online Learning in Discrete Hidden Markov Models

    OpenAIRE

    Alamino, Roberto C.; Caticha, Nestor

    2007-01-01

    We present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking b...

  6. Quantum Computing and Hidden Variables II: The Complexity of Sampling Histories

    CERN Document Server

    Aaronson, S

    2004-01-01

    This paper shows that, if we could examine the entire history of a hidden variable, then we could efficiently solve problems that are believed to be intractable even for quantum computers. In particular, under any hidden-variable theory satisfying a reasonable axiom called "indifference to the identity," we could solve the Graph Isomorphism and Approximate Shortest Vector problems in polynomial time, as well as an oracle problem that is known to require quantum exponential time. We could also search an N-item database using O(N^{1/3}) queries, as opposed to O(N^{1/2}) queries with Grover's search algorithm. On the other hand, the N^{1/3} bound is optimal, meaning that we could probably not solve NP-complete problems in polynomial time. We thus obtain the first good example of a model of computation that appears slightly more powerful than the quantum computing model.

  7. Hidden Symmetries of Stochastic Models

    Directory of Open Access Journals (Sweden)

    Boyka Aneva

    2007-05-01

    Full Text Available In the matrix product states approach to $n$ species diffusion processes the stationary probability distribution is expressed as a matrix product state with respect to a quadratic algebra determined by the dynamics of the process. The quadratic algebra defines a noncommutative space with a $SU_q(n$ quantum group action as its symmetry. Boundary processes amount to the appearance of parameter dependent linear terms in the algebraic relations and lead to a reduction of the $SU_q(n$ symmetry. We argue that the boundary operators of the asymmetric simple exclusion process generate a tridiagonal algebra whose irriducible representations are expressed in terms of the Askey-Wilson polynomials. The Askey-Wilson algebra arises as a symmetry of the boundary problem and allows to solve the model exactly.

  8. Hidden Markov Models for indirect classification of occupant behaviour

    DEFF Research Database (Denmark)

    Liisberg, Jon Anders Reichert; Møller, Jan Kloppenborg; Bloem, H.

    2016-01-01

    Even for similar residential buildings, a huge variability in the energy consumption can be observed. This variability is mainly due to the different behaviours of the occupants and this impacts the thermal (temperature setting, window opening, etc.) as well as the electrical (appliances, TV....... This paper focuses on the use of Hidden Markov Models (HMMs) to create methods for indirect observations and characterisation of occupant behaviour. By applying homogeneous HMMs on the electricity consumption of fourteen apartments, three states describing the data were found suitable. The most likely...

  9. Bardeen variables and hidden gauge symmetries in linearized massive gravity

    CERN Document Server

    Jaccard, Maud; Mitsou, Ermis

    2012-01-01

    We give a detailed discussion of the use of the (3+1) decomposition and of Bardeen's variables in massive gravity linearized over a Minkowski as well as over a de Sitter background. In Minkowski space the Bardeen "potential" \\Phi, that in the massless case is a non-radiative degree of freedom, becomes radiative and describes the helicity-0 component of the massive graviton. Its dynamics is governed by a simple Klein-Gordon action, supplemented by a term (\\Box \\Phi)^2 if we do not make the Fierz-Pauli tuning of the mass term. In de Sitter the identification of the variable that describes the radiative degree of freedom in the scalar sector is more subtle, and even involves expressions non-local in time. The use of this new variable provides a simple and transparent derivation of the Higuchi bound and of the disappearance of the scalar degree of freedom at a special value of $m_g^2/H^2$. The use of this formalism also allows us to uncover the existence of a hidden gauge symmetry of the massive theory, that beco...

  10. Evolving the structure of hidden Markov Models

    DEFF Research Database (Denmark)

    won, K. J.; Prugel-Bennett, A.; Krogh, A.

    2006-01-01

    A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission...... and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature....

  11. Detecting Structural Breaks using Hidden Markov Models

    DEFF Research Database (Denmark)

    Ntantamis, Christos

    Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another....... The locations of the breaks are subsequently obtained by assigning states to data points according to the Maximum Posterior Mode (MPM) algorithm. The Integrated Classification Likelihood-Bayesian Information Criterion (ICL-BIC) allows for the determination of the number of regimes by taking into account...... in the monetary policy of United States, the dierent functional form being variants of the Taylor (1993) rule....

  12. Epitope discovery with phylogenetic hidden Markov models.

    LENUS (Irish Health Repository)

    Lacerda, Miguel

    2010-05-01

    Existing methods for the prediction of immunologically active T-cell epitopes are based on the amino acid sequence or structure of pathogen proteins. Additional information regarding the locations of epitopes may be acquired by considering the evolution of viruses in hosts with different immune backgrounds. In particular, immune-dependent evolutionary patterns at sites within or near T-cell epitopes can be used to enhance epitope identification. We have developed a mutation-selection model of T-cell epitope evolution that allows the human leukocyte antigen (HLA) genotype of the host to influence the evolutionary process. This is one of the first examples of the incorporation of environmental parameters into a phylogenetic model and has many other potential applications where the selection pressures exerted on an organism can be related directly to environmental factors. We combine this novel evolutionary model with a hidden Markov model to identify contiguous amino acid positions that appear to evolve under immune pressure in the presence of specific host immune alleles and that therefore represent potential epitopes. This phylogenetic hidden Markov model provides a rigorous probabilistic framework that can be combined with sequence or structural information to improve epitope prediction. As a demonstration, we apply the model to a data set of HIV-1 protein-coding sequences and host HLA genotypes.

  13. Hidden Markov Modeling for Weigh-In-Motion Estimation

    Energy Technology Data Exchange (ETDEWEB)

    Abercrombie, Robert K [ORNL; Ferragut, Erik M [ORNL; Boone, Shane [ORNL

    2012-01-01

    This paper describes a hidden Markov model to assist in the weight measurement error that arises from complex vehicle oscillations of a system of discrete masses. Present reduction of oscillations is by a smooth, flat, level approach and constant, slow speed in a straight line. The model uses this inherent variability to assist in determining the true total weight and individual axle weights of a vehicle. The weight distribution dynamics of a generic moving vehicle were simulated. The model estimation converged to within 1% of the true mass for simulated data. The computational demands of this method, while much greater than simple averages, took only seconds to run on a desktop computer.

  14. Pruning Boltzmann networks and hidden Markov models

    DEFF Research Database (Denmark)

    Pedersen, Morten With; Stork, D.

    1996-01-01

    We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear...... Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...

  15. Genetic Algorithms Principles Towards Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2011-10-01

    Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
    out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

  16. Hidden variable problem for a family of continuously many spin 1 measurements

    OpenAIRE

    Kurzynski, Pawel; Soeda, Akihito; Bzdega, Bartlomiej; Kaszlikowski, Dagomir

    2012-01-01

    We study a continuous set of spin 1 measurements and show that for a special family of measurements parametrized by a single variable $\\theta$ the possibility of hidden-variable description is a discontinuous property.

  17. ADAPTIVE LEARNING OF HIDDEN MARKOV MODELS FOR EMOTIONAL SPEECH

    Directory of Open Access Journals (Sweden)

    A. V. Tkachenia

    2014-01-01

    Full Text Available An on-line unsupervised algorithm for estimating the hidden Markov models (HMM parame-ters is presented. The problem of hidden Markov models adaptation to emotional speech is solved. To increase the reliability of estimated HMM parameters, a mechanism of forgetting and updating is proposed. A functional block diagram of the hidden Markov models adaptation algorithm is also provided with obtained results, which improve the efficiency of emotional speech recognition.

  18. Error statistics of hidden Markov model and hidden Boltzmann model results

    Directory of Open Access Journals (Sweden)

    Newberg Lee A

    2009-07-01

    Full Text Available Abstract Background Hidden Markov models and hidden Boltzmann models are employed in computational biology and a variety of other scientific fields for a variety of analyses of sequential data. Whether the associated algorithms are used to compute an actual probability or, more generally, an odds ratio or some other score, a frequent requirement is that the error statistics of a given score be known. What is the chance that random data would achieve that score or better? What is the chance that a real signal would achieve a given score threshold? Results Here we present a novel general approach to estimating these false positive and true positive rates that is significantly more efficient than are existing general approaches. We validate the technique via an implementation within the HMMER 3.0 package, which scans DNA or protein sequence databases for patterns of interest, using a profile-HMM. Conclusion The new approach is faster than general naïve sampling approaches, and more general than other current approaches. It provides an efficient mechanism by which to estimate error statistics for hidden Markov model and hidden Boltzmann model results.

  19. Error statistics of hidden Markov model and hidden Boltzmann model results

    Science.gov (United States)

    Newberg, Lee A

    2009-01-01

    Background Hidden Markov models and hidden Boltzmann models are employed in computational biology and a variety of other scientific fields for a variety of analyses of sequential data. Whether the associated algorithms are used to compute an actual probability or, more generally, an odds ratio or some other score, a frequent requirement is that the error statistics of a given score be known. What is the chance that random data would achieve that score or better? What is the chance that a real signal would achieve a given score threshold? Results Here we present a novel general approach to estimating these false positive and true positive rates that is significantly more efficient than are existing general approaches. We validate the technique via an implementation within the HMMER 3.0 package, which scans DNA or protein sequence databases for patterns of interest, using a profile-HMM. Conclusion The new approach is faster than general naïve sampling approaches, and more general than other current approaches. It provides an efficient mechanism by which to estimate error statistics for hidden Markov model and hidden Boltzmann model results. PMID:19589158

  20. On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model%基于变长度移动窗口和HMM的工业过程故障在线诊断

    Institute of Scientific and Technical Information of China (English)

    周韶园; 谢磊; 王树青

    2005-01-01

    An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

  1. Inference in Hidden Markov Models with Explicit State Duration Distributions

    CERN Document Server

    Dewar, Michael; Wood, Frank

    2012-01-01

    In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting of both discrete state-indicator and discrete state-duration random variables. In contrast to the implicit geometric state duration distribution possessed by the standard HMM, EDHMMs allow the direct parameterisation and estimation of per-state duration distributions. As most duration distributions are defined over the positive integers, truncation or other approximations are usually required to perform EDHMM inference.

  2. An introduction to hidden Markov models for biological sequences

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose

    1998-01-01

    A non-matematical tutorial on hidden Markov models (HMMs) plus a description of one of the applications of HMMs: gene finding.......A non-matematical tutorial on hidden Markov models (HMMs) plus a description of one of the applications of HMMs: gene finding....

  3. Probabilistic Resilience in Hidden Markov Models

    Science.gov (United States)

    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.

  4. A Constraint Model for Constrained Hidden Markov Models

    DEFF Research Database (Denmark)

    Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp

    2009-01-01

    A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we extend HMMs with constraints and show how the familiar Viterbi algorithm can be generalized, based on constraint solving...

  5. Hidden Markov models in automatic speech recognition

    Science.gov (United States)

    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.

  6. A Hidden-Removal Model of Dam Perspective Drawing

    Institute of Scientific and Technical Information of China (English)

    WANG Zi-ru; ZHOU Hui-cheng; LI Ming-qiu

    2011-01-01

    Aming at water conservancy project visualization, a hidden-removal method of dam perspective drawings is realized by building a hidden-removal mathematical model for overlapping points location to set up the hidden relationship among point and plane, plane and plane in space. On this basis, as an example of panel rockfill dam, a dam hidden-removal perspective drawing is generated in different directions and different visual angles through adapting VC++ and OpenGL visualizing technology. The results show that the data construction of the model is simple which can overcome the disadvantages of considerable and complicated calculation. This method also provides the new means to draw hidden-removal perspective drawings for those landforms and ground objects.

  7. Bianchi Cosmologies New Variables and a Hidden Supersymmetry

    CERN Document Server

    Obregón, O; Ryan, M P; Obregon, Octavio; Pullin, Jorge; Ryan, Michael P.

    1993-01-01

    We find a supersymmetrization of the Bianchi IX cosmology in terms of Ashtekar's new variables. This provides a framework for connecting the recent results of Graham and those of Ryan and Moncrief for quantum states of this model. These states are also related with the states obtained particularizing supergravity for a minisuperspace. Implications for the general theory are also briefly discussed.

  8. Hidden Markov Models with Factored Gaussian Mixtures Densities

    Institute of Scientific and Technical Information of China (English)

    LI Hao-zheng; LIU Zhi-qiang; ZHU Xiang-hua

    2004-01-01

    We present a factorial representation of Gaussian mixture models for observation densities in Hidden Markov Models(HMMs), which uses the factorial learning in the HMM framework. We derive the reestimation formulas for estimating the factorized parameters by the Expectation Maximization (EM) algorithm. We conduct several experiments to compare the performance of this model structure with Factorial Hidden Markov Models(FHMMs) and HMMs, some conclusions and promising empirical results are presented.

  9. Reduced-Rank Hidden Markov Models

    CERN Document Server

    Siddiqi, Sajid M; Gordon, Geoffrey J

    2009-01-01

    We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n discrete observations, with a transition matrix of rank k <= m. This implies the dynamics evolve in a k-dimensional subspace, while the shape of the set of predictive distributions is determined by m. Latent state belief is represented with a k-dimensional state vector and inference is carried out entirely in R^k, making RR-HMMs as computationally efficient as k-state HMMs yet more expressive. To learn RR-HMMs, we relax the assumptions of a recently proposed spectral learning algorithm for HMMs (Hsu, Kakade and Zhang 2009) and apply it to learn k-dimensional observable representations of rank-k RR-HMMs. The algorithm is consistent and free of local optima, and we extend its performance guarantees to cover the RR-...

  10. Recent Applications of Hidden Markov Models in Computational Biology

    Institute of Scientific and Technical Information of China (English)

    Khar Heng Choo; Joo Chuan Tong; Louxin Zhang

    2004-01-01

    This paper examines recent developments and applications of Hidden Markov Models (HMMs) to various problems in computational biology, including multiple sequence alignment, homology detection, protein sequences classification, and genomic annotation.

  11. Disease surveillance using a hidden Markov model

    Directory of Open Access Journals (Sweden)

    Wright Graeme

    2009-08-01

    Full Text Available Abstract Background Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. Methods A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS algorithms and a negative binomial cusum. Results Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms. Conclusion Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.

  12. Hidden Markov models: the best models for forager movements?

    Directory of Open Access Journals (Sweden)

    Rocio Joo

    Full Text Available One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs. We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs. They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (∼1 record per hour, while their behavioural modes (fishing, searching and cruising were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%, significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance.

  13. Modelling proteins' hidden conformations to predict antibiotic resistance

    Science.gov (United States)

    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.

  14. Variational Infinite Hidden Conditional Random Fields

    NARCIS (Netherlands)

    Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja; Ghahramani, Zoubin

    2015-01-01

    Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of

  15. Chaos, solitons and fractals in hidden symmetry models

    Energy Technology Data Exchange (ETDEWEB)

    Maccari, Attilio [Technical Institute ' G. Cardano' , Piazza della Resistenza 1, 00015 Monterotondo, Rome (Italy)] e-mail: solitone@yahoo.it

    2006-01-01

    A spontaneous symmetry breaking (or hidden symmetry) model is reduced to a system nonlinear evolution equations integrable via an appropriate change of variables, by means of the asymptotic perturbation (AP) method, based on spatio-temporal rescaling and Fourier expansion. It is demonstrated the existence of coherent solutions as well as chaotic and fractal patterns, due to the possibility of selecting appropriately some arbitrary functions. Dromion, lump, breather, instanton and ring soliton solutions are derived and the interaction between these coherent solutions are completely elastic, because they pass through each other and preserve their shapes and velocities, the only change being a phase shift. Finally, one can construct lower dimensional chaotic patterns such as chaotic-chaotic patterns, periodic-chaotic patterns, chaotic soliton and dromion patterns. In a similar way, fractal dromion and lump patterns as well as stochastic fractal excitations can appear in the solution.

  16. Riboswitch Detection Using Profile Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Krishnamachari A

    2009-10-01

    Full Text Available Abstract Background Riboswitches are a type of noncoding RNA that regulate gene expression by switching from one structural conformation to another on ligand binding. The various classes of riboswitches discovered so far are differentiated by the ligand, which on binding induces a conformational switch. Every class of riboswitch is characterized by an aptamer domain, which provides the site for ligand binding, and an expression platform that undergoes conformational change on ligand binding. The sequence and structure of the aptamer domain is highly conserved in riboswitches belonging to the same class. We propose a method for fast and accurate identification of riboswitches using profile Hidden Markov Models (pHMM. Our method exploits the high degree of sequence conservation that characterizes the aptamer domain. Results Our method can detect riboswitches in genomic databases rapidly and accurately. Its sensitivity is comparable to the method based on the Covariance Model (CM. For six out of ten riboswitch classes, our method detects more than 99.5% of the candidates identified by the much slower CM method while being several hundred times faster. For three riboswitch classes, our method detects 97-99% of the candidates relative to the CM method. Our method works very well for those classes of riboswitches that are characterized by distinct and conserved sequence motifs. Conclusion Riboswitches play a crucial role in controlling the expression of several prokaryotic genes involved in metabolism and transport processes. As more and more new classes of riboswitches are being discovered, it is important to understand the patterns of their intra and inter genomic distribution. Understanding such patterns will enable us to better understand the evolutionary history of these genetic regulatory elements. However, a complete picture of the distribution pattern of riboswitches will emerge only after accurate identification of riboswitches across genomes

  17. Modeling Movement Primitives with Hidden Markov Models for Robotic and Biomedical Applications.

    Science.gov (United States)

    Karg, Michelle; Kulić, Dana

    2017-01-01

    Movement primitives are elementary motion units and can be combined sequentially or simultaneously to compose more complex movement sequences. A movement primitive timeseries consist of a sequence of motion phases. This progression through a set of motion phases can be modeled by Hidden Markov Models (HMMs). HMMs are stochastic processes that model time series data as the evolution of a hidden state variable through a discrete set of possible values, where each state value is associated with an observation (emission) probability. Each motion phase is represented by one of the hidden states and the sequential order by their transition probabilities. The observations of the MP-HMM are the sensor measurements of the human movement, for example, motion capture or inertial measurements. The emission probabilities are modeled as Gaussians. In this chapter, the MP-HMM modeling framework is described and applications to motion recognition and motion performance assessment are discussed. The selected applications include parametric MP-HMMs for explicitly modeling variability in movement performance and the comparison of MP-HMMs based on the loglikelihood, the Kullback-Leibler divergence, the extended HMM-based F-statistic, and gait-specific reference-based measures.

  18. Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model

    CERN Document Server

    DelRose, Michael; Frederick, Philip; 10.5121/ijaia.2011.2101

    2011-01-01

    The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. The intelligence of current computer algorithms has not reached this level of complexity, but there are several research efforts that are working towards it. With the number of classification algorithms available, it is hard to determine which algorithm works best for a particular situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their variants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a big downfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability to summarize the actions, and thus determine, with pretty good accuracy, the intention of the person performing the action. These visual cues and linkages are important...

  19. Parameter estimation of hidden periodic model in random fields

    Institute of Scientific and Technical Information of China (English)

    何书元

    1999-01-01

    Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.

  20. Evidence Feed Forward Hidden Markov Model: A New Type Of Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Michael Del Rose

    2011-01-01

    Full Text Available The ability to predict the intentions of people based solely on their visual actions is a skill only performed by humans and animals. The intelligence of current computer algorithms has not reached this level of complexity, but there are several research efforts that are working towards it. With the number of classification algorithms available, it is hard to determine which algorithm works best for a particular situation. In classification of visual human intent data, Hidden Markov Models (HMM, and their variants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a big downfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability to summarize the actions, and thus determine, with pretty good accuracy, the intention of the person performing the action. These visual cues and linkages are important in creating intelligent algorithms for determining human actions based on visual observations. The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which provides observation to observation linkages. The following research addresses the theory behind Evidence Feed Forward HMMs, provides mathematical proofs of their learning of these parameters to optimize the likelihood of observations with a Evidence Feed Forwards HMM, which is important in all computational intelligence algorithm, and gives comparative examples with standard HMMs in classification of both visual action data and measurement data; thus providing a strong base for Evidence Feed Forward HMMs in classification of many types of problems.

  1. Image Coding using Markov Models with Hidden States

    DEFF Research Database (Denmark)

    Forchhammer, Søren Otto

    1999-01-01

    The Cylinder Partially Hidden Markov Model (CPH-MM) is applied to lossless coding of bi-level images. The original CPH-MM is relaxed for the purpose of coding by not imposing stationarity, but otherwise the model description is the same.......The Cylinder Partially Hidden Markov Model (CPH-MM) is applied to lossless coding of bi-level images. The original CPH-MM is relaxed for the purpose of coding by not imposing stationarity, but otherwise the model description is the same....

  2. Clustering Multivariate Time Series Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Shima Ghassempour

    2014-03-01

    Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

  3. A Proposal for Testing Local Realism Without Using Assumptions Related to Hidden Variable States

    Science.gov (United States)

    Ryff, Luiz Carlos

    1996-01-01

    A feasible experiment is discussed which allows us to prove a Bell's theorem for two particles without using an inequality. The experiment could be used to test local realism against quantum mechanics without the introduction of additional assumptions related to hidden variables states. Only assumptions based on direct experimental observation are needed.

  4. Reheating the Standard Model from a hidden sector

    Science.gov (United States)

    Tenkanen, Tommi; Vaskonen, Ville

    2016-10-01

    We consider a scenario where the inflaton decays to a hidden sector thermally decoupled from the visible Standard Model sector. A tiny portal coupling between the hidden and the visible sectors later heats the visible sector so that the Standard Model degrees of freedom come to dominate the energy density of the Universe before big bang nucleosynthesis. We find that this scenario is viable, although obtaining the correct dark matter abundance and retaining successful big bang nucleosynthesis is not obvious. We also show that the isocurvature perturbations constituted by a primordial Higgs condensate are not problematic for the viability of the scenario.

  5. Reheating the Standard Model from a Hidden Sector

    CERN Document Server

    Tenkanen, Tommi

    2016-01-01

    We consider a scenario where the inflaton decays to a hidden sector thermally decoupled from the visible Standard Model sector. A tiny portal coupling between the hidden and the visible sectors later heats the visible sector so that the Standard Model degrees of freedom come to dominate the energy density of the Universe before Big Bang Nucleosynthesis. We find that this scenario is viable, although obtaining the correct dark matter abundance and retaining successful Big Bang Nucleosynthesis is not obvious. We also show that the isocurvature perturbations constituted by a primordial Higgs condensate are not problematic for the viability of the scenario.

  6. Inference with constrained hidden Markov models in PRISM

    DEFF Research Database (Denmark)

    Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp

    2010-01-01

    A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. De...

  7. K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

    NARCIS (Netherlands)

    Perrone, M.P.; Connell, S.D.

    2004-01-01

    An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as

  8. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...

  9. Modeling Driver Behavior near Intersections in Hidden Markov Model.

    Science.gov (United States)

    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.

  10. Modeling promoter grammars with evolving hidden Markov models

    DEFF Research Database (Denmark)

    Won, Kyoung-Jae; Sandelin, Albin; Marstrand, Troels Torben

    2008-01-01

    MOTIVATION: Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several...... factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation. RESULTS: With the goal of automatically deciphering such regulatory structures......, we present a method that iteratively evolves an ensemble of regulatory grammars using a hidden Markov Model (HMM) architecture composed of interconnected blocks representing transcription factor binding sites (TFBSs) and background regions of promoter sequences. The ensemble approach reduces the risk...

  11. Best-first Model Merging for Hidden Markov Model Induction

    CERN Document Server

    Stolcke, A; Stolcke, Andreas; Omohundro, Stephen M.

    1994-01-01

    This report describes a new technique for inducing the structure of Hidden Markov Models from data which is based on the general `model merging' strategy (Omohundro 1992). The process begins with a maximum likelihood HMM that directly encodes the training data. Successively more general models are produced by merging HMM states. A Bayesian posterior probability criterion is used to determine which states to merge and when to stop generalizing. The procedure may be considered a heuristic search for the HMM structure with the highest posterior probability. We discuss a variety of possible priors for HMMs, as well as a number of approximations which improve the computational efficiency of the algorithm. We studied three applications to evaluate the procedure. The first compares the merging algorithm with the standard Baum-Welch approach in inducing simple finite-state languages from small, positive-only training samples. We found that the merging procedure is more robust and accurate, particularly with a small a...

  12. Recognizing Strokes in Tennis Videos Using Hidden Markov Models

    NARCIS (Netherlands)

    Petkovic, M.; Jonker, W.; Zivkovic, Z.

    2001-01-01

    This paper addresses content-based video retrieval with an emphasis on recognizing events in tennis game videos. In particular, we aim at recognizing different classes of tennis strokes using automatic learning capability of Hidden Markov Models. Driven by our domain knowledge, a robust player segme

  13. Evolving the Topology of Hidden Markov Models using Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Thomsen, Réne

    2002-01-01

    Hidden Markov models (HMM) are widely used for speech recognition and have recently gained a lot of attention in the bioinformatics community, because of their ability to capture the information buried in biological sequences. Usually, heuristic algorithms such as Baum-Welch are used to estimate...

  14. Bayesian online algorithms for learning in discrete Hidden Markov Models

    OpenAIRE

    Alamino, Roberto C.; Caticha, Nestor

    2008-01-01

    We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

  15. Coulomb gauge model for hidden charm tetraquarks

    Science.gov (United States)

    Xie, W.; Mo, L. Q.; Wang, Ping; Cotanch, Stephen R.

    2013-08-01

    The spectrum of tetraquark states with hidden charm is studied within an effective Coulomb gauge Hamiltonian approach. Of the four independent color schemes, two are investigated, the (qcbar)1(cqbar)1 singlet-singlet (molecule) and the (qc)3(qbarcbar)3 triplet-triplet (diquark), for selected JPC states using a variational method. The predicted masses of triplet-triplet tetraquarks are roughly a GeV heavier than the singlet-singlet states. There is also an interesting flavor dependence with (qqbar)1 (ccbar1) states about half a GeV lighter than (qcbar)1(qbarc)1. The lightest 1++ and 1-- predictions are in agreement with the observed X (3872) and Y (4008) masses suggesting they are molecules with ωJ / ψ and ηhc, rather than D*Dbar* and DDbar, type structure, respectively. Similarly, the lightest isovector 1++ molecule, having a ρJ / ψ flavor composition, has mass near the recently observed charged Zc (3900) value. These flavor configurations are consistent with observed X, Y and Zc decays to ππJ / ψ.

  16. Imaginary part of action, Future functioning as hidden variables

    OpenAIRE

    Nielsen, H.B.

    2009-01-01

    A model -- by myself and Masao Ninomiya -- which in principle predicts the initial conditions in a way as to minimze a certain functional of the history of the Universe through both past and future -- a functional conceived of as an imaginary part of the action -- is suggested to be also helpful in solving some problems for quantum mechanics. Especially as our model almost makes it possible in principle to calculate the full history of the universe, it even makes it in principle calculable, w...

  17. Imaginary part of action, Future functioning as hidden variables

    CERN Document Server

    Nielsen, H B

    2009-01-01

    A model -- by myself and Masao Ninomiya -- which in principle predicts the initial conditions in a way as to minimze a certain functional of the history of the Universe through both past and future -- a functional conceived of as an imaginary part of the action -- is suggested to be also helpful in solving some problems for quantum mechanics. Especially as our model almost makes it possible in principle to calculate the full history of the universe, it even makes it in principle calculable, which one among several measurement results in a quantum experiment will actually be realized! Our "complex action model" thus is a special case of superdeterminism - in Bells way - and does not have true causality, but rather even in some cases true backward causation. In fact we claim in our model that the SSC (Superconducting Supercollider) were stopped by the US Congress due to the backward causation from the big amounts of Higgs particles, which it would have produced, if it had been allowed to run. The noumenon ("das...

  18. The incompatibility between local hidden variable theories and the fundamental conservation laws

    Indian Academy of Sciences (India)

    C S Unnikrishnan

    2005-09-01

    I discuss in detail the result that the Bell's inequalities derived in the context of local hidden variable theories for discrete quantized observables can be satisfied only if a fundamental conservation law is violated on the average. This result shows that such theories are physically nonviable, and makes the demarcating criteria of the Bell's inequalities redundant. I show that a unique correlation function can be derived from the validity of the conservation law alone and this coincides with the quantum mechanical correlation function. Thus, any theory with a different correlation function, like any local hidden variable theory, is incompatible with the fundamental conservation laws and space-time symmetries. The results are discussed in the context of two-particle singlet and triplet states, GHZ states, and two-particle double slit interferometry. Some observations on quantum entropy, entanglement, and nonlocality are also discussed.

  19. Post-relativistic gravity a hidden variable theory for general relativity

    CERN Document Server

    Schmelzer, I

    1996-01-01

    Post-relativistic gravity is a hidden variable theory for general relativity. It introduces the pre-relativistic notions absolute space, absolute time, and ether as hidden variables into general relativity. Evolution is defined by the equations of general relativity and the harmonic coordinate condition interpreted as a physical equation. There are minor differences in predictions compared with general relativity (i.e. trivial topology of the universe is predicted). The unobservable absolute time is designed to solve the problem of time in quantization of general relativity. Background space and time define a Newtonian frame for the quantization of the gravitational field. By the way, a lot of other conceptual problems of quantization will be solved (i.e. no constraints, no topological foam, no black hole and bib bang singularities, natural vacuum definition for quantum fields on classical background).

  20. Critical analysis of the empirical tests of local hidden-variable theories

    Science.gov (United States)

    Santos, Emilio

    1992-10-01

    A local hidden-variable model is exhibited for the experiments by Aspect, Grangier, and Roger [Phys. Rev. Lett. 47, 460 (1981); 49, 91 (1982)] and Aspect, Dalibard, and Roger [Phys. Rev. Lett. 49, 1804 (1982)] measuring polarization correlation of optical-photon pairs. The model agrees with quantum-mechanical predictions for all measurable quantities even with ideal polarizers and detectors, and emphasizes the need of a high degree of directional correlation, besides the correlation of spin (or polarization or other quantities), in any test of locality. It is proved that homogeneous inequalities, involving only coincidence detection rates, cannot discriminate between quantum mechanics and local theories, which invalidates all previously used empirical tests. The role of supplementary assumptions, like the so-called no enhancement, for the derivation of Bell's inequalities is discussed. Finally it is conjectured that quantum mechanics might be compatible with local realism, if we assume that not all self-adjoint operators represent observables and not all density operators represent states.

  1. Engineering of Algorithms for Hidden Markov models and Tree Distances

    DEFF Research Database (Denmark)

    Sand, Andreas

    grown exponentially because of drastic improvements in the technology behind DNA and RNA sequencing, and focus on the research field has increased due to its potential to expand our knowledge about biological mechanisms and to improve public health. There has therefore been a continuously growing demand...... of the algorithms to exploit the parallel architecture of modern computers. In this PhD dissertation, I present my work with algorithmic optimizations and parallelizations in primarily two areas in algorithmic bioinformatics: algorithms for analyzing hidden Markov models and algorithms for computing distance...... measures between phylogenetic trees. Hidden Markov models is a class of probabilistic models that is used in a number of core applications in bioinformatics such as modeling of proteins, gene finding and reconstruction of species and population histories. I show how a relatively simple parallelization can...

  2. Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models

    Science.gov (United States)

    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.

  3. HMMEditor: a visual editing tool for profile hidden Markov model

    Directory of Open Access Journals (Sweden)

    Cheng Jianlin

    2008-03-01

    Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

  4. Hidden Markov Model Based Automated Fault Localization for Integration Testing

    OpenAIRE

    Ge, Ning; NAKAJIMA, SHIN; Pantel, Marc

    2013-01-01

    International audience; Integration testing is an expensive activity in software testing, especially for fault localization in complex systems. Model-based diagnosis (MBD) provides various benefits in terms of scalability and robustness. In this work, we propose a novel MBD approach for the automated fault localization in integration testing. Our method is based on Hidden Markov Model (HMM) which is an abstraction of system's component to simulate component's behaviour. The core of this metho...

  5. Drum Sound Detection in Polyphonic Music with Hidden Markov Models

    OpenAIRE

    Jouni Paulus; Anssi Klapuri

    2009-01-01

    This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combination...

  6. Analysis of animal accelerometer data using hidden Markov models

    OpenAIRE

    2016-01-01

    Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data there is a natural dependence between observations of movement or behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (H...

