Bell's theorem and the problem of decidability between the views of Einstein and Bohr.

PubMed

Hess, K; Philipp, W

2001-12-04

Einstein, Podolsky, and Rosen (EPR) have designed a gedanken experiment that suggested a theory that was more complete than quantum mechanics. The EPR design was later realized in various forms, with experimental results close to the quantum mechanical prediction. The experimental results by themselves have no bearing on the EPR claim that quantum mechanics must be incomplete nor on the existence of hidden parameters. However, the well known inequalities of Bell are based on the assumption that local hidden parameters exist and, when combined with conflicting experimental results, do appear to prove that local hidden parameters cannot exist. This fact leaves only instantaneous actions at a distance (called "spooky" by Einstein) to explain the experiments. The Bell inequalities are based on a mathematical model of the EPR experiments. They have no experimental confirmation, because they contradict the results of all EPR experiments. In addition to the assumption that hidden parameters exist, Bell tacitly makes a variety of other assumptions; for instance, he assumes that the hidden parameters are governed by a single probability measure independent of the analyzer settings. We argue that the mathematical model of Bell excludes a large set of local hidden variables and a large variety of probability densities. Our set of local hidden variables includes time-like correlated parameters and a generalized probability density. We prove that our extended space of local hidden variables does permit derivation of the quantum result and is consistent with all known experiments.

A possible loophole in the theorem of Bell.

PubMed

Hess, K; Philipp, W

2001-12-04

The celebrated inequalities of Bell are based on the assumption that local hidden parameters exist. When combined with conflicting experimental results, these inequalities appear to prove that local hidden parameters cannot exist. This contradiction suggests to many that only instantaneous action at a distance can explain the Einstein, Podolsky, and Rosen type of experiments. We show that, in addition to the assumption that hidden parameters exist, Bell tacitly makes a variety of other assumptions that contribute to his being able to obtain the desired contradiction. For instance, Bell assumes that the hidden parameters do not depend on time and are governed by a single probability measure independent of the analyzer settings. We argue that the exclusion of time has neither a physical nor a mathematical basis but is based on Bell's translation of the concept of Einstein locality into the language of probability theory. Our additional set of local hidden variables includes time-like correlated parameters and a generalized probability density. We prove that our extended space of local hidden variables does not permit Bell-type proofs to go forward.

Electronic Tuning In The Hidden Order Compound URu2Si 2 Through Si → P substitution

NASA Astrophysics Data System (ADS)

Gallagher, Andrew

Crystalline materials that include 4f- and 5 f-electron elements frequently exhibit a variety of intriguing phenomena including spin and charge orderings, spin and valence fluctuations, heavy fermion behavior, breakdown of Fermi liquid behavior, and unconventional superconductivity. [5, 6, 7, 8, 9, 10, 11, 12, 13] Amongst such materials, the Kondo lattice system URu2Si2 stands out as being particularly unusual. [14, 15, 16] While at high temperature it exhibits behavior that is typical for an f-electron lattice immersed in a sea of conduction electrons, at T0 = 17:5 K there is a second order phase transition that is followed by unconventional superconductivity near Tc ≈ 1:5 K. [15] Despite three decades of work, the order parameter for the transition at T0 remains unknown and hence, it has been named "hidden order". There have been a multitude of experimental attempts to unravel hidden order, mainly through tuning of the electronic state via pressure, applied magnetic field, and chemical substitution. [17, 18] While these strategies reveal interesting phase diagrams, a longstanding challenge is that any such approach explores the phase space along an unknown vector: i.e., many different factors are affected. To address this issue, we developed a new organizational map for the U-based ThCr2Si2-type compounds that are related to URu2Si2 and thus guided, we explored a new chemical tuning axis: Si -> P. Our studies were enabled by the development of a new molten metal crystal growth method for URu2Si2 which produces high quality single crystals and allows us to introduce high vapor pressure elements, such as phosphorous. [19, 20] Si → P tuning reveals that while the high temperature Kondo lattice behavior is robust, the low temperature phenomena are remarkably sensitive to electronic tuning. [21, 22] In the URu2Si2-xPx phase diagram we find that while hidden order is monotonically suppressed and destroyed for x < 0.035, the superconducting strength evolves non-monotonically with a maximum near x = 0.01 and that superconductivity is destroyed near x ≈ 0.028. For 0.03 < x < 0.26 there is a region with Kondo coherence but no ordered state. Antiferromagnetism abruptly appears for x = 0.26. This phase diagram differs significantly from those produced by most other tuning strategies in URu2Si2, including applied pressure, and isoelectronic chemical substitution (i.e. Ru→Fe and Os), where hidden order and magnetism share a common phase boundary. [2, 23, 24] We discuss implications for understanding hidden order, its relationship to magnetism, and prospects for uncovering novel sibling electronic states.

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

NASA Astrophysics Data System (ADS)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Discovering Hidden Controlling Parameters using Data Analytics and Dimensional Analysis

NASA Astrophysics Data System (ADS)

Del Rosario, Zachary; Lee, Minyong; Iaccarino, Gianluca

2017-11-01

Dimensional Analysis is a powerful tool, one which takes a priori information and produces important simplifications. However, if this a priori information - the list of relevant parameters - is missing a relevant quantity, then the conclusions from Dimensional Analysis will be incorrect. In this work, we present novel conclusions in Dimensional Analysis, which provide a means to detect this failure mode of missing or hidden parameters. These results are based on a restated form of the Buckingham Pi theorem that reveals a ridge function structure underlying all dimensionless physical laws. We leverage this structure by constructing a hypothesis test based on sufficient dimension reduction, allowing for an experimental data-driven detection of hidden parameters. Both theory and examples will be presented, using classical turbulent pipe flow as the working example. Keywords: experimental techniques, dimensional analysis, lurking variables, hidden parameters, buckingham pi, data analysis. First author supported by the NSF GRFP under Grant Number DGE-114747.

A practical iterative PID tuning method for mechanical systems using parameter chart

NASA Astrophysics Data System (ADS)

Kang, M.; Cheong, J.; Do, H. M.; Son, Y.; Niculescu, S.-I.

2017-10-01

In this paper, we propose a method of iterative proportional-integral-derivative parameter tuning for mechanical systems that possibly possess hidden mechanical resonances, using a parameter chart which visualises the closed-loop characteristics in a 2D parameter space. We employ a hypothetical assumption that the considered mechanical systems have their upper limit of the derivative feedback gain, from which the feasible region in the parameter chart becomes fairly reduced and thus the gain selection can be extremely simplified. Then, a two-directional parameter search is carried out within the feasible region in order to find the best set of parameters. Experimental results show the validity of the assumption used and the proposed parameter tuning method.

Symmetry and singularity properties of second-order ordinary differential equations of Lie's type III

NASA Astrophysics Data System (ADS)

Andriopoulos, K.; Leach, P. G. L.

2007-04-01

We extend the work of Abraham-Shrauner [B. Abraham-Shrauner, Hidden symmetries and linearization of the modified Painleve-Ince equation, J. Math. Phys. 34 (1993) 4809-4816] on the linearization of the modified Painleve-Ince equation to a wider class of nonlinear second-order ordinary differential equations invariant under the symmetries of time translation and self-similarity. In the process we demonstrate a remarkable connection with the parameters obtained in the singularity analysis of this class of equations.

Birefringence and hidden photons

NASA Astrophysics Data System (ADS)

Arza, Ariel; Gamboa, J.

2018-05-01

We study a model where photons interact with hidden photons and millicharged particles through a kinetic mixing term. Particularly, we focus on vacuum birefringence effects and we find a bound for the millicharged parameter assuming that hidden photons are a piece of the local dark matter density.