  7. Limits of performance for the model reduction problem of hidden Markov models

    KAUST Repository

    Kotsalis, Georgios

    2015-12-15

    We introduce system theoretic notions of a Hankel operator, and Hankel norm for hidden Markov models. We show how the related Hankel singular values provide lower bounds on the norm of the difference between a hidden Markov model of order n and any lower order approximant of order n̂ < n.

  8. Stock Market Trend Analysis Using Hidden Markov Models

    OpenAIRE

    Kavitha, G.; Udhayakumar, A.; D. Nagarajan

    2013-01-01

    Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock market behaviour using Hidden Markov Model (HMM). The trend once followed over a particular period will sure repeat in future. The one day difference in close value of stocks for a...

  9. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Olivier Aycard

    2004-12-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  10. Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Richard Washington

    2008-11-01

    Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T- intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

  11. Hidden Symmetry of a Fluid Dynamical Model

    CERN Document Server

    Neves, C

    2001-01-01

    A connection between solutions of the relativistic d-brane system in (d+1) dimensions with the solutions of a Galileo invariant fluid in d-dimensions is by now well established. However, the physical nature of the light-cone gauge description of a relativistic membrane changes after the reduction to the fluid dynamical model since the gauge symmetry is lost. In this work we argue that the original gauge symmetry present in a relativistic d-brane system can be recovered after the reduction process to a d-dimensional fluid model. To this end we propose, without introducing Wess-Zumino fields, a gauge invariant theory of isentropic fluid dynamics and show that this symmetry corresponds to the invariance under local translation of the velocity potential in the fluid dynamics picture. We show that different but equivalent choices of the sympletic sector lead to distinct representations of the embedded gauge algebra.

  12. Hidden Grassmann structure in the XXZ model

    CERN Document Server

    Boos, H; Miwa, T; Smirnov, F A; Takeyama, Y

    2006-01-01

    For the critical XXZ model, we consider the space W of operators which are products of local operators with a disorder operator. We introduce two anti-commutative family of operators b(z), c(z) which act on the space W. These operators are constructed as traces over representations of the q-oscillator algebra, in close analogy with Baxter's Q-operators. We show that the vacuum expectation values of operators in W can be expressed in terms of an exponential of a quadratic form of b(z), c(z).

  13. Quantum hidden Markov models based on transition operation matrices

    Science.gov (United States)

    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.

  14. A Duration Hidden Markov Model for the Identification of Regimes in Stock Market Returns

    DEFF Research Database (Denmark)

    Ntantamis, Christos

    This paper introduces a Duration Hidden Markov Model to model bull and bear market regime switches in the stock market; the duration of each state of the Markov Chain is a random variable that depends on a set of exogenous variables. The model not only allows the endogenous determination...... of the different regimes and but also estimates the effect of the explanatory variables on the regimes' durations. The model is estimated here on NYSE returns using the short-term interest rate and the interest rate spread as exogenous variables. The bull market regime is assigned to the identified state...... with the higher mean and lower variance; bull market duration is found to be negatively dependent on short-term interest rates and positively on the interest rate spread, while bear market duration depends positively the short-term interest rate and negatively on the interest rate spread....

  15. Hidden charm octet tetraquarks from a diquark-antidiquark model

    CERN Document Server

    Zhu, Ruilin

    2016-01-01

    Four exotic charmonium-like states, i.e. $X(4140)$, $X(4274)$, $X(4500)$, and $X(4700)$, have been observed very recently by LHCb Collaboration in the decay process $B^+\\to J/\\psi \\phi K^+$ using the 3${\\rm fb}^{-1}$ data of $p\\bar p$ collision at $\\sqrt s= 7$ and $8$ TeV. In this paper, we investigate systematically the hidden charm tetraquark states. The hidden charm tetraquarks form an octet and a singlet representation according to flavor $SU(3)$ symmetry. Based on a diquark-antidiquark model, the hidden charm tetraquarks spectra are given. The previous XYZ exotic states altogether with the newly ones $X(4140)$, $X(4274)$, $X(4500)$, and $X(4700)$, can be well classified into certain representations. The spin-parities and masses of the XYZ are predicted, most of which are in agreement with the data. We particularly find that $Z_c(4430)$ can be treated as the first radial excitation of $Z_c(3900)$, while the $Y(1^{--})$ states can be obtained by the first orbital excitation of $X/Z$. Besides, we calculate ...

  16. Entanglement without hidden nonlocality

    Science.gov (United States)

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

    2016-11-01

    We consider Bell tests in which the distant observers can perform local filtering before testing a Bell inequality. Notably, in this setup, certain entangled states admitting a local hidden variable model in the standard Bell scenario can nevertheless violate a Bell inequality after filtering, displaying so-called hidden nonlocality. Here we ask whether all entangled states can violate a Bell inequality after well-chosen local filtering. We answer this question in the negative by showing that there exist entangled states without hidden nonlocality. Specifically, we prove that some two-qubit Werner states still admit a local hidden variable model after any possible local filtering on a single copy of the state.

  17. Inference with Constrained Hidden Markov Models in PRISM

    CERN Document Server

    Christiansen, Henning; Lassen, Ole Torp; Petit, Matthieu

    2010-01-01

    A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.

  18. Models with hidden regular variation: Generation and detection

    Directory of Open Access Journals (Sweden)

    Bikramjit Das

    2015-12-01

    Full Text Available We review the notions of multivariate regular variation (MRV and hidden regular variation (HRV for distributions of random vectors and then discuss methods for generating models exhibiting both properties concentrating on the non-negative orthant in dimension two. Furthermore we suggest diagnostic techniques that detect these properties in multivariate data and indicate when models exhibiting both MRV and HRV are plausible fits for the data. We illustrate our techniques on simulated data, as well as two real Internet data sets.

  19. AIRWAY LABELING USING A HIDDEN MARKOV TREE MODEL

    Science.gov (United States)

    Ross, James C.; Díaz, Alejandro A.; Okajima, Yuka; Wassermann, Demian; Washko, George R.; Dy, Jennifer; San José Estépar, Raúl

    2014-01-01

    We present a novel airway labeling algorithm based on a Hidden Markov Tree Model (HMTM). We obtain a collection of discrete points along the segmented airway tree using particles sampling [1] and establish topology using Kruskal’s minimum spanning tree algorithm. Following this, our HMTM algorithm probabilistically assigns labels to each point. While alternative methods label airway branches out to the segmental level, we describe a general method and demonstrate its performance out to the subsubsegmental level (two generations further than previously published approaches). We present results on a collection of 25 computed tomography (CT) datasets taken from a Chronic Obstructive Pulmonary Disease (COPD) study. PMID:25436039

  20. Learning Hidden Markov Models using Non-Negative Matrix Factorization

    CERN Document Server

    Cybenko, George

    2008-01-01

    The Baum-Welsh algorithm together with its derivatives and variations has been the main technique for learning Hidden Markov Models (HMM) from observational data. We present an HMM learning algorithm based on the non-negative matrix factorization (NMF) of higher order Markovian statistics that is structurally different from the Baum-Welsh and its associated approaches. The described algorithm supports estimation of the number of recurrent states of an HMM and iterates the non-negative matrix factorization (NMF) algorithm to improve the learned HMM parameters. Numerical examples are provided as well.

  1. Hidden Markov Model Based Visual Perception Filtering in Robotic Soccer

    Directory of Open Access Journals (Sweden)

    Can Kavaklioglu

    2009-02-01

    Full Text Available Autonomous robots can initiate their mission plans only after gathering sufficient information about the environment. Therefore reliable perception information plays a major role in the overall success of an autonomous robot. The Hidden Markov Model based post-perception filtering module proposed in this paper aims to identify and remove spurious perception information in a given perception sequence using the generic metapose definition. This method allows representing uncertainty in more abstract terms compared to the common physical representations. Our experiments with the four legged AIBO robot indicated that the proposed module improved perception and localization performance significantly.

  2. Time separation as a hidden variable to the Copenhagen school of quantum mechanics

    CERN Document Server

    Kim, Y S

    2010-01-01

    The Bohr radius is a space-like separation between the proton and electron in the hydrogen atom. According to the Copenhagen school of quantum mechanics, the proton is sitting in the absolute Lorentz frame. If this hydrogen atom is observed from a different Lorentz frame, there is a time-like separation linearly mixed with the Bohr radius. Indeed, the time-separation is one of the essential variables in high-energy hadronic physics where the hadron is a bound state of the quarks, while thoroughly hidden in the present form of quantum mechanics. It will be concluded that this variable is hidden in Feynman's rest of the universe. It is noted first that Feynman's Lorentz-invariant differential equation for the bound-state quarks has a set of solutions which describe all essential features of hadronic physics. These solutions explicitly depend on the time separation between the quarks. This set also forms the mathematical basis for two-mode squeezed states in quantum optics, where both photons are observable, but...

  3. Hidden Markov models for prediction of protein features

    DEFF Research Database (Denmark)

    Bystroff, Christopher; Krogh, Anders

    2008-01-01

    Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represented by the Markov states. For protein...... structure prediction, states have been chosen to represent either homologous sequence positions, local or secondary structure types, or transmembrane locality. The resulting models can be used to predict common ancestry, secondary or local structure, or membrane topology by applying one of the two standard...... algorithms for comparing a sequence to a model. In this chapter, we review those algorithms and discuss how HMMs have been constructed and refined for the purpose of protein structure prediction....

  4. A first study of Hidden Valley models at the LHC

    CERN Document Server

    Seth, Morgan Svensson

    2011-01-01

    New stable particles with fairly low masses could exist if the coupling to the Standard Model is weak, and with suitable parameters they might be possible to produce at the LHC. Here we study a selection of models with the new particles being charged under a new gauge group, either U(1) or SU(N). In the Abelian case there will be radiation of gammavs, which decay back into the SM. In the non-Abelian case the particles will undergo hadronization into mesons like states piv/rhov that subsequently decays. We consider three different scenarios for interaction between the new sector and the SM sector and perform simulations using a Hidden Valley model previously implemented in PYTHIA. In this study we illustrate how one can distinguish the different models and measure different parameters of the models under conditions like those at the LHC.

  5. Promoter recognition based on the maximum entropy hidden Markov model.

    Science.gov (United States)

    Zhao, Xiao-yu; Zhang, Jin; Chen, Yuan-yuan; Li, Qiang; Yang, Tao; Pian, Cong; Zhang, Liang-yun

    2014-08-01

    Since the fast development of genome sequencing has produced large scale data, the current work uses the bioinformatics methods to recognize different gene regions, such as exon, intron and promoter, which play an important role in gene regulations. In this paper, we introduce a new method based on the maximum entropy Markov model (MEMM) to recognize the promoter, which utilizes the biological features of the promoter for the condition. However, it leads to a high false positive rate (FPR). In order to reduce the FPR, we provide another new method based on the maximum entropy hidden Markov model (ME-HMM) without the independence assumption, which could also accommodate the biological features effectively. To demonstrate the precision, the new methods are implemented by R language and the hidden Markov model (HMM) is introduced for comparison. The experimental results show that the new methods may not only overcome the shortcomings of HMM, but also have their own advantages. The results indicate that, MEMM is excellent for identifying the conserved signals, and ME-HMM can demonstrably improve the true positive rate.

  6. Generalized Hidden Markov Models To Handwritten Devanagari Word Recognition

    Directory of Open Access Journals (Sweden)

    Mr. Pradeep Singh Thakur

    2012-06-01

    Full Text Available Hidden Markov Models (HMM have long been a popular choice for Western cursive handwriting recognition following their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, Hidden Markov Models are increasingly being used to model substrokes of characters. However, when it comes to Indic script recognition, the published work employing HMMs is limited, and generally focused on isolated character recognition. In this effort, a data-driven HMM-based handwritten word recognition system for Hindi, an Indic script, is proposed. Though Devanagari is the script for Hindi, which is the official language of India, its character and word recognition pose great challenges due to large variety of symbols and their proximity in appearance. The accuracies obtained ranged from 30�0to 60�0with lexicon. These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

  7. Application of the Viterbi Algorithm in Hidden Markov Models for Exploring Irrigation Decision Series

    Science.gov (United States)

    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

  8. Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R

    DEFF Research Database (Denmark)

    O'Connell, Jarad Michael; Højsgaard, Søren

    2011-01-01

    This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov...... models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows...

  9. Hidden Markov Models Incorporating Fuzzy Measures and Integrals for Protein Sequence Identification and Alignment

    Institute of Scientific and Technical Information of China (English)

    Niranjan P.Bidargaddi; Madlhu Chetty; Joarder Kamruzzaman

    2008-01-01

    Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forwardand backward variables, we propose a fuzzy Baum-Welch parameter estimation al-gorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.

  10. A Dependent Hidden Markov Model of Credit Quality

    Directory of Open Access Journals (Sweden)

    Małgorzata Wiktoria Korolkiewicz

    2012-01-01

    Full Text Available We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

  11. Hidden Markov Model Application to Transfer The Trader Online Forex Brokers

    Directory of Open Access Journals (Sweden)

    Farida Suharleni

    2012-05-01

    Full Text Available Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A from every broker forex online. Next step is making a probability of observation matrix (B by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1 is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.

  12. Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

    OpenAIRE

    Genon-Catalot, Valentine; Jeantheau, Thierry; Laredo, Catherine

    2003-01-01

    ABSTRACT. This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimat...

  13. Permutation Complexity and Coupling Measures in Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Taichi Haruna

    2013-09-01

    Full Text Available Recently, the duality between values (words and orderings (permutations has been proposed by the authors as a basis to discuss the relationship between information theoretic measures for finite-alphabet stationary stochastic processes and their permutatio nanalogues. It has been used to give a simple proof of the equality between the entropy rate and the permutation entropy rate for any finite-alphabet stationary stochastic process and to show some results on the excess entropy and the transfer entropy for finite-alphabet stationary ergodic Markov processes. In this paper, we extend our previous results to hidden Markov models and show the equalities between various information theoretic complexity and coupling measures and their permutation analogues. In particular, we show the following two results within the realm of hidden Markov models with ergodic internal processes: the two permutation analogues of the transfer entropy, the symbolic transfer entropy and the transfer entropy on rank vectors, are both equivalent to the transfer entropy if they are considered as the rates, and the directed information theory can be captured by the permutation entropy approach.

  14. PELACAKAN DAN PENGENALAN WAJAH MENGGUNAKAN METODE EMBEDDED HIDDEN MARKOV MODELS

    Directory of Open Access Journals (Sweden)

    Arie Wirawan Margono

    2004-01-01

    Full Text Available Tracking and recognizing human face becomes one of the important research subjects nowadays, where it is applicable in security system like room access, surveillance, as well as searching for person identity in police database. Because of applying in security case, it is necessary to have robust system for certain conditions such as: background influence, non-frontal face pose of male or female in different age and race. The aim of this research is to develop software which combines human face tracking using CamShift algorithm and face recognition system using Embedded Hidden Markov Models. The software uses video camera (webcam for real-time input, video AVI for dynamic input, and image file for static input. The software uses Object Oriented Programming (OOP coding style with C++ programming language, Microsoft Visual C++ 6.0® compiler, and assisted by some libraries of Intel Image Processing Library (IPL and Intel Open Source Computer Vision (OpenCV. System testing shows that object tracking based on skin complexion using CamShift algorithm comes out well, for tracking of single or even two face objects at once. Human face recognition system using Embedded Hidden Markov Models method has reach accuracy percentage of 82.76%, using 341 human faces in database that consists of 31 individuals with 11 poses and 29 human face testers. Abstract in Bahasa Indonesia : Pelacakan dan pengenalan wajah manusia merupakan salah satu bidang yang cukup berkembang dewasa ini, dimana aplikasi dapat diterapkan dalam bidang keamanan (security system seperti ijin akses masuk ruangan, pengawasan lokasi (surveillance, maupun pencarian identitas individu pada database kepolisian. Karena diterapkan dalam kasus keamanan, dibutuhkan sistem yang handal terhadap beberapa kondisi, seperti: pengaruh latar belakang, pose wajah non-frontal terhadap pria maupun wanita dalam perbedaan usia dan ras. Tujuan penelitiam ini adalah untuk membuat perangkat lunak yang menggabungkan

  15. Scale invariant extension of the standard model with a strongly interacting hidden sector.

    Science.gov (United States)

    Hur, Taeil; Ko, P

    2011-04-08

    We present a scale invariant extension of the standard model with a new QCD-like strong interaction in the hidden sector. A scale Λ(H) is dynamically generated in the hidden sector by dimensional transmutation, and chiral symmetry breaking occurs in the hidden sector. This scale is transmitted to the SM sector by a real singlet scalar messenger S and can trigger electroweak symmetry breaking. Thus all the mass scales in this model arise from the hidden sector scale Λ(H), which has quantum mechanical origin. Furthermore, the lightest hadrons in the hidden sector are stable by the flavor conservation of the hidden sector strong interaction, and could be the cold dark matter (CDM). We study collider phenomenology, relic density, and direct detection rates of the CDM of this model.

  16. The Hierarchical Dirichlet Process Hidden Semi-Markov Model

    CERN Document Server

    Johnson, Matthew J

    2012-01-01

    There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi- Markovianity, which has been developed in the parametric setting to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicitduration HDP-HSMM and develop posterior sampling algorithms for efficient inference in both the direct-assignment and weak-limit approximation settings. We demonstrate the utility of the model and our inference methods on synthetic data as well as experiments on a speaker diarization problem and an example of learning the patterns in Morse code.

  17. Characterization of prokaryotic and eukaryotic promoters using hidden Markov models

    DEFF Research Database (Denmark)

    Pedersen, Anders Gorm; Baldi, P.; Chauvin, Y.

    1996-01-01

    that bind to them. We find that HMMs trained on such subclasses of Escherichia coli promoters (specifically, the so-called sigma 70 and sigma 54 classes) give an excellent classification of unknown promoters with respect to sigma-class. HMMs trained on eukaryotic sequences from human genes also model nicely......In this paper we utilize hidden Markov models (HMMs) and information theory to analyze prokaryotic and eukaryotic promoters. We perform this analysis with special emphasis on the fact that promoters are divided into a number of different classes, depending on which polymerase-associated factors...... have at the same time the ability to find clusters and the ability to model the sequential structure in the input data. This is highly relevant in situations where the variance in the data is high, as is the case for the subclass structure in for example promoter sequences....

  18. Sequence alignments and pair hidden Markov models using evolutionary history.

    Science.gov (United States)

    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.

  19. Understanding eye movements in face recognition using hidden Markov models.

    Science.gov (United States)

    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.

  20. Optimal State-Space Reduction for Pedigree Hidden Markov Models

    CERN Document Server

    Kirkpatrick, Bonnie

    2012-01-01

    To analyze whole-genome genetic data inherited in families, the likelihood is typically obtained from a Hidden Markov Model (HMM) having a state space of 2^n hidden states where n is the number of meioses or edges in the pedigree. There have been several attempts to speed up this calculation by reducing the state-space of the HMM. One of these methods has been automated in a calculation that is more efficient than the naive HMM calculation; however, that method treats a special case and the efficiency gain is available for only those rare pedigrees containing long chains of single-child lineages. The other existing state-space reduction method treats the general case, but the existing algorithm has super-exponential running time. We present three formulations of the state-space reduction problem, two dealing with groups and one with partitions. One of these problems, the maximum isometry group problem was discussed in detail by Browning and Browning. We show that for pedigrees, all three of these problems hav...

  1. Shadow networks: Discovering hidden nodes with models of information flow

    CERN Document Server

    Bagrow, James P; Frank, Morgan R; Manukyan, Narine; Mitchell, Lewis; Reagan, Andrew; Bloedorn, Eric E; Booker, Lashon B; Branting, Luther K; Smith, Michael J; Tivnan, Brian F; Danforth, Christopher M; Dodds, Peter S; Bongard, Joshua C

    2013-01-01

    Complex, dynamic networks underlie many systems, and understanding these networks is the concern of a great span of important scientific and engineering problems. Quantitative description is crucial for this understanding yet, due to a range of measurement problems, many real network datasets are incomplete. Here we explore how accidentally missing or deliberately hidden nodes may be detected in networks by the effect of their absence on predictions of the speed with which information flows through the network. We use Symbolic Regression (SR) to learn models relating information flow to network topology. These models show localized, systematic, and non-random discrepancies when applied to test networks with intentionally masked nodes, demonstrating the ability to detect the presence of missing nodes and where in the network those nodes are likely to reside.

  2. The Consensus String Problem and the Complexity of Comparing Hidden Markov Models

    DEFF Research Database (Denmark)

    Lyngsø, Rune Bang; Pedersen, Christian Nørgaard Storm

    2002-01-01

    The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing...... the probability of generating a given string, or computing the most likely path generating a given string. In this paper we consider the problem of computing the most likely string, or consensus string, generated by a given model, and its implications on the complexity of comparing hidden Markov models. We show...... that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models. Furthermore, we establish the NP-hardness of comparing two hidden Markov models under the L∞- and L1...

  3. The Consensus String Problem and the Complexity of Comparing Hidden Markov Models

    DEFF Research Database (Denmark)

    Lyngsø, Rune Bang; Pedersen, Christian Nørgaard Storm

    2002-01-01

    The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing...... the probability of generating a given string, or computing the most likely path generating a given string. In this paper we consider the problem of computing the most likely string, or consensus string, generated by a given model, and its implications on the complexity of comparing hidden Markov models. We show...... that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models. Furthermore, we establish the NP-hardness of comparing two hidden Markov models under the L∞- and L1...

  4. A Bayesian Approach for Structural Learning with Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Cen Li

    2002-01-01

    Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

  5. Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

    Directory of Open Access Journals (Sweden)

    Jérôme Boudy

    2007-01-01

    Full Text Available This work aims at providing new insights on the electrocardiogram (ECG segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.

  6. A context dependent pair hidden Markov model for statistical alignment

    CERN Document Server

    Arribas-Gil, Ana

    2011-01-01

    This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. We propose to estimate alignments and context dependent mutation rates relying on the observation of two homologous sequences. The procedure is based on a generalized pair-hidden Markov structure, where conditional on the alignment path, the nucleotide sequences follow a Markov distribution. We use a stochastic approximation expectation maximization (saem) algorithm to give accurate estimators of parameters and alignments. We provide results both on simulated data and vertebrate genomes, which are known to have a high mutation rate from CG dinucleotide. In particular, we establish that the method improves the accuracy of the alignment of a human pseudogene and its functional gene.

  7. Topic Information Collection based on the Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Hai-yan Jiang

    2013-02-01

    Full Text Available Specific-subject oriented information collection is one of the key technologies of vertical search engines, which directly affects the speed and relevance of search results. The topic information collection algorithm is widely used for its accuracy. The Hidden Markov Model (HMM is used to learn and judge the relevance between the Uniform Resource Locator (URL and the topic information. The Rocchio method is used to construct the prototype vectors relevant to the topic information, and the HMM is used to learn the preferred browsing paths. The concept maps including the semantics of the webpage are constructed and the web's link structures can be decided. The validity of the algorithm is proved by the experiment at last. Comparing with the Best-First algorithm, this algorithm can get more information pages and has higher precision ratio.

  8. Image segmentation and classification based on a 2D distributed hidden Markov model

    Science.gov (United States)

    Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq

    2008-01-01

    In this paper, we propose a two-dimensional distributed hidden Markovmodel (2D-DHMM), where dependency of the state transition probability on any state is allowed as long as causality is preserved. The proposed 2D-DHMM model is result of a novel solution to a more general non-causal two-dimensional hidden Markovmodel (2D-HMM) that we proposed. Our proposed models can capture, for example, dependency among diagonal states, which can be critical in many image processing applications, for example, image segmentation. A new sets of basic image patterns are designed to enrich the variability of states, which in return largely improves the accuracy of state estimations and segmentation performance. We provide three algorithms for the training and classification of our proposed model. A new Expectation-Maximization (EM) algorithm suitable for estimation of the new model is derived, where a novel General Forward-Backward (GFB) algorithm is proposed for recursive estimation of the model parameters. A new conditional independent subset-state sequence structure decomposition of state sequences is proposed for the 2D Viterbi algorithm. Application to aerial image segmentation shows the superiority of our model compared to the existing models.

  9. Drum Sound Detection in Polyphonic Music with Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Jouni Paulus

    2009-01-01

    Full Text Available This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs. The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations.

  10. Landmine detection using mixture of discrete hidden Markov models

    Science.gov (United States)

    Frigui, Hichem; Hamdi, Anis; Missaoui, Oualid; Gader, Paul

    2009-05-01

    We propose a landmine detection algorithm that uses a mixture of discrete hidden Markov models. We hypothesize that the data are generated by K models. These different models reflect the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Model identification could be achieved through clustering in the parameters space or in the feature space. However, this approach is inappropriate as it is not trivial to define a meaningful distance metric for model parameters or sequence comparison. Our proposed approach is based on clustering in the log-likelihood space, and has two main steps. First, one HMM is fit to each of the R individual sequence. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an R×R log-likelihood distance matrix that will be partitioned into K groups using a hierarchical clustering algorithm. In the second step, we pool the sequences, according to which cluster they belong, into K groups, and we fit one HMM to each group. The mixture of these K HMMs would be used to build a descriptive model of the data. An artificial neural networks is then used to fuse the output of the K models. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent HMM models that describe different properties of the data. Each HMM models a group of alarm signatures that share common attributes such as clutter, mine type, and burial depth. Our initial experiments have also indicated that the proposed mixture model outperform the baseline HMM that uses one model for the mine and one model for the background.

  11. Landmine detection using discrete hidden Markov models with Gabor features

    Science.gov (United States)

    Frigui, Hichem; Missaoui, Oualid; Gader, Paul

    2007-04-01

    We propose a general method for detecting landmine signatures in vehicle mounted ground penetrating radar (GPR) using discrete hidden Markov models and Gabor wavelet features. Observation vectors are constructed based on the expansion of the signature's B-scan using a bank of scale and orientation selective Gabor filters. This expansion provides localized frequency description that gets encoded in the observation sequence. These observations do not impose an explicit structure on the mine model, and are used to naturally model the time-varying signatures produced by the interaction of the GPR and the landmines as the vehicle moves. The proposed method is evaluated on real data collected by a GPR mounted on a moving vehicle at three different geographical locations that include several lanes. The model parameters are optimized using the BaumWelch algorithm, and lane-based cross-validation, in which each mine lane is in turn treated as a test set with the rest of the lanes used for training, is used to train and test the model. Preliminary results show that observations encoded with Gabor wavelet features perform better than observation encoded with gradient-based edge features.

  12. Adaptive Partially Hidden Markov Models with Application to Bilevel Image Coding

    DEFF Research Database (Denmark)

    Forchhammer, Søren Otto; Rasmussen, Tage

    1999-01-01

    Adaptive Partially Hidden Markov Models (APHMM) are introduced extending the PHMM models. The new models are applied to lossless coding of bi-level images achieving resluts which are better the JBIG standard.......Adaptive Partially Hidden Markov Models (APHMM) are introduced extending the PHMM models. The new models are applied to lossless coding of bi-level images achieving resluts which are better the JBIG standard....

  13. Predicting nucleosome positioning using a duration Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Widom Jonathan

    2010-06-01

    Full Text Available Abstract Background The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function. Results We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download. Conclusions Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

  14. On Parsing Visual Sequences with the Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Naomi Harte

    2009-01-01

    Full Text Available Hidden Markov Models have been employed in many vision applications to model and identify events of interest. Their use is common in applications where HMMs are used to classify previously divided segments of video as one of a set of events being modelled. HMMs can also simultaneously segment and classify events within a continuous video, without the need for a separate first step to identify the start and end of the events. This is significantly less common. This paper is an exploration of the development of HMM frameworks for such complete event recognition. A review of how HMMs have been applied to both event classification and recognition is presented. The discussion evolves in parallel with an example of a real application in psychology for illustration. The complete videos depict sessions where candidates perform a number of different exercises under the instruction of a psychologist. The goal is to isolate portions of video containing just one of these exercises. The exercise involves rotating the head of a kneeling subject to the left, back to centre, to the right, to the centre, and repeating a number of times. By designing a HMM system to automatically isolate portions of video containing this exercise, issues such as the strategy of choice of event to be modelled, feature design and selection, as well as training and testing are reviewed. Thus this paper shows how HMMs can be more extensively applied in the domain of event recognition in video.

  15. Hidden Markov Models for the Activity Profile of Terrorist Groups

    CERN Document Server

    Raghavan, Vasanthan; Tartakovsky, Alexander G

    2012-01-01

    The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and in general, tracking it over a period of time. Toward this goal, a d-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of d = 2 corresponds to the case where the dynamics are coarsely quantized as Active and Inactive, respectively. Two strategies for spurt detection and tracking are developed here: a model-independent strategy that uses the exponential weighted moving-average (EWMA) filter to track the strength of the group as measured by the number of attacks perpetrated by it, and a state estimation strategy that exploits the underlying HMM structure. The EWMA strategy is robust to modeling uncertainties and errors, and tracks persistent changes (changes that last for a sufficiently long duration) in the strength of the group. On the othe...

  16. Hidden Semi-Markov Models for Predictive Maintenance

    Directory of Open Access Journals (Sweden)

    Francesco Cartella

    2015-01-01

    Full Text Available Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs with (i no constraints on the state duration density function and (ii being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL of the machine is calculated.

  17. Recognition of surgical skills using hidden Markov models

    Science.gov (United States)

    Speidel, Stefanie; Zentek, Tom; Sudra, Gunther; Gehrig, Tobias; Müller-Stich, Beat Peter; Gutt, Carsten; Dillmann, Rüdiger

    2009-02-01

    Minimally invasive surgery is a highly complex medical discipline and can be regarded as a major breakthrough in surgical technique. A minimally invasive intervention requires enhanced motor skills to deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To recognize and analyze the current situation for context-aware assistance, we need intraoperative sensor data and a model of the intervention. Characteristics of a situation are the performed activity, the used instruments, the surgical objects and the anatomical structures. Important information about the surgical activity can be acquired by recognizing the surgical gesture performed. Surgical gestures in minimally invasive surgery like cutting, knot-tying or suturing are here referred to as surgical skills. We use the motion data from the endoscopic instruments to classify and analyze the performed skill and even use it for skill evaluation in a training scenario. The system uses Hidden Markov Models (HMM) to model and recognize a specific surgical skill like knot-tying or suturing with an average recognition rate of 92%.

  18. Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures

    NARCIS (Netherlands)

    Bousmalis, K.; Zafeiriou, S.; Morency, L.P.; Pantic, Maja; Ghahramani, Z.

    2013-01-01

    Hidden Conditional Random Fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An infinite HCRF is an HCRF with a countably infinite number of hidden states, which rids us not only of the necessit

  19. Ensemble hidden Markov models with application to landmine detection

    Science.gov (United States)

    Hamdi, Anis; Frigui, Hichem

    2015-12-01

    We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models' outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM. Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model's parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

  20. Prediction of signal peptides and signal anchors by a hidden Markov model

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Nielsen, Henrik

    1998-01-01

    A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our ...... is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model....

  1. Optical character recognition of handwritten Arabic using hidden Markov models

    Energy Technology Data Exchange (ETDEWEB)

    Aulama, Mohannad M. [University of Jordan; Natsheh, Asem M. [University of Jordan; Abandah, Gheith A. [University of Jordan; Olama, Mohammed M [ORNL

    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.

  2. Optical character recognition of handwritten Arabic using hidden Markov models

    Science.gov (United States)

    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.

  3. Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

    DEFF Research Database (Denmark)

    Tataru, Paula; Sand, Andreas; Hobolth, Asger;

    2013-01-01

    Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed...... data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction...

  4. Bayesian inversion of seismic attributes for geological facies using a Hidden Markov Model

    Science.gov (United States)

    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

  5. Use of Hidden Markov Mobility Model for Location Based Services

    Directory of Open Access Journals (Sweden)

    Bhakti D. Shelar

    2014-07-01

    Full Text Available These days people prefer to use portable and wireless devices such as laptops, mobile phones, They are connected through satellites. As user moves from one point to other, task of updating stored information becomes difficult. Provision of Location based services to users, faces some challenges like limited bandwidth and limited client power. To optimize data accessibility and to minimize access cost, we can store frequently accessed data item in cache of client. So small size of cache is introduced in mobile devices. Data fetched from server is stored on cache. So requested data from user is provided from cache and not from remote server. Question arises that which data should be kept in the cache? Performance of cache majorly depends on the cache replacement policies which select data suitable for eviction from cache. This paper presents use of Hidden Markov Models(HMMs for prediction of user‟s future location. Then data item irrelevant to this predicted location is fetched out from the cache. The proposed approach clusters location histories according to their location characteristics and also it considers each user‟s previous actions. This results in producing high packet delivery ratio and minimum delay.

  6. pHMM-tree: phylogeny of profile hidden Markov models.

    Science.gov (United States)

    Huo, Luyang; Zhang, Han; Huo, Xueting; Yang, Yasong; Li, Xueqiong; Yin, Yanbin

    2017-04-01

    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. This software is implemented in C/C ++ and is available at http://cys.bios.niu.edu/pHMM-Tree/source/. zhanghan@nankai.edu.cn or yyin@niu.edu. Supplementary data are available at Bioinformatics online.

  7. Liver Disease Recognition: A Discrete Hidden Markov Model Approach

    Directory of Open Access Journals (Sweden)

    Farzan Madadizadeh

    2016-03-01

    Full Text Available The liver alongside the heart and the brain is the largest and the most vital organ within the human body whose absence leads to certain death. In addition, diagnosis of liver diseases takes a long time and requires sufficient expertise of physicians. To this end, statistical methods as automatic prediction systems can help specialists to diagnose liver diseases quickly and accurately. The Discrete Hidden Markov Model (DHMM is an intelligent and a strong statistical model used to predict the types of liver diseases in patients in this study. The data in this crosssectional study included information elicited from the records of 1143 patients with 5 different types of liver diseases including cirrhosis of the liver, liver cancer, acute hepatitis, chronic hepatitis, and fatty liver disease admitted to Afzalipour Hospital in the city of Kerman in the time period of 2006-2013. At first, the type of diseases for each patient was identified; however, it was assumed that the type of diseases is unknown and there were attempts to diagnose the type of the disease through the DHMM to examine its accuracy. Therefore, the DHMM was fitted to the data and its performance was evaluated by using the parameters of accuracy, sensitivity, and specificity. Such parameters of the model were separately calculated for the diagnosis of liver diseases. The highest levels of accuracy, sensitivity, and specificity were associated with the diagnosis of cirrhosis of the liver and equal to 0.77, 0.82, 0.96, respectively; and the lowest levels were related to the diagnosis of fatty liver disease with an accuracy level of 0.65 and a sensitivity level of 0.69. As well, the specificity level in the diagnosis of fatty liver disease was 0.94. The results of this study indicated the potential ability of the DHMM; thus, the use of this model in terms of diagnosing liver diseases was strongly recommended.

  8. Efficient decoding algorithms for generalized hidden Markov model gene finders

    Directory of Open Access Journals (Sweden)

    Delcher Arthur L

    2005-01-01

    Full Text Available Abstract 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

  9. A TWO-STATE MIXED HIDDEN MARKOV MODEL FOR RISKY TEENAGE DRIVING BEHAVIOR

    Science.gov (United States)

    Jackson, John C.; Albert, Paul S.; Zhang, Zhiwei

    2016-01-01

    This paper proposes a joint model for longitudinal binary and count outcomes. We apply the model to a unique longitudinal study of teen driving where risky driving behavior and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and predicting crash and near crash outcomes. We propose a two-state mixed hidden Markov model whereby the hidden state characterizes the mean for the joint longitudinal crash/near crash outcomes and elevated g-force events which are a proxy for risky driving. Heterogeneity is introduced in both the conditional model for the count outcomes and the hidden process using a shared random effect. An estimation procedure is presented using the forward–backward algorithm along with adaptive Gaussian quadrature to perform numerical integration. The estimation procedure readily yields hidden state probabilities as well as providing for a broad class of predictors.

  10. Belief Bisimulation for Hidden Markov Models Logical Characterisation and Decision Algorithm

    DEFF Research Database (Denmark)

    Jansen, David N.; Nielson, Flemming; Zhang, Lijun

    2012-01-01

    This paper establishes connections between logical equivalences and bisimulation relations for hidden Markov models (HMM). Both standard and belief state bisimulations are considered. We also present decision algorithms for the bisimilarities. For standard bisimilarity, an extension of the usual...

  11. Locality or non-locality in quantum mechanics: hidden variables without ''spooky action-at-a-distance''

    Energy Technology Data Exchange (ETDEWEB)

    Aharonov, Y. [Texas A and M Univ., College Station, TX (United States); South Carolina Univ., Columbia, SC (United States). Dept. of Physics and Astronomy; Tel Aviv Univ. (Israel). School of Physics and Astronomy; Botero, A. [South Carolina Univ., Columbia, SC (United States). Dept. of Physics and Astronomy; Centro Internacional de Fisica, Ciudad Univ., Bogota (Colombia); Scully, M. [Texas A and M Univ., College Station, TX (United States); Max-Planck-Institut fuer Quantenoptik (MPQ), Garching (Germany)

    2001-02-01

    The folklore notion of the ''Non-Locality of Quantum Mechanics'' is examined from the point of view of hidden-variables theories according to Belinfante's classification in his Survey of Hidden Variables Theories. It is here shown that in the case of EPR, there exist hidden variables theories that successfully reproduce quantum-mechanical predictions, but which are explicitly local. Since such theories do not fall into Belinfante's classification, we propose an expanded classification which includes similar theories, which we term as theories of the ''third'' kind. Causal implications of such theories are explored. (orig.)

  12. A Hidden Markov Model for avalanche forecasting on Chowkibal–Tangdhar road axis in Indian Himalayas

    Indian Academy of Sciences (India)

    Jagdish Chandra Joshi; Sunita Srivastava

    2014-12-01

    A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal–Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992–2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.

  13. Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

    CERN Document Server

    van de Meent, Jan-Willem; Wood, Frank; Gonzalez, Ruben L; Wiggins, Chris H

    2013-01-01

    We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes a...

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

    Institute of Scientific and Technical Information of China (English)

    Hongyan Wang; Xiaobo Zhou

    2013-01-01

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

  15. Hidden Markov models applied to a subsequence of the Xylella fastidiosa genome

    Directory of Open Access Journals (Sweden)

    Silva Cibele Q. da

    2003-01-01

    Full Text Available Dependencies in DNA sequences are frequently modeled using Markov models. However, Markov chains cannot account for heterogeneity that may be present in different regions of the same DNA sequence. Hidden Markov models are more realistic than Markov models since they allow for the identification of heterogeneous regions of a DNA sequence. In this study we present an application of hidden Markov models to a subsequence of the Xylella fastidiosa DNA data. We found that a three-state model provides a good description for the data considered.

  16. Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

    DEFF Research Database (Denmark)

    Nystrup, Peter; Madsen, Henrik; Lindström, Erik

    2016-01-01

    estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact...... to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations....

  17. Algorithms for a parallel implementation of Hidden Markov Models with a small state space

    DEFF Research Database (Denmark)

    Nielsen, Jesper; Sand, Andreas

    2011-01-01

    Two of the most important algorithms for Hidden Markov Models are the forward and the Viterbi algorithms. We show how formulating these using linear algebra naturally lends itself to parallelization. Although the obtained algorithms are slow for Hidden Markov Models with large state spaces......, they require very little communication between processors, and are fast in practice on models with a small state space. We have tested our implementation against two other imple- mentations on artificial data and observe a speed-up of roughly a factor of 5 for the forward algorithm and more than 6...... for the Viterbi algorithm. We also tested our algorithm in the Coalescent Hidden Markov Model framework, where it gave a significant speed-up....

  18. Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R

    DEFF Research Database (Denmark)

    O'Connell, Jarad Michael; Højsgaard, Søren

    2011-01-01

    models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows...

  19. The detection of hidden visual loss in optic neuropathy: VISTECH test at variable illuminations.

    Science.gov (United States)

    Leys, M J; Candaele, C M; Uvijls, A G; Heintz, B F; De Rouck, A F; Odom, J V

    1990-01-01

    The sensitivity of the VISTECH chart in the detection of hidden visual loss is debated. We tried to evaluate the diagnostic value of the test by using different illumination levels. Twelve MS-patients with normal acuity but a pathological VEP were examined at 9 different illuminations. We did not identify more abnormalities among patients, using VISTECH test at other illumination levels than the one recommended by the manufacturer.

  20. Using frame correlation algorithm in a duration distribution based hidden Markov model

    Institute of Scientific and Technical Information of China (English)

    王作英; 崔小东

    2000-01-01

    The assumption of frame independence is a widely known weakness of traditional hidden Markov model (HMM). In this paper, a frame correlation algorithm based on the duration distribution based hidden Markov model (DDBHMM) is proposed. In the algorithm, an AR model is used to depict the low pass effect of vocal tract from which stems the inertia leading to frame correlation. In the preliminary experiment of middle vocabulary speaker dependent isolated word recognition, our frame correlation algorithm outperforms the frame independent one. The average error reduction is about 20% .

  1. Long memory of financial time series and hidden Markov models with time-varying parameters

    DEFF Research Database (Denmark)

    Nystrup, Peter; Madsen, Henrik; Lindström, Erik

    facts have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time-varying. It is shown that a two-state Gaussian hidden Markov model with time-varying parameters is able to reproduce the long memory of squared...... daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time-varying behavior of the parameters also leads to improved one-step predictions....

  2. Model-Independent Indirect Detection Constraints on Hidden Sector Dark Matter

    CERN Document Server

    Elor, Gilly; Slatyer, Tracy R; Xue, Wei

    2015-01-01

    If dark matter inhabits an expanded "hidden sector", annihilations may proceed through sequential decays or multi-body final states. We map out the potential signals and current constraints on such a framework in indirect searches, using a model-independent setup based on multi-step hierarchical cascade decays. While remaining agnostic to the details of the hidden sector model, our framework captures the generic broadening of the spectrum of secondary particles (photons, neutrinos, e+e- and antiprotons) relative to the case of direct annihilation to Standard Model particles. We explore how indirect constraints on dark matter annihilation limit the parameter space for such cascade/multi-particle decays. We investigate limits from the cosmic microwave background by Planck, the Fermi measurement of photons from the dwarf galaxies, and positron data from AMS-02. The presence of a hidden sector can change the constraints on the dark matter annihilation cross section by up to an order of magnitude in either directi...

  3. Discrete channel modelling based on genetic algorithm and simulated annealing for training hidden Markov model

    Institute of Scientific and Technical Information of China (English)

    Zhao Zhi-Jin; Zheng Shi-Lian; Xu Chun-Yun; Kong Xian-Zheng

    2007-01-01

    Hidden Markov models (HMMs) have been used to model burst error sources of wireless channels. This paper proposes a hybrid method of using genetic algorithm (GA) and simulated annealing (SA) to train HMM for discrete channel modelling. The proposed method is compared with pure GA, and experimental results show that the HMMs trained by the hybrid method can better describe the error sequences due to SA's ability of facilitating hill-climbing at the later stage of the search. The burst error statistics of the HMMs trained by the proposed method and the corresponding error sequences are also presented to validate the proposed method.

  4. Comparison of the Beta and the Hidden Markov Models of Trust in Dynamic Environments

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  6. Feature learning for a hidden Markov model approach to landmine detection

    Science.gov (United States)

    Zhang, Xuping; Gader, Paul; Frigui, Hichem

    2007-04-01

    Hidden Markov Models (HMMs) are useful tools for landmine detection and discrimination using Ground Penetrating Radar (GPR). The performance of HMMs, as well as other feature-based methods, depends not only on the design of the classifier but on the features. Traditionally, algorithms for learning the parameters of classifiers have been intensely investigated while algorithms for learning parameters of the feature extraction process have been much less intensely investigated. In this paper, we describe experiments for learning feature extraction and classification parameters simultaneously in the context of using hidden Markov models for landmine detection.

  7. Modeling Pacific Decadal Variability

    Science.gov (United States)

    Schneider, N.

    2002-05-01

    Hypotheses for decadal variability rely on the large thermal inertia of the ocean to sequester heat and provide the long memory of the climate system. Understanding decadal variability requires the study of the generation of ocean anomalies at decadal frequencies, the evolution of oceanic signals, and the response of the atmosphere to oceanic perturbations. A sample of studies relevant for Pacific decadal variability will be reviewed in this presentation. The ocean integrates air-sea flux anomalies that result from internal atmospheric variability or broad-band coupled processes such as ENSO, or are an intrinsic part of the decadal feedback loop. Anomalies of Ekman pumping lead to deflections of the ocean thermocline and accompanying changes of the ocean circulation; perturbations of surface layer heat and fresh water budgets cause anomalies of T/S characteristics of water masses. The former process leads to decadal variability due to the dynamical adjustment of the mid latitude gyres or thermocline circulation; the latter accounts for the low frequency climate variations by the slow propagation of anomalies in the thermocline from the mid-latitude outcrops to the equatorial upwelling regions. Coupled modeling studies and ocean model hindcasts suggest that the adjustment of the North Pacific gyres to variation of Ekman pumping causes low frequency variations of surface temperature in the Kuroshio-Oyashio extension region. These changes appear predictable a few years in advance, and affect the local upper ocean heat budget and precipitation. The majority of low frequency variance is explained by the ocean's response to stochastic atmospheric forcing, the additional variance explained by mid-latitude ocean to atmosphere feedbacks appears to be small. The coupling of subtropical and tropical regions by the equator-ward motion in the thermocline can support decadal anomalies by changes of its speed and path, or by transporting water mass anomalies to the equatorial

  8. Automatic categorization of web pages and user clustering with mixtures of hidden Markov models

    NARCIS (Netherlands)

    Ypma, A.; Heskes, T.M.

    2003-01-01

    We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static

  9. 438 Optimal Number of States in Hidden Markov Models and its ...

    African Journals Online (AJOL)

    (Al-Ani, et al., 2007) or Artificial Neural Networks (Zheng & Koenig, n.d.) can ... A Hidden Markov Model (R.Rabiner, 1989) is a stochastic finite state machine ..... likelihood of other models (i.e. for different states), the learning procedure is.

  10. Automatic categorization of web pages and user clustering with mixtures of hidden Markov models

    NARCIS (Netherlands)

    Ypma, A.; Heskes, T.M.

    2003-01-01

    We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static

  11. Stylised facts of financial time series and hidden Markov models in continuous time

    DEFF Research Database (Denmark)

    Nystrup, Peter; Madsen, Henrik; Lindström, Erik

    2015-01-01

    Hidden Markov models are often applied in quantitative finance to capture the stylised facts of financial returns. They are usually discrete-time models and the number of states rarely exceeds two because of the quadratic increase in the number of parameters with the number of states. This paper...

  12. Construction of classical and quantum integrable field models unravelling hidden possibilities

    Indian Academy of Sciences (India)

    Anjan Kundu

    2015-11-01

    Reviewing briefly the concept of classical and quantum integrable systems, we propose an alternative Lax operator approach, leading to quasi-higher-dimensional integrable model, unravelling some hidden dimensions in integrable systems. As an example, we construct a novel integrable quasi-two-dimensional NLS equation at the classical and the quantum levels with intriguing application in rogue wave modelling.

  13. Privilege Flow Oriented Intrusion Detection Based on Hidden Semi- Markov Model

    Institute of Scientific and Technical Information of China (English)

    ZHONG An-ming; JIA Chun-fu

    2005-01-01

    A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusion Detection System). Using the data set of DARPA 1998, our experiment results reveal good detection performance and acceptable computation cost.

  14. Using Bayesian Nonparametric Hidden Semi-Markov Models to Disentangle Affect Processes during Marital Interaction.

    Directory of Open Access Journals (Sweden)

    William A Griffin

    Full Text Available 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.

  15. Hidden Sector Dark Matter Models for the Galactic Center Gamma-Ray Excess

    Energy Technology Data Exchange (ETDEWEB)

    Berlin, Asher [Chicago U.; Gratia, Pierre [Chicago U.; Hooper, Dan [Chicago U., Astron. Astrophys. Ctr.; McDermott, Samuel D. [Michigan U., MCTP

    2014-07-24

    The gamma-ray excess observed from the Galactic Center can be interpreted as dark matter particles annihilating into Standard Model fermions with a cross section near that expected for a thermal relic. Although many particle physics models have been shown to be able to account for this signal, the fact that this particle has not yet been observed in direct detection experiments somewhat restricts the nature of its interactions. One way to suppress the dark matter's elastic scattering cross section with nuclei is to consider models in which the dark matter is part of a hidden sector. In such models, the dark matter can annihilate into other hidden sector particles, which then decay into Standard Model fermions through a small degree of mixing with the photon, Z, or Higgs bosons. After discussing the gamma-ray signal from hidden sector dark matter in general terms, we consider two concrete realizations: a hidden photon model in which the dark matter annihilates into a pair of vector gauge bosons that decay through kinetic mixing with the photon, and a scenario within the generalized NMSSM in which the dark matter is a singlino-like neutralino that annihilates into a pair of singlet Higgs bosons, which decay through their mixing with the Higgs bosons of the MSSM.

  16. UNIVERSAL APPROXIMATION WITH NON-SIGMOID HIDDEN LAYER ACTIVATION FUNCTIONS BY USING ARTIFICIAL NEURAL NETWORK MODELING

    Directory of Open Access Journals (Sweden)

    R. Murugadoss

    2014-10-01

    Full Text Available Neural networks are modeled on the way the human brain. They are capable of learning and can automatically recognize by skillfully training and design complex relationships and hidden dependencies based on historical example patterns and use this information for forecasting. The main difference, and at the same time is biggest advantage of the model of neural networks over statistical techniques seen that the forecaster the exact functional structure between input and Output variables need not be specified, but this by the system with certain Learning algorithms is "learned" using a kind of threshold logic. Goal of the learning procedure is to define the training phase while those parameters of the network, with Help the network has one of those adequate for the problem behavior. Mathematically, the training phase is an iterative, converging towards a minimum error value process. They identify the processors of the network, minimize the "total error". The currently the most popular and most widely for business applications algorithm is the backpropagation algorithm. This paper opens the black box of Backpropagation networks and makes the optimization process in the network over time and locally comprehensible.

  17. Evidence Feed Forward Hidden Markov Models for Visual Human Action Classification (Preprint)

    Science.gov (United States)

    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

  18. Exact Sampling and Decoding in High-Order Hidden Markov Models

    NARCIS (Netherlands)

    Carter, S.; Dymetman, M.; Bouchard, G.

    2012-01-01

    We present a method for exact optimization and sampling from high order Hidden Markov Models (HMMs), which are generally handled by approximation techniques. Motivated by adaptive rejection sampling and heuristic search, we propose a strategy based on sequentially refining a lower-order language mod

  19. Finding cis-regulatory modules in Drosophila using phylogenetic hidden Markov models

    DEFF Research Database (Denmark)

    Wong, Wendy S W; Nielsen, Rasmus

    2007-01-01

    of the increasing availability of comparative genomic data. RESULTS: We develop a method for finding regulatory modules in Eukaryotic species using phylogenetic data. Using computer simulations and analysis of real data, we show that the use of phylogenetic hidden Markov model can lead to an increase in accuracy...

  20. A Generative Approach to the Modeling of Isomorphic Hidden-Figure Items.

    Science.gov (United States)

    Bejar, Isaac I.; Yocom, Peter

    1991-01-01

    An approach to test modeling is illustrated that encompasses both response consistency and response difficulty. This generative approach makes validation an ongoing process. An analysis of hidden figure items with 60 high school students supports the feasibility of the method. (SLD)

  1. Reversibility of time series: revealing the hidden messages in X-ray binaries and cataclysmic variables

    CERN Document Server

    Scaringi, S; Middleton, M

    2014-01-01

    We explore the non-linear, high-frequency, aperiodic variability properties in the three cataclysmic variables MV Lyr, KIC 8751494 and V1504 Cyg observed with Kepler, as well as the X-ray binary Cyg X-1 observed with RXTE. This is done through the use of a high-order Fourier statistic called the bispectrum and its related biphase and bicoherence, as well as the time-skewness statistic. We show how all objects display qualitatively similar biphase trends. In particular all biphase amplitudes are found to be smaller than $\\pi/2$, suggesting that the flux distributions for all sources are positively skewed on all observed timescales, consistent with the log-normal distributions expected from the fluctuating accretion disk model. We also find that for all objects the biphases are positive at frequencies where the corresponding power spectral densities display their high frequency break. This suggests that the noise-like flaring observed is rising more slowly than it is falling, and thus not time-reversible. This ...

  2. Model-independent indirect detection constraints on hidden sector dark matter

    Science.gov (United States)

    Elor, Gilly; Rodd, Nicholas L.; Slatyer, Tracy R.; Xue, Wei

    2016-06-01

    If dark matter inhabits an expanded ``hidden sector'', annihilations may proceed through sequential decays or multi-body final states. We map out the potential signals and current constraints on such a framework in indirect searches, using a model-independent setup based on multi-step hierarchical cascade decays. While remaining agnostic to the details of the hidden sector model, our framework captures the generic broadening of the spectrum of secondary particles (photons, neutrinos, e+e- and bar p p) relative to the case of direct annihilation to Standard Model particles. We explore how indirect constraints on dark matter annihilation limit the parameter space for such cascade/multi-particle decays. We investigate limits from the cosmic microwave background by Planck, the Fermi measurement of photons from the dwarf galaxies, and positron data from AMS-02. The presence of a hidden sector can change the constraints on the dark matter by up to an order of magnitude in either direction (although the effect can be much smaller). We find that generally the bound from the Fermi dwarfs is most constraining for annihilations to photon-rich final states, while AMS-02 is most constraining for electron and muon final states; however in certain instances the CMB bounds overtake both, due to their approximate independence on the details of the hidden sector cascade. We provide the full set of cascade spectra considered here as publicly available code with examples at http://web.mit.edu/lns/research/CascadeSpectra.html.

  3. Modeling Shared Variables in VHDL

    DEFF Research Database (Denmark)

    Madsen, Jan; Brage, Jens P.

    1994-01-01

    A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set of guide......A set of concurrent processes communicating through shared variables is an often used model for hardware systems. This paper presents three modeling techniques for representing such shared variables in VHDL, depending on the acceptable constraints on accesses to the variables. Also a set...

  4. Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic

    Science.gov (United States)

    Bessac, Julie; Ailliot, Pierre; Cattiaux, Julien; Monbet, Valerie

    2016-02-01

    Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. A regime-switching framework is introduced to account for the alternation of intensity and variability that is observed in wind conditions due to the existence of different weather types. This modeling blocks time series into periods in which the series is described by a single model. The regime-switching is modeled by a discrete variable that can be introduced as a latent (or hidden) variable or as an observed variable. In the latter case a clustering algorithm is used before fitting the model to extract the regime. Conditional on the regimes, the observed wind conditions are assumed to evolve as a linear Gaussian vector autoregressive (VAR) model. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes derived from a descriptor of the atmospheric circulation. We also discuss the relative advantages of hidden and observed regime-switching models. For artificial stochastic generation of wind sequences, we show that the proposed models reproduce the average space-time motions of wind conditions, and we highlight the advantage of regime-switching models in reproducing the alternation of intensity and variability in wind conditions.

  5. Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Daniel Fernando Tello Gamarra

    2016-12-01

    Full Text Available We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM. We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.

  6. Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping.

    Science.gov (United States)

    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.

  7. A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

    Institute of Scientific and Technical Information of China (English)

    DONG Ming

    2008-01-01

    As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and prac-tice in industry is effective diagnostics and prognostics. Recently, a pattern recog-nition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equip-ment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1)It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations' independence assumption by accom-modating a link between consecutive observations. 3) It does not follow the unre-alistic Markov chain's memoryless assumption and therefore provides more pow-erful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forwardbackward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision-making in equipment health management.

  8. Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers

    Directory of Open Access Journals (Sweden)

    M. Beyreuther

    2011-02-01

    Full Text Available Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms (earthquakes are often hard to detect and classify. Here, we propose to use models from speech synthesis which extend the double stochastic models from speech recognition by integrating a more realistic duration of the target waveforms. The method, which has general applicability, is applied to earthquake detection and classification. First, we generate characteristic functions from the time-series. The Hidden semi-Markov Models are estimated from the characteristic functions and Weighted Finite-State Transducers are constructed for the classification. We test our scheme on one month of continuous seismic data, which corresponds to 370 151 classifications, showing that incorporating the time dependency explicitly in the models significantly improves the results compared to Hidden Markov Models.

  9. A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies.

    Science.gov (United States)

    Fieberg, John R; Conn, Paul B

    2014-05-01

    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

  10. Condition monitoring of oil-impregnated paper bushings using extension neural network, Gaussian mixture and hidden Markov models

    CSIR Research Space (South Africa)

    Miya, WS

    2008-10-01

    Full Text Available In this paper, a comparison between Extension Neural Network (ENN), Gaussian Mixture Model (GMM) and Hidden Markov model (HMM) is conducted for bushing condition monitoring. The monitoring process is a two-stage implementation of a classification...

  11. Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline

    Science.gov (United States)

    2016-11-28

    Title: Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline Christopher J. Smalt...to utilize computational models of the auditory periphery and auditory cortex to study the effect of low spontaneous rate ANF loss on the cortical...clinical hearing thresholds is difficulty in understanding speech in noise. Recent animal studies have shown that noise exposure causes selective loss

  12. An Intelligent Web Pre-fetching Based on Hidden Markov Model

    Institute of Scientific and Technical Information of China (English)

    许欢庆; 金鑫

    2004-01-01

    Web pre-fetching is one of the most popular strategies,which are proposed for reducing the perceived access delay and improving the service quality of web server. In this paper, we present a pre-fetching model based on the hidden Markov model, which mines the latent information requirement concepts that the user's access path contains and makes semantic-based pre-fetching decisions.Experimental results show that our scheme has better predictive pre-fetching precision.

  13. Preparation of name and address data for record linkage using hidden Markov models

    Directory of Open Access Journals (Sweden)

    Lim Kim

    2002-12-01

    Full Text Available Abstract Background Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs. Methods HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems. Results Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed. Conclusion Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve.

  14. Characterization of Caenorhabditis elegans behavior in response to chemical stress by using hidden Markov model

    Science.gov (United States)

    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.

  15. Hidden Markov models and other machine learning approaches in computational molecular biology

    Energy Technology Data Exchange (ETDEWEB)

    Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

    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.

  16. A Hidden Markov Movement Model for rapidly identifying behavioral states from animal tracks

    DEFF Research Database (Denmark)

    Whoriskey, Kim; Auger-Méthé, Marie; Albertsen, Christoffer Moesgaard

    2017-01-01

    1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic....... 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum...... animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common...

  17. Research on identification method of heavy vehicle rollover based on hidden Markov model

    Science.gov (United States)

    Zhao, Zhiguo; Wang, Yeqin; Hu, Xiaoming; Tao, Yukai; Wang, Jinsheng

    2017-07-01

    Aiming at the problem of early warning credibility degradation as the heavy vehicle load and its center of gravity change greatly; the heavy vehicle rollover state identification method based on the Hidden Markov Model (HMM, is introduced to identify heavy vehicle lateral conditions dynamically in this paper. In this method, the lateral acceleration and roll angle are taken as the observation values of the model base. The Viterbi algorithm is used to predict the state sequence with the highest probability in the observed sequence, and the Markov prediction algorithm is adopted to calculate the state transition law and to predict the state of the vehicle in a certain period of time in the future. According to combination conditions of Double lane change and steering, applying Trucksim and Matlab trained hidden Markov model, the model is applied to the online identification of heavy vehicle rollover states. The identification results show that the model can accurately and efficiently identify the vehicle rollover state, and has good applicability. This study provides a novel method and a general strategy for active safety early warning and control of vehicles, which has reference significance for the application of the Hidden Markov theory in collision, rear-end and lane departure warning system.

  18. Optimizing the Forward Algorithm for Hidden Markov Model on IBM Roadrunner clusters

    Directory of Open Access Journals (Sweden)

    SOIMAN, S.-I.

    2015-05-01

    Full Text Available In this paper we present a parallel solution of the Forward Algorithm for Hidden Markov Models. The Forward algorithm compute a probability of a hidden state from Markov model at a certain time, this process being recursively. The whole process requires large computational resources for those models with a large number of states and long observation sequences. Our solution in order to reduce the computational time is a multilevel parallelization of Forward algorithm. Two types of cores were used in our implementation, for each level of parallelization, cores that are graved on the same chip of PowerXCell8i processor. This hybrid architecture of processors permitted us to obtain a speedup factor over 40 relative to the sequential algorithm for a model with 24 states and 25 millions of observable symbols. Experimental results showed that the parallel Forward algorithm can evaluate the probability of an observation sequence on a hidden Markov model 40 times faster than the classic one does. Based on the performance obtained, we demonstrate the applicability of this parallel implementation of Forward algorithm in complex problems such as large vocabulary speech recognition.

  19. Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments

    OpenAIRE

    Ismail Shahin

    2010-01-01

    Speaker identification performance is almost perfect in neutral talking environments. However, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing, and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) to alleviate the deteriorated performance in the shouted talking environments. These proposed models possess the characteristics of both Circular Suprasegmental Hidden Mark...

  20. Wavelet-based SAR images despeckling using joint hidden Markov model

    Science.gov (United States)

    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.

  1. Gender Based Emotion Recognition System for Telugu Rural Dialects Using Hidden Markov Models

    CERN Document Server

    D, Prasad Reddy P V G; Srinivas, Y; Brahmaiah, P

    2010-01-01

    Automatic emotion recognition in speech is a research area with a wide range of applications in human interactions. The basic mathematical tool used for emotion recognition is Pattern recognition which involves three operations, namely, pre-processing, feature extraction and classification. This paper introduces a procedure for emotion recognition using Hidden Markov Models (HMM), which is used to divide five emotional states: anger, surprise, happiness, sadness and neutral state. The approach is based on standard speech recognition technology using hidden continuous markov model by selection of low level features and the design of the recognition system. Emotional Speech Database from Telugu Rural Dialects of Andhra Pradesh (TRDAP) was designed using several speaker's voices comprising the emotional states. The accuracy of recognizing five different emotions for both genders of classification is 80% for anger-emotion which is achieved by using the best combination of 39-dimensioanl feature vector for every f...

  2. Hidden Markov Model of atomic quantum jump dynamics in an optically probed cavity

    DEFF Research Database (Denmark)

    Gammelmark, S.; Molmer, K.; Alt, W.

    2014-01-01

    We analyze the quantum jumps of an atom interacting with a cavity field. The strong atom- field interaction makes the cavity transmission depend on the time dependent atomic state, and we present a Hidden Markov Model description of the atomic state dynamics which is conditioned in a Bayesian......, the atomic state is determined in a Bayesian manner from the measurement data, and we present an iterative protocol, which determines both the atomic state and the model parameters. As a new element in the treatment of observed quantum systems, we employ a Bayesian approach that conditions the atomic state...... manner on the detected signal. We suggest that small variations in the observed signal may be due to spatial motion of the atom within the cavity, and we represent the atomic system by a number of hidden states to account for both the small variations and the internal state jump dynamics. In our theory...

  3. Statistical Inference in Hidden Markov Models Using k-Segment Constraints.

    Science.gov (United States)

    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.

  4. Hidden Markov Model-based Packet Loss Concealment for Voice over IP

    DEFF Research Database (Denmark)

    Rødbro, Christoffer A.; Murthi, Manohar N.; Andersen, Søren Vang

    2006-01-01

    As voice over IP proliferates, packet loss concealment (PLC) at the receiver has emerged as an important factor in determining voice quality of service. Through the use of heuristic variations of signal and parameter repetition and overlap-add interpolation to handle packet loss, conventional PLC...... systems largely ignore the dynamics of the statistical evolution of the speech signal, possibly leading to perceptually annoying artifacts. To address this problem, we propose the use of hidden Markov models for PLC. With a hidden Markov model (HMM) tracking the evolution of speech signal parameters, we...... demonstrate how PLC is performed within a statistical signal processing framework. Moreover, we show how the HMM is used to index a specially designed PLC module for the particular signal context, leading to signal-contingent PLC. Simulation examples, objective tests, and subjective listening tests...

  5. Hidden Markov Model of atomic quantum jump dynamics in an optically probed cavity

    DEFF Research Database (Denmark)

    Gammelmark, S.; Molmer, K.; Alt, W.