Passing from Mesoscopy to Macroscopy. The Mesoscopic Parameter \\bar k

NASA Astrophysics Data System (ADS)

Maslov, V. P.

2018-01-01

In previous papers of the author it was shown that, depending on the hidden parameter, purely quantum problems behave like classical ones. In the present paper, it is shown that the Bose-Einstein and the Fermi-Dirac distributions, which until now were regarded as dealing with quantum particles, describe, for the appropriate values of the hidden parameter, the macroscopic thermodynamics of classical molecules.

Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes

PubMed Central

Katahira, Kentaro; Suzuki, Kenta; Okanoya, Kazuo; Okada, Masato

2011-01-01

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies. PMID:21915345

Hidden-Symmetry-Protected Topological Semimetals on a Square Lattice

NASA Astrophysics Data System (ADS)

Hou, Jing-Min

2013-09-01

We study a two-dimensional fermionic square lattice, which supports the existence of a two-dimensional Weyl semimetal, quantum anomalous Hall effect, and 2π-flux topological semimetal in different parameter ranges. We show that the band degenerate points of the two-dimensional Weyl semimetal and 2π-flux topological semimetal are protected by two distinct novel hidden symmetries, which both correspond to antiunitary composite operations. When these hidden symmetries are broken, a gap opens between the conduction and valence bands, turning the system into a insulator. With appropriate parameters, a quantum anomalous Hall effect emerges. The degenerate point at the boundary between the quantum anomalous Hall insulator and trivial band insulator is also protected by the hidden symmetry.

Hidden symmetries of the extended Kitaev-Heisenberg model: Implications for the honeycomb-lattice iridates A2IrO3

NASA Astrophysics Data System (ADS)

Chaloupka, Jiří; Khaliullin, Giniyat

2015-07-01

We have explored the hidden symmetries of a generic four-parameter nearest-neighbor spin model, allowed in honeycomb-lattice compounds under trigonal compression. Our method utilizes a systematic algorithm to identify all dual transformations of the model that map the Hamiltonian on itself, changing the parameters and providing exact links between different points in its parameter space. We have found the complete set of points of hidden SU(2) symmetry at which a seemingly highly anisotropic model can be mapped back on the Heisenberg model and inherits therefore its properties such as the presence of gapless Goldstone modes. The procedure used to search for the hidden symmetries is quite general and may be extended to other bond-anisotropic spin models and other lattices, such as the triangular, kagome, hyperhoneycomb, or harmonic-honeycomb lattices. We apply our findings to the honeycomb-lattice iridates Na2IrO3 and Li2IrO3 , and illustrate how they help to identify plausible values of the model parameters that are compatible with the available experimental data.

Simultaneous escaping of explicit and hidden free energy barriers: application of the orthogonal space random walk strategy in generalized ensemble based conformational sampling.

PubMed

Zheng, Lianqing; Chen, Mengen; Yang, Wei

2009-06-21

To overcome the pseudoergodicity problem, conformational sampling can be accelerated via generalized ensemble methods, e.g., through the realization of random walks along prechosen collective variables, such as spatial order parameters, energy scaling parameters, or even system temperatures or pressures, etc. As usually observed, in generalized ensemble simulations, hidden barriers are likely to exist in the space perpendicular to the collective variable direction and these residual free energy barriers could greatly abolish the sampling efficiency. This sampling issue is particularly severe when the collective variable is defined in a low-dimension subset of the target system; then the "Hamiltonian lagging" problem, which reveals the fact that necessary structural relaxation falls behind the move of the collective variable, may be likely to occur. To overcome this problem in equilibrium conformational sampling, we adopted the orthogonal space random walk (OSRW) strategy, which was originally developed in the context of free energy simulation [L. Zheng, M. Chen, and W. Yang, Proc. Natl. Acad. Sci. U.S.A. 105, 20227 (2008)]. Thereby, generalized ensemble simulations can simultaneously escape both the explicit barriers along the collective variable direction and the hidden barriers that are strongly coupled with the collective variable move. As demonstrated in our model studies, the present OSRW based generalized ensemble treatments show improved sampling capability over the corresponding classical generalized ensemble treatments.

Driving style recognition method using braking characteristics based on hidden Markov model

PubMed Central

Wu, Chaozhong; Lyu, Nengchao; Huang, Zhen

2017-01-01

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style. PMID:28837580

Model-independent indirect detection constraints on hidden sector dark matter

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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 suchmore » 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.« less

Model-independent indirect detection constraints on hidden sector dark matter

DOE PAGES

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

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 +e - andmore » $$\\overline{p}$$ 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.« less

Market impact and trading profile of hidden orders in stock markets.

PubMed

Moro, Esteban; Vicente, Javier; Moyano, Luis G; Gerig, Austin; Farmer, J Doyne; Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N

2009-12-01

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

Market impact and trading profile of hidden orders in stock markets

NASA Astrophysics Data System (ADS)

Moro, Esteban; Vicente, Javier; Moyano, Luis G.; Gerig, Austin; Farmer, J. Doyne; Vaglica, Gabriella; Lillo, Fabrizio; Mantegna, Rosario N.

2009-12-01

We empirically study the market impact of trading orders. We are specifically interested in large trading orders that are executed incrementally, which we call hidden orders. These are statistically reconstructed based on information about market member codes using data from the Spanish Stock Market and the London Stock Exchange. We find that market impact is strongly concave, approximately increasing as the square root of order size. Furthermore, as a given order is executed, the impact grows in time according to a power law; after the order is finished, it reverts to a level of about 0.5-0.7 of its value at its peak. We observe that hidden orders are executed at a rate that more or less matches trading in the overall market, except for small deviations at the beginning and end of the order.

Hastatic Order in URu2Si2

NASA Astrophysics Data System (ADS)

Chandra, Premala; Coleman, Piers; Flint, Rebecca

2012-02-01

The hidden order that develops below 17.5K in URu2Si2 has eluded identification for twenty-five years. Here we show that the recent observation of Ising quasiparticles in URu2Si2 suggests a novel ``hastatic order'' (Latin:spear),with a two-component order parameter describing hybridization between electrons and the Ising 5f^2 states of the uranium atoms. Hastatic order breaks time-reversal symmetry by mixing states of different Kramers parity; this accounts for the magnetic anomalies observed in torque magnetometry and the pseudo-Goldstone mode observed in neutron scattering. Hastatic order is predicted to induce a basal-plane magnetic moment of order 0.01μB, a gap to longitudinal spin fluctuations that vanishes continuously at the first-order antiferromagnetic transition and a narrow resonant nematic feature in the scanning tunneling spectra.

Hidden-Symmetry-Protected Topological Semimetals on a Square Lattice

NASA Astrophysics Data System (ADS)

Hou, Jing-Min

2014-03-01

We study a two-dimensional fermionic square lattice, which supports the existence of two-dimensional Weyl semimetal, quantum anomalous Hall effect, and 2 π -flux topological semimetal in different parameter ranges. We show that the band degenerate points of the two-dimensional Weyl semimetal and 2 π -flux topological semimetal are protected by two distinct novel hidden symmetries, which both corresponds to antiunitary composite operations. When these hidden symmetries are broken, a gap opens between the conduction and valence bands, turning the system into a insulator. With appropriate parameters, a quantum anomalous Hall effect emerges. The degenerate point at the boundary between the quantum anomalous Hall insulator and trivial band insulator is also protected by the hidden symmetry. [PRL 111, 130403(2013)] This work was supported by the National Natural Science Foundation of China under Grants No. 11004028 and No. 11274061.