    2014-01-01

    manner on the detected signal. We suggest that small variations in the observed signal may be due to spatial motion of the atom within the cavity, and we represent the atomic system by a number of hidden states to account for both the small variations and the internal state jump dynamics. In our theory......We analyze the quantum jumps of an atom interacting with a cavity field. The strong atom- field interaction makes the cavity transmission depend on the time dependent atomic state, and we present a Hidden Markov Model description of the atomic state dynamics which is conditioned in a Bayesian......, the atomic state is determined in a Bayesian manner from the measurement data, and we present an iterative protocol, which determines both the atomic state and the model parameters. As a new element in the treatment of observed quantum systems, we employ a Bayesian approach that conditions the atomic state...

  6. Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models

    OpenAIRE

    Mehta, Pankaj; Schwab, David J.; Sengupta, Anirvan M.

    2011-01-01

    Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the "inverse" statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where th...

  7. Fault detection and diagnosis in a food pasteurization process with Hidden Markov Models

    OpenAIRE

    Tokatlı, Figen; Cinar, Ali

    2004-01-01

    Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a ...

  8. Hidden Degeneracy in the Brick Wall Model of Black Holes

    CERN Document Server

    Sen-Gupta, K; Gupta, Kumar S.; Sen, Siddhartha

    2003-01-01

    Quantum field theory in the near-horizon region of a black hole predicts the existence of an infinite number of degenerate modes. Such a degeneracy is regulated in the brick wall model by the introduction of a short distance cutoff. In this Letter we show that states of the brick wall model with non zero energy admit a further degeneracy for any given finite value of the cutoff. The black hole entropy is calculated within the brick wall model taking this degeneracy into account. Modes with complex frequencies however do not exhibit such a degeneracy.

  9. A Hidden Valley model of cold dark matter

    CERN Document Server

    Krolikowski, Wojciech

    2008-01-01

    In the discussed model, the cold dark matter consists of Dirac spin-1/2 fermions, sterile from all Standard Model charges, where masses are generated by a nonzero vacuum expectation value of a field of scalar bosons, also assumed to be sterile. For convenience, these sterile particles have beeen called sterinos and sterons, respectively. It has been conjectured that our sterile world of sterinos and sterons can communicate with the familiar Standard Model world not only through gravity, but also through a photonic portal provided by a very weak effective interaction involving the electromagnetic field F_{\\mu \

  10. A Multilayer Hidden Markov Models-Based Method for Human-Robot Interaction

    Directory of Open Access Journals (Sweden)

    Chongben Tao

    2013-01-01

    Full Text Available To achieve Human-Robot Interaction (HRI by using gestures, a continuous gesture recognition approach based on Multilayer Hidden Markov Models (MHMMs is proposed, which consists of two parts. One part is gesture spotting and segment module, the other part is continuous gesture recognition module. Firstly, a Kinect sensor is used to capture 3D acceleration and 3D angular velocity data of hand gestures. And then, a Feed-forward Neural Networks (FNNs and a threshold criterion are used for gesture spotting and segment, respectively. Afterwards, the segmented gesture signals are respectively preprocessed and vector symbolized by a sliding window and a K-means clustering method. Finally, symbolized data are sent into Lower Hidden Markov Models (LHMMs to identify individual gestures, and then, a Bayesian filter with sequential constraints among gestures in Upper Hidden Markov Models (UHMMs is used to correct recognition errors created in LHMMs. Five predefined gestures are used to interact with a Kinect mobile robot in experiments. The experimental results show that the proposed method not only has good effectiveness and accuracy, but also has favorable real-time performance.

  11. Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments

    Directory of Open Access Journals (Sweden)

    Ismail Shahin

    2010-01-01

    Full Text Available Speaker identification performance is almost perfect in neutral talking environments. However, the performance is deteriorated significantly in shouted talking environments. This work is devoted to proposing, implementing, and evaluating new models called Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s to alleviate the deteriorated performance in the shouted talking environments. These proposed models possess the characteristics of both Circular Suprasegmental Hidden Markov Models (CSPHMMs and Second-Order Suprasegmental Hidden Markov Models (SPHMM2s. The results of this work show that CSPHMM2s outperform each of First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s, Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM2s, and First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s in the shouted talking environments. In such talking environments and using our collected speech database, average speaker identification performance based on LTRSPHMM1s, LTRSPHMM2s, CSPHMM1s, and CSPHMM2s is 74.6%, 78.4%, 78.7%, and 83.4%, respectively. Speaker identification performance obtained based on CSPHMM2s is close to that obtained based on subjective assessment by human listeners.

  12. Model-independent indirect detection constraints on hidden sector dark matter

    Energy Technology Data Exchange (ETDEWEB)

    Elor, Gilly; Rodd, Nicholas L.; Slatyer, Tracy R.; Xue, Wei [Center for Theoretical Physics, Massachusetts Institute of Technology,77 Massachusetts Ave, Cambridge, MA 02139 (United States)

    2016-06-10

    If dark matter inhabits an expanded “hidden sector”, annihilations may proceed through sequential decays or multi-body final states. We map out the potential signals and current constraints on such a framework in indirect searches, using a model-independent setup based on multi-step hierarchical cascade decays. While remaining agnostic to the details of the hidden sector model, our framework captures the generic broadening of the spectrum of secondary particles (photons, neutrinos, e{sup +}e{sup −} and p-barp) relative to the case of direct annihilation to Standard Model particles. We explore how indirect constraints on dark matter annihilation limit the parameter space for such cascade/multi-particle decays. We investigate limits from the cosmic microwave background by Planck, the Fermi measurement of photons from the dwarf galaxies, and positron data from AMS-02. The presence of a hidden sector can change the constraints on the dark matter by up to an order of magnitude in either direction (although the effect can be much smaller). We find that generally the bound from the Fermi dwarfs is most constraining for annihilations to photon-rich final states, while AMS-02 is most constraining for electron and muon final states; however in certain instances the CMB bounds overtake both, due to their approximate independence on the details of the hidden sector cascade. We provide the full set of cascade spectra considered here as publicly available code with examples at http://web.mit.edu/lns/research/CascadeSpectra.html.

  13. Lung Cancer Pathological Image Analysis Using a Hidden Potts Model

    Directory of Open Access Journals (Sweden)

    Qianyun Li

    2017-06-01

    Full Text Available Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-Hastings algorithm allows us to simulate samples approximately from a distribution with an intractable normalizing constant. Our numerical results indicate that the spatial interaction between the lymphocyte and tumor cells is significantly associated with the patient’s survival time, and it can be used together with the cell count information to predict the survival of the patients.

  14. Lung Cancer Pathological Image Analysis Using a Hidden Potts Model.

    Science.gov (United States)

    Li, Qianyun; Yi, Faliu; Wang, Tao; Xiao, Guanghua; Liang, Faming

    2017-01-01

    Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-Hastings algorithm allows us to simulate samples approximately from a distribution with an intractable normalizing constant. Our numerical results indicate that the spatial interaction between the lymphocyte and tumor cells is significantly associated with the patient's survival time, and it can be used together with the cell count information to predict the survival of the patients.

  15. Gene finding with a hidden Markov model of genome structure and evolution

    DEFF Research Database (Denmark)

    Pedersen, Jakob Skou; Hein, Jotun

    2003-01-01

    annotation. The modelling of evolution by the existing comparative gene finders leaves room for improvement. Results: A probabilistic model of both genome structure and evolution is designed. This type of model is called an Evolutionary Hidden Markov Model (EHMM), being composed of an HMM and a set of region......Motivation: A growing number of genomes are sequenced. The differences in evolutionary pattern between functional regions can thus be observed genome-wide in a whole set of organisms. The diverse evolutionary pattern of different functional regions can be exploited in the process of genomic...

  16. A hidden Markov model approach for determining expression from genomic tiling micro arrays

    DEFF Research Database (Denmark)

    Terkelsen, Kasper Munch; Gardner, P. P.; Arctander, Peter;

    2006-01-01

    HMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non......]. Results can be downloaded and viewed from our web site [2]. Conclusion The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms...

  17. The $BC_{1}$ Elliptic model: algebraic forms, hidden algebra $sl(2)$, polynomial eigenfunctions

    CERN Document Server

    Turbiner, Alexander V

    2014-01-01

    The potential of the $BC_1$ elliptic model is a superposition of two Weierstrass functions with doubling of both periods (two coupling constants), the model degenerates to $A_1$ elliptic model characterized by the Lame Hamiltonian. It is shown that in space of $BC_1$ elliptic invariant the potential becomes a rational function while the flat space metric is polynomial. The model possesses the hidden $sl_2$ algebra for arbitrary coupling constants: it is equivalent to $sl_2$-quantum top in three different magnetic fields. It is shown that there exist three one-parametric families of coupling constants for which a finite number of polynomial eigenfunctions (up to a factor) occur.

  18. Hidden Markov modelling of movement data from fish

    DEFF Research Database (Denmark)

    Pedersen, Martin Wæver

    Movement data from marine animals tagged with electronic tags are becoming increasingly diverse and plentiful. This trend entails a need for statistical methods that are able to filter the observations to extract the ecologically relevant content. This dissertation focuses on the development...... state HMM is employed to deal with this task. Specifically, the continuous horizontal plane is discretised into grid cells, which enables a state-space model for the geographical location to be estimated on this grid. The estimation model for location is extended with an additional state representing...

  19. On the problem of periodicity and hidden solitons for the KdV model.

    Science.gov (United States)

    Engelbrecht, Jüri; Salupere, Andrus

    2005-03-01

    In continuum limit, the Fermi-Pasta-Ulam lattice is modeled by a Korteweg-de Vries (KdV) equation. It is shown that the long-time behavior of a KdV soliton train emerging from a harmonic excitation has a regular periodicity of right- and left-going trajectories. In a soliton train not all the solitons are visible, the solitons with smaller amplitude are hidden and their influence is seen through the changes of phase shifts of larger solitons. In the case of an external harmonic force several resonance schemes are revealed where both visible and hidden solitons have important roles. The weak, moderate, strong, and dominating fields are distinguished and the corresponding solution types presented.

  20. Optimisation of Hidden Markov Model using Baum–Welch algorithm for prediction of maximum and minimum temperature over Indian Himalaya

    Indian Academy of Sciences (India)

    J C Joshi; Tankeshwar Kumar; Sunita Srivastava; Divya Sachdeva

    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.

  1. A hidden Markov model for prediction transmembrane helices in proteinsequences

    DEFF Research Database (Denmark)

    Sonnhammer, Erik L.L.; von Heijne, Gunnar; Krogh, Anders Stærmose

    1998-01-01

    and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77...

  2. Hidden Markov Models for Time Series An Introduction Using R

    CERN Document Server

    Zucchini, Walter

    2009-01-01

    Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

  3. Two-stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Nhan Nguyen-Duc-Thanh

    2012-07-01

    Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human‐Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2‐stages Hidden Markov Model. The 1st HMM is to recognize the prime command‐like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.

  4. Two-Stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction

    Directory of Open Access Journals (Sweden)

    Nhan Nguyen-Duc-Thanh

    2012-07-01

    Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.

  5. Modeling and online recognition of surgical phases using Hidden Markov Models.

    Science.gov (United States)

    Blum, Tobias; Padoy, Nicolas; Feussner, Hubertus; Navab, Nassir

    2008-01-01

    The amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. These signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. This is a crucial issue for designing future systems that provide context-sensitive information and user interfaces. In this paper, Hidden Markov Models (HMM) are used to model a laparoscopic cholecystectomy. Seventeen signals, representing tool usage, from twelve surgeries are used to train the model. The use of a model merging approach is proposed to build the HMM topology and compared to other methods of initializing a HMM. The merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. Results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.

  6. MODELING SUPPLY CHAIN PERFORMANCE VARIABLES

    Directory of Open Access Journals (Sweden)

    Ashish Agarwal

    2005-01-01

    Full Text Available In order to understand the dynamic behavior of the variables that can play a major role in the performance improvement in a supply chain, a System Dynamics-based model is proposed. The model provides an effective framework for analyzing different variables affecting supply chain performance. Among different variables, a causal relationship among different variables has been identified. Variables emanating from performance measures such as gaps in customer satisfaction, cost minimization, lead-time reduction, service level improvement and quality improvement have been identified as goal-seeking loops. The proposed System Dynamics-based model analyzes the affect of dynamic behavior of variables for a period of 10 years on performance of case supply chain in auto business.

  7. On the equivalence between standard and sequentially ordered hidden Markov models

    CERN Document Server

    Chopin, Nicolas

    2012-01-01

    Chopin (2007) introduced a sequentially ordered hidden Markov model, for which states are ordered according to their order of appearance, and claimed that such a model is a re-parametrisation of a standard Markov model. This note gives a formal proof that this equivalence holds in Bayesian terms, as both formulations generate equivalent posterior distributions, but does not hold in Frequentist terms, as both formulations generate incompatible likelihood functions. Perhaps surprisingly, this shows that Bayesian re-parametrisation and Frequentist re-parametrisation are not identical concepts.

  8. A hidden Ising model for ChIP-chip data analysis

    KAUST Repository

    Mo, Q.

    2010-01-28

    Motivation: Chromatin immunoprecipitation (ChIP) coupled with tiling microarray (chip) experiments have been used in a wide range of biological studies such as identification of transcription factor binding sites and investigation of DNA methylation and histone modification. Hidden Markov models are widely used to model the spatial dependency of ChIP-chip data. However, parameter estimation for these models is typically either heuristic or suboptimal, leading to inconsistencies in their applications. To overcome this limitation and to develop an efficient software, we propose a hidden ferromagnetic Ising model for ChIP-chip data analysis. Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient. Availability: A software called iChip is freely available at http://www.bioconductor.org/. Contact: moq@mskcc.org. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.

  9. Detecting Hidden Diversification Shifts in Models of Trait-Dependent Speciation and Extinction.

    Science.gov (United States)

    Beaulieu, Jeremy M; O'Meara, Brian C

    2016-07-01

    The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE (Binary State Speciation and Extinction) methods. Specifically, our model, which we refer to as HiSSE (Hidden State Speciation and Extinction), assumes that related to each observed state in the model are "hidden" states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved

  10. Modelling the Hidden Magnetic Field of Low-Mass Stars

    CERN Document Server

    Lang, P; Morin, J; Donati, J-F; Jeffers, S; Vidotto, A A; Fares, R

    2014-01-01

    Zeeman-Doppler imaging is a spectropolarimetric technique that is used to map the large-scale surface magnetic fields of stars. These maps in turn are used to study the structure of the stars' coronae and winds. This method, however, misses any small-scale magnetic flux whose polarisation signatures cancel out. Measurements of Zeeman broadening show that a large percentage of the surface magnetic flux may be neglected in this way. In this paper we assess the impact of this 'missing flux' on the predicted coronal structure and the possible rates of spin down due to the stellar wind. To do this we create a model for the small-scale field and add this to the Zeeman-Doppler maps of the magnetic fields of a sample of 12 M dwarfs. We extrapolate this combined field and determine the structure of a hydrostatic, isothermal corona. The addition of small-scale surface field produces a carpet of low-lying magnetic loops that covers most of the surface, including the stellar equivalent of solar 'coronal holes' where the ...

  11. Hidden markov model for the prediction of transmembrane proteins using MATLAB.

    Science.gov (United States)

    Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath

    2011-01-01

    Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

  12. A close hidden stellar companion to the SX Phe-type variable star DW Psc

    Energy Technology Data Exchange (ETDEWEB)

    Qian, S.-B.; Li, L.-J.; Wang, S.-M.; He, J.-J.; Zhou, X.; Jiang, L.-Q., E-mail: qsb@ynao.ac.cn [Yunnan Observatories, Chinese Academy of Sciences (CAS), P.O. Box 110, 650011 Kunming (China)

    2015-01-01

    DW Psc is a high-amplitude SX Phe-type variable with a period of pulsation of 0.05875 days. Using a few newly determined times of maximum light together with those collected from the literature, the changes in the observed-calculated (O-C) diagram are analyzed. It is discovered that the O-C curve of DW Psc shows a cyclic variation with a period of 6.08 years and a semi-amplitude of 0.0066 days. The periodic variation is analyzed for the light travel time effect, which is due to the presence of a stellar companion (M{sub 2}sini∼0.45(±0.03) M{sub ⊙}). The two-component stars in the binary system are orbiting each other in an eccentric orbit (e ∼ 0.4) at an orbital separation of about 2.7(±0.3) AU. The detection of a close stellar companion to an SX Phe-type star supports the idea that SX Phe-type pulsating stars are blue stragglers that were formed from the merging of close binaries. The stellar companion has played an important role in the merging of the original binary by removing angular momentum from the central binary during early dynamical interaction or/and late dynamical evolution. After the more massive component in DW Psc evolves into a red giant, the cool close companion should help to remove the giant envelope via possible critical Roche-lobe overflow, and the system may be a progenitor of a cataclysmic variable. The detection of a close stellar companion to DW Psc makes it a very interesting system to study in the future.

  13. Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya

    Science.gov (United States)

    Joshi, J. C.; Tankeshwar, K.; Srivastava, Sunita

    2017-04-01

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992-2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum-Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012-2013 and 2013-2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.

  14. Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya

    Indian Academy of Sciences (India)

    J C Joshi; K Tankeshwar; Sunita Srivastava

    2017-04-01

    A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six observations and six states of the model. The most probable observation and state sequence has been computed using Forward and Viterbi algorithms, respectively. Baum–Welch algorithm has been used for optimizing the model parameters. The model has been validated for two winters (2012–2013 and 2013–2014) by computing root mean square error (RMSE), accuracy measures such as percent correct (PC), critical success index (CSI) and Heidke skill score (HSS). The RMSE of the model has also been calculated using leave-one-out cross-validation method. Snowfall predicted by the model during hazardous snowfall events in different parts of the Himalaya matches well with the observed one. The HSS of the model for all the stations implies that the optimized model has better forecasting skill than random forecast for both the days. The RMSE of the optimized model has also been found smaller than the persistence forecast and standard deviation for both the days.

  15. The 750 GeV Diphoton excess in a $U(1)$ hidden symmetry model

    CERN Document Server

    Das, Kasinath

    2015-01-01

    Recent results from the experimental collaborations at LHC give hints of a resonance in the diphoton channel at an invariant mass of 750 GeV. We show that such a scalar resonance would be possible in an $U(1)$ extension of the SM where the extended symmetry is hidden and yet to be discovered. We explore the possibilities of accommodating this excess by introducing a minimal extension to the matter content and highlight the parameter space that can accommodate the observed diphoton resonance in the model. The model also predicts new interesting signals that may be observed at the current LHC run.

  16. Multiple instance learning for hidden Markov models: application to landmine detection

    Science.gov (United States)

    Bolton, Jeremy; Yuksel, Seniha Esen; Gader, Paul

    2013-06-01

    Multiple instance learning is a recently researched learning paradigm in machine intelligence which operates under conditions of uncertainty. A Multiple Instance Hidden Markov Model (MI-HMM) is investigated with applications to landmine detection using ground penetrating radar data. Without introducing any additional parameters, the MI-HMM provides an elegant and simple way to learn the parameters of an HMM in a multiple instance framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is effective.

  17. Constraints on Hidden Photon Models from Electron g-2 and Hydrogen Spectroscopy

    CERN Document Server

    Endo, Motoi; Mishima, Go

    2012-01-01

    The hidden photon model is one of the simplest models which can explain the anomaly of the muon anomalous magnetic moment (g-2). The experimental constraints are studied in detail, which come from the electron g-2 and the hydrogen transition frequencies. The input parameters are set carefully in order to take dark photon contributions into account and to prevent the analysis from being self-inconsistent. It is shown that the new analysis provides a constraint severer by more than one order of magnitude than the previous result.

  18. HIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTS

    OpenAIRE

    2014-01-01

    Monthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM) in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a “defective” or “non-defective” state. The number of machine parts errors were collected from a thermo plastic injection molding machine in a car bumper auto parts manufacturer in Liberec city, Czech Re...

  19. Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

    Science.gov (United States)

    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.

  20. Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

    Directory of Open Access Journals (Sweden)

    Zhang Yingjun

    2015-02-01

    Full Text Available 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.

  1. Hidden Gauged U(1) Model: Unifying Scotogenic Neutrino and Flavor Dark Matter

    CERN Document Server

    Yu, Jiang-Hao

    2016-01-01

    In both scotogenic neutrino and flavor dark matter models, the dark sector communicates with the standard model fermions via Yukawa portal couplings. We propose an economic scenario that scotogenic neutrino and flavored mediator share the same inert Higgs doublet and all are charged under a hidden gauged $U(1)$ symmetry. The dark Z2 symmetry in dark sector is regarded as the remnant of this hidden $U(1)$ symmetry breaking. In particular, we investigate a dark $U(1)_D$ (and also a $U(1)_{B-L}$) model which unifies scotogenic neutrino and top-flavored mediator. In this model dark tops and dark neutrinos are the standard model fermion partners, and the dark matter could be inert Higgs or the lightest dark neutrino. This model has rich collider signatures on dark tops, inert Higgs and Z' gauge boson, etc. Moreover, the scalar associated to the $U(1)_D$ (and also $U(1)_{B-L}$) symmetry breaking could explain the 750 GeV diphoton excess reported by ATLAS and CMS recently.

  2. Hybrid Unifying Variable Supernetwork Model

    Institute of Scientific and Technical Information of China (English)

    LIU; Qiang; FANG; Jin-qing; LI; Yong

    2015-01-01

    In order to compare new phenomenon of topology change,evolution,hybrid ratio and network characteristics of unified hybrid network theoretical model with unified hybrid supernetwork model,this paper constructed unified hybrid variable supernetwork model(HUVSM).The first layer introduces a hybrid ratio dr,the

  3. Hidden Markov model analysis of force/torque information in telemanipulation

    Science.gov (United States)

    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.

  4. Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2015-04-01

    Full Text Available Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.

  5. Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2015-04-01

    Full Text Available Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker’s hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM method is adopted to recognize patterns via data streams and identify workers’ gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.

  6. 2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A new feature extraction method based on 2D-hidden Markov model(HMM) is proposed.Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

  7. Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models

    Science.gov (United States)

    Mehta, Pankaj; Schwab, David J.; Sengupta, Anirvan M.

    2011-04-01

    Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the "inverse" statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it.

  8. [Classification of human sleep stages based on EEG processing using hidden Markov models].

    Science.gov (United States)

    Doroshenkov, L G; Konyshev, V A; Selishchev, S V

    2007-01-01

    The goal of this work was to describe an automated system for classification of human sleep stages. Classification of sleep stages is an important problem of diagnosis and treatment of human sleep disorders. The developed classification method is based on calculation of characteristics of the main sleep rhythms. It uses hidden Markov models. The method is highly accurate and provides reliable identification of the main stages of sleep. The results of automatic classification are in good agreement with the results of sleep stage identification performed by an expert somnologist using Rechtschaffen and Kales rules. This substantiates the applicability of the developed classification system to clinical diagnosis.

  9. Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models.

    Science.gov (United States)

    Mehta, Pankaj; Schwab, David J; Sengupta, Anirvan M

    2011-04-01

    Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the "inverse" statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it.

  10. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    Science.gov (United States)

    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.

  11. Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models

    Directory of Open Access Journals (Sweden)

    Yulong Qiao

    2016-11-01

    Full Text Available Multiresolution models such as the wavelet-domain hidden Markov tree (HMT model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM, the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods.

  12. The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce

    Directory of Open Access Journals (Sweden)

    Liu Chang

    2013-01-01

    Full Text Available Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems.

  13. Detecting Gait Phases from RGB-D Images Based on Hidden Markov Model.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

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

    2016-09-01

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

  15. LDA Based Face Recognition by Using Hidden Markov Model in Current Trends

    Directory of Open Access Journals (Sweden)

    S.Sharavanan

    2009-10-01

    Full Text Available Hidden Markov model (HMM is a promising method that works well for images with variations in lighting, facial expression, and orientation. Face recognition draws attention as a complex task due to noticeable changes produced on appearance by illumination, facial expression, size, orientation and other external factors. To process images using HMM, the temporal or space sequences are to be considered. In simple terms HMM can be defined as set of finite states with associated probability distributions. Only the outcome is visible to the external user not the states and hence the name Hidden Markov Model. The paper deals with various techniques and methodologies used for resolving the problem .We discuss about appearance based, feature based, model based and hybrid methods for face identification. Conventional techniques such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Independent Component Analysis (ICA, and feature based Elastic Bunch Graph Matching (EBGM and 2D and 3D face models are well-known for face detection and recognition.

  16. Quasi-exact-solvability of the $A_{2}$ Elliptic model: algebraic form, $sl(3)$ hidden algebra, polynomial eigenfunctions

    CERN Document Server

    Sokolov, Vladimir V

    2014-01-01

    The potential of the $A_2$ quantum elliptic model (3-body Calogero elliptic model) is defined by the pairwise three-body interaction through Weierstrass $\\wp$-function and has a single coupling constant. A change of variables has been found, which are $A_2$ elliptic invariants. In those, the potential becomes a rational function, while the flat space metric as well as its associated vector are polynomials in two variables. It is shown the model possesses the hidden $sl_3$ algebra - the Hamiltonian is an element of the universal enveloping algebra $U_{sl_3}$ for arbitrary coupling constant - being equivalent to $sl_3$-quantum top. The integral in a form of the third order differential operator with polynomial coefficients is constructed explicitly, being also an element of the universal enveloping algebra $U_{sl_3}$. It is shown that there exists a discrete sequence of coupling constants for which a finite number of polynomial eigenfunctions up to a (non-singular) gauge factor occur.

  17. Modeling Strategic Use of Human Computer Interfaces with Novel Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Laura Jane Mariano

    2015-07-01

    Full Text Available 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

  18. Analyzing development of working models for disrupted attachments: the case of hidden family violence.

    Science.gov (United States)

    Ayoub, Catherine C; Fischer, Kurt W; O'Connor, Erin E

    2003-06-01

    This article offers a developmental model of attachment theory rooted in dynamic skill theory. Dynamic skill theory is based on the assumption that people do not have integrated, fundamentally logical minds, but instead develop along naturally fractionated strands of a web. Contrary to traditional interpretations of attachment theory, dynamic skill theory proposes that individuals continue to modify their working models of attachments throughout the lifespan. In particular, working models of close relationships develop systematically through a series of skill levels such that the skills vary across strands in the web and will not automatically form a unified whole. The continual modification of working models is particularly pertinent for the consequences of hidden family violence for individuals' development. Dynamic skill theory shows how trauma can produce not developmental delay or fixation, as has been proposed previously, but instead the construction of advanced, complex working models.

  19. Products of Hidden Markov Models: It Takes N>1 to Tango

    CERN Document Server

    Taylor, Graham W

    2012-01-01

    Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction. This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under the model. In this paper, we demonstrate how the partition function can be estimated reliably via Annealed Importance Sampling. We perform experiments using contrastive divergence learning on rainfall data and data captured from pairs of people dancing. Our results suggest that advances in learning and evaluation for undirected graphical models and recent increases in available computing power make PoHMMs worth considering for complex time-series modeling tasks.

  20. A language independent acronym extraction from biomedical texts with hidden Markov models.

    Science.gov (United States)

    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.

  1. A hidden Markov model-based algorithm for identifying tumour subtype using array CGH data

    Directory of Open Access Journals (Sweden)

    Zhang Ke

    2011-12-01

    Full Text Available Abstract Background The recent advancement in array CGH (aCGH research has significantly improved tumor identification using DNA copy number data. A number of unsupervised learning methods have been proposed for clustering aCGH samples. Two of the major challenges for developing aCGH sample clustering are the high spatial correlation between aCGH markers and the low computing efficiency. A mixture hidden Markov model based algorithm was developed to address these two challenges. Results The hidden Markov model (HMM was used to model the spatial correlation between aCGH markers. A fast clustering algorithm was implemented and real data analysis on glioma aCGH data has shown that it converges to the optimal cluster rapidly and the computation time is proportional to the sample size. Simulation results showed that this HMM based clustering (HMMC method has a substantially lower error rate than NMF clustering. The HMMC results for glioma data were significantly associated with clinical outcomes. Conclusions We have developed a fast clustering algorithm to identify tumor subtypes based on DNA copy number aberrations. The performance of the proposed HMMC method has been evaluated using both simulated and real aCGH data. The software for HMMC in both R and C++ is available in ND INBRE website http://ndinbre.org/programs/bioinformatics.php.

  2. Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model.

    Science.gov (United States)

    Zhang, Yu-Chen; Zhang, Shao-Wu; Liu, Lian; Liu, Hui; Zhang, Lin; Cui, Xiaodong; Huang, Yufei; Meng, Jia

    2015-01-01

    With the development of new sequencing technology, the entire N6-methyl-adenosine (m(6)A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset.

  3. Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models

    Energy Technology Data Exchange (ETDEWEB)

    Riber Marklund, A. [CEA, Cadarache, DEN/DTN/STCP/LIET, Batiment 202, 13108 St Paul-lez-Durance, (France); Kishore, S. [Fast Reactor Technology Group of IGCAR, (India); Prakash, V. [Vibrations Diagnostics Division, Fast Reactor Technology Group of IGCAR, (India); Rajan, K.K. [Fast Reactor Technology Group and Engineering Services Group of IGCAR, (India)

    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)

  4. Speech-To-Text Conversion STT System Using Hidden Markov Model HMM

    Directory of Open Access Journals (Sweden)

    Su Myat Mon

    2015-06-01

    Full Text Available Abstract Speech is an easiest way to communicate with each other. Speech processing is widely used in many applications like security devices household appliances cellular phones ATM machines and computers. The human computer interface has been developed to communicate or interact conveniently for one who is suffering from some kind of disabilities. Speech-to-Text Conversion STT systems have a lot of benefits for the deaf or dumb people and find their applications in our daily lives. In the same way the aim of the system is to convert the input speech signals into the text output for the deaf or dumb students in the educational fields. This paper presents an approach to extract features by using Mel Frequency Cepstral Coefficients MFCC from the speech signals of isolated spoken words. And Hidden Markov Model HMM method is applied to train and test the audio files to get the recognized spoken word. The speech database is created by using MATLAB.Then the original speech signals are preprocessed and these speech samples are extracted to the feature vectors which are used as the observation sequences of the Hidden Markov Model HMM recognizer. The feature vectors are analyzed in the HMM depending on the number of states.

  5. Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

    Directory of Open Access Journals (Sweden)

    S. Parkash  Kumar

    2011-01-01

    Full Text Available Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.

  6. Metode Linear Predictive Coding (LPC Pada klasifikasi Hidden Markov Model (HMM Untuk Kata Arabic pada penutur Indonesia

    Directory of Open Access Journals (Sweden)

    Ririn Kusumawati

    2016-05-01

    In the classification, using Hidden Markov Model, voice signal is analyzed and searched the maximum possible value that can be recognized. The modeling results obtained parameters are used to compare with the sound of Arabic speakers. From the test results' Classification, Hidden Markov Models with Linear Predictive Coding extraction average accuracy of 78.6% for test data sampling frequency of 8,000 Hz, 80.2% for test data sampling frequency of 22050 Hz, 79% for frequencies sampling test data at 44100 Hz.

  7. Incorporating teleconnection information into reservoir operating policies using Stochastic Dynamic Programming and a Hidden Markov Model

    Science.gov (United States)

    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

  8. AdS/QCD approach to the scale invariant extension of the standard model with a strongly interacting hidden sector

    CERN Document Server

    Hatanaka, Hisaki; Ko, Pyungwon

    2016-01-01

    In this paper, we revisit a scale-invariant extension of the standard model (SM) with a strongly interacting hidden sector within AdS/QCD approach. Using the AdS/QCD, we reduce the number of input parameters to three, i.e. hidden pion decay constant, hidden pion mass and $\\tan\\beta$ that is defined as the ratio of the vacuum expectation values (VEV) of the singlet scalar field and the SM Higgs boson. As a result, our model has sharp predictability. We perform the phenomenological analysis of the hidden pions which is one of the dark matter (DM) candidates in this model. With various theoretical and experimental constraints we search for the allowed parameter space and find that both resonance and non-resonance solutions are possible. Some typical correlations among various observables such as thermal relic density of hidden pions, Higgs signal strengths and DM-nucleon cross section are investigated. We provide some benchmark points for experimental tests.

  9. Peculiar Quantum Phase Transitions and Hidden Supersymmetry in a Lipkin-Meshkov-Glick Model

    Institute of Scientific and Technical Information of China (English)

    CHEN Gang; LIANG Jiu-Qing

    2009-01-01

    In this paper we theoretically report an unconventional quantum phase transition of a simple Lipkin-Meshkov-Glick model: an interacting collective spin system without external magnetic field. It is shown that this model with integer-spin can exhibit a first-order quantum phase transition between different disordered phases, and more intriguingly, possesses a hidden supersymmetry at the critical point. However, for half-integer spin we predict another first-order quantum phase transition between two different long-range-ordered phases with a vanishing energy gap, which is induced by the destructive topological quantum interference between the intanton and anti-instanton tunneling paths and accompanies spontaneously breaking of supersymmetry at the same critical point. We also show that, when the total spin-value varies from half-integer to integer this model can exhibit an abrupt variation of Berry phase from π to zero.

  10. ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm.

    Science.gov (United States)

    Passera, Katia M; Potepan, Paolo; Brambilla, Luca; Mainardi, Luca T

    2008-01-01

    In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy.

  11. A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms

    DEFF Research Database (Denmark)

    Fang, Jiakun; Su, Chi; Hu, Weihao

    2015-01-01

    To accommodate the increasing wind energy with stochastic nature becomes a major issue on power system reliability. This paper proposes a methodology to characterize the spatiotemporal correlation of multiple wind farms. First, a hierarchical clustering method based on self-organizing maps...... is adopted to categorize the similar output patterns of several wind farms into joint states. Then the hidden Markov model (HMM) is then designed to describe the temporal correlations among these joint states. Unlike the conventional Markov chain model, the accumulated wind power is taken into consideration....... The proposed statistical modeling framework is compatible with the sequential power system reliability analysis. A case study on optimal sizing and location of fast-response regulation sources is presented....

  12. Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules

    CERN Document Server

    Noe, Frank; Prinz, Jan-Hendrik; Plattner, Nuria

    2013-01-01

    Markov state models (MSMs) have been successful in computing metastable states, slow relaxation timescales and associated structural changes, and stationary or kinetic experimental observables of complex molecules from large amounts of molecular dynamics simulation data. However, MSMs approximate the true dynamics by assuming a Markov chain on a clusters discretization of the state space. This approximation is difficult to make for high-dimensional biomolecular systems, and the quality and reproducibility of MSMs has therefore been limited. Here, we discard the assumption that dynamics are Markovian on the discrete clusters. Instead, we only assume that the full phase- space molecular dynamics is Markovian, and a projection of this full dynamics is observed on the discrete states, leading to the concept of Projected Markov Models (PMMs). Robust estimation methods for PMMs are not yet available, but we derive a practically feasible approximation via Hidden Markov Models (HMMs). It is shown how various molecula...

  13. Autoregressive hidden Markov models for the early detection of neonatal sepsis.

    Science.gov (United States)

    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.

  14. Dark matter, dark radiation and Higgs phenomenology in the hidden sector DM models

    CERN Document Server

    Ko, P

    2015-01-01

    I present a class of hidden sector dark matter (DM) models with local dark gauge symmetries, where DM is stable due to unbroken local dark gauge symmetry, or due topology, or it is long-lived because of some accidental symme- tries, and the particle contents and their dynamics are completely fixed by local gauge symmetries. In these models, one have two types of natural force mediators, dark gauge bosons and dark Higgs boson, which would affect DM and Higgs phenomenology in important ways. I discuss various phenomenological issues including the GeV scale gamma-ray excess from the galactic center (GC), (in)direct detection signatures, dark radiation, Higgs phenomenology and Higgs inflation assisted by dark Higgs.

  15. Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

    Science.gov (United States)

    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

  16. Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Hyesuk Kim

    2015-01-01

    Full Text Available We introduce a vision-based arm gesture recognition (AGR system using Kinect. The AGR system learns the discrete Hidden Markov Model (HMM, an effective probabilistic graph model for gesture recognition, from the dynamic pose of the arm joints provided by the Kinect API. Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles of the corresponding arm part to obtain view-invariant and more discriminative features. We confirmed high recognition performance of the proposed AGR system through experiments with two different datasets.

  17. A Method for Driving Route Predictions Based on Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Ning Ye

    2015-01-01

    Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

  18. A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective

    Directory of Open Access Journals (Sweden)

    Kyung-Eun Lee

    2014-12-01

    Full Text Available Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip and chromatin immunoprecipitation-sequencing (ChIP-seq, have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.

  19. Weak-scale hidden sector and energy transport in fireball models of gamma-ray bursts

    Energy Technology Data Exchange (ETDEWEB)

    Demir, Durmus A. [The Abdus Salam International Centre for Theoretical Physics, Trieste (Italy); Mosquera Cuesta, Herman J. [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil). Lab. de Fisica de Altas Energias (LAFEX)

    2000-12-01

    The annihilation of pairs of very weakly interacting particles in the neighborhood of gamma-ray sources is introduced here as a plausible mechanism to overcome the baryon load problem. This way we can explain how these very high energy gamma-ray bursts can be powered at the onset of very energetic events like supernovae (collapsars) explosions or coalescences of binary neutron stars. Our approach uses the weak-scale hidden sector models in which the Higgs sector of the standard model is extended to include a gauge singlet that only interacts with the Higgs particle. These particles would be produced either during the implosion of the red supergiant star core or at the aftermath of a neutron star binary merger. The whole energetics and timescales of the relativistic blast wave, the fireball, are reproduced. (author)

  20. Weak-scale hidden sector and energy transport in fireball models of gamma-ray bursts

    Energy Technology Data Exchange (ETDEWEB)

    Demir, Durmus A. [The Abdus Salam International Centre for Theoretical Physics, Trieste (Italy); Mosquera Cuesta, Herman J. [Centro Brasileiro de Pesquisas Fisicas (CBPF), Rio de Janeiro, RJ (Brazil). Lab. de Fisica de Altas Energias (LAFEX)

    2000-12-01

    The annihilation of pairs of very weakly interacting particles in the neighborhood of gamma-ray sources is introduced here as a plausible mechanism to overcome the baryon load problem. This way we can explain how these very high energy gamma-ray bursts can be powered at the onset of very energetic events like supernovae (collapsars) explosions or coalescences of binary neutron stars. Our approach uses the weak-scale hidden sector models in which the Higgs sector of the standard model is extended to include a gauge singlet that only interacts with the Higgs particle. These particles would be produced either during the implosion of the red supergiant star core or at the aftermath of a neutron star binary merger. The whole energetics and timescales of the relativistic blast wave, the fireball, are reproduced. (author)

  1. A computationally efficient approach for hidden-Markov model-augmented fingerprint-based positioning

    Science.gov (United States)

    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.

  2. Hidden Markov Model and Forward-Backward Algorithm in Crude Oil Price Forecasting

    Science.gov (United States)

    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.

  3. Characterising the Transmission Dynamics of Acinetobacter baumannii in Intensive Care Units Using Hidden Markov Models.

    Directory of Open Access Journals (Sweden)

    Tan N Doan

    Full Text Available Little is known about the transmission dynamics of Acinetobacter baumannii in hospitals, despite such information being critical for designing effective infection control measures. In the absence of comprehensive epidemiological data, mathematical modelling is an attractive approach to understanding transmission process. The statistical challenge in estimating transmission parameters from infection data arises from the fact that most patients are colonised asymptomatically and therefore the transmission process is not fully observed. Hidden Markov models (HMMs can overcome this problem. We developed a continuous-time structured HMM to characterise the transmission dynamics, and to quantify the relative importance of different acquisition sources of A. baumannii in intensive care units (ICUs in three hospitals in Melbourne, Australia. The hidden states were the total number of patients colonised with A. baumannii (both detected and undetected. The model input was monthly incidence data of the number of detected colonised patients (observations. A Bayesian framework with Markov chain Monte Carlo algorithm was used for parameter estimations. We estimated that 96-98% of acquisition in Hospital 1 and 3 was due to cross-transmission between patients; whereas most colonisation in Hospital 2 was due to other sources (sporadic acquisition. On average, it takes 20 and 31 days for each susceptible individual in Hospital 1 and Hospital 3 to become colonised as a result of cross-transmission, respectively; whereas it takes 17 days to observe one new colonisation from sporadic acquisition in Hospital 2. The basic reproduction ratio (R0 for Hospital 1, 2 and 3 was 1.5, 0.02 and 1.6, respectively. Our study is the first to characterise the transmission dynamics of A. baumannii using mathematical modelling. We showed that HMMs can be applied to sparse hospital infection data to estimate transmission parameters despite unobserved events and imperfect detection of

  4. Landmine detection using ensemble discrete hidden Markov models with context dependent training methods

    Science.gov (United States)

    Hamdi, Anis; Missaoui, Oualid; Frigui, Hichem; Gader, Paul

    2010-04-01

    We propose a landmine detection algorithm that uses ensemble discrete hidden Markov models with context dependent training schemes. We hypothesize that the data are generated by K models. These different models reflect the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Model identification is based on clustering in the log-likelihood space. First, one HMM is fit to each of the N individual sequence. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N x N log-likelihood distance matrix that will be partitioned into K groups. In the second step, we learn the parameters of one discrete HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we will investigate the maximum likelihood, and the MCE-based discriminative training approaches. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent HMM models that describe different properties of the data. Each HMM models a group of alarm signatures that share common attributes such as clutter, mine type, and burial depth. Our initial experiments have also indicated that the proposed mixture model outperform the baseline HMM that uses one model for the mine and one model for the background.

  5. Multiple instance hidden Markov models for GPR-based landmine detection

    Science.gov (United States)

    Manandhar, Achut; Morton, Kenneth D.; Collins, Leslie M.; Torrione, Peter A.

    2013-06-01

    Ground Penetrating Radar (GPR) is a widely used technology for the detection of subsurface buried threats. Although GPR data contains a representation of 3D space, during training, target and false alarm locations are usually only provided in 2D space along the surface of the earth. To overcome uncertainty in target depth location, many algorithms simply extract features from multiple depth regions that are then independently used to make mine/non-mine decisions. A similar technique is employed in hidden Markov models (HMM) based landmine detection. In this approach, sequences of downtrack GPR responses over multiple depth regions are utilized to train an HMM, which learns the probability of a particular sequence of GPR responses being generated by a buried target. However, the uncertainty in object depth complicates learning for discriminating targets/non-targets since features at the (unknown) target depth can be significantly different from features at other depths but in the same volume. To mitigate the negative impact of the uncertainty in object depth, mixture models based on Multiple Instance Learning (MIL) have previously been developed. MIL is also applicable in the landmine detection problem using HMMs because features that are extracted independently from sequences of GPR signals over several depth bins can be viewed as a set of unlabeled time series, where the entire set either corresponds to a buried threat or a false alarm. In this work, a novel framework termed as multiple instance hidden Markov model (MIHMM) is developed. We show that the performance of the proposed approach for discriminating targets from non-targets in GPR data is promising.

  6. Progression of liver cirrhosis to HCC: an application of hidden Markov model

    Directory of Open Access Journals (Sweden)

    Serio Gabriella

    2011-04-01

    Full Text Available Abstract Background Health service databases of administrative type can be a useful tool for the study of progression of a disease, but the data reported in such sources could be affected by misclassifications of some patients' real disease states at the time. Aim of this work was to estimate the transition probabilities through the different degenerative phases of liver cirrhosis using health service databases. Methods We employed a hidden Markov model to determine the transition probabilities between two states, and of misclassification. The covariates inserted in the model were sex, age, the presence of comorbidities correlated with alcohol abuse, the presence of diagnosis codes indicating hepatitis C virus infection, and the Charlson Index. The analysis was conducted in patients presumed to have suffered the onset of cirrhosis in 2000, observing the disease evolution and, if applicable, death up to the end of the year 2006. Results The incidence of hepatocellular carcinoma (HCC in cirrhotic patients was 1.5% per year. The probability of developing HCC is higher in males (OR = 2.217 and patients over 65 (OR = 1.547; over 65-year-olds have a greater probability of death both while still suffering from cirrhosis (OR = 2.379 and if they have developed HCC (OR = 1.410. A more severe casemix affects the transition from HCC to death (OR = 1.714. The probability of misclassifying subjects with HCC as exclusively affected by liver cirrhosis is 14.08%. Conclusions The hidden Markov model allowing for misclassification is well suited to analyses of health service databases, since it is able to capture bias due to the fact that the quality and accuracy of the available information are not always optimal. The probability of evolution of a cirrhotic subject to HCC depends on sex and age class, while hepatitis C virus infection and comorbidities correlated with alcohol abuse do not seem to have an influence.

  7. Gauge symmetry breaking in the hidden sector of the flipped SU(5)xU(1) superstring model

    Energy Technology Data Exchange (ETDEWEB)

    Antoniadis, I.; Rizos, J. (Centre de Physique Theorique, Ecole Polytechnique, 91 - Palaiseau (France)); Tamvakis, K. (Theoretical Physics Div., Univ. Ioannina (Greece))

    1992-03-26

    We analyze the SU(5)xU(1)'xU(1){sup 4}xSO(10)xSU(4) superstring model with a spontaneously broken hidden sector down to SO(7)xSO(5) taking into account non-renormalizable superpotential terms up to eight order. As a result of the hidden sector breaking the 'exotic' states get a mass and the 'observable' spectrum is composed of the standard three families. In addition, Cabibbo mixing arises at sixth order and an improved fermion mass hierarchy emerges. (orig.).

  8. Study of the ${\\eta}^{\\prime}$ ${\\to}$ $Ve^{+}e^{-}$ decay with hidden local symmetry model

    CERN Document Server

    Yang, Yueling; Lu, Gongru

    2014-01-01

    Within the hidden local symmetry framework, the Dalitz decay ${\\eta}^{\\prime}$ ${\\to}$ $Ve^{+}e^{-}$ is studied with the vector meson dominance model. It is found that the partial width ${\\Gamma}({\\eta}^{\\prime}{\\to}{\\omega}e^{+}e^{-})$ ${\\approx}$ $40$ eV and branching ratio ${\\cal B}({\\eta}^{\\prime}{\\to}{\\omega}e^{+}e^{-})$ ${\\approx}$ $2{\\times}10^{-4}$, and ${\\Gamma}({\\eta}^{\\prime}{\\to}{\\rho}e^{+}e^{-})$ ${\\approx}$ $10{\\Gamma}({\\eta}^{\\prime}{\\to}{\\omega}e^{+}e^{-})$ and ${\\cal B}({\\eta}^{\\prime}{\\to}{\\rho}e^{+}e^{-})$ ${\\approx}$ $10{\\cal B}({\\eta}^{\\prime}{\\to}{\\omega}e^{+}e^{-})$. The maximum position of the dilepton distribution is $m_{e^{+}e^{-}}$ ${\\approx}$ $1.33$ MeV. These decays are measurable with the advent of high statistics ${\\eta}^{\\prime}$ experiments.

  9. Confidence level fusion of edge histogram descriptor, hidden Markov model, spectral correlation feature, and NUKEv6

    Science.gov (United States)

    Ho, K. C.; Gader, P. D.; Frigui, H.; Wilson, J. N.

    2007-04-01

    This paper examines the confidence level fusion of several promising algorithms for the vehiclemounted ground penetrating radar landmine detection system. The detection algorithms considered here include Edge Histogram Descriptor (EHD), Hidden Markov Model (HMM), Spectral Correlation Feature (SCF) and NUKEv6. We first form a confidence vector by collecting the confidence values from the four individual detectors. The fused confidence is assigned to be the difference in the square of the Mahalanobis distance to the non-mine class and the square of the Mahalanobis distance to the mine class. Experimental results on a data collection that contains over 1500 mine encounters indicate that the proposed fusion technique can reduce the false alarm rate by a factor of two at 90% probability of detection when compared to the best individual detector.

  10. On-line monitoring of pharmaceutical production processes using Hidden Markov Model.

    Science.gov (United States)

    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.

  11. The discovery of processing stages: analyzing EEG data with hidden semi-Markov models.

    Science.gov (United States)

    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.

  12. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

  13. Mathematical Modeling of Hidden Intimate Partner Violence in Spain: A Quantitative and Qualitative Approach

    Directory of Open Access Journals (Sweden)

    E. De la Poza

    2016-01-01

    Full Text Available The fact that women are abused by their male partner is something that happens worldwide in the 21st century. In numerous cases, abuse only becomes publicly known when a fatal event occurs and is beyond any possible remedy, that is, when men murder their female partner. Since 2003, 793 (September 4, 2015 women have been assassinated by their significant other or excouple in Spain. Only 7.2% of murdered women had reported their fear and previous intimate partner violence (IPV to the police. Even when the number of female victims is comparable to the number of victims by terrorism, the Government has not assigned an equal amount of resources to diminish the magnitude of this hidden social problem. In this paper, a mathematical epidemiological model to forecast intimate partner violence in Spain is constructed. Both psychological and physical aggressor subpopulations are predicted and simulated. The model’s robustness versus uncertain parameters is studied by a sensitivity analysis.

  14. A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos

    KAUST Repository

    Wu, Baoyuan

    2016-10-25

    Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.

  15. Tinnitus with a normal audiogram: physiological evidence for hidden hearing loss and computational model.

    Science.gov (United States)

    Schaette, Roland; McAlpine, David

    2011-09-21

    Ever since Pliny the Elder coined the term tinnitus, the perception of sound in the absence of an external sound source has remained enigmatic. Traditional theories assume that tinnitus is triggered by cochlear damage, but many tinnitus patients present with a normal audiogram, i.e., with no direct signs of cochlear damage. Here, we report that in human subjects with tinnitus and a normal audiogram, auditory brainstem responses show a significantly reduced amplitude of the wave I potential (generated by primary auditory nerve fibers) but normal amplitudes of the more centrally generated wave V. This provides direct physiological evidence of "hidden hearing loss" that manifests as reduced neural output from the cochlea, and consequent renormalization of neuronal response magnitude within the brainstem. Employing an established computational model, we demonstrate how tinnitus could arise from a homeostatic response of neurons in the central auditory system to reduced auditory nerve input in the absence of elevated hearing thresholds.

  16. Detection of selective cationic amphipatic antibacterial peptides by Hidden Markov models.

    Science.gov (United States)

    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.

  17. Isolated Word Recognition Using Ergodic Hidden Markov Models and Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Warih Maharani

    2012-03-01

    Full Text Available Speech to Text was one of speech recognition applications which speech signal was processed, recognized and converted into a textual representation. Hidden Markov Model (HMM was the widely used method in speech recognition. However, the level of accuracy using HMM was strongly influenced by the optimalization of extraction process and modellling methods. Hence in this research, the use of genetic algorithm (GA method to optimize the Ergodic HMM was tested. In Hybrid HMM-GA, GA was used to optimize the Baum-welch method in the training process. It was useful to improve the accuracy of the recognition result which is produced by the HMM parameters that generate the low accuracy when the HMM are tested. Based on the research, the percentage increases the level of accuracy of 20% to 41%. Proved that the combination of GA in HMM method can gives more optimal results when compared with the HMM system that not combine with any method.

  18. An alternative to the Baum-Welch recursions for hidden Markov models

    CERN Document Server

    Bartolucci, Francesco

    2012-01-01

    We develop a recursion for hidden Markov model of any order h, which allows us to obtain the posterior distribution of the latent state at every occasion, given the previous h states and the observed data. With respect to the well-known Baum-Welch recursions, the proposed recursion has the advantage of being more direct to use and, in particular, of not requiring dummy renormalizations to avoid numerical problems. We also show how this recursion may be expressed in matrix notation, so as to allow for an efficient implementation, and how it may be used to obtain the manifest distribution of the observed data and for parameter estimation within the Expectation-Maximization algorithm. The approach is illustrated by an application to financial data which is focused on the study of the dynamics of the volatility level of log-returns.

  19. Video object's behavior analyzing based on motion history image and hidden markov model

    Institute of Scientific and Technical Information of China (English)

    Meng Fanfeng; Qu Zhenshen; Zeng Qingshuang; Li li

    2009-01-01

    A novel method was proposed, which extracted video object's track and analyzed video object's behavior. Firstly, this method tracked the video object based on motion history image, and obtained the coordinate-based track sequence and orientation-based track sequence of the video object. Then the proposed hidden markov model (HMM) based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects' behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object's various behaviors accurately. It provides a base for further research on video object behavior.

  20. Name segmentation using hidden Markov models and its application in record linkage

    Directory of Open Access Journals (Sweden)

    Rita de Cassia Braga Gonçalves

    2014-10-01

    Full Text Available This study aimed to evaluate the use of hidden Markov models (HMM for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM, Information Subsystem for High Complexity Procedures (APAC, and Hospital Information System (AIH. A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.

  1. Concomitant variables in finite mixture models

    NARCIS (Netherlands)

    Wedel, M

    The standard mixture model, the concomitant variable mixture model, the mixture regression model and the concomitant variable mixture regression model all enable simultaneous identification and description of groups of observations. This study reviews the different ways in which dependencies among

  2. Hidden symmetries in dilaton-axion gravity

    CERN Document Server

    Kechkin, O V

    1996-01-01

    Four--dimensional Einstein--Maxwell--dilaton--axion system restricted to space--times with one non--null Killing symmetry is formulated as the three--dimensional gravity coupled sigma--model. Several alternative representations are discussed and the associated hidden symmetries are revealed. The action of target space isometries on the initial set of (non--dualized ) variables is found. New mulicenter solutions are obtained via generating technique based on the formulation in terms of the non--dualized variables.

  3. A hidden state space modeling approach for improving glacier surface velocity estimates using remotely sensed data

    Science.gov (United States)

    Henke, D.; Schubert, A.; Small, D.; Meier, E.; Lüthi, M. P.; Vieli, A.

    2014-12-01

    A new method for glacier surface velocity (GSV) estimates is proposed here which combines ground- and space-based measurements with hidden state space modeling (HSSM). Examples of such a fusion of physical models with remote sensing (RS) observations were described in (Henke & Meier, Hidden State Space Models for Improved Remote Sensing Applications, ITISE 2014, p. 1242-1255) and are currently adapted for GSV estimation. GSV can be estimated using in situ measurements, RS methods or numerical simulations based on ice-flow models. In situ measurements ensure high accuracy but limited coverage and time consuming field work, while RS methods offer regular observations with high spatial coverage generally not possible with in situ methods. In particular, spaceborne Synthetic Aperture Radar (SAR) can obtain useful images independent of daytime and cloud cover. A ground portable radar interferometer (GPRI) is useful for investigating a particular area in more detail than is possible from space, but provides local coverage only. Several processing methods for deriving GSV from radar sensors have been established, including interferometry and offset tracking (Schubert et al, Glacier surface velocity estimation using repeat TerraSAR-X images. ISPRS Journal of P&RS, p. 49-62, 2013). On the other hand, it is also possible to derive glacier parameters from numerical ice-flow modeling alone. Given a well-parameterized model, GSV can in theory be derived and propagated continuously in time. However, uncertainties in the glacier flow dynamics and model errors increase with excessive propagation. All of these methods have been studied independently, but attempts to combine them have only rarely been made. The HSSM we propose recursively estimates the GSV based on 1) a process model making use of temporal and spatial interdependencies between adjacent states, and 2) observations (RS and optional in situ). The in situ and GPRI images currently being processed were acquired in the

  4. A hidden Markov model for decoding and the analysis of replay in spike trains.

    Science.gov (United States)

    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.

  5. Adaptive hidden Markov model with anomaly States for price manipulation detection.

    Science.gov (United States)

    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.

  6. Hypovigilance Detection for UCAV Operators Based on a Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Yerim Choi

    2014-01-01

    Full Text Available 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.

  7. Infinite hidden Markov models for unusual-event detection in video.

    Science.gov (United States)

    Pruteanu-Malinici, Iulian; Carin, Lawrence

    2008-05-01

    We address the problem of unusual-event detection in a video sequence. Invariant subspace analysis (ISA) is used to extract features from the video, and the time-evolving properties of these features are modeled via an infinite hidden Markov model (iHMM), which is trained using "normal"/"typical" video. The iHMM retains a full posterior density function on all model parameters, including the number of underlying HMM states. Anomalies (unusual events) are detected subsequently if a low likelihood is observed when associated sequential features are submitted to the trained iHMM. A hierarchical Dirichlet process framework is employed in the formulation of the iHMM. The evaluation of posterior distributions for the iHMM is achieved in two ways: via Markov chain Monte Carlo and using a variational Bayes formulation. Comparisons are made to modeling based on conventional maximum-likelihood-based HMMs, as well as to Dirichlet-process-based Gaussian-mixture models.

  8. Automated recognition of bird song elements from continuous recordings using dynamic time warping and hidden Markov models: a comparative study.

    Science.gov (United States)

    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.

  9. HIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTS

    Directory of Open Access Journals (Sweden)

    Pornpit Sirima

    2014-01-01

    Full Text Available Monthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a “defective” or “non-defective” state. The number of machine parts errors were collected from a thermo plastic injection molding machine in a car bumper auto parts manufacturer in Liberec city, Czech Republic from January 2012 to November 2012. A Bayesian method is used for parameter estimation. The results of this study indicate that the machine part error correction and the amount of time spent on the error correction do not improve the machine part status of the individual part, but there is a very strong month-to-month dependence of the machine part states. Using the Mean Absolute Error (MAE criterion, the performance of the proposed model (MAE = 1.62 and the HMM including machine part error correction only (MAE = 1.68, from our previous study, is not significantly different. However, the proposed model has more advantage in the fact that the machine part state can be explained by both the machine part error correction and the amount of time spent on the error correction.

  10. A hidden Markov model approach for determining expression from genomic tiling micro arrays

    Directory of Open Access Journals (Sweden)

    Krogh Anders

    2006-05-01

    Full Text Available Abstract Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. Results We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005 tiling array experiments on ten Human chromosomes 1. Results can be downloaded and viewed from our web site 2. Conclusion The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

  11. A Bayesian hierarchical nonhomogeneous hidden Markov model for multisite streamflow reconstructions

    Science.gov (United States)

    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.

  12. A Coupled Hidden Conditional Random Field Model for Simultaneous Face Clustering and Naming in Videos

    KAUST Repository

    Zhang, Yifan

    2016-08-18

    For face naming in TV series or movies, a typical way is using subtitles/script alignment to get the time stamps of the names, and tagging them to the faces. We study the problem of face naming in videos when subtitles are not available. To this end, we divide the problem into two tasks: face clustering which groups the faces depicting a certain person into a cluster, and name assignment which associates a name to each face. Each task is formulated as a structured prediction problem and modeled by a hidden conditional random field (HCRF) model. We argue that the two tasks are correlated problems whose outputs can provide prior knowledge of the target prediction for each other. The two HCRFs are coupled in a unified graphical model called coupled HCRF where the joint dependence of the cluster labels and face name association is naturally embedded in the correlation between the two HCRFs. We provide an effective algorithm to optimize the two HCRFs iteratively and the performance of the two tasks on real-world data set can be both improved.

  13. A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Yi Lu

    2016-11-01

    Full Text Available 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.

  14. Recognition-based online Kurdish character recognition using hidden Markov model and harmony search

    Directory of Open Access Journals (Sweden)

    Rina D. Zarro

    2017-04-01

    Full Text Available In this paper a hidden Markov model and harmony search algorithms are combined for writer independent online Kurdish character recognition. The Markov model is integrated as an intermediate group classifier instead of a main character classifier/recognizer as in most of previous works. Markov model is used to classify each group of characters, according to their forms, into smaller sub groups based on common directional feature vector. This process reduced the processing time taken by the later recognition stage. The small number of candidate characters are then processed by harmony search recognizer. The harmony search recognizer uses a dominant and common movement pattern as a fitness function. The objective function is used to minimize the matching score according to the fitness function criteria and according to the least score for each segmented group of characters. Then, the system displays the generated word which has the lowest score from the generated character combinations. The system was tested on a dataset of 4500 words structured with 21,234 characters in different positions or forms (isolated, start, middle and end. The system scored 93.52% successful recognition rate with an average of 500 ms. The system showed a high improvement in recognition rate when compared to similar systems that use HMM as its main recognizer.

  15. Object trajectory-based activity classification and recognition using hidden Markov models.

    Science.gov (United States)

    Bashir, Faisal I; Khokhar, Ashfaq A; Schonfeld, Dan

    2007-07-01

    Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.

  16. Reverse engineering a social agent-based hidden markov model--visage.

    Science.gov (United States)

    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.

  17. Revelatory aspects when innovating the “as – is” business model – actualizing hidden knowledge

    DEFF Research Database (Denmark)

    Saghaug, Kristin Margrethe; Lindgren, Peter

    This paper combines the area of innovation of business models (BM) and revelation. It explains the importance of discovering ones current, “as is” BM in relation to actualization of the company’s hidden knowledge and potential. The biblical revelations concern truly seeing, knowing and experience...... this as creating profound and meaningful relations (Pattison 2005;Tillich 1951). When one discovers the “as is” BM‘s potentials, it is revelatory in the sense that one is able to discover new potential relations and relate these to one’s future - “to be” business model. This is a participative act of knowing...... in line with Polanyi’s focus on practice related to knowing and also addressed directly by (Spender 2009a;Sveiby 2001;Weick 1996) or indirectly by (Roos et al. 2005). For the innovation leader of a company our empirical findings show that the discovering of one’s current business (model)’s “as is” seems...

  18. A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model.

    Science.gov (United States)

    Lu, Yi; Wei, Dongyan; Lai, Qifeng; Li, Wen; Yuan, Hong

    2016-11-30

    Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian's location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian's starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time.

  19. Evaluation of various feature extraction methods for landmine detection using hidden Markov models

    Science.gov (United States)

    Hamdi, Anis; Frigui, Hichem

    2012-06-01

    Hidden Markov Models (HMM) have proved to be eective for detecting buried land mines using data collected by a moving-vehicle-mounted ground penetrating radar (GPR). The general framework for a HMM-based landmine detector consists of building a HMM model for mine signatures and a HMM model for clutter signatures. A test alarm is assigned a condence proportional to the probability of that alarm being generated by the mine model and inversely proportional to its probability in the clutter model. The HMM models are built based on features extracted from GPR training signatures. These features are expected to capture the salient properties of the 3-dimensional alarms in a compact representation. The baseline HMM framework for landmine detection is based on gradient features. It models the time varying behavior of GPR signals, encoded using edge direction information, to compute the likelihood that a sequence of measurements is consistent with a buried landmine. In particular, the HMM mine models learns the hyperbolic shape associated with the signature of a buried mine by three states that correspond to the succession of an increasing edge, a at edge, and a decreasing edge. Recently, for the same application, other features have been used with dierent classiers. In particular, the Edge Histogram Descriptor (EHD) has been used within a K-nearest neighbor classier. Another descriptor is based on Gabor features and has been used within a discrete HMM classier. A third feature, that is closely related to the EHD, is the Bar histogram feature. This feature has been used within a Neural Networks classier for handwritten word recognition. In this paper, we propose an evaluation of the HMM based landmine detection framework with several feature extraction techniques. We adapt and evaluate the EHD, Gabor, Bar, and baseline gradient feature extraction methods. We compare the performance of these features using a large and diverse GPR data collection.

  20. Look-Back and Look-Ahead in the Conversion of Hidden Markov Models into Finite State Transducers

    CERN Document Server

    Kempe, A

    1999-01-01

    This paper describes the conversion of a Hidden Markov Model into a finite state transducer that closely approximates the behavior of the stochastic model. In some cases the transducer is equivalent to the HMM. This conversion is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also improved. The described methods have been implemented and successfully tested.

  1. Implementing EM and Viterbi algorithms for Hidden Markov Model in linear memory

    Directory of Open Access Journals (Sweden)

    Winters-Hilt Stephen

    2008-04-01

    Full Text Available Abstract Background The Baum-Welch learning procedure for Hidden Markov Models (HMMs provides a powerful tool for tailoring HMM topologies to data for use in knowledge discovery and clustering. A linear memory procedure recently proposed by Miklós, I. and Meyer, I.M. describes a memory sparse version of the Baum-Welch algorithm with modifications to the original probabilistic table topologies to make memory use independent of sequence length (and linearly dependent on state number. The original description of the technique has some errors that we amend. We then compare the corrected implementation on a variety of data sets with conventional and checkpointing implementations. Results We provide a correct recurrence relation for the emission parameter estimate and extend it to parameter estimates of the Normal distribution. To accelerate estimation of the prior state probabilities, and decrease memory use, we reverse the originally proposed forward sweep. We describe different scaling strategies necessary in all real implementations of the algorithm to prevent underflow. In this paper we also describe our approach to a linear memory implementation of the Viterbi decoding algorithm (with linearity in the sequence length, while memory use is approximately independent of state number. We demonstrate the use of the linear memory implementation on an extended Duration Hidden Markov Model (DHMM and on an HMM with a spike detection topology. Comparing the various implementations of the Baum-Welch procedure we find that the checkpointing algorithm produces the best overall tradeoff between memory use and speed. In cases where sequence length is very large (for Baum-Welch, or state number is very large (for Viterbi, the linear memory methods outlined may offer some utility. Conclusion Our performance-optimized Java implementations of Baum-Welch algorithm are available at http://logos.cs.uno.edu/~achurban. The described method and implementations will aid

  2. Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models.