Hidden hyperchaos and electronic circuit application in a 5D self-exciting homopolar disc dynamo

NASA Astrophysics Data System (ADS)

Wei, Zhouchao; Moroz, Irene; Sprott, J. C.; Akgul, Akif; Zhang, Wei

2017-03-01

We report on the finding of hidden hyperchaos in a 5D extension to a known 3D self-exciting homopolar disc dynamo. The hidden hyperchaos is identified through three positive Lyapunov exponents under the condition that the proposed model has just two stable equilibrium states in certain regions of parameter space. The new 5D hyperchaotic self-exciting homopolar disc dynamo has multiple attractors including point attractors, limit cycles, quasi-periodic dynamics, hidden chaos or hyperchaos, as well as coexisting attractors. We use numerical integrations to create the phase plane trajectories, produce bifurcation diagram, and compute Lyapunov exponents to verify the hidden attractors. Because no unstable equilibria exist in two parameter regions, the system has a multistability and six kinds of complex dynamic behaviors. To the best of our knowledge, this feature has not been previously reported in any other high-dimensional system. Moreover, the 5D hyperchaotic system has been simulated using a specially designed electronic circuit and viewed on an oscilloscope, thereby confirming the results of the numerical integrations. Both Matlab and the oscilloscope outputs produce similar phase portraits. Such implementations in real time represent a new type of hidden attractor with important consequences for engineering applications.

Hidden hyperchaos and electronic circuit application in a 5D self-exciting homopolar disc dynamo.

PubMed

Wei, Zhouchao; Moroz, Irene; Sprott, J C; Akgul, Akif; Zhang, Wei

2017-03-01

We report on the finding of hidden hyperchaos in a 5D extension to a known 3D self-exciting homopolar disc dynamo. The hidden hyperchaos is identified through three positive Lyapunov exponents under the condition that the proposed model has just two stable equilibrium states in certain regions of parameter space. The new 5D hyperchaotic self-exciting homopolar disc dynamo has multiple attractors including point attractors, limit cycles, quasi-periodic dynamics, hidden chaos or hyperchaos, as well as coexisting attractors. We use numerical integrations to create the phase plane trajectories, produce bifurcation diagram, and compute Lyapunov exponents to verify the hidden attractors. Because no unstable equilibria exist in two parameter regions, the system has a multistability and six kinds of complex dynamic behaviors. To the best of our knowledge, this feature has not been previously reported in any other high-dimensional system. Moreover, the 5D hyperchaotic system has been simulated using a specially designed electronic circuit and viewed on an oscilloscope, thereby confirming the results of the numerical integrations. Both Matlab and the oscilloscope outputs produce similar phase portraits. Such implementations in real time represent a new type of hidden attractor with important consequences for engineering applications.

On some dynamical chameleon systems

NASA Astrophysics Data System (ADS)

Burkin, I. M.; Kuznetsova, O. I.

2018-03-01

It is now well known that dynamical systems can be categorized into systems with self-excited attractors and systems with hidden attractors. A self-excited attractor has a basin of attraction that is associated with an unstable equilibrium, while a hidden attractor has a basin of attraction that does not intersect with small neighborhoods of any equilibrium points. Hidden attractors play the important role in engineering applications because they allow unexpected and potentially disastrous responses to perturbations in a structure like a bridge or an airplane wing. In addition, complex behaviors of chaotic systems have been applied in various areas from image watermarking, audio encryption scheme, asymmetric color pathological image encryption, chaotic masking communication to random number generator. Recently, researchers have discovered the so-called “chameleon systems”. These systems were so named because they demonstrate self-excited or hidden oscillations depending on the value of parameters. The present paper offers a simple algorithm of synthesizing one-parameter chameleon systems. The authors trace the evolution of Lyapunov exponents and the Kaplan-Yorke dimension of such systems which occur when parameters change.

Extracting hidden messages in steganographic images

DOE PAGES

Quach, Tu-Thach

2014-07-17

The eventual goal of steganalytic forensic is to extract the hidden messages embedded in steganographic images. A promising technique that addresses this problem partially is steganographic payload location, an approach to reveal the message bits, but not their logical order. It works by finding modified pixels, or residuals, as an artifact of the embedding process. This technique is successful against simple least-significant bit steganography and group-parity steganography. The actual messages, however, remain hidden as no logical order can be inferred from the located payload. This paper establishes an important result addressing this shortcoming: we show that the expected mean residualsmore » contain enough information to logically order the located payload provided that the size of the payload in each stego image is not fixed. The located payload can be ordered as prescribed by the mean residuals to obtain the hidden messages without knowledge of the embedding key, exposing the vulnerability of these embedding algorithms. We provide experimental results to support our analysis.« less

Momentum-resolved hidden-order gap reveals symmetry breaking and origin of entropy loss in URu2Si2

NASA Astrophysics Data System (ADS)

Bareille, C.; Boariu, F. L.; Schwab, H.; Lejay, P.; Reinert, F.; Santander-Syro, A. F.

2014-07-01

Spontaneous symmetry breaking in physical systems leads to salient phenomena at all scales, from the Higgs mechanism and the emergence of the mass of the elementary particles, to superconductivity and magnetism in solids. The hidden-order state arising below 17.5 K in URu2Si2 is a puzzling example of one of such phase transitions: its associated broken symmetry and gap structure have remained longstanding riddles. Here we directly image how, across the hidden-order transition, the electronic structure of URu2Si2 abruptly reconstructs. We observe an energy gap of 7 meV opening over 70% of a large diamond-like heavy-fermion Fermi surface, resulting in the formation of four small Fermi petals, and a change in the electronic periodicity from body-centred tetragonal to simple tetragonal. Our results explain the large entropy loss in the hidden-order phase, and the similarity between this phase and the high-pressure antiferromagnetic phase found in quantum-oscillation experiments.

Hidden order and unconventional superconductivity in URu2Si2

NASA Astrophysics Data System (ADS)

Rau, Jeffrey; Kee, Hae-Young

2012-02-01

The nature of the so-called hidden order in URu2Si2 and the subsequent superconducting phase have remained a puzzle for over two decades. Motivated by evidence for rotational symmetry breaking seen in recent magnetic torque measurements [Okazaki et al. Science 331, 439 (2011)], we derive a simple tight-binding model consistent with experimental Fermi surface probes and ab-initio calculations. From this model we use mean-field theory to examine the variety of hidden orders allowed by existing experimental results, including the torque measurements. We then construct a phase diagram in temperature and pressure and discuss relevant experimental consequences.

Minimum of the order parameter fluctuations of seismicity before major earthquakes in Japan.

PubMed

Sarlis, Nicholas V; Skordas, Efthimios S; Varotsos, Panayiotis A; Nagao, Toshiyasu; Kamogawa, Masashi; Tanaka, Haruo; Uyeda, Seiya

2013-08-20

It has been shown that some dynamic features hidden in the time series of complex systems can be uncovered if we analyze them in a time domain called natural time χ. The order parameter of seismicity introduced in this time domain is the variance of χ weighted for normalized energy of each earthquake. Here, we analyze the Japan seismic catalog in natural time from January 1, 1984 to March 11, 2011, the day of the M9 Tohoku earthquake, by considering a sliding natural time window of fixed length comprised of the number of events that would occur in a few months. We find that the fluctuations of the order parameter of seismicity exhibit distinct minima a few months before all of the shallow earthquakes of magnitude 7.6 or larger that occurred during this 27-y period in the Japanese area. Among the minima, the minimum before the M9 Tohoku earthquake was the deepest. It appears that there are two kinds of minima, namely precursory and nonprecursory, to large earthquakes.