    Science.gov (United States)

    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.

  3. Landmine detection with ground penetrating radar using discrete hidden Markov models with symbol dependent features

    Science.gov (United States)

    Frigui, Hichem; Missaoui, Oualid; Gader, Paul

    2008-04-01

    In this paper, we propose an efficient Discrete Hidden Markov Models (DHMM) for landmine detection that rely on training data to learn the relevant features that characterize different signatures (mines and non-mines), and can adapt to different environments and different radar characteristics. Our work is motivated by the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Thus, ideally different sets of specialized features may be needed to achieve high detection and low false alarm rates. The proposed approach includes three main components: feature extraction, clustering, and DHMM. First, since we do not assume that the relevant features for the different signatures are known a priori, we proceed by extracting several sets of features for each signature. Then, we apply a clustering and feature discrimination algorithm to the training data to quantize it into a set of symbols and learn feature relevance weights for each symbol. These symbols and their weights are then used in a DHMM framework to learn the parameters of the mine and the background models. Preliminary results on large and diverse ground penetrating radar data show that the proposed method outperforms the basic DHMM where all the features are treated equally important.

  4. Characterization of the crawling activity of Caenorhabditis elegans using a Hidden Markov model.

    Science.gov (United States)

    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.

  5. Extracting duration information in a picture category decoding task using hidden Markov Models

    Science.gov (United States)

    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.

  6. A Hidden Markov model web application for analysing bacterial genomotyping DNA microarray experiments.

    Science.gov (United States)

    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.

  7. Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales.

    Science.gov (United States)

    Quick, Nicola J; Isojunno, Saana; Sadykova, Dina; Bowers, Matthew; Nowacek, Douglas P; Read, Andrew J

    2017-03-31

    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.

  8. ISO observations and models of galaxies with Hidden Broad Line Regions

    CERN Document Server

    Efstathiou, A

    2005-01-01

    In this paper we present ISO mid-infrared spectrophotometry and far-infrared photometry of galaxies with Hidden Broad Line Regions (HBLR). We also present radiative transfer models of their spectral energy distributions which enable us to separate the contributions from the dusty disc of the AGN and the dusty starbursts. We find that the combination of tapered discs (discs whose thickness increases with distance from the central source in the inner part but stays constant in the outer part) and starbursts provide good fits to the data. The tapered discs dominate in the mid-infrared part of the spectrum and the starbursts in the far-infrared. After correcting the AGN luminosity for anisotropic emission we find that the ratio of the AGN luminosity to the starburst luminosity, L(AGN)/L(SB), ranges from about unity for IRAS14454-4343 to about 13 for IRAS01475-0740. Our results suggest that the warm IRAS colours of HBLR are due to the relatively high L(AGN)/L(SB). Our fits are consistent with the unified model and...

  9. Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales

    Science.gov (United States)

    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

  10. Automatic detection of avalanches in seismic data using Hidden Markov Models

    Science.gov (United States)

    Heck, Matthias; Hammer, Conny; van Herwijnen, Alec; Schweizer, Jürg; Fäh, Donat

    2017-04-01

    Seismic monitoring systems are well suited for the remote detection of mass movements, such as landslides, rockfalls and debris flows. For snow avalanches, this has been known since the 1970s and seismic monitoring could potentially provide valuable information for avalanche forecasting. We thus explored continuous seismic data from a string of vertical component geophones in an avalanche starting zone above Davos, Switzerland. The overall goal is to automatically detect avalanches with a Hidden Markov Model (HMM), a statistical pattern recognition tool widely used for speech recognition. A HMM uses a classifier to determine the likelihood that input objects belong to a finite number of classes. These classes are obtained by learning a multidimensional Gaussian mixture model representation of the overall observable feature space. This model is then used to derive the HMM parameters for avalanche waveforms using a single training sample to build the final classifier. We classified data from the winter seasons of 2010 and compared the results to several hundred avalanches manually identified in the seismic data. First results of a classification of a single day have shown, that the model is good in terms of probability of detection while having a relatively low false alarm rate. We further implemented a voting based classification approach to neglect events detected only by one sensor to further improve the model performance. For instance, on 22 March 2010, a day with particular high avalanche activity, 17 avalanches were positively identified by at least three sensors with no false alarms. These results show, that the automatic detection of avalanches in seismic data is feasible, bringing us one step closer to implementing seismic monitoring system in operational forecasting.

  11. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    Full Text Available BACKGROUND: The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions. CONCLUSIONS/SIGNIFICANCE: Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

  12. Higgs potential and hidden light Higgs scenario in two Higgs doublet models

    CERN Document Server

    Chang, Sanghyeon; Lee, Jong-Phil; Song, Jeonghyeon

    2015-01-01

    In two Higgs doublet models (2HDM), there exists an interesting possibility, the hidden light Higgs scenario, that the discovered SM-like Higgs boson is the heavier CP-even Higgs boson $H^0$ and the lighter CP-even $h^0$ has not been observed yet in any experiment. We study the current status of this scenario in Types I, II, X, and Y, through the scans of the 2HDM parameters with all relevant theoretical and experimental constraints. We employ not only the most up-to-date Higgs signal strength measurements with the feed-down effects, but also all the available LHC exclusion limits from heavy Higgs searches. Adjusting the heavier $H^0$ to the 125 GeV state while hiding the lighter $h^0$ from the LEP Higgs search prohibits the extreme decoupling limit: there exist upper bounds on the masses of the pseudoscalar $A^0$ and the charged Higgs $H^\\pm$ below about 550 GeV. In addition, the $Z_2$ symmetry, which was introduced to avoid the tree-level FCNC, is shown to be a good approximate symmetry since the soft $Z_2$...

  13. Conserved Functional Motifs and Homology Modeling to Predict Hidden Moonlighting Functional Sites

    KAUST Repository

    Wong, Aloysius Tze

    2015-06-09

    Moonlighting functional centers within proteins can provide them with hitherto unrecognized functions. Here, we review how hidden moonlighting functional centers, which we define as binding sites that have catalytic activity or regulate protein function in a novel manner, can be identified using targeted bioinformatic searches. Functional motifs used in such searches include amino acid residues that are conserved across species and many of which have been assigned functional roles based on experimental evidence. Molecules that were identified in this manner seeking cyclic mononucleotide cyclases in plants are used as examples. The strength of this computational approach is enhanced when good homology models can be developed to test the functionality of the predicted centers in silico, which, in turn, increases confidence in the ability of the identified candidates to perform the predicted functions. Computational characterization of moonlighting functional centers is not diagnostic for catalysis but serves as a rapid screening method, and highlights testable targets from a potentially large pool of candidates for subsequent in vitro and in vivo experiments required to confirm the functionality of the predicted moonlighting centers.

  14. An Enhanced Informed Watermarking Scheme Using the Posterior Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Chuntao Wang

    2014-01-01

    Full Text Available Designing a practical watermarking scheme with high robustness, feasible imperceptibility, and large capacity remains one of the most important research topics in robust watermarking. This paper presents a posterior hidden Markov model (HMM- based informed image watermarking scheme, which well enhances the practicability of the prior-HMM-based informed watermarking with favorable robustness, imperceptibility, and capacity. To make the encoder and decoder use the (nearly identical posterior HMM, each cover image at the encoder and each received image at the decoder are attacked with JPEG compression at an equivalently small quality factor (QF. The attacked images are then employed to estimate HMM parameter sets for both the encoder and decoder, respectively. Numerical simulations show that a small QF of 5 is an optimum setting for practical use. Based on this posterior HMM, we develop an enhanced posterior-HMM-based informed watermarking scheme. Extensive experimental simulations show that the proposed scheme is comparable to its prior counterpart in which the HMM is estimated with the original image, but it avoids the transmission of the prior HMM from the encoder to the decoder. This thus well enhances the practical application of HMM-based informed watermarking systems. Also, it is demonstrated that the proposed scheme has the robustness comparable to the state-of-the-art with significantly reduced computation time.

  15. Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Lokesh Selvaraj

    2014-01-01

    Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

  16. Identifying bubble collapse in a hydrothermal system using hidden Markov models

    Science.gov (United States)

    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.

  17. A classification of marked hijaiyah letters' pronunciation using hidden Markov model

    Science.gov (United States)

    Wisesty, Untari N.; Mubarok, M. Syahrul; Adiwijaya

    2017-08-01

    Hijaiyah letters are the letters that arrange the words in Al Qur'an consisting of 28 letters. They symbolize the consonant sounds. On the other hand, the vowel sounds are symbolized by harokat/marks. Speech recognition system is a system used to process the sound signal to be data so that it can be recognized by computer. To build the system, some stages are needed i.e characteristics/feature extraction and classification. In this research, LPC and MFCC extraction method, K-Means Quantization vector and Hidden Markov Model classification are used. The data used are the 28 letters and 6 harakat with the total class of 168. After several are testing done, it can be concluded that the system can recognize the pronunciation pattern of marked hijaiyah letter very well in the training data with its highest accuracy of 96.1% using the feature of LPC extraction and 94% using the MFCC. Meanwhile, when testing system is used, the accuracy decreases up to 41%.

  18. A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading

    Directory of Open Access Journals (Sweden)

    Yong Lian

    2005-06-01

    Full Text Available 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.

  19. An enhanced informed watermarking scheme using the posterior hidden Markov model.

    Science.gov (United States)

    Wang, Chuntao

    2014-01-01

    Designing a practical watermarking scheme with high robustness, feasible imperceptibility, and large capacity remains one of the most important research topics in robust watermarking. This paper presents a posterior hidden Markov model (HMM-) based informed image watermarking scheme, which well enhances the practicability of the prior-HMM-based informed watermarking with favorable robustness, imperceptibility, and capacity. To make the encoder and decoder use the (nearly) identical posterior HMM, each cover image at the encoder and each received image at the decoder are attacked with JPEG compression at an equivalently small quality factor (QF). The attacked images are then employed to estimate HMM parameter sets for both the encoder and decoder, respectively. Numerical simulations show that a small QF of 5 is an optimum setting for practical use. Based on this posterior HMM, we develop an enhanced posterior-HMM-based informed watermarking scheme. Extensive experimental simulations show that the proposed scheme is comparable to its prior counterpart in which the HMM is estimated with the original image, but it avoids the transmission of the prior HMM from the encoder to the decoder. This thus well enhances the practical application of HMM-based informed watermarking systems. Also, it is demonstrated that the proposed scheme has the robustness comparable to the state-of-the-art with significantly reduced computation time.

  20. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    Science.gov (United States)

    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.

  1. Conserved functional motifs and homology modelling to predict hidden moonlighting functional sites

    Directory of Open Access Journals (Sweden)

    Helen R Irving

    2015-06-01

    Full Text Available Moonlighting functional centers within proteins can provide them with hitherto unrecognized functions. Here we review how hidden moonlighting functional centers which we define as binding sites that have catalytic activity or regulate protein function in a novel manner, can be identified using targeted bioinformatic searches. Functional motifs used in such searches include amino acid residues that are conserved across species and many of which have been assigned functional roles based on experimental evidence. Molecules that were identified in this manner seeking cyclic mononucleotide cyclases in plants are used as examples. The strength of this computational approach is enhanced when good homology models can be developed to test the functionality of the predicted centers in silico which in turn, increases confidence in the ability of the identified candidates to perform the predicted functions. Computational characterization of moonlighting functional centers is not diagnostic for catalysis but serves as a rapid screening method, and highlights testable targets from a potentially large pool of candidates for subsequent in vitro and in vivo experiments required to confirm the functionality of the predicted moonlighting centers.

  2. Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Dongha Lim

    2014-01-01

    Full Text Available Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL. The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

  3. Snoring detection using a piezo snoring sensor based on hidden Markov models.

    Science.gov (United States)

    Lee, Hyo-Ki; Lee, Jeon; Kim, Hojoong; Ha, Jin-Young; Lee, Kyoung-Joung

    2013-05-01

    This study presents a snoring detection method based on hidden Markov models (HMMs) using a piezo snoring sensor. Snoring is a major symptom of obstructive sleep apnea (OSA). In most sleep studies, snoring is detected with a microphone. Since these studies analyze the acoustic properties of snoring, they need to acquire data at high sampling rates, so a large amount of data should be processed. Recently, several sleep studies have monitored snoring using a piezo snoring sensor. However, an automatic method for snoring detection using a piezo snoring sensor has not been reported in the literature. This study proposed the HMM-based method to detect snoring using this sensor, which is attached to the neck. The data from 21 patients with OSA were gathered for training and test sets. The short-time Fourier transform and short-time energy were computed so they could be applied to HMMs. The data were classified as snoring, noise and silence according to their HMMs. As a result, the sensitivity and the positive predictivity values were 93.3% and 99.1% for snoring detection, respectively. The results demonstrated that the method produced simple, portable and user-friendly detection tools that provide an alternative to the microphone-based method.

  4. Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin

    CERN Document Server

    Suvorova, S; Melatos, A; Moran, W; Evans, R J

    2016-01-01

    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 T_drift = 10d over a total observation time of T_obs = 1yr can detect signals with wave strains h0 > 2e-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...

  5. Hidden photons in connection to dark matter

    Energy Technology Data Exchange (ETDEWEB)

    Andreas, Sarah; Ringwald, Andreas [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Goodsell, Mark D. [CPhT, Ecole Polytechnique, Palaiseau (France)

    2013-06-15

    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.

  6. The nonstrange dibaryon and hidden-color effect in a chiral quark model

    Energy Technology Data Exchange (ETDEWEB)

    Dai, L.R.; Zhang, Y.N.; Sun, Y.L.; Shao, S.J. [Liaoning Normal University, Department of Physics, Dalian (China)

    2016-09-15

    The exotic nonstrange ΔΔ dibaryon with I(J{sup P}) = 0(3{sup +}) has been confirmed by the experimental data reported by WASA-at-COSY Collaboration, and the result is consistent with our theoretical prediction in the chiral SU(3) quark model and extended chiral SU(3) quark model, showing that the effect from hidden-color channel (CC) is important. In the present work, we further investigate another exotic nonstrange ΔΔ dibaryon with I(J{sup P}) = 3(0{sup +}) in the chiral SU(3) quark model that describes the energies of baryon ground states and the nucleon-nucleon (NN) scattering data satisfactorily. We perform a dynamical coupled-channel study of the ΔΔ-CC system with I(J{sup P}) = 3(0{sup +}) within the framework of resonating group method (RGM). We find that the binding energy of I(J{sup P}) = 3(0{sup +}) state is about 22.3 MeV and a root-mean-square radius (RMS) of 1.03 fm in single-channel calculation. Then we extend the model to include the CC channel to further study the I(J{sup P}) = 3(0{sup +}) state and find that the binding energy is about 31.3 MeV and RMS is 0.97 fm in coupled-channel calculation. We can see that the CC channel coupling has a relatively large effect on this state. The color screening effect is further considered and we find that the bound state property will not change much. It is shown that the binding energy of this state is stably ranged around several tens of MeV; it means that its mass is always lower than the threshold of the ΔΔ channel and higher than the mass of NΔπ. (orig.)

  7. The nonstrange dibaryon and hidden-color effect in a chiral quark model

    Science.gov (United States)

    Dai, L. R.; Zhang, Y. N.; Sun, Y. L.; Shao, S. J.

    2016-09-01

    The exotic nonstrange ΔΔ dibaryon with I(JP) = 0(3+) has been confirmed by the experimental data reported by WASA-at-COSY Collaboration, and the result is consistent with our theoretical prediction in the chiral SU(3) quark model and extended chiral SU(3) quark model, showing that the effect from hidden-color channel ( CC is important. In the present work, we further investigate another exotic nonstrange ΔΔ dibaryon with I(JP) = 3(0+) in the chiral SU(3) quark model that describes the energies of baryon ground states and the nucleon-nucleon (NN) scattering data satisfactorily. We perform a dynamical coupled-channel study of the ΔΔ - CC system with I(JP) = 3(0+) within the framework of resonating group method (RGM). We find that the binding energy of I(JP) = 3(0+) state is about 22.3 MeV and a root-mean-square radius (RMS) of 1.03 fm in single-channel calculation. Then we extend the model to include the CC channel to further study the I(JP) = 3(0+) state and find that the binding energy is about 31.3 MeV and RMS is 0.97 fm in coupled-channel calculation. We can see that the CC channel coupling has a relatively large effect on this state. The color screening effect is further considered and we find that the bound state property will not change much. It is shown that the binding energy of this state is stably ranged around several tens of MeV; it means that its mass is always lower than the threshold of the ΔΔ channel and higher than the mass of NΔπ.

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

    Directory of Open Access Journals (Sweden)

    Yen-Jen Lin

    Full Text Available 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.

  9. Multi-stream continuous hidden Markov models with application to landmine detection

    Science.gov (United States)

    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.

  10. HaplotypeCN: Copy Number Haplotype Inference with Hidden Markov Model and Localized Haplotype Clustering

    Science.gov (United States)

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

    2014-01-01

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

  11. Self-similarity of complex networks and hidden metric spaces

    CERN Document Server

    Serrano, M Angeles; Boguna, Marian

    2007-01-01

    We demonstrate that the self-similarity of some scale-free networks with respect to a simple degree-thresholding renormalization scheme finds a natural interpretation in the assumption that network nodes exist in hidden metric spaces. Clustering, i.e., cycles of length three, plays a crucial role in this framework as a topological reflection of the triangle inequality in the hidden geometry. We prove that a class of hidden variable models with underlying metric spaces are able to accurately reproduce the self-similarity properties that we measured in the real networks. Our findings indicate that hidden geometries underlying these real networks are a plausible explanation for their observed topologies and, in particular, for their self-similarity with respect to the degree-based renormalization.

  12. Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Da Liu

    2013-01-01

    Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

  13. Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models

    Science.gov (United States)

    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.

  14. Analysis of Decision Trees in Context Clustering of Hidden Markov Model Based Thai Speech Synthesis

    Directory of Open Access Journals (Sweden)

    Suphattharachai Chomphan

    2011-01-01

    Full Text Available Problem statement: In Thai speech synthesis using Hidden Markov model (HMM based synthesis system, the tonal speech quality is degraded due to tone distortion. This major problem must be treated appropriately to preserve the tone characteristics of each syllable unit. Since tone brings about the intelligibility of the synthesized speech. It is needed to establish the tone questions and other phonetic questions in tree-based context clustering process accordingly. Approach: This study describes the analysis of questions in tree-based context clustering process of an HMM-based speech synthesis system for Thai language. In the system, spectrum, pitch or F0 and state duration are modeled simultaneously in a unified framework of HMM, their parameter distributions are clustered independently by using a decision-tree based context clustering technique. The contextual factors which affect spectrum, pitch and duration, i.e., part of speech, position and number of phones in a syllable, position and number of syllables in a word, position and number of words in a sentence, phone type and tone type, are taken into account for constructing the questions of the decision tree. All in all, thirteen sets of questions are analyzed in comparison. Results: In the experiment, we analyzed the decision trees by counting the number of questions in each node coming from those thirteen sets and by calculating the dominance score given to each question as the reciprocal of the distance from the root node to the question node. The highest number and dominance score are of the set of phonetic type, while the second, third highest ones are of the set of part of speech and tone type. Conclusion: By counting the number of questions in each node and calculating the dominance score, we can set the priority of each question set. All in all, the analysis results bring about further development of Thai speech synthesis with efficient context clustering process in

  15. Identification of temporal patterns in the seismicity of Sumatra using Poisson Hidden Markov models

    Directory of Open Access Journals (Sweden)

    Katerina Orfanogiannaki

    2014-05-01

    Full Text Available On 26 December 2004 and 28 March 2005 two large earthquakes occurred between the Indo-Australian and the southeastern Eurasian plates with moment magnitudes Mw=9.1 and Mw=8.6, respectively. Complete data (mb≥4.2 of the post-1993 time interval have been used to apply Poisson Hidden Markov models (PHMMs for identifying temporal patterns in the time series of the two earthquake sequences. Each time series consists of earthquake counts, in given and constant time units, in the regions determined by the aftershock zones of the two mainshocks. In PHMMs each count is generated by one of m different Poisson processes that are called states. The series of states is unobserved and is in fact a Markov chain. The model incorporates a varying seismicity rate, it assigns a different rate to each state and it detects the changes on the rate over time. In PHMMs unobserved factors, related to the local properties of the region are considered affecting the earthquake occurrence rate. Estimation and interpretation of the unobserved sequence of states that underlie the data contribute to better understanding of the geophysical processes that take place in the region. We applied PHMMs to the time series of the two mainshocks and we estimated the unobserved sequences of states that underlie the data. The results obtained showed that the region of the 26 December 2004 earthquake was in state of low seismicity during almost the entire observation period. On the contrary, in the region of the 28 March 2005 earthquake the seismic activity is attributed to triggered seismicity, due to stress transfer from the region of the 2004 mainshock.

  16. Rainfall variability modelling in Rwanda

    Science.gov (United States)

    Nduwayezu, E.; Kanevski, M.; Jaboyedoff, M.

    2012-04-01

    Support to climate change adaptation is a priority in many International Organisations meetings. But is the international approach for adaptation appropriate with field reality in developing countries? In Rwanda, the main problems will be heavy rain and/or long dry season. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). The spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front » mechanism. The torrential rainfall that occurs every year in Rwanda disturbs the circulation for many days, damages the houses and, more seriously, causes heavy losses of people. All districts are affected by bad weather (heavy rain) but the costs of such events are the highest in mountains districts. The objective of the current research is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. The research will include rainfalls variability mapping and probabilistic analyses of extreme events.

  17. A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified.

    Science.gov (United States)

    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.

  18. Modeling anger and aggressive driving behavior in a dynamic choice-latent variable model.

    Science.gov (United States)

    Danaf, Mazen; Abou-Zeid, Maya; Kaysi, Isam

    2015-02-01

    This paper develops a hybrid choice-latent variable model combined with a Hidden Markov model in order to analyze the causes of aggressive driving and forecast its manifestations accordingly. The model is grounded in the state-trait anger theory; it treats trait driving anger as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, and state anger as a dynamic latent variable that evolves over time and affects driving behavior, and that is expressed as a function of trait anger, frustrating events, and contextual variables (e.g., geometric roadway features, flow conditions, etc.). This model may be used in order to test measures aimed at reducing aggressive driving behavior and improving road safety, and can be incorporated into micro-simulation packages to represent aggressive driving. The paper also presents an application of this model to data obtained from a driving simulator experiment performed at the American University of Beirut. The results derived from this application indicate that state anger at a specific time period is significantly affected by the occurrence of frustrating events, trait anger, and the anger experienced at the previous time period. The proposed model exhibited a better goodness of fit compared to a similar simple joint model where driving behavior and decisions are expressed as a function of the experienced events explicitly and not the dynamic latent variable.

  19. Search for Hidden Particles

    CERN Multimedia

    The SHiP Experiment is a new general-purpose fixed target facility at the SPS to search for hidden particles as predicted by a very large number of recently elaborated models of Hidden Sectors which are capable of accommodating dark matter, neutrino oscillations, and the origin of the full baryon asymmetry in the Universe. Specifically, the experiment is aimed at searching for very weakly interacting long lived particles including Heavy Neutral Leptons - right-handed partners of the active neutrinos; light supersymmetric particles - sgoldstinos, etc.; scalar, axion and vector portals to the hidden sector. The high intensity of the SPS and in particular the large production of charm mesons with the 400 GeV beam allow accessing a wide variety of light long-lived exotic particles of such models and of SUSY. Moreover, the facility is ideally suited to study the interactions of tau neutrinos.

  20. Limited dependent variable models for panel data

    NARCIS (Netherlands)

    Charlier, E.

    1997-01-01

    Many economic phenomena require limited variable models for an appropriate treatment. In addition, panel data models allow the inclusion of unobserved individual-specific effects. These models are combined in this thesis. Distributional assumptions in the limited dependent variable models are

  1. BEYOND SEM: GENERAL LATENT VARIABLE MODELING

    National Research Council Canada - National Science Library

    Muthén, Bengt O

    2002-01-01

    This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts...

  2. Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

    Directory of Open Access Journals (Sweden)

    Surovcik Katharina

    2006-03-01

    Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired

  3. Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.

    Science.gov (United States)

    Llanos, Fernando; Xie, Zilong; Chandrasekaran, Bharath

    2017-08-12

    The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch processing. Due to low signal-to-noise ratio at the single-trial level, FFRs are typically averaged across thousands of stimulus repetitions. Prior work using this approach has shown that subcortical encoding of linguistically-relevant pitch patterns is modulated by long-term language experience. We examine the extent to which a machine learning approach using hidden Markov modeling (HMM) can be utilized to decode Mandarin tone-categories from scalp-record electrophysiolgical activity. We then assess the extent to which the HMM can capture biologically-relevant effects (language experience-driven plasticity). To this end, we recorded FFRs to four Mandarin tones from 14 adult native speakers of Chinese and 14 of native English. We trained a HMM to decode tone categories from the FFRs with varying size of averages. Tone categories were decoded with above-chance accuracies using HMM. The HMM derived metric (decoding accuracy) revealed a robust effect of language experience, such that FFRs from native Chinese speakers yielded greater accuracies than native English speakers. Critically, the language experience-driven plasticity was captured with average sizes significantly smaller than those used in the extant literature. Our results demonstrate the feasibility of HMM in assessing the robustness of neural pitch. Machine-learning approaches can complement extant analytical methods that capture auditory function and could reduce the number of trials needed to capture biological phenomena. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Hidden Markov model tracking of continuous gravitational waves from a neutron star with wandering spin

    Science.gov (United States)

    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 .

  5. Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

    DEFF Research Database (Denmark)

    O'Connell, Jared Michael; Tøgersen, Frede Aakmann; Friggens, Nic

    2011-01-01

    Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular...

  6. Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition

    Science.gov (United States)

    Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq

    2008-01-01

    In this paper, we propose a novel distributed causal multi-dimensional hidden Markov model (DHMM). The proposed model can represent, for example, multiple motion trajectories of objects and their interaction activities in a scene; it is capable of conveying not only dynamics of each trajectory, but also interactions information between multiple trajectories, which can be critical in many applications. We firstly provide a solution for non-causal, multi-dimensional hidden Markov model (HMM) by distributing the non-causal model into multiple distributed causal HMMs. We approximate the simultaneous solution of multiple HMMs on a sequential processor by an alternate updating scheme. Subsequently we provide three algorithms for the training and classification of our proposed model. A new Expectation-Maximization (EM) algorithm suitable for estimation of the new model is derived, where a novel General Forward-Backward (GFB) algorithm is proposed for recursive estimation of the model parameters. A new conditional independent subset-state sequence structure decomposition of state sequences is proposed for the 2D Viterbi algorithm. The new model can be applied to many other areas such as image segmentation and image classification. Simulation results in classification of multiple interacting trajectories demonstrate the superior performance and higher accuracy rate of our distributed HMM in comparison to previous models.

  7. Cardinality-dependent Variability in Orthogonal Variability Models

    DEFF Research Database (Denmark)

    Mærsk-Møller, Hans Martin; Jørgensen, Bo Nørregaard

    2012-01-01

    During our work on developing and running a software product line for eco-sustainable greenhouse-production software tools, which currently have three products members we have identified a need for extending the notation of the Orthogonal Variability Model (OVM) to support what we refer to as car......During our work on developing and running a software product line for eco-sustainable greenhouse-production software tools, which currently have three products members we have identified a need for extending the notation of the Orthogonal Variability Model (OVM) to support what we refer...

  8. A QoS-Satisfied Prediction Model for Cloud-Service Composition Based on a Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Qingtao Wu

    2013-01-01

    Full Text Available Various significant issues in cloud computing, such as service provision, service matching, and service assessment, have attracted researchers’ attention recently. Quality of service (QoS plays an increasingly important role in the provision of cloud-based services, by aiming for the seamless and dynamic integration of cloud-service components. In this paper, we focus on QoS-satisfied predictions about the composition of cloud-service components and present a QoS-satisfied prediction model based on a hidden Markov model. In providing a cloud-based service for a user, if the user’s QoS cannot be satisfied by a single cloud-service component, component composition should be considered, where its QoS-satisfied capability needs to be proactively predicted to be able to guarantee the user’s QoS. We discuss the proposed model in detail and prove some aspects of the model. Simulation results show that our model can achieve high prediction accuracies.

  9. A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

    Directory of Open Access Journals (Sweden)

    Hamodrakas Stavros J

    2004-03-01

    Full Text Available Abstract Background Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. Results The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. Conclusion Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for

  10. Hidden loss

    DEFF Research Database (Denmark)

    Kieffer-Kristensen, Rikke; Johansen, Karen Lise Gaardsvig

    2013-01-01

    to participate. RESULTS: All children were affected by their parents' ABI and the altered family situation. The children's expressions led the authors to identify six themes, including fear of losing the parent, distress and estrangement, chores and responsibilities, hidden loss, coping and support. The main...... the ill parent. These findings contribute to a deeper understanding of the traumatic process of parental ABI that some children experience and emphasize the importance of family-centred interventions that include the children....

  11. Variable Fidelity Aeroelastic Toolkit - Structural Model Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation is a methodology to incorporate variable fidelity structural models into steady and unsteady aeroelastic and aeroservoelastic analyses in...

  12. Hidden Statistics of Schroedinger Equation

    Science.gov (United States)

    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.

  13. Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

    Science.gov (United States)

    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.

  14. Using hidden markov models to improve quantifying physical activity in accelerometer data - a simulation study.

    Directory of Open Access Journals (Sweden)

    Vitali Witowski

    Full Text Available INTRODUCTION: The use of accelerometers to objectively measure physical activity (PA has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. METHODS: 1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois], the generalized Poisson distribution (HMM[GenPois] and the Gaussian distribution (HMM[Gauss] with regard to misclassification rate (MCR, bout detection, detection of the number of activities performed during the day and runtime. RESULTS: The cutpoint method had a misclassification rate (MCR of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint, 2.0 minutes (HMM[Gauss] and 14.2 minutes (HMM[GenPois]. CONCLUSIONS: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.