How hidden are hidden processes? A primer on crypticity and entropy convergence

NASA Astrophysics Data System (ADS)

Mahoney, John R.; Ellison, Christopher J.; James, Ryan G.; Crutchfield, James P.

2011-09-01

We investigate a stationary process's crypticity—a measure of the difference between its hidden state information and its observed information—using the causal states of computational mechanics. Here, we motivate crypticity and cryptic order as physically meaningful quantities that monitor how hidden a hidden process is. This is done by recasting previous results on the convergence of block entropy and block-state entropy in a geometric setting, one that is more intuitive and that leads to a number of new results. For example, we connect crypticity to how an observer synchronizes to a process. We show that the block-causal-state entropy is a convex function of block length. We give a complete analysis of spin chains. We present a classification scheme that surveys stationary processes in terms of their possible cryptic and Markov orders. We illustrate related entropy convergence behaviors using a new form of foliated information diagram. Finally, along the way, we provide a variety of interpretations of crypticity and cryptic order to establish their naturalness and pervasiveness. This is also a first step in developing applications in spatially extended and network dynamical systems.

Camouflaging in Digital Image for Secure Communication

NASA Astrophysics Data System (ADS)

Jindal, B.; Singh, A. P.

2013-06-01

The present paper reports on a new type of camouflaging in digital image for hiding crypto-data using moderate bit alteration in the pixel. In the proposed method, cryptography is combined with steganography to provide a two layer security to the hidden data. The novelty of the algorithm proposed in the present work lies in the fact that the information about hidden bit is reflected by parity condition in one part of the image pixel. The remaining part of the image pixel is used to perform local pixel adjustment to improve the visual perception of the cover image. In order to examine the effectiveness of the proposed method, image quality measuring parameters are computed. In addition to this, security analysis is also carried by comparing the histograms of cover and stego images. This scheme provides a higher security as well as robustness to intentional as well as unintentional attacks.

Role of band filling in tuning the high-field phases of URu 2 Si 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wartenbe, M. R.; Chen, K. -W.; Gallagher, A.

2017-08-28

We present a detailed study of the low temperature and high magnetic eld phases in the chemical substitution series URu 2Si 2-xPx using electrical transport and magnetization in pulsed magnetic elds up to 65T. Within the hidden order x-regime (0 < x ≲ 0.035) the eld induced ordering that was earlier seen for x = 0 is robust, even as the hidden order temperature is suppressed. Earlier work shows that for 0.035 ≲ x ≲ 0.26 there is a Kondo lattice with a no-ordered state that is replaced by antiferromagnetism for 0.26 ≲ x ≲ 0.5. We observe a simplimore » ed continuation of the eld induced order in the no-order x-regime and an enhancement of the field induced order upon the destruction of the antiferromagnetism with magnetic field. These results closely resemble what is seen for URu 2-xRhxSi 2 a, from which we infer that charge tuning dominantly controls the ground state of URu 2Si 2, regardless of whether s/p or d-electrons are replaced. Contraction of the unit cell volume may also play a role at large x. This provides guidance for determining the specific factors that lead to hidden order versus magnetism in this family of materials and constrains possible models for hidden order.« less

Raman scattering study on the hidden order and antiferromagnetic phases in URu2-xFexSi2

NASA Astrophysics Data System (ADS)

Kung, Hsiang-Hsi; Ran, Sheng; Kanchanavatee, Noravee; Lee, Alexander; Krapivin, Viktor; Haule, Kristjan; Maple, M. Brian; Blumberg, Girsh

The heavy fermion compound URu2Si2 possesses an unusual ground state known as the ``hidden order'' (HO) phase below T = 17 . 5 K, which evolves into an large moment antiferromagnetic (LMAFM) phase under pressure. A recent Raman scattering study shows that an A2 g symmetry (D4 h) in-gap mode emerges in the HO phase, characterizing the excitation from a chirality density wave. Here, we report Raman scattering results for single crystal URu2-xFexSi2 with x <= 0 . 2 , where the Fe substitution acts as chemical pressure, shifting the system's ground state from HO to LMAFM. We found that the A2 g mode softens with doping, vanishes at the HO and LMAFM phase boundary, then re-emerges and hardens with doping in the LMAFM phase. The relations between the A2 g mode energy and the strength of the HO/LMAFM order parameters will be discussed in this talk. GB and HHK acknowledge support from DOE BES Award DE-SC0005463. AL and VK acknowledge NSF Award DMR-1104884. KH acknowledges NSF Award DMR-1405303. MBM, SR and NK acknowledge DOE BES Award DE-FG02-04ER46105 and NSF Award DMR 1206553.

Neural net classification of x-ray pistachio nut data

NASA Astrophysics Data System (ADS)

Casasent, David P.; Sipe, Michael A.; Schatzki, Thomas F.; Keagy, Pamela M.; Le, Lan Chau

1996-12-01

Classification results for agricultural products are presented using a new neural network. This neural network inherently produces higher-order decision surfaces. It achieves this with fewer hidden layer neurons than other classifiers require. This gives better generalization. It uses new techniques to select the number of hidden layer neurons and adaptive algorithms that avoid other such ad hoc parameter selection problems; it allows selection of the best classifier parameters without the need to analyze the test set results. The agriculture case study considered is the inspection and classification of pistachio nuts using x- ray imagery. Present inspection techniques cannot provide good rejection of worm damaged nuts without rejecting too many good nuts. X-ray imagery has the potential to provide 100% inspection of such agricultural products in real time. Only preliminary results are presented, but these indicate the potential to reduce major defects to 2% of the crop with 1% of good nuts rejected. Future image processing techniques that should provide better features to improve performance and allow inspection of a larger variety of nuts are noted. These techniques and variations of them have uses in a number of other agricultural product inspection problems.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

NASA Astrophysics Data System (ADS)

Mulyadi, Y.; Abdullah, A. G.; Rohmah, K. A.

2017-03-01

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Unfolding the physics of URu 2Si 2 through silicon to phosphorus substitution

DOE PAGES

Gallagher, A.; Chen, K. -W.; Moir, C. M.; ...

2016-02-19

The heavy fermion intermetallic compound URu 2Si 2 exhibits a hidden-order phase below the temperature of 17.5 K, which supports both anomalous metallic behavior and unconventional superconductivity. While these individual phenomena have been investigated in detail, it remains unclear how they are related to each other and to what extent uranium f-electron valence fluctuations influence each one. Here we use ligand site substituted URu 2Si 2-xP x to establish their evolution under electronic tuning. We find that while hidden order is monotonically suppressed and destroyed for x ≤ 0.035, the superconducting strength evolves non-monotonically with a maximum near x ≈more » 0.01 and that superconductivity is destroyed near x ≈ 0.028. This behavior reveals that hidden order depends strongly on tuning outside of the U f-electron shells. Furthermore, it also suggests that while hidden order provides an environment for superconductivity and anomalous metallic behavior, it’s fluctuations may not be solely responsible for their progression.« less

Understanding overlay signatures using machine learning on non-lithography context information

NASA Astrophysics Data System (ADS)

Overcast, Marshall; Mellegaard, Corey; Daniel, David; Habets, Boris; Erley, Georg; Guhlemann, Steffen; Thrun, Xaver; Buhl, Stefan; Tottewitz, Steven

2018-03-01

Overlay errors between two layers can be caused by non-lithography processes. While these errors can be compensated by the run-to-run system, such process and tool signatures are not always stable. In order to monitor the impact of non-lithography context on overlay at regular intervals, a systematic approach is needed. Using various machine learning techniques, significant context parameters that relate to deviating overlay signatures are automatically identified. Once the most influential context parameters are found, a run-to-run simulation is performed to see how much improvement can be obtained. The resulting analysis shows good potential for reducing the influence of hidden context parameters on overlay performance. Non-lithographic contexts are significant contributors, and their automatic detection and classification will enable the overlay roadmap, given the corresponding control capabilities.

PVWatts Version 1 Technical Reference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobos, A. P.

2013-10-01

The NREL PVWatts(TM) calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes several hidden assumptions about performance parameters. This technical reference details the individual sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimation.

A coupled hidden Markov model for disease interactions

PubMed Central

Sherlock, Chris; Xifara, Tatiana; Telfer, Sandra; Begon, Mike

2013-01-01

To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly. Although trapping sessions were regular, a different set of voles was caught at each session, leading to incomplete profiles for all subjects. We use a discrete time hidden Markov model for each disease with transition probabilities dependent on covariates via a set of logistic regressions. For each disease the hidden states for each of the other diseases at a given time point form part of the covariate set for the Markov transition probabilities from that time point. This allows us to gauge the influence of each parasite species on the transition probabilities for each of the other parasite species. Inference is performed via a Gibbs sampler, which cycles through each of the diseases, first using an adaptive Metropolis–Hastings step to sample from the conditional posterior of the covariate parameters for that particular disease given the hidden states for all other diseases and then sampling from the hidden states for that disease given the parameters. We find evidence for interactions between several pairs of parasites and of an acquired immune response for two of the parasites. PMID:24223436

Charge Density Waves and the Hidden Nesting of Purple Bronze KMo6O17

NASA Astrophysics Data System (ADS)

Su, Lei; Pereira, Vitor

The layered purple bronze KMo6O17, with its robust triple CDW phase up to high temperatures, became the emblematic example of the ''hidden nesting'' concept. Recent experiments suggest that, on the surface layers, its CDW phase can be stabilized at much higher temperatures, and with a tenfold increase in the electronic gap in comparison with the bulk. Despite such interesting fermiology and properties, the K and Na purple bronzes remain largely unexplored systems, most particularly so at the theoretical level. We introduce the first multi-orbital effective tight-binding model to describe the effect of electron-electron interactions in this system. Upon fixing all the effective hopping parameters in the normal state against an ab-initio band structure, and with only the overall scale of the interactions as sole adjustable parameter, we find that a self-consistent Hartree-Fock solution reproduces extremely well the experimental behavior of the charge density wave (CDW) order parameter in the full range 0 < T < Tc , as well as the precise reciprocal space locations of the partial gap opening and Fermi arc development. The interaction strengths extracted from fitting to the experimental CDW gap are consistent with those derived from an independent Stoner-type analysis This work was supported by the Singapore National Research Foundation under Grant NRF-CRP6-2010-05.

Experimental search for hidden photon CDM in the eV mass range with a dish antenna

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suzuki, J.; Horie, T.; Inoue, Y.

2015-09-15

A search for hidden photon cold dark matter (HP CDM) using a new technique with a dish antenna is reported. From the result of the measurement, we found no evidence for the existence of HP CDM and set an upper limit on the photon-HP mixing parameter χ of ∼6×10{sup −12} for the hidden photon mass m{sub γ}=3.1±1.2 eV.

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

Heating up the Galaxy with hidden photons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubovsky, Sergei; Hernández-Chifflet, Guzmán, E-mail: dubovsky@nyu.edu, E-mail: ghc236@nyu.edu

2015-12-01

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

Heating up the Galaxy with hidden photons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubovsky, Sergei; Hernández-Chifflet, Guzmán; Instituto de Física, Facultad de Ingeniería, Universidad de la República,Montevideo, 11300

2015-12-29

We elaborate on the dynamics of ionized interstellar medium in the presence of hidden photon dark matter. Our main focus is the ultra-light regime, where the hidden photon mass is smaller than the plasma frequency in the Milky Way. We point out that as a result of the Galactic plasma shielding direct detection of ultra-light photons in this mass range is especially challenging. However, we demonstrate that ultra-light hidden photon dark matter provides a powerful heating source for the ionized interstellar medium. This results in a strong bound on the kinetic mixing between hidden and regular photons all the waymore » down to the hidden photon masses of order 10{sup −20} eV.« less

Experimental search for hidden photon CDM in the eV mass range with a dish antenna

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suzuki, J.; Horie, T.; Minowa, M.

2015-09-01

A search for hidden photon cold dark matter (HP CDM) using a new technique with a dish antenna is reported. From the result of the measurement, we found no evidence for the existence of HP CDM and set an upper limit on the photon-HP mixing parameter χ of ∼ 6× 10{sup −12} for the hidden photon mass m{sub γ} = 3.1 ± 1.2 eV.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Emergence of higher order rotational symmetry in the hidden order phase of URu 2Si 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanchanavatee, N.; Janoschek, M.; Huang, K.

2016-09-30

Electrical resistivity measurements were performed in this paper as functions of temperature, magnetic field, and angle θ between the magnetic field and the c-axis of a URu 2Si 2 single crystal. The resistivity exhibits a two-fold oscillation as a function of θ at high temperatures, which undergoes a 180°-phase shift (sign change) with decreasing temperature at around 35 K. The hidden order transition is manifested as a minimum in the magnetoresistance and amplitude of the two-fold oscillation. Interestingly, the resistivity also showed four-fold, six-fold, and eight-fold symmetries at the hidden order transition. These higher order symmetries were also detected atmore » low temperatures, which could be a sign of the formation of another pseudogap phase above the superconducting transition, consistent with recent evidence for a pseudogap from point-contact spectroscopy measurements and NMR. Measurements of the magnetisation of single crystalline URu 2Si 2 with the magnetic field applied parallel and perpendicular to the crystallographic c-axis revealed regions with linear temperature dependencies between the hidden order transition temperature and about 25 K. Finally, this T-linear behaviour of the magnetisation may be associated with the formation of a precursor phase or ‘pseudogap’ in the density of states in the vicinity of 30–35 K.« less

Phase diagram of URu 2-xFe xSi 2 in high magnetic fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ran, S.; Jeon, I.; Kanchanavatee, N.

2017-03-01

The search for the order parameter of the hidden order (HO) phase in URu 2Si 2 has attracted an enormous amount of attention for the past three decades. Measurements in high magnetic fields H up to 45~T reveal that URu 2Si 2 displays behavior that is consistent with quantum criticality at a field near 35~T, where a cascade of novel quantum phases was found at and around the quantum critical point, suggesting the existence of competing order parameters. Experiments at high pressure P reveal that a first order transition from the HO phase to a large moment antiferromagnetic (LMAFM) phasemore » occurs under pressure at a critical pressure Pc. We have recently demonstrated that tuning URu 2Si 2 by substitution of Fe for Ru offers an opportunity to study the HO and LMAFM phases at atmospheric pressure. In this study, we conducted electrical resistance measurements on URu 2-xFe xSi 2 for H < 65 T using the pulsed field facility at the NHMFL in Los Alamos, in order to establish the temperature T vs. H phase diagram of URu 2-xFe xSi 2 under magnetic fields.« less

A hidden pseudogap under the 'dome' of superconductivity in electron-doped high-temperature superconductors.