  15. A Survey on Hidden Markov Model (HMM Based Intention Prediction Techniques

    Directory of Open Access Journals (Sweden)

    Mrs. Manisha Bharati

    2016-01-01

    Full Text Available The extensive use of virtualization in implementing cloud infrastructure brings unrivaled security concerns for cloud tenants or customers and introduces an additional layer that itself must be completely configured and secured. Intruders can exploit the large amount of cloud resources for their attacks. This paper discusses two approaches In the first three features namely ongoing attacks, autonomic prevention actions, and risk measure are Integrated to our Autonomic Cloud Intrusion Detection Framework (ACIDF as most of the current security technologies do not provide the essential security features for cloud systems such as early warnings about future ongoing attacks, autonomic prevention actions, and risk measure. The early warnings are signaled through a new finite State Hidden Markov prediction model that captures the interaction between the attackers and cloud assets. The risk assessment model measures the potential impact of a threat on assets given its occurrence probability. The estimated risk of each security alert is updated dynamically as the alert is correlated to prior ones. This enables the adaptive risk metric to evaluate the cloud’s overall security state. The prediction system raises early warnings about potential attacks to the autonomic component, controller. Thus, the controller can take proactive corrective actions before the attacks pose a serious security risk to the system. In another Attack Sequence Detection (ASD approach as Tasks from different users may be performed on the same machine. Therefore, one primary security concern is whether user data is secure in cloud. On the other hand, hacker may facilitate cloud computing to launch larger range of attack, such as a request of port scan in cloud with multiple virtual machines executing such malicious action. In addition, hacker may perform a sequence of attacks in order to compromise his target system in cloud, for example, evading an easy-to-exploit machine in a

  16. Handbook of latent variable and related models

    CERN Document Server

    Lee, Sik-Yum

    2011-01-01

    This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

  17. Mining adverse drug reactions from online healthcare forums using hidden Markov model.

    Science.gov (United States)

    Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

    2014-10-23

    Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also

  18. Fitting hidden Markov models of protein domains to a target species: application to Plasmodium falciparum

    Directory of Open Access Journals (Sweden)

    Terrapon Nicolas

    2012-05-01

    Full Text Available Abstract Background Hidden Markov Models (HMMs are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion We show that the new approaches allow identification of several domain families previously absent in

  19. Profile hidden Markov models for the detection of viruses within metagenomic sequence data.

    Directory of Open Access Journals (Sweden)

    Peter Skewes-Cox

    Full Text Available Rapid, sensitive, and specific virus detection is an important component of clinical diagnostics. Massively parallel sequencing enables new diagnostic opportunities that complement traditional serological and PCR based techniques. While massively parallel sequencing promises the benefits of being more comprehensive and less biased than traditional approaches, it presents new analytical challenges, especially with respect to detection of pathogen sequences in metagenomic contexts. To a first approximation, the initial detection of viruses can be achieved simply through alignment of sequence reads or assembled contigs to a reference database of pathogen genomes with tools such as BLAST. However, recognition of highly divergent viral sequences is problematic, and may be further complicated by the inherently high mutation rates of some viral types, especially RNA viruses. In these cases, increased sensitivity may be achieved by leveraging position-specific information during the alignment process. Here, we constructed HMMER3-compatible profile hidden Markov models (profile HMMs from all the virally annotated proteins in RefSeq in an automated fashion using a custom-built bioinformatic pipeline. We then tested the ability of these viral profile HMMs ("vFams" to accurately classify sequences as viral or non-viral. Cross-validation experiments with full-length gene sequences showed that the vFams were able to recall 91% of left-out viral test sequences without erroneously classifying any non-viral sequences into viral protein clusters. Thorough reanalysis of previously published metagenomic datasets with a set of the best-performing vFams showed that they were more sensitive than BLAST for detecting sequences originating from more distant relatives of known viruses. To facilitate the use of the vFams for rapid detection of remote viral homologs in metagenomic data, we provide two sets of vFams, comprising more than 4,000 vFams each, in the HMMER3

  20. A Core Language for Separate Variability Modeling

    DEFF Research Database (Denmark)

    Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina

    2014-01-01

    Separate variability modeling adds variability to a modeling language without requiring modifications of the language or the supporting tools. We define a core language for separate variability modeling using a single kind of variation point to define transformations of software artifacts in object...... hierarchical dependencies between variation points via copying and flattening. Thus, we reduce a model with intricate dependencies to a flat executable model transformation consisting of simple unconditional local variation points. The core semantics is extremely concise: it boils down to two operational rules...

  1. Detection of UXO contaminated land fields using hidden Markov models in the SAR images generated by airborne radar system

    Science.gov (United States)

    Damarla, Thyagaraju; Nguyen, Lam H.; Ranney, Kenneth I.

    2001-08-01

    We present an algorithm based on hidden Markov models (HMM) to detect several types of unexploded ordinance (UXO). We use the synthetic aperture radar (SAR) images simulated for 155 mm artillery shell, 2.75 in rocket and 105 mm mortar to generate the codebook. The algorithm is used on the data collected at Yuma Proving ground (YPG). YPG is seeded with several types of UXOs for testing purposes. The data is collected using an ultra wideband SAR mounted on a telescoping boom to simulate the airborne radar. The algorithm has detected all the targets for which it is trained for and it also detected other UXOs that are similar in shape.

  2. Experimental falsification of Leggett's nonlocal variable model.

    Science.gov (United States)

    Branciard, Cyril; Ling, Alexander; Gisin, Nicolas; Kurtsiefer, Christian; Lamas-Linares, Antia; Scarani, Valerio

    2007-11-23

    Bell's theorem guarantees that no model based on local variables can reproduce quantum correlations. Also, some models based on nonlocal variables, if subject to apparently "reasonable" constraints, may fail to reproduce quantum physics. In this Letter, we introduce a family of inequalities, which use a finite number of measurement settings, and which therefore allow testing Leggett's nonlocal model versus quantum physics. Our experimental data falsify Leggett's model and are in agreement with quantum predictions.

  3. A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data.

    Science.gov (United States)

    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.

  4. A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection.

    Science.gov (United States)

    Bulla, Ingo; Schultz, Anne-Kathrin; Chesneau, Christophe; Mark, Tanya; Serea, Florin

    2014-06-19

    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. 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. 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.

  5. Decision variables analysis for structured modeling

    Institute of Scientific and Technical Information of China (English)

    潘启树; 赫东波; 张洁; 胡运权

    2002-01-01

    Structured modeling is the most commonly used modeling method, but it is not quite addaptive to significant changes in environmental conditions. Therefore, Decision Variables Analysis(DVA), a new modelling method is proposed to deal with linear programming modeling and changing environments. In variant linear programming , the most complicated relationships are those among decision variables. DVA classifies the decision variables into different levels using different index sets, and divides a model into different elements so that any change can only have its effect on part of the whole model. DVA takes into consideration the complicated relationships among decision variables at different levels, and can therefore sucessfully solve any modeling problem in dramatically changing environments.

  6. Generalized latent variable modeling multilevel, longitudinal, and structural equation models

    CERN Document Server

    Skrondal, Anders

    2004-01-01

    METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction 'True' variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses Generating flexible distributions Combining information Summary MODELING DIFFERENT RESPONSE PROCESSES Introduction Generalized linear models Extensions of generalized linear models Latent response formulation Modeling durations or survival Summary and further reading CLASSICAL LATENT VARIABLE MODELS Introduction Multilevel regression models Factor models and item respons

  7. A twist on folding: Predicting optimal sequences and optimal folds of simple protein models with the hidden-force algorithm

    CERN Document Server

    Kolossváry, István

    2012-01-01

    We propose a new way of looking at global optimization of off-lattice protein models. We present a dual optimization concept of predicting optimal sequences as well as optimal folds. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We emphasize that while the protein model we used is minimalist, the methodology is applicable to detailed protein models, and sequence optimization may yield novel folds and aid de novo protein design.

  8. Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

    Science.gov (United States)

    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.

  9. Assessing the limits of hidden Markov model analysis for multi-state particle tracks in living systems

    Science.gov (United States)

    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.

  10. Hidden worlds in quantum physics

    CERN Document Server

    Gouesbet, Gérard

    2014-01-01

    The past decade has witnessed a resurgence in research and interest in the areas of quantum computation and entanglement. This new book addresses the hidden worlds or variables of quantum physics. Author Gérard Gouesbet studied and worked with a former student of Louis de Broglie, a pioneer of quantum physics. His presentation emphasizes the history and philosophical foundations of physics, areas that will interest lay readers as well as professionals and advanced undergraduate and graduate students of quantum physics. The introduction is succeeded by chapters offering background on relevant concepts in classical and quantum mechanics, a brief history of causal theories, and examinations of the double solution, pilot wave, and other hidden-variables theories. Additional topics include proofs of possibility and impossibility, contextuality, non-locality, classification of hidden-variables theories, and stochastic quantum mechanics. The final section discusses how to gain a genuine understanding of quantum mec...

  11. A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

    Science.gov (United States)

    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.

  12. Interpreting the 750 GeV diphoton resonance using photon jets in hidden-valley-like models

    Science.gov (United States)

    Chang, Jung; Cheung, Kingman; Lu, Chih-Ting

    2016-04-01

    Motivated by the diphoton resonance recently reported by the ATLAS and CMS collaborations at √{s }=13 TeV , we interpret the resonance as a scalar boson X (750 ) in hidden-valley-like models. The scalar boson X can mix with the standard model Higgs boson and thus can be produced via gluon fusion. It then decays into a pair of very light hidden particles Y of O (1 GeV ) , each of which in turn decays to a pair of collimated π0's, and these two π0's decay into photons which then form photon jets. A photon jet (γ jet) is a special feature that consists of a cluster of collinear photons from the decay of a fast moving light particle [O (1 GeV )]. Because these photons inside the photon jet are so collimated that it cannot be distinguished from a single photon, in the final state of the decay of X (750 ) a pair of photon jets looks like a pair of single photons, which the experimentalists observed and reconstructed the 750 GeV diphoton resonance. Prospects for LHC Run-2 about other new and testable features are also discussed.

  13. AdS/QCD approach to the scale-invariant extension of the standard model with a strongly interacting hidden sector

    Science.gov (United States)

    Hatanaka, Hisaki; Jung, Dong-Won; Ko, Pyungwon

    2016-08-01

    In this paper, we revisit a scale-invariant extension of the standard model (SM) with a strongly interacting hidden sector within AdS/QCD approach. Using the AdS/QCD, we reduce the number of input parameters to three, i.e. hidden pion decay constant, hidden pion mass and tan β that is defined as the ratio of the vacuum expectation values (VEV) of the singlet scalar field and the SM Higgs boson. As a result, our model has sharp predictability. We perform the phenomenological analysis of the hidden pions which is one of the dark matter (DM) candidates in this model. With various theoretical and experimental constraints we search for the allowed parameter space and find that both resonance and non-resonance solutions are possible. Some typical correlations among various observables such as thermal relic density of hidden pions, Higgs boson signal strengths and DM-nucleon cross section are investigated. We provide some benchmark points for experimental tests.

  14. PT -breaking threshold in spatially asymmetric Aubry-André and Harper models: Hidden symmetry and topological states

    Science.gov (United States)

    Harter, Andrew K.; Lee, Tony E.; Joglekar, Yogesh N.

    2016-06-01

    Aubry-André-Harper lattice models, characterized by a reflection-asymmetric sinusoidally varying nearest-neighbor tunneling profile, are well known for their topological properties. We consider the fate of such models in the presence of balanced gain and loss potentials ±i γ located at reflection-symmetric sites. We predict that these models have a finite PT -breaking threshold only for specific locations of the gain-loss potential and uncover a hidden symmetry that is instrumental to the finite threshold strength. We also show that the topological edge states remain robust in the PT -symmetry-broken phase. Our predictions substantially broaden the possible experimental realizations of a PT -symmetric system.

  15. Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM).

    Science.gov (United States)

    Malesevic, Nebojsa; Markovic, Dimitrije; Kanitz, Gunter; Controzzi, Marco; Cipriani, Christian; Antfolk, Christian

    2017-07-01

    In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.

  16. Random Effect and Latent Variable Model Selection

    CERN Document Server

    Dunson, David B

    2008-01-01

    Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models

  17. Sampling Weights in Latent Variable Modeling

    Science.gov (United States)

    Asparouhov, Tihomir

    2005-01-01

    This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The…

  18. Dark energy and dark matter from hidden symmetry of gravity model with a non-Riemannian volume form

    Energy Technology Data Exchange (ETDEWEB)

    Guendelman, Eduardo [Ben-Gurion University of the Negev, Department of Physics, Beersheba (Israel); Nissimov, Emil; Pacheva, Svetlana [Bulgarian Academy of Sciences, Institute for Nuclear Research and Nuclear Energy, Sofia (Bulgaria)

    2015-10-15

    We show that dark energy and dark matter can be described simultaneously by ordinary Einstein gravity interacting with a single scalar field provided the scalar field Lagrangian couples in a symmetric fashion to two different spacetime volume forms (covariant integration measure densities) on the spacetime manifold - one standard Riemannian given by √(-g) (square root of the determinant of the pertinent Riemannian metric) and another non-Riemannian volume form independent of the Riemannian metric, defined in terms of an auxiliary antisymmetric tensor gauge field of maximal rank. Integration of the equations of motion of the latter auxiliary gauge field produce an a priori arbitrary integration constant that plays the role of a dynamically generated cosmological constant or dark energy. Moreover, the above modified scalar field action turns out to possess a hidden Noether symmetry whose associated conserved current describes a pressureless ''dust'' fluid which we can identify with the dark matter completely decoupled from the dark energy. The form of both the dark energy and dark matter that results from the above class of models is insensitive to the specific form of the scalar field Lagrangian. By adding an appropriate perturbation, which breaks the above hidden symmetry and along with this couples dark matter and dark energy, we also suggest a way to obtain growing dark energy in the present universe's epoch without evolution pathologies. (orig.)

  19. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  20. A Model for Positively Correlated Count Variables

    DEFF Research Database (Denmark)

    Møller, Jesper; Rubak, Ege Holger

    2010-01-01

    An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields and their poten......An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields...

  1. On Determining the Order of Markov Dependence of an Observed Process Governed by a Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    R.J. Boys

    2002-01-01

    Full Text Available This paper describes a Bayesian approach to determining the order of a finite state Markov chain whose transition probabilities are themselves governed by a homogeneous finite state Markov chain. It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. The method we describe uses a Markov chain Monte Carlo algorithm to obtain samples from the (posterior distribution for both the order of Markov dependence in the observed sequence and the other governing model parameters. These samples allow coherent inferences to be made straightforwardly in contrast to those which use information criteria. The methods are illustrated by their application to both simulated and real data sets.

  2. A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Sofia Siachalou

    2015-03-01

    Full Text Available Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece.

  3. Integrating models that depend on variable data

    Science.gov (United States)

    Banks, A. T.; Hill, M. C.

    2016-12-01

    Models of human-Earth systems are often developed with the goal of predicting the behavior of one or more dependent variables from multiple independent variables, processes, and parameters. Often dependent variable values range over many orders of magnitude, which complicates evaluation of the fit of the dependent variable values to observations. Many metrics and optimization methods have been proposed to address dependent variable variability, with little consensus being achieved. In this work, we evaluate two such methods: log transformation (based on the dependent variable being log-normally distributed with a constant variance) and error-based weighting (based on a multi-normal distribution with variances that tend to increase as the dependent variable value increases). Error-based weighting has the advantage of encouraging model users to carefully consider data errors, such as measurement and epistemic errors, while log-transformations can be a black box for typical users. Placing the log-transformation into the statistical perspective of error-based weighting has not formerly been considered, to the best of our knowledge. To make the evaluation as clear and reproducible as possible, we use multiple linear regression (MLR). Simulations are conducted with MatLab. The example represents stream transport of nitrogen with up to eight independent variables. The single dependent variable in our example has values that range over 4 orders of magnitude. Results are applicable to any problem for which individual or multiple data types produce a large range of dependent variable values. For this problem, the log transformation produced good model fit, while some formulations of error-based weighting worked poorly. Results support previous suggestions fthat error-based weighting derived from a constant coefficient of variation overemphasizes low values and degrades model fit to high values. Applying larger weights to the high values is inconsistent with the log

  4. An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters

    Directory of Open Access Journals (Sweden)

    Ojcius David M

    2009-08-01

    Full Text Available Abstract Background Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort. Results Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability. Conclusion This strategy and resulting model support the conjecture that DNA biophysical properties

  5. The Hidden Costs of Offshoring

    DEFF Research Database (Denmark)

    Møller Larsen, Marcus; Manning, Stephan; Pedersen, Torben

    2011-01-01

    This study seeks to explain hidden costs of offshoring, i.e. unexpected costs resulting from the relocation of business tasks and activities outside the home country. We develop a model that highlights the role of complexity, design orientation and experience in explaining hidden costs of offshor...... of our study is to suggest how hidden costs of offshoring can be mitigated through an explicit orientation towards improving organizational processes and structures as well as experience with offshoring.......This study seeks to explain hidden costs of offshoring, i.e. unexpected costs resulting from the relocation of business tasks and activities outside the home country. We develop a model that highlights the role of complexity, design orientation and experience in explaining hidden costs...... of offshoring. Specifically, we propose that hidden costs can be explained by the combination of increasing structural, operational and social complexity of offshoring activities. In addition, we suggest that firm orientation towards organizational design as part of an offshoring strategy and offshoring...

  6. Matter Power Spectrum in Hidden Neutrino Interacting Dark Matter Models: A Closer Look at the Collision term

    CERN Document Server

    Binder, Tobias; Kamada, Ayuki; Murayama, Hitoshi; Takahashi, Tomo; Yoshida, Naoki

    2016-01-01

    Dark Matter (DM) models providing possible alternative solutions to the small-scale crisis of standard cosmology are nowadays of growing interest. We consider DM interacting with light hidden fermions via well motivated fundamental operators showing the resultant matter power spectrum is suppressed on subgalactic scales within a plausible parameter region. Our basic description of evolution of cosmological perturbations relies on a fully consistent first principle derivation of a perturbed Fokker-Planck type equation, generalizing existing literature. The cosmological perturbation of the Fokker-Planck equation is presented for the first time in two different gauges, where the results transform into each other according to the rules of gauge transformation. Furthermore, our focus lies on a derivation of a broadly applicable and easily computable collision term showing important phenomenological differences to other existing approximations. As one of the main results and concerning the small-scale crisis, we sh...

  7. Hidden attractors in dynamical models of phase-locked loop circuits: Limitations of simulation in MATLAB and SPICE

    Science.gov (United States)

    Kuznetsov, N. V.; Leonov, G. A.; Yuldashev, M. V.; Yuldashev, R. V.

    2017-10-01

    During recent years it has been shown that hidden oscillations, whose basin of attraction does not overlap with small neighborhoods of equilibria, may significantly complicate simulation of dynamical models, lead to unreliable results and wrong conclusions, and cause serious damage in drilling systems, aircrafts control systems, electromechanical systems, and other applications. This article provides a survey of various phase-locked loop based circuits (used in satellite navigation systems, optical, and digital communication), where such difficulties take place in MATLAB and SPICE. Considered examples can be used for testing other phase-locked loop based circuits and simulation tools, and motivate the development and application of rigorous analytical methods for the global analysis of phase-locked loop based circuits.

  8. Backward transfer entropy: Informational measure for detecting hidden Markov models and its interpretations in thermodynamics, gambling and causality

    Science.gov (United States)

    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.

  9. Hidden Markov model-based approach for generation of Pitman shorthand language symbols for consonants and vowels from spoken English

    Indian Academy of Sciences (India)

    G Hemantha Kumar; M Ravishankar; P Nagabushan; Basavaraj S Anami

    2006-06-01

    Pitman shorthand language (PSL) is a widely practised medium for transcribing/recording speech to text (StT) in English. This recording medium continues to exist in spite of considerable development in speech processing systems (SPS), because of its ability to record spoken/dictated text at high speeds of more than 120 words per minute. Hence, scope exists for exploiting this potential of PSL in present SPS. In this paper, an approach for feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using hidden Markov models (HMM) for generating strokes comprising consonants and vowels (CV) in the process of production of Pitman shorthand language from spoken English is proposed. The proposed method is tested on a large number of samples, drawn from different speakers and the results are encouraging. The work is useful in total automation of PSL processing.

  10. Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

    CERN Document Server

    Karaman, Svebor; Dovgalecs, Vladislavs; Mégret, Rémi; Pinquier, Julien; André-Obrecht, Régine; Gaëstel, Yann; Dartigues, Jean-François

    2011-01-01

    This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach.

  11. Probing the hidden Higgs bosons of the Y=0 triplet- and singlet-extended Supersymmetric Standard Model at the LHC

    Energy Technology Data Exchange (ETDEWEB)

    Bandyopadhyay, Priyotosh [Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento and INFN-Lecce,Via Arnesano, 73100 Lecce (Italy); Corianò, Claudio [Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento and INFN-Lecce,Via Arnesano, 73100 Lecce (Italy); STAG Research Centre and Mathematical Sciences, University of Southampton,Southampton SO17 1BJ (United Kingdom); Costantini, Antonio [Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento and INFN-Lecce,Via Arnesano, 73100 Lecce (Italy)

    2015-12-18

    We investigate the scalar sector in an extension of the Minimal Supersymmetric Standard Model (MSSM) containing a SU(2) Higgs triplet of zero hypercharge and a gauge singlet beside the SU(2) scalar doublets. In particular, we focus on a scenario of this model which allows a light pseudoscalar and/or a scalar below 100 GeV, consistent with the most recent data from the LHC and the earlier data from the LEP experiments. We analyze the exotic decay of the discovered Higgs (h{sub 125}) into two light (hidden) Higgs bosons present in the extension. The latter are allowed by the uncertainties in the Higgs decay h{sub 125}→WW{sup ∗}, h{sub 125}→ZZ{sup ∗} and h{sub 125}→γγ. The study of the parameter space for such additional scalars/pseudoscalars decay of the Higgs is performed in the gluon fusion channel. The extra hidden Higgs bosons of the enlarged scalar sector, if they exist, will then decay into lighter fermion paris, i.e., bb̄, ττ̄ and μμ̄ via the mixing with the doublets. A detailed simulation using PYTHIA of the 2b+2τ, ≥3τ, 2b+2μ and 2τ+2μ final states is presented. From our analysis we conclude that, depending on the selected benchmark points, such decay modes can be explored with an integrated luminosity of 25 fb{sup −1} at the LHC at a center of mass energy of 13 TeV.

  12. Classification of changes occurring in lung patient during radiotherapy using relative γ analysis and hidden Markov models.

    Science.gov (United States)

    Varfalvy, Nicolas; Piron, Ophelie; Cyr, Marc François; Dagnault, Anne; Archambault, Louis

    2017-07-26

    To present a new automated patient classification method based on relative gamma analysis and hidden Markov models (HMM) to identify patients undergoing important anatomical changes during radiation therapy. Daily EPID images of every treatment field were acquired for 52 patients treated for lung cancer. In addition, CBCT were acquired on a regular basis. Gamma analysis was performed relative to the first fraction given that no significant anatomical change was observed on the CBCT of the first fraction compared to the planning CT. Several parameters were extracted from the gamma analysis (e.g., average gamma value, standard deviation, percent above 1). These parameters formed patient-specific time series. Data from the first 24 patients were used as a training set for the HMM. The trained HMM was then applied to the remaining 28 patients and compared to manual clinical evaluation and fixed thresholds. A three-category system was used for patient classification ranging from minor deviations (category 1) to severe deviations (category 3) from the treatment plan. Patient classified using the HMM lead to the same result as the classification made by a human expert 83% of the time. The HMM overestimate the category 10% of the time and underestimate 7% of the time. Both methods never disagree by more than one category. In addition, the information provided by the HMM is richer than the simple threshold-based approach. HMM provides information on the likelihood that a patient will improve or deteriorate as well as the expected time the patient will remain in that state. We showed a method to classify patients during the course of radiotherapy based on relative changes in EPID images and a hidden Markov model. Information obtained through this automated classification can complement the clinical information collected during treatment and help identify patients in need of a plan adaptation. © 2017 American Association of Physicists in Medicine.

  13. Gait variability: methods, modeling and meaning

    Directory of Open Access Journals (Sweden)

    Hausdorff Jeffrey M

    2005-07-01

    Full Text Available Abstract The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting.

  14. Searching for hidden sectors in multiparticle production at the LHC

    CERN Document Server

    Sanchis-Lozano, Miguel-Angel; Moreno-Picot, Salvador

    2015-01-01

    We study the impact of a hidden sector beyond the Standard Model, e.g. a Hidden Valley model, on factorial moments and cumulants of multiplicity distributions in multiparticle production with a special emphasis on the prospects for LHC results.

  15. Gaussian mixture model of heart rate variability.

    Directory of Open Access Journals (Sweden)

    Tommaso Costa

    Full Text Available Heart rate variability (HRV is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters.

  16. Bayesian variable selection for latent class models.

    Science.gov (United States)

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  17. Ancestry inference in complex admixtures via variable-length Markov chain linkage models.

    Science.gov (United States)

    Rodriguez, Jesse M; Bercovici, Sivan; Elmore, Megan; Batzoglou, Serafim

    2013-03-01

    Inferring the ancestral origin of chromosomal segments in admixed individuals is key for genetic applications, ranging from analyzing population demographics and history, to mapping disease genes. Previous methods addressed ancestry inference by using either weak models of linkage disequilibrium, or large models that make explicit use of ancestral haplotypes. In this paper we introduce ALLOY, an efficient method that incorporates generalized, but highly expressive, linkage disequilibrium models. ALLOY applies a factorial hidden Markov model to capture the parallel process producing the maternal and paternal admixed haplotypes, and models the background linkage disequilibrium in the ancestral populations via an inhomogeneous variable-length Markov chain. We test ALLOY in a broad range of scenarios ranging from recent to ancient admixtures with up to four ancestral populations. We show that ALLOY outperforms the previous state of the art, and is robust to uncertainties in model parameters.

  18. Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Paul Gader

    2005-07-01

    Full Text Available We propose a real-time software system for landmine detection using ground-penetrating radar (GPR. The system includes an efficient and adaptive preprocessing component; a hidden Markov model- (HMM- based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline. It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m2 of simulated dirt and gravel roads for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%.

  19. Modelling variability in hospital bed occupancy.

    Science.gov (United States)

    Harrison, Gary W; Shafer, Andrea; Mackay, Mark

    2005-11-01

    A stochastic version of the Harrison-Millard multistage model of the flow of patients through a hospital division is developed in order to model correctly not only the average but also the variability in occupancy levels, since it is the variability that makes planning difficult and high percent occupancy levels increase the risk of frequent overflows. The model is fit to one year of data from the medical division of an acute care hospital in Adelaide, Australia. Admissions can be modeled as a Poisson process with rates varying by day of the week and by season. Methods are developed to use the entire annual occupancy profile to estimate transition rate parameters when admission rates are not constant and to estimate rate parameters that vary by day of the week and by season, which are necessary for the model variability to be as large as in the data. The final model matches well the mean, standard deviation and autocorrelation function of the occupancy data and also six months of data not used to estimate the parameters. Repeated simulations are used to construct percentiles of the daily occupancy distributions and thus identify ranges of normal fluctuations and those that are substantive deviations from the past, and also to investigate the trade-offs between frequency of overflows and the percent occupancy for both fixed and flexible bed allocations. Larger divisions can achieve more efficient occupancy levels than smaller ones with the same frequency of overflows. Seasonal variations are more significant than day-of-the-week variations and variable discharge rates are more significant than variable admission rates in contributing to overflows.

  20. Interpolation of climate variables and temperature modeling

    Science.gov (United States)

    Samanta, Sailesh; Pal, Dilip Kumar; Lohar, Debasish; Pal, Babita

    2012-01-01

    Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4° × 4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.

  1. Modeling Variability in Immunocompetence and Immunoresponsiveness

    NARCIS (Netherlands)

    Ask, B.; Waaij, van der E.H.; Bishop, S.C.

    2008-01-01

    The purposes of this paper were to 1) develop a stochastic model that would reflect observed variation between animals and across ages in immunocompetence and responsiveness; and 2) illustrate consequences of this variability for the statistical power of genotype comparisons and selection. A stochas

  2. Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

    Directory of Open Access Journals (Sweden)

    Juri Taborri

    2015-09-01

    Full Text Available 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.

  3. Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

    Directory of Open Access Journals (Sweden)

    Yuan Yuan

    2015-11-01

    Full Text Available In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

  4. The effects of motivation on response rate: a hidden semi-Markov model analysis of behavioral dynamics.

    Science.gov (United States)

    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.

  5. Probing hidden sector photons through the Higgs window

    Energy Technology Data Exchange (ETDEWEB)

    Ahlers, M. [Oxford Univ. (United Kingdom). Rudolf Peierls Centre for Theoretical Physics; Jaeckel, J. [Durham Univ. (United Kingdom). Inst. for Particle Physics and Phenomenology; Redondo, J.; Ringwald, A. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)

    2008-07-15

    We investigate the possibility that a (light) hidden sector extra photon receives its mass via spontaneous symmetry breaking of a hidden sector Higgs boson, the so-called hidden-Higgs. The hidden-photon can mix with the ordinary photon via a gauge kinetic mixing term. The hidden-Higgs can couple to the Standard Model Higgs via a renormalizable quartic term - sometimes called the Higgs Portal. We discuss the implications of this light hidden-Higgs in the context of laser polarization and light-shining-through-the-wall experiments as well as cosmological, astrophysical, and non-Newtonian force measurements. For hidden-photons receiving their mass from a hidden-Higgs we find in the small mass regime significantly stronger bounds than the bounds on massive hidden sector photons alone. (orig.)

  6. Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.

    Directory of Open Access Journals (Sweden)

    Michael Seifert

    2012-01-01

    Full Text Available Array-based comparative genomic hybridization (Array-CGH is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM.

  7. Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.

    Science.gov (United States)

    Seifert, Michael; Gohr, André; Strickert, Marc; Grosse, Ivo

    2012-01-01

    Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM).

  8. Probing Hidden Sector Photons through the Higgs Window.

    OpenAIRE

    Ahlers, M.; Jaeckel, J; Redondo, J.; Ringwald, A.

    2008-01-01

    We investigate the possibility that a (light) hidden sector extra photon receives its mass via spontaneous symmetry breaking of a hidden sector Higgs boson, the so-called hidden-Higgs. The hidden-photon can mix with the ordinary photon via a gauge kinetic mixing term. The hidden-Higgs can couple to the Standard Model Higgs via a renormalizable quartic term - sometimes called the Higgs Portal. We discuss the implications of this light hidden-Higgs in the context of laser polarization and light...

  9. Bayesian modeling of ChIP-chip data using latent variables.

    KAUST Repository

    Wu, Mingqi

    2009-10-26

    BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the

  10. Simple nonlinear models suggest variable star universality

    CERN Document Server

    Lindner, John F; Kia, Behnam; Hippke, Michael; Learned, John G; Ditto, William L

    2015-01-01

    Dramatically improved data from observatories like the CoRoT and Kepler spacecraft have recently facilitated nonlinear time series analysis and phenomenological modeling of variable stars, including the search for strange (aka fractal) or chaotic dynamics. We recently argued [Lindner et al., Phys. Rev. Lett. 114 (2015) 054101] that the Kepler data includes "golden" stars, whose luminosities vary quasiperiodically with two frequencies nearly in the golden ratio, and whose secondary frequencies exhibit power-law scaling with exponent near -1.5, suggesting strange nonchaotic dynamics and singular spectra. Here we use a series of phenomenological models to make plausible the connection between golden stars and fractal spectra. We thereby suggest that at least some features of variable star dynamics reflect universal nonlinear phenomena common to even simple systems.

  11. Genomic relationships and speciation times of human, chimpanzee, and gorilla inferred from a coalescent hidden Markov model.

    Directory of Open Access Journals (Sweden)

    Asger Hobolth

    2007-02-01

    Full Text Available The genealogical relationship of human, chimpanzee, and gorilla varies along the genome. We develop a hidden Markov model (HMM that incorporates this variation and relate the model parameters to population genetics quantities such as speciation times and ancestral population sizes. Our HMM is an analytically tractable approximation to the coalescent process with recombination, and in simulations we see no apparent bias in the HMM estimates. We apply the HMM to four autosomal contiguous human-chimp-gorilla-orangutan alignments comprising a total of 1.9 million base pairs. We find a very recent speciation time of human-chimp (4.1 +/- 0.4 million years, and fairly large ancestral effective population sizes (65,000 +/- 30,000 for the human-chimp ancestor and 45,000 +/- 10,000 for the human-chimp-gorilla ancestor. Furthermore, around 50% of the human genome coalesces with chimpanzee after speciation with gorilla. We also consider 250,000 base pairs of X-chromosome alignments and find an effective population size much smaller than 75% of the autosomal effective population sizes. Finally, we find that the rate of transitions between different genealogies correlates well with the region-wide present-day human recombination rate, but does not correlate with the fine-scale recombination rates and recombination hot spots, suggesting that the latter are evolutionarily transient.

  12. Disfavouring Electroweak Baryogenesis and a hidden Higgs in a CP-violating Two-Higgs-Doublet Model

    CERN Document Server

    Haarr, Anders; Petersen, Troels C

    2016-01-01

    A strongly first-order electroweak phase transition is a necessary requirement for Electroweak Baryogenesis. We investigate the plausibility of obtaining a strong phase transition in a Two-Higgs-Doublet Model of type II with a minimal amount of $CP$ violation. By performing a Bayesian fit where we constrain the scalar sector with indirect and direct measurements, we find that current data disfavours a first-order phase transition in this model. This result is mainly driven by the interplay of three effects: Constraints from the LHC Higgs data on the magnitude of the quartic couplings, the requirement of a $H^\\pm$ heavier than around 490 GeV to avoid large contributions to $BR(b \\rightarrow s\\gamma)$ and the fact that a first-order phase transition requires relatively light scalar states in addition to the 125 GeV Higgs. For similar reasons we find that a "hidden-Higgs" scenario, in which the 125 GeV state is identified with the next-to-lightest scalar, is disfavoured by current data independent of any require...

  13. Dissecting magnetar variability with Bayesian hierarchical models

    CERN Document Server

    Huppenkothen, D; Hogg, D W; Murray, I; Frean, M; Elenbaas, C; Watts, A L; Levin, Y; van der Horst, A J; Kouveliotou, C

    2015-01-01

    Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behaviour, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favoured models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture afte...