PubMed

Alff, L; Krockenberger, Y; Welter, B; Schonecke, M; Gross, R; Manske, D; Naito, M

2003-04-17

The ground state of superconductors is characterized by the long-range order of condensed Cooper pairs: this is the only order present in conventional superconductors. The high-transition-temperature (high-T(c)) superconductors, in contrast, exhibit more complex phase behaviour, which might indicate the presence of other competing ground states. For example, the pseudogap--a suppression of the accessible electronic states at the Fermi level in the normal state of high-T(c) superconductors-has been interpreted as either a precursor to superconductivity or as tracer of a nearby ground state that can be separated from the superconducting state by a quantum critical point. Here we report the existence of a second order parameter hidden within the superconducting phase of the underdoped (electron-doped) high-T(c) superconductor Pr2-xCe(x)CuO4-y and the newly synthesized electron-doped material La2-xCe(x)CuO4-y (ref. 8). The existence of a pseudogap when superconductivity is suppressed excludes precursor superconductivity as its origin. Our observation is consistent with the presence of a (quantum) phase transition at T = 0, which may be a key to understanding high-T(c) superconductivity. This supports the picture that the physics of high-T(c) superconductors is determined by the interplay between competing and coexisting ground states.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Synchronisation, electronic circuit implementation, and fractional-order analysis of 5D ordinary differential equations with hidden hyperchaotic attractors

NASA Astrophysics Data System (ADS)

Wei, Zhouchao; Rajagopal, Karthikeyan; Zhang, Wei; Kingni, Sifeu Takougang; Akgül, Akif

2018-04-01

Hidden hyperchaotic attractors can be generated with three positive Lyapunov exponents in the proposed 5D hyperchaotic Burke-Shaw system with only one stable equilibrium. To the best of our knowledge, this feature has rarely been previously reported in any other higher-dimensional systems. Unidirectional linear error feedback coupling scheme is used to achieve hyperchaos synchronisation, which will be estimated by using two indicators: the normalised average root-mean squared synchronisation error and the maximum cross-correlation coefficient. The 5D hyperchaotic system has been simulated using a specially designed electronic circuit and viewed on an oscilloscope, thereby confirming the results of the numerical integration. In addition, fractional-order hidden hyperchaotic system will be considered from the following three aspects: stability, bifurcation analysis and FPGA implementation. Such implementations in real time represent hidden hyperchaotic attractors with important consequences for engineering applications.

Measurement problem and local hidden variables with entangled photons

NASA Astrophysics Data System (ADS)

Muchowski, Eugen

2017-12-01

It is shown that there is no remote action with polarization measurements of photons in singlet state. A model is presented introducing a hidden parameter which determines the polarizer output. This model is able to explain the polarization measurement results with entangled photons. It is not ruled out by Bell's Theorem.

Dissipative hidden sector dark matter

NASA Astrophysics Data System (ADS)

Foot, R.; Vagnozzi, S.

2015-01-01

A simple way of explaining dark matter without modifying known Standard Model physics is to require the existence of a hidden (dark) sector, which interacts with the visible one predominantly via gravity. We consider a hidden sector containing two stable particles charged under an unbroken U (1 )' gauge symmetry, hence featuring dissipative interactions. The massless gauge field associated with this symmetry, the dark photon, can interact via kinetic mixing with the ordinary photon. In fact, such an interaction of strength ε ˜10-9 appears to be necessary in order to explain galactic structure. We calculate the effect of this new physics on big bang nucleosynthesis and its contribution to the relativistic energy density at hydrogen recombination. We then examine the process of dark recombination, during which neutral dark states are formed, which is important for large-scale structure formation. Galactic structure is considered next, focusing on spiral and irregular galaxies. For these galaxies we modeled the dark matter halo (at the current epoch) as a dissipative plasma of dark matter particles, where the energy lost due to dissipation is compensated by the energy produced from ordinary supernovae (the core-collapse energy is transferred to the hidden sector via kinetic mixing induced processes in the supernova core). We find that such a dynamical halo model can reproduce several observed features of disk galaxies, including the cored density profile and the Tully-Fisher relation. We also discuss how elliptical and dwarf spheroidal galaxies could fit into this picture. Finally, these analyses are combined to set bounds on the parameter space of our model, which can serve as a guideline for future experimental searches.

PVWatts Version 5 Manual

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobos, A. P.

2014-09-01

The NREL PVWatts calculator is a web application developed by the National Renewable Energy Laboratory (NREL) that estimates the electricity production of a grid-connected photovoltaic system based on a few simple inputs. PVWatts combines a number of sub-models to predict overall system performance, and makes includes several built-in parameters that are hidden from the user. This technical reference describes the sub-models, documents assumptions and hidden parameters, and explains the sequence of calculations that yield the final system performance estimate. This reference is applicable to the significantly revised version of PVWatts released by NREL in 2014.

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

NASA Astrophysics Data System (ADS)

Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

Comparison of statistical algorithms for detecting homogeneous river reaches along a longitudinal continuum

NASA Astrophysics Data System (ADS)

Leviandier, Thierry; Alber, A.; Le Ber, F.; Piégay, H.

2012-02-01

Seven methods designed to delineate homogeneous river segments, belonging to four families, namely — tests of homogeneity, contrast enhancing, spatially constrained classification, and hidden Markov models — are compared, firstly on their principles, then on a case study, and on theoretical templates. These templates contain patterns found in the case study but not considered in the standard assumptions of statistical methods, such as gradients and curvilinear structures. The influence of data resolution, noise and weak satisfaction of the assumptions underlying the methods is investigated. The control of the number of reaches obtained in order to achieve meaningful comparisons is discussed. No method is found that outperforms all the others on all trials. However, the methods with sequential algorithms (keeping at order n + 1 all breakpoints found at order n) fail more often than those running complete optimisation at any order. The Hubert-Kehagias method and Hidden Markov Models are the most successful at identifying subpatterns encapsulated within the templates. Ergodic Hidden Markov Models are, moreover, liable to exhibit transition areas.

Pseudo-random bit generator based on lag time series

NASA Astrophysics Data System (ADS)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Electric Field Effects on the Hidden Order of Microstructured URu 2Si 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stritzinger, Laurel Elaine Winter; Mcdonald, Ross David; Harrison, Neil

2017-03-23

Despite being studied for over 30 years there is still continual interest in they heavy-fermion URu 2Si 2 due largely in part to the still disagreed upon origin of the so-called hidden-order (HO) state that arises below THO = 17.5 K. While both the application of pressure and high magnetic fields have been shown to suppress the HO state, one mechanism that has yet to be explored is the application of an electric field, most likely due to the difficulty of measuring such an effect in a metal. To overcome this challenge we have used focused ion beam (FIB) lithographymore » to obtain the necessary sample geometry to create an electric field across a small section of the sample by applying a voltage. Our results suggest that at low temperatures the application of an electric field is able to suppress the hidden order state.« less

The Hidden Cost of Buying a Computer.

ERIC Educational Resources Information Center

Johnson, Michael

1983-01-01

In order to process data in a computer, application software must be either developed or purchased. Costs for modifications of the software package and maintenance are often hidden. The decision to buy or develop software packages should be based upon factors of time and maintenance. (MLF)

Memory and foraging theory: Chimpanzee utilization of optimality heuristics in the rank-order recovery of hidden foods

PubMed Central

Sayers, Ken; Menzel, Charles R.

2012-01-01

Many models from foraging theory and movement ecology assume that resources are encountered randomly. If food locations, types and values are retained in memory, however, search time could be significantly reduced, with concurrent effects on biological fitness. Despite this, little is known about what specific characteristics of foods, particularly those relevant to profitability, nonhuman animals can remember. Building upon previous observations, we hypothesized that chimpanzees (Pan troglodytes), after observing foods being hidden in a large wooded test area they could not enter, and after long delays, would direct (through gesture and vocalization) experimentally naïve humans to the reward locations in an order that could be predicted beforehand by the spatial and physical characteristics of those items. In the main experiment, various quantities of almonds, both in and out of shells and sealed in transparent bags, were hidden in the test area. The chimpanzees later directed searchers to those items in a nonrandom order related to quantity, shell presence/absence, and the distance they were hidden from the subject. The recovery sequences were closely related to the actual e/h profitability of the foods. Predicted recovery orders, based on the energetic value of almonds and independently-measured, individual-specific expected pursuit and processing times, were closely related to observed recovery orders. We argue that the information nonhuman animals possess regarding their environment can be extensive, and that further comparative study is vital for incorporating realistic cognitive variables into models of foraging and movement. PMID:23226837

Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks

NASA Astrophysics Data System (ADS)

Zhu, Shijia; Wang, Yadong

2015-12-01

Dynamic Bayesian Networks (DBN) have been widely used to recover gene regulatory relationships from time-series data in computational systems biology. Its standard assumption is ‘stationarity’, and therefore, several research efforts have been recently proposed to relax this restriction. However, those methods suffer from three challenges: long running time, low accuracy and reliance on parameter settings. To address these problems, we propose a novel non-stationary DBN model by extending each hidden node of Hidden Markov Model into a DBN (called HMDBN), which properly handles the underlying time-evolving networks. Correspondingly, an improved structural EM algorithm is proposed to learn the HMDBN. It dramatically reduces searching space, thereby substantially improving computational efficiency. Additionally, we derived a novel generalized Bayesian Information Criterion under the non-stationary assumption (called BWBIC), which can help significantly improve the reconstruction accuracy and largely reduce over-fitting. Moreover, the re-estimation formulas for all parameters of our model are derived, enabling us to avoid reliance on parameter settings. Compared to the state-of-the-art methods, the experimental evaluation of our proposed method on both synthetic and real biological data demonstrates more stably high prediction accuracy and significantly improved computation efficiency, even with no prior knowledge and parameter settings.

First Direct-Detection Constraints on eV-Scale Hidden-Photon Dark Matter with DAMIC at SNOLAB

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aguilar-Arevalo, A.; Amidei, D.; Bertou, X.

We present direct detection constraints on the absorption of hidden-photon dark matter with particle masses in the range 1.2-30 eVmore » $$c^{-2}$$ with the DAMIC experiment at SNOLAB. Under the assumption that the local dark matter is entirely constituted of hidden photons, the sensitivity to the kinetic mixing parameter $$\\kappa$$ is competitive with constraints from solar emission, reaching a minimum value of 2.2$$\\times$$$10^{-14}$$ at 17 eV$$c^{-2}$$. These results are the most stringent direct detection constraints on hidden-photon dark matter with masses 3-12 eV$$c^{-2}$$ and the first demonstration of direct experimental sensitivity to ionization signals $<$12 eV from dark matter interactions.« less

URu2Si2 under intense magnetic fields: From hidden order to spin-density wave

NASA Astrophysics Data System (ADS)

Knafo, W.; Aoki, D.; Scheerer, G. W.; Duc, F.; Bourdarot, F.; Kuwahara, K.; Nojiri, H.; Regnault, L.-P.; Flouquet, J.

2018-05-01

A review of recent state-of-the-art pulsed field experiments performed on URu2Si2 under a magnetic field applied along its easy magnetic axis c is given. Resistivity, magnetization, magnetic susceptibility, Shubnikov-de Haas, and neutron diffraction experiments are presented, permitting to emphasize the relationship between Fermi surface reconstructions, the destruction of the hidden-order and the appearance of a spin-density wave state in a high magnetic field.

Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks.

PubMed

Conesa, D; Martínez-Beneito, M A; Amorós, R; López-Quílez, A

2015-04-01

Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness. © The Author(s) 2011 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Perspective: Sloppiness and emergent theories in physics, biology, and beyond.

PubMed

Transtrum, Mark K; Machta, Benjamin B; Brown, Kevin S; Daniels, Bryan C; Myers, Christopher R; Sethna, James P

2015-07-07

Large scale models of physical phenomena demand the development of new statistical and computational tools in order to be effective. Many such models are "sloppy," i.e., exhibit behavior controlled by a relatively small number of parameter combinations. We review an information theoretic framework for analyzing sloppy models. This formalism is based on the Fisher information matrix, which is interpreted as a Riemannian metric on a parameterized space of models. Distance in this space is a measure of how distinguishable two models are based on their predictions. Sloppy model manifolds are bounded with a hierarchy of widths and extrinsic curvatures. The manifold boundary approximation can extract the simple, hidden theory from complicated sloppy models. We attribute the success of simple effective models in physics as likewise emerging from complicated processes exhibiting a low effective dimensionality. We discuss the ramifications and consequences of sloppy models for biochemistry and science more generally. We suggest that the reason our complex world is understandable is due to the same fundamental reason: simple theories of macroscopic behavior are hidden inside complicated microscopic processes.

Learning to represent spatial transformations with factored higher-order Boltzmann machines.

PubMed

Memisevic, Roland; Hinton, Geoffrey E

2010-06-01

To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicative interactions that use the intensity of a pixel in the first image as a multiplicative gain on a learned, symmetric weight between a pixel in the second image and a hidden unit. This creates cubically many parameters, which form a three-dimensional interaction tensor. We describe a low-rank approximation to this interaction tensor that uses a sum of factors, each of which is a three-way outer product. This approximation allows efficient learning of transformations between larger image patches. Since each factor can be viewed as an image filter, the model as a whole learns optimal filter pairs for efficiently representing transformations. We demonstrate the learning of optimal filter pairs from various synthetic and real image sequences. We also show how learning about image transformations allows the model to perform a simple visual analogy task, and we show how a completely unsupervised network trained on transformations perceives multiple motions of transparent dot patterns in the same way as humans.

Behavior of supercooled aqueous solutions stemming from hidden liquid-liquid transition in water.

PubMed

Biddle, John W; Holten, Vincent; Anisimov, Mikhail A

2014-08-21

A popular hypothesis that explains the anomalies of supercooled water is the existence of a metastable liquid-liquid transition hidden below the line of homogeneous nucleation. If this transition exists and if it is terminated by a critical point, the addition of a solute should generate a line of liquid-liquid critical points emanating from the critical point of pure metastable water. We have analyzed thermodynamic consequences of this scenario. In particular, we consider the behavior of two systems, H2O-NaCl and H2O-glycerol. We find the behavior of the heat capacity in supercooled aqueous solutions of NaCl, as reported by Archer and Carter [J. Phys. Chem. B 104, 8563 (2000)], to be consistent with the presence of the metastable liquid-liquid transition. We elucidate the non-conserved nature of the order parameter (extent of "reaction" between two alternative structures of water) and the consequences of its coupling with conserved properties (density and concentration). We also show how the shape of the critical line in a solution controls the difference in concentration of the coexisting liquid phases.