  14. Conditional Random Fields versus Hidden Markov Models for activity recognition in temporal sensor data

    NARCIS (Netherlands)

    van Kasteren, T.L.M.; Noulas, A.K.; Kröse, B.J.A.; Smit, G.J.M.; Epema, D.H.J.; Lew, M.S.

    2008-01-01

    Conditional Random Fields are a discriminative probabilistic model which recently gained popularity in applications that require modeling nonindependent observation sequences. In this work, we present the basic advantages of this model over generative models and argue about its suitability in the do

  15. Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

    Energy Technology Data Exchange (ETDEWEB)

    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.

  16. Matter power spectrum in hidden neutrino interacting dark matter models: a closer look at the collision term

    Energy Technology Data Exchange (ETDEWEB)

    Binder, Tobias; Covi, Laura [Institute for Theoretical Physics, Georg-August University Göttingen,Friedrich-Hund-Platz 1, Göttingen, D-37077 (Germany); Kamada, Ayuki [Department of Physics and Astronomy, University of California,Riverside, California 92521 (United States); Murayama, Hitoshi [Kavli Institute for the Physics and Mathematics of the Universe (WPI),University of Tokyo Institutes for Advanced Study, University of Tokyo,Kashiwa 277-8583 (Japan); Department of Physics, University of California, Berkeley,Berkeley, California 94720 (United States); Theoretical Physics Group, Lawrence Berkeley National Laboratory,Berkeley, California 94720 (United States); Takahashi, Tomo [Department of Physics, Saga University,Saga 840-8502 (Japan); Yoshida, Naoki [Kavli Institute for the Physics and Mathematics of the Universe (WPI),University of Tokyo Institutes for Advanced Study, University of Tokyo,Kashiwa 277-8583 (Japan); Department of Physics, University of Tokyo,Tokyo 113-0033 (Japan); CREST, Japan Science and Technology Agency,4-1-8 Honcho, Kawaguchi, Saitama, 332-0012 (Japan)

    2016-11-21

    Dark Matter (DM) models providing possible alternative solutions to the small-scale crisis of the standard cosmology are nowadays of growing interest. We consider DM interacting with light hidden fermions via well-motivated fundamental operators showing the resultant matter power spectrum is suppressed on subgalactic scales within a plausible parameter region. Our basic description of the evolution of cosmological perturbations relies on a fully consistent first principles derivation of a perturbed Fokker-Planck type equation, generalizing existing literature. The cosmological perturbation of the Fokker-Planck equation is presented for the first time in two different gauges, where the results transform into each other according to the rules of gauge transformation. Furthermore, our focus lies on a derivation of a broadly applicable and easily computable collision term showing important phenomenological differences to other existing approximations. As one of the main results and concerning the small-scale crisis, we show the equal importance of vector and scalar boson mediated interactions between the DM and the light fermions.

  17. The study of gesture recognition based on hidden Markov model%基于HMM的手势识别研究

    Institute of Scientific and Technical Information of China (English)

    严焰; 刘蓉; 黄璐; 陈婷

    2012-01-01

    This paper designs a wearable gesture recognition system. Improved SWAB automatic endpoint detection algorithm is proposed in this paper. The K-means is employed to compute gesture vector quantization. Then the data as the input of hidden Markov model (HMM) is used to acquire the classification information. Experiments show that is an effective method for gesture recognition.%利用可穿戴式加速度传感器采集手势动作信息,研究了基于隐马尔可夫模型的手势识别技术.首先采集手势加速度数据,采用改进的SWAB算法进行自动端点检测,通过提取相应的手势特征,利用HMM对手势指令建模,并采用K-means算法矢量量化手势特征序列,以提高手势识别性能.实验表明,本文采用的方法能够有效识别手势动作.

  18. Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network

    CERN Document Server

    Khademi, Mahmoud; Kiapour, Mohammad H; Kiaei, Ali A

    2010-01-01

    Facial Action Coding System consists of 44 action units (AUs) and more than 7000 combinations. Hidden Markov models (HMMs) classifier has been used successfully to recognize facial action units (AUs) and expressions due to its ability to deal with AU dynamics. However, a separate HMM is necessary for each single AU and each AU combination. Since combinations of AU numbering in thousands, a more efficient method will be needed. In this paper an accurate real-time sequence-based system for representation and recognition of facial AUs is presented. Our system has the following characteristics: 1) employing a mixture of HMMs and neural network, we develop a novel accurate classifier, which can deal with AU dynamics, recognize subtle changes, and it is also robust to intensity variations, 2) although we use an HMM for each single AU only, by employing a neural network we can recognize each single and combination AU, and 3) using both geometric and appearance-based features, and applying efficient dimension reducti...

  19. Application of T2 Control Charts and Hidden Markov Models in Condition-Based Maintenance at Thermoelectric Power Plants

    Directory of Open Access Journals (Sweden)

    Emilija Kisić

    2015-01-01

    Full Text Available An innovative approach to condition-based maintenance of coal grinding subsystems at thermoelectric power plants is proposed in the paper. Coal mill grinding tables become worn over time and need to be replaced through time-based maintenance, after a certain number of service hours. At times such replacement is necessary earlier or later than prescribed, depending on the quality of the coal and of the grinding table itself. Considerable financial losses are incurred when the entire coal grinding subsystem is shut down and the grinding table found to not actually require replacement. The only way to determine whether replacement is necessary is to shut down and open the entire subsystem for visual inspection. The proposed algorithm supports condition-based maintenance and involves the application of T2 control charts to distinct acoustic signal parameters in the frequency domain and the construction of Hidden Markov Models whose observations are coded samples from the control charts. In the present research, the acoustic signals were collected by coal mill monitoring at the thermoelectric power plant “Kostolac” in Serbia. The proposed approach provides information about the current condition of the grinding table.

  20. Integrated Hidden Markov Model and Bayes Packet Classifier for effective Mitigation of Application DDoS attacks

    Directory of Open Access Journals (Sweden)

    S Prabha

    2011-07-01

    Full Text Available Resisting distributed denial of service (DDoS attacks become more challenging with the availability of resources and techniques to attackers. The application-layer-based DDoS attacks utilize legitimate HTTP requests to overwhelm victim resources are more undetectable and are protocol compliant and non-intrusive. Focusing on the detection for application layer DDoS attacks, the existing scheme provide an access matrix which capture the spatial-temporal patterns of a normal flash crowd on non stationary object. The access matrix captures the spatial-temporal patterns of the normal flash crowd and the anomaly detector based on hidden Markov model (HMM described the dynamics of Access Matrix (AM to detect the application DDoS attacks. However current application layer attacks have high influence on the stationary object as well. In addition the detection threshold for non stationary object should be reevaluated to improve the performance of false positive rate and detection rate of the DDoS attacks.

  1. From hidden symmetry to extra dimensions: a five dimensional formulation of the Degenerate BESS model

    CERN Document Server

    Coradeschi, Francesco; Dominici, Daniele

    2010-01-01

    We consider the continuum limit of a moose model corresponding to a generalization to N sites of the Degenerate BESS model. The five dimensional formulation emerging in this limit is a realization of a RS1 type model with SU(2)_L x SU(2)_R in the bulk, broken by boundary conditions and a vacuum expectation value on the infrared brane. A low energy effective Lagrangian is derived by means of the holographic technique and corresponding bounds on the model parameters are obtained.

  2. Bridging Models and Business: Understanding heterogeneity in hidden drivers of customer purchase behavior

    NARCIS (Netherlands)

    E. Korkmaz (Evsen)

    2014-01-01

    markdownabstract__Abstract__ Recent years have seen many advances in quantitative models in the marketing literature. Even though these advances enable model building for a better understanding of customer purchase behavior and customer heterogeneity such that firms develop optimal targeting and pr

  3. Bridging Models and Business: Understanding heterogeneity in hidden drivers of customer purchase behavior

    NARCIS (Netherlands)

    E. Korkmaz (Evsen)

    2014-01-01

    markdownabstract__Abstract__ Recent years have seen many advances in quantitative models in the marketing literature. Even though these advances enable model building for a better understanding of customer purchase behavior and customer heterogeneity such that firms develop optimal targeting and

  4. Geolocation of North Sea cod (Gadus morhua) using Hidden Markov Models and behavioural switching

    DEFF Research Database (Denmark)

    Pedersen, Martin Wæver; Righton, David; Thygesen, Uffe Høgsbro;

    2008-01-01

    . In addition to the tidal component of the geolocation, the model incoporates two behavioural states, either high or low activity, estimated directly from the depth data, that affect the diffusivity parameter of the model and improves the precision and realism of the geolocation significantly. The new method...

  5. Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework

    Directory of Open Access Journals (Sweden)

    Humblot Fabrice

    2006-01-01

    Full Text Available This paper presents a new method for super-resolution (SR reconstruction of a high-resolution (HR image from several low-resolution (LR images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM and a Potts Markov model (PMM for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.

  6. A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Papasaikas Panagiotis K

    2005-04-01

    Full Text Available Abstract Background G- Protein coupled receptors (GPCRs comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins. Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies. Results Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins. Conclusion Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G

  7. Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In airborne tracking, the blind Doppler makes the target undetectable, resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types: targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model (IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

  8. Pentaquark states with hidden charm

    Science.gov (United States)

    Bijker, Roelof

    2017-07-01

    I develop an extension of the usual three-flavor quark model to four flavors (u, d, s and c), and discuss the classification of pentaquark states with hidden charm. This work is motivated by the recent observation of such states by the LHCb Collatoration at CERN.

  9. Generalized linear models for categorical and continuous limited dependent variables

    CERN Document Server

    Smithson, Michael

    2013-01-01

    Introduction and OverviewThe Nature of Limited Dependent VariablesOverview of GLMsEstimation Methods and Model EvaluationOrganization of This BookDiscrete VariablesBinary VariablesLogistic RegressionThe Binomial GLMEstimation Methods and IssuesAnalyses in R and StataExercisesNominal Polytomous VariablesMultinomial Logit ModelConditional Logit and Choice ModelsMultinomial Processing Tree ModelsEstimation Methods and Model EvaluationAnalyses in R and StataExercisesOrdinal Categorical VariablesModeling Ordinal Variables: Common Practice versus Best PracticeOrdinal Model AlternativesCumulative Mod

  10. Modeling Hidden Circuits: An Authentic Research Experience in One Lab Period

    Science.gov (United States)

    Moore, J. Christopher; Rubbo, Louis J.

    2016-10-01

    Two wires exit a black box that has three exposed light bulbs connected together in an unknown configuration. The task for students is to determine the circuit configuration without opening the box. In the activity described in this paper, we navigate students through the process of making models, developing and conducting experiments that can support or falsify models, and confronting ways of distinguishing between two different models that make similar predictions. We also describe a twist that forces students to confront new phenomena, requiring revision of their mental model of electric circuits. This activity is designed to mirror the practice of science by actual scientists and expose students to the "messy" side of science, where our simple explanations of reality often require expansion and/or revision based on new evidence. The purpose of this paper is to present a simple classroom activity within the context of electric circuits that supports students as they learn to test hypotheses and refine and revise models based on evidence.

  11. BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.

    Science.gov (United States)

    Sinha, Arijit; Chi, Zhiyi; Chen, Ming-Hui

    2015-10-01

    Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.

  12. Quantifying Numerical Model Accuracy and Variability

    Science.gov (United States)

    Montoya, L. H.; Lynett, P. J.

    2015-12-01

    The 2011 Tohoku tsunami event has changed the logic on how to evaluate tsunami hazard on coastal communities. Numerical models are a key component for methodologies used to estimate tsunami risk. Model predictions are essential for the development of Tsunami Hazard Assessments (THA). By better understanding model bias and uncertainties and if possible minimizing them, a more accurate and reliable THA will result. In this study we compare runup height, inundation lines and flow velocity field measurements between GeoClaw and the Method Of Splitting Tsunami (MOST) predictions in the Sendai plain. Runup elevation and average inundation distance was in general overpredicted by the models. However, both models agree relatively well with each other when predicting maximum sea surface elevation and maximum flow velocities. Furthermore, to explore the variability and uncertainties in numerical models, MOST is used to compare predictions from 4 different grid resolutions (30m, 20m, 15m and 12m). Our work shows that predictions of particular products (runup and inundation lines) do not require the use of high resolution (less than 30m) Digital Elevation Maps (DEMs). When predicting runup heights and inundation lines, numerical convergence was achieved using the 30m resolution grid. On the contrary, poor convergence was found in the flow velocity predictions, particularly the 1 meter depth maximum flow velocities. Also, runup height measurements and elevations from the DEM were used to estimate model bias. The results provided in this presentation will help understand the uncertainties in model predictions and locate possible sources of errors within a model.

  13. Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures.

    Science.gov (United States)

    Chuk, Tim; Crookes, Kate; Hayward, William G; Chan, Antoni B; Hsiao, Janet H

    2017-12-01

    It remains controversial whether culture modulates eye movement behavior in face recognition. Inconsistent results have been reported regarding whether cultural differences in eye movement patterns exist, whether these differences affect recognition performance, and whether participants use similar eye movement patterns when viewing faces from different ethnicities. These inconsistencies may be due to substantial individual differences in eye movement patterns within a cultural group. Here we addressed this issue by conducting individual-level eye movement data analysis using hidden Markov models (HMMs). Each individual's eye movements were modeled with an HMM. We clustered the individual HMMs according to their similarities and discovered three common patterns in both Asian and Caucasian participants: holistic (looking mostly at the face center), left-eye-biased analytic (looking mostly at the two individual eyes in addition to the face center with a slight bias to the left eye), and right-eye-based analytic (looking mostly at the right eye in addition to the face center). The frequency of participants adopting the three patterns did not differ significantly between Asians and Caucasians, suggesting little modulation from culture. Significantly more participants (75%) showed similar eye movement patterns when viewing own- and other-race faces than different patterns. Most importantly, participants with left-eye-biased analytic patterns performed significantly better than those using either holistic or right-eye-biased analytic patterns. These results suggest that active retrieval of facial feature information through an analytic eye movement pattern may be optimal for face recognition regardless of culture. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Talking about Reading as Thinking: Modeling the Hidden Complexities of Online Reading Comprehension

    Science.gov (United States)

    Coiro, Julie

    2011-01-01

    This article highlights four cognitive processes key to online reading comprehension and how one might begin to transform existing think-aloud strategy models to encompass the challenges of reading for information on the Internet. Informed by principles of cognitive apprenticeship and an emerging taxonomy of online reading comprehension…

  15. Investigating the Relationship between Dialogue Structure and Tutoring Effectiveness: A Hidden Markov Modeling Approach

    Science.gov (United States)

    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…

  16. Molecular tools and bumble bees: revealing hidden details of ecology and evolution in a model system

    Science.gov (United States)

    Bumble bees are a longstanding model system for studies on behavior, ecology, and evolution, due to their well-studied social lifestyle, invaluable roles as both wild and managed pollinators, and their ubiquity and diversity across temperate ecosystems. Yet despite their importance, many aspects of ...

  17. Multi-Observation Continuous Density Hidden Markov Models for Anomaly Detection in Full Motion Video

    Science.gov (United States)

    2012-06-01

    Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are used to...distribution analysis for scoring Log-likelihood • Automatically select MOCDHMM model via Akaike Information Criteria (AIC) or Bayesian Information Criteria...limited to any particular graphical structure. For example, Xiang uses Bayesian Information Criterion (BICr) and Completed Likelihood Akaike’s Information

  18. The Hidden Quantum Group of the 8-vertex Free Fermion Model q-Clifford Algebras

    CERN Document Server

    Cuerno, R; López, E; Sierra, G

    1993-01-01

    We prove in this paper that the elliptic $R$--matrix of the eight vertex free fermion model is the intertwiner $R$--matrix of a quantum deformed Clifford--Hopf algebra. This algebra is constructed by affinization of a quantum Hopf deformation of the Clifford algebra.

  19. Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies.

    Science.gov (United States)

    Barros de Andrade E Sousa, Lisa C; Kühn, Clemens; Tyc, Katarzyna M; Klipp, Edda

    2015-01-01

    The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

  20. Dosage and dose schedule screening of drug combinations in agent-based models reveals hidden synergies

    Directory of Open Access Journals (Sweden)

    Lisa Corina Barros de Andrade e Sousa1

    2016-01-01

    Full Text Available The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

  1. Helioscope bounds on hidden sector photons

    Energy Technology Data Exchange (ETDEWEB)

    Redondo, J.

    2007-12-15

    The flux of hypothetical ''hidden photons'' from the Sun is computed under the assumption that they interact with normal matter only through kinetic mixing with the ordinary standard model photon. Requiring that the exotic luminosity is smaller than the standard photon luminosity provides limits for the mixing parameter down to {chi} hidden photon mass. Furthermore, it is pointed point out that helioscopes looking for solar axions are also sensitive to hidden photons. The recent results of the CAST collaboration are used to further constrain the mixing parameter {chi} at low masses (m{sub {gamma}}{sub '}<1 eV) where the luminosity bound is weaker. In this regime the solar hidden photon ux has a sizable contribution of longitudinally polarized hidden photons of low energy which are invisible for current helioscopes. (orig.)

  2. Helioscope bounds on hidden sector photons

    Energy Technology Data Exchange (ETDEWEB)

    Redondo, J.

    2007-12-15

    The flux of hypothetical ''hidden photons'' from the Sun is computed under the assumption that they interact with normal matter only through kinetic mixing with the ordinary standard model photon. Requiring that the exotic luminosity is smaller than the standard photon luminosity provides limits for the mixing parameter down to {chi} hidden photon mass. Furthermore, it is pointed point out that helioscopes looking for solar axions are also sensitive to hidden photons. The recent results of the CAST collaboration are used to further constrain the mixing parameter {chi} at low masses (m{sub {gamma}}{sub '}<1 eV) where the luminosity bound is weaker. In this regime the solar hidden photon ux has a sizable contribution of longitudinally polarized hidden photons of low energy which are invisible for current helioscopes. (orig.)

  3. A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Haitao Guo

    2017-01-01

    Full Text Available The discovery of cis-regulatory modules (CRMs is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.

  4. Elusive present: Hidden past and future dependency and why we build models.

    Science.gov (United States)

    Ara, Pooneh M; James, Ryan G; Crutchfield, James P

    2016-02-01

    Modeling a temporal process as if it is Markovian assumes that the present encodes all of a process's history. When this occurs, the present captures all of the dependency between past and future. We recently showed that if one randomly samples in the space of structured processes, this is almost never the case. So, how does the Markov failure come about? That is, how do individual measurements fail to encode the past? and How many are needed to capture dependencies between the past and future? Here, we investigate how much information can be shared between the past and the future but not reflected in the present. We quantify this elusive information, give explicit calculational methods, and outline the consequences, the most important of which is that when the present hides past-future correlation or dependency we must move beyond sequence-based statistics and build state-based models.

  5. Robust Transmission of Speech LSFs Using Hidden Markov Model-Based Multiple Description Index Assignments

    Directory of Open Access Journals (Sweden)

    Rondeau Paul

    2008-01-01

    Full Text Available Speech coding techniques capable of generating encoded representations which are robust against channel losses play an important role in enabling reliable voice communication over packet networks and mobile wireless systems. In this paper, we investigate the use of multiple description index assignments (MDIAs for loss-tolerant transmission of line spectral frequency (LSF coefficients, typically generated by state-of-the-art speech coders. We propose a simulated annealing-based approach for optimizing MDIAs for Markov-model-based decoders which exploit inter- and intraframe correlations in LSF coefficients to reconstruct the quantized LSFs from coded bit streams corrupted by channel losses. Experimental results are presented which compare the performance of a number of novel LSF transmission schemes. These results clearly demonstrate that Markov-model-based decoders, when used in conjunction with optimized MDIA, can yield average spectral distortion much lower than that produced by methods such as interleaving/interpolation, commonly used to combat the packet losses.

  6. The role of hidden ambiguities in the linear sigma model with fermions

    Energy Technology Data Exchange (ETDEWEB)

    Hiller, Brigitte [Centro de Fisica Teorica, Departamento de Fisica da Universidade de Coimbra, 3004-516 Coimbra (Portugal); Mota, A.L. [Departamento de Ciencias Naturais, Universidade Federal de Sao Joao del Rei, Sao Joao del Rei, MG (Brazil) and Departamento de Fisica Moderna, Faculdad de Ciencias, Universidad de Granada, Granada (Spain)]. E-mail: motaal@ufsj.edu.br; Nemes, M.C. [Centro de Fisica Teorica, Departamento de Fisica da Universidade de Coimbra, 3004-516 Coimbra (Portugal); Departamento de Fisica, Instituto de Ciencias Exactas, Universidade Federal de Minas Gerais, BH, CEP 30161-970, MG (Brazil); Osipov, Alexander A. [Centro de Fisica Teorica, Departamento de Fisica da Universidade de Coimbra, 3004-516 Coimbra (Portugal); Joint Institute for Nuclear Research, Laboratory of Nuclear Problems, 141980 Dubna, Moscow Region (Russian Federation); Sampaio, Marcos [Departamento de Fisica, Instituto de Ciencias Exactas, Universidade Federal de Minas Gerais, BH, CEP 30161-970, MG (Brazil)

    2006-04-17

    The U{sub L}(3)xU{sub R}(3) linear sigma model (LSM) with quark degrees of freedom is used to show that radiative corrections generate undetermined finite contributions. Their origin is related to surface terms which are differences between divergent integrals with the same degree of divergence. The technique used to detect these ambiguities is an implicit regularization on basic divergent integrals that do not depend on external momenta. We show that such contributions are absorbed by renormalization or fixed by symmetry requirements. The general expression for surface terms is derived. Renormalization group coefficients are calculated, as well as relevant observables for this model, such as f{sub {pi}}, f{sub {kappa}} and the pion and kaon form factors.

  7. Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models.

    Science.gov (United States)

    Régnier, Mireille; Furletova, Evgenia; Yakovlev, Victor; Roytberg, Mikhail

    2014-01-01

    Finding new functional fragments in biological sequences is a challenging problem. Methods addressing this problem commonly search for clusters of pattern occurrences that are statistically significant. A measure of statistical significance is the P-value of a number of pattern occurrences, i.e. the probability to find at least S occurrences of words from a pattern in a random text of length N generated according to a given probability model. All words of the pattern are supposed to be of same length. We present a novel algorithm SufPref that computes an exact P-value for Hidden Markov models (HMM). The algorithm is based on recursive equations on text sets related to pattern occurrences; the equations can be used for any probability model. The algorithm inductively traverses a specific data structure, an overlap graph. The nodes of the graph are associated with the overlaps of words from . The edges are associated to the prefix and suffix relations between overlaps. An originality of our data structure is that pattern need not be explicitly represented in nodes or leaves. The algorithm relies on the Cartesian product of the overlap graph and the graph of HMM states; this approach is analogous to the automaton approach from JBCB 4: 553-569. The gain in size of SufPref data structure leads to significant improvements in space and time complexity compared to existent algorithms. The algorithm SufPref was implemented as a C++ program; the program can be used both as Web-server and a stand alone program for Linux and Windows. The program interface admits special formats to describe probability models of various types (HMM, Bernoulli, Markov); a pattern can be described with a list of words, a PSSM, a degenerate pattern or a word and a number of mismatches. It is available at http://server2.lpm.org.ru/bio/online/sf/. The program was applied to compare sensitivity and specificity of methods for TFBS prediction based on P-values computed for Bernoulli models, Markov

  8. GPCR-GRAPA-LIB--a refined library of hidden Markov Models for annotating GPCRs.

    Science.gov (United States)

    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.

  9. Reconstructing the hidden states in time course data of stochastic models.

    Science.gov (United States)

    Zimmer, Christoph

    2015-11-01

    Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time.

  10. A novel seizure detection algorithm informed by hidden Markov model event states

    Science.gov (United States)

    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.

  11. A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays

    Directory of Open Access Journals (Sweden)

    Torring Niels

    2007-11-01

    Full Text Available Abstract Background Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copy-numbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion. Results We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97.71%. Moreover, although the known copy-numbers could be well recovered in simulated cancer samples with more than 70% cancer cells (and less than 30% normal cells, we demonstrate that including the mixture proportion in the HMM increases the accuracy of the method. Finally, the method is tested on HapMap samples and on bladder and prostate cancer samples. Conclusion The HMM method developed here uses the genotype calls of germline DNA and the allelic SNP intensities from the tumour DNA to estimate allelic copy-numbers (including changes in the tumour. It differentiates between different events like uniparental disomy and allelic imbalances. Moreover, the HMM can estimate the mixture proportion, and thus inform about the purity of the tumour sample.

  12. Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation.

    Science.gov (United States)

    Häme, Yrjö; Pollari, Mika

    2012-01-01

    A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.

  13. Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

    Science.gov (United States)

    Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

    2017-05-04

    The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Distributed smart home activity recommender system using hidden Markov model principles

    DEFF Research Database (Denmark)

    Lynggaard, Per

    2013-01-01

    A smart home is able to propose learned activities to its user and learn new activities by observing the user’s behavioral patterns, that is, the user’s actions. Most of today’s discussed systems use some more or less complex classifier algorithms to predict user activities from contextual...... information provided by sensors. However, an alternative concept using a distributed framework is presented in this paper. It offers the possibility of combining simple low level activity classifiers with a more sophisticated one. The high level classifier has been modeled in Java and tested on a publicly...... available data set that offers approximately seven months of annotated activity including 6468 sensor events produced by a women living in the test home. Using this data set, it has been shown that this system can achieve good performance with a recognition probability of 75%....

  15. A Unified Hidden-Sector-Electroweak Model, Paraphotons and the X-Boson

    CERN Document Server

    Neves, M J

    2016-01-01

    Our contribution sets out to investigate a gauge model based on an $SU_{L}(2) \\times U_{R}(1)_{J} \\times U(1)_{K}$-symmetry group whose main goal is to accommodate, in the distinct phases the Higgs sector sets up according to different symmetry-breaking patterns, the para-photon of the dark matter sector, a heavy scalar whose mass is upper-bounded by $830 \\, \\mbox{GeV}$ and the recently discussed $17 \\, \\mbox{MeV}$ X-boson. As a result, there also emerge in the spectrum an extra massive charged fermion along with an additional neutral Higgs; their masses are fixed according to the particular way the symmetry breakings take place. In all situations contemplated here, we are committed with the $246 \\, \\mbox{GeV}$ electroweak breaking scale.

  16. Numerical modeling of cold magmatic CO2 flux measurements for the exploration of hidden geothermal systems

    Science.gov (United States)

    Peiffer, Loïc.; Wanner, Christoph; Pan, Lehua

    2015-10-01

    The most accepted conceptual model to explain surface degassing of cold magmatic CO2 in volcanic-geothermal systems involves the presence of a gas reservoir. In this study, numerical simulations using the TOUGH2-ECO2N V2.0 package are performed to get quantitative insights into how cold CO2 soil flux measurements are related to reservoir and fluid properties. Although the modeling is based on flux data measured at a specific geothermal site, the Acoculco caldera (Mexico), some general insights have been gained. Both the CO2 fluxes at the surface and the depth at which CO2 exsolves are highly sensitive to the dissolved CO2 content of the deep fluid. If CO2 mainly exsolves above the reservoir within a fracture zone, the surface CO2 fluxes are not sensitive to the reservoir size but depend on the CO2 dissolved content and the rock permeability. For gas exsolution below the top of the reservoir, surface CO2 fluxes also depend on the gas saturation of the deep fluid as well as the reservoir size. The absence of thermal anomalies at the surface is mainly a consequence of the low enthalpy of CO2. The heat carried by CO2 is efficiently cooled down by heat conduction and to a certain extent by isoenthalpic volume expansion depending on the temperature gradient. Thermal anomalies occur at higher CO2 fluxes (>37,000 g m-2 d-1) when the heat flux of the rising CO2 is not balanced anymore. Finally, specific results are obtained for the Acoculco area (reservoir depth, CO2 dissolved content, and gas saturation state).

  17. Predicting Group-Level Outcome Variables from Variables Measured at the Individual Level: A Latent Variable Multilevel Model

    Science.gov (United States)

    Croon, Marcel A.; van Veldhoven, Marc J. P. M.

    2007-01-01

    In multilevel modeling, one often distinguishes between macro-micro and micro-macro situations. In a macro-micro multilevel situation, a dependent variable measured at the lower level is predicted or explained by variables measured at that lower or a higher level. In a micro-macro multilevel situation, a dependent variable defined at the higher…

  18. Subdivision of the MDR superfamily of medium-chain dehydrogenases/reductases through iterative hidden Markov model refinement

    Directory of Open Access Journals (Sweden)

    Persson Bengt

    2010-10-01

    Full Text Available Abstract Background The Medium-chain Dehydrogenases/Reductases (MDR form a protein superfamily whose size and complexity defeats traditional means of subclassification; it currently has over 15000 members in the databases, the pairwise sequence identity is typically around 25%, there are members from all kingdoms of life, the chain-lengths vary as does the oligomericity, and the members are partaking in a multitude of biological processes. There are profile hidden Markov models (HMMs available for detecting MDR superfamily members, but none for determining which MDR family each protein belongs to. The current torrential influx of new sequence data enables elucidation of more and more protein families, and at an increasingly fine granularity. However, gathering good quality training data usually requires manual attention by experts and has therefore been the rate limiting step for expanding the number of available models. Results We have developed an automated algorithm for HMM refinement that produces stable and reliable models for protein families. This algorithm uses relationships found in data to generate confident seed sets. Using this algorithm we have produced HMMs for 86 distinct MDR families and 34 of their subfamilies which can be used in automated annotation of new sequences. We find that MDR forms with 2 Zn2+ ions in general are dehydrogenases, while MDR forms with no Zn2+ in general are reductases. Furthermore, in Bacteria MDRs without Zn2+ are more frequent than those with Zn2+, while the opposite is true for eukaryotic MDRs, indicating that Zn2+ has been recruited into the MDR superfamily after the initial life kingdom separations. We have also developed a web site http://mdr-enzymes.org that provides textual and numeric search against various characterised MDR family properties, as well as sequence scan functions for reliable classification of novel MDR sequences. Conclusions Our method of refinement can be readily applied to

  19. Molecular tools and bumble bees: revealing hidden details of ecology and evolution in a model system.

    Science.gov (United States)

    Woodard, S Hollis; Lozier, Jeffrey D; Goulson, David; Williams, Paul H; Strange, James P; Jha, Shalene

    2015-06-01

    Bumble bees are a longstanding model system for studies on behaviour, ecology and evolution, due to their well-studied social lifestyle, invaluable role as wild and managed pollinators, and ubiquity and diversity across temperate ecosystems. Yet despite their importance, many aspects of bumble bee biology have remained enigmatic until the rise of the genetic and, more recently, genomic eras. Here, we review and synthesize new insights into the ecology, evolution and behaviour of bumble bees that have been gained using modern genetic and genomic techniques. Special emphasis is placed on four areas of bumble bee biology: the evolution of eusociality in this group, population-level processes, large-scale evolutionary relationships and patterns, and immunity and resistance to pesticides. We close with a prospective on the future of bumble bee genomics research, as this rapidly advancing field has the potential to further revolutionize our understanding of bumble bees, particularly in regard to adaptation and resilience. Worldwide, many bumble bee populations are in decline. As such, throughout the review, connections are drawn between new molecular insights into bumble bees and our understanding of the causal factors involved in their decline. Ongoing and potential applications to bumble bee management and conservation are also included to demonstrate how genetics- and genomics-enabled research aids in the preservation of this threatened group. © 2015 John Wiley & Sons Ltd.

  20. Classification of Transient Events of Nuclear Reactor Using Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    P. Bečvář

    2000-01-01

    Full Text Available This article describes a part of on-line system for a residual life of a pressure vessel shell. In this system there appears a need for determining of a real history of a pressure vessel described as a sequence of so called transient events. Each event (there are 23 events now is given by its template. It is theoretically necessary to compare data measured in a real history with all possible sequences of transient events. This solution in intractable and that is why a more sophisticated solution had to be used. Because this task is very similar to task of an automatic speech recognition, models and algorithms used to solve speech recognition tasks can be efficiently used to solve our task. Of course there are some different circumstances caused by the fact that the transient events take much longer than words and their number is much smaller than the number of words in speech recognition system's vocabulary. But the inspiration from speech recognition was very useful and the known algorithms rapidly decreased the design time.