Hidden sector dark matter and the Galactic Center gamma-ray excess: a closer look

DOE PAGES

Escudero, Miguel; Witte, Samuel J.; Hooper, Dan

2017-11-24

Stringent constraints from direct detection experiments and the Large Hadron Collider motivate us to consider models in which the dark matter does not directly couple to the Standard Model, but that instead annihilates into hidden sector particles which ultimately decay through small couplings to the Standard Model. We calculate the gamma-ray emission generated within the context of several such hidden sector models, including those in which the hidden sector couples to the Standard Model through the vector portal (kinetic mixing with Standard Model hypercharge), through the Higgs portal (mixing with the Standard Model Higgs boson), or both. In each case,more » we identify broad regions of parameter space in which the observed spectrum and intensity of the Galactic Center gamma-ray excess can easily be accommodated, while providing an acceptable thermal relic abundance and remaining consistent with all current constraints. Here, we also point out that cosmic-ray antiproton measurements could potentially discriminate some hidden sector models from more conventional dark matter scenarios.« less

Clustering coefficients of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Miller, Gerald A.; Shi, Yi Y.; Qian, Hong; Bomsztyk, Karol

2007-05-01

The properties of certain networks are determined by hidden variables that are not explicitly measured. The conditional probability (propagator) that a vertex with a given value of the hidden variable is connected to k other vertices determines all measurable properties. We study hidden variable models and find an averaging approximation that enables us to obtain a general analytical result for the propagator. Analytic results showing the validity of the approximation are obtained. We apply hidden variable models to protein-protein interaction networks (PINs) in which the hidden variable is the association free energy, determined by distributions that depend on biochemistry and evolution. We compute degree distributions as well as clustering coefficients of several PINs of different species; good agreement with measured data is obtained. For the human interactome two different parameter sets give the same degree distributions, but the computed clustering coefficients differ by a factor of about 2. This shows that degree distributions are not sufficient to determine the properties of PINs.

Hidden Sector Dark Matter and the Galactic Center Gamma-Ray Excess: A Closer Look

DOE Office of Scientific and Technical Information (OSTI.GOV)

Escudero, Miguel; Witte, Samuel J.; Hooper, Dan

2017-09-20

Stringent constraints from direct detection experiments and the Large Hadron Collider motivate us to consider models in which the dark matter does not directly couple to the Standard Model, but that instead annihilates into hidden sector particles which ultimately decay through small couplings to the Standard Model. We calculate the gamma-ray emission generated within the context of several such hidden sector models, including those in which the hidden sector couples to the Standard Model through the vector portal (kinetic mixing with Standard Model hypercharge), through the Higgs portal (mixing with the Standard Model Higgs boson), or both. In each case,more » we identify broad regions of parameter space in which the observed spectrum and intensity of the Galactic Center gamma-ray excess can easily be accommodated, while providing an acceptable thermal relic abundance and remaining consistent with all current constraints. We also point out that cosmic-ray antiproton measurements could potentially discriminate some hidden sector models from more conventional dark matter scenarios.« less

Hidden sector dark matter and the Galactic Center gamma-ray excess: a closer look

NASA Astrophysics Data System (ADS)

Escudero, Miguel; Witte, Samuel J.; Hooper, Dan

2017-11-01

Stringent constraints from direct detection experiments and the Large Hadron Collider motivate us to consider models in which the dark matter does not directly couple to the Standard Model, but that instead annihilates into hidden sector particles which ultimately decay through small couplings to the Standard Model. We calculate the gamma-ray emission generated within the context of several such hidden sector models, including those in which the hidden sector couples to the Standard Model through the vector portal (kinetic mixing with Standard Model hypercharge), through the Higgs portal (mixing with the Standard Model Higgs boson), or both. In each case, we identify broad regions of parameter space in which the observed spectrum and intensity of the Galactic Center gamma-ray excess can easily be accommodated, while providing an acceptable thermal relic abundance and remaining consistent with all current constraints. We also point out that cosmic-ray antiproton measurements could potentially discriminate some hidden sector models from more conventional dark matter scenarios.

Hidden sector dark matter and the Galactic Center gamma-ray excess: a closer look

DOE Office of Scientific and Technical Information (OSTI.GOV)

Escudero, Miguel; Witte, Samuel J.; Hooper, Dan

Stringent constraints from direct detection experiments and the Large Hadron Collider motivate us to consider models in which the dark matter does not directly couple to the Standard Model, but that instead annihilates into hidden sector particles which ultimately decay through small couplings to the Standard Model. We calculate the gamma-ray emission generated within the context of several such hidden sector models, including those in which the hidden sector couples to the Standard Model through the vector portal (kinetic mixing with Standard Model hypercharge), through the Higgs portal (mixing with the Standard Model Higgs boson), or both. In each case,more » we identify broad regions of parameter space in which the observed spectrum and intensity of the Galactic Center gamma-ray excess can easily be accommodated, while providing an acceptable thermal relic abundance and remaining consistent with all current constraints. Here, we also point out that cosmic-ray antiproton measurements could potentially discriminate some hidden sector models from more conventional dark matter scenarios.« less

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Signatures of a hidden cosmic microwave background.

PubMed

Jaeckel, Joerg; Redondo, Javier; Ringwald, Andreas

2008-09-26

If there is a light Abelian gauge boson gamma' in the hidden sector its kinetic mixing with the photon can produce a hidden cosmic microwave background (HCMB). For meV masses, resonant oscillations gamma<-->gamma' happen after big bang nucleosynthesis (BBN) but before CMB decoupling, increasing the effective number of neutrinos Nnu(eff) and the baryon to photon ratio, and distorting the CMB blackbody spectrum. The agreement between BBN and CMB data provides new constraints. However, including Lyman-alpha data, Nnu(eff) > 3 is preferred. It is tempting to attribute this effect to the HCMB. The interesting parameter range will be tested in upcoming laboratory experiments.

First Direct-Detection Constraints on eV-Scale Hidden-Photon Dark Matter with DAMIC at SNOLAB.

PubMed

Aguilar-Arevalo, A; Amidei, D; Bertou, X; Butner, M; Cancelo, G; Castañeda Vázquez, A; Cervantes Vergara, B A; Chavarria, A E; Chavez, C R; de Mello Neto, J R T; D'Olivo, J C; Estrada, J; Fernandez Moroni, G; Gaïor, R; Guardincerri, Y; Hernández Torres, K P; Izraelevitch, F; Kavner, A; Kilminster, B; Lawson, I; Letessier-Selvon, A; Liao, J; Matalon, A; Mello, V B B; Molina, J; Privitera, P; Ramanathan, K; Sarkis, Y; Schwarz, T; Settimo, M; Sofo Haro, M; Thomas, R; Tiffenberg, J; Tiouchichine, E; Torres Machado, D; Trillaud, F; You, X; Zhou, J

2017-04-07

We present direct detection constraints on the absorption of hidden-photon dark matter with particle masses in the range 1.2-30  eV c^{-2} with the DAMIC experiment at SNOLAB. Under the assumption that the local dark matter is entirely constituted of hidden photons, the sensitivity to the kinetic mixing parameter κ is competitive with constraints from solar emission, reaching a minimum value of 2.2×10^{-14} at 17  eV c^{-2}. These results are the most stringent direct detection constraints on hidden-photon dark matter in the galactic halo with masses 3-12  eV c^{-2} and the first demonstration of direct experimental sensitivity to ionization signals <12  eV from dark matter interactions.

An actual load forecasting methodology by interval grey modeling based on the fractional calculus.

PubMed

Yang, Yang; Xue, Dingyü

2017-07-17

The operation processes for thermal power plant are measured by the real-time data, and a large number of historical interval data can be obtained from the dataset. Within defined periods of time, the interval information could provide important information for decision making and equipment maintenance. Actual load is one of the most important parameters, and the trends hidden in the historical data will show the overall operation status of the equipments. However, based on the interval grey parameter numbers, the modeling and prediction process is more complicated than the one with real numbers. In order not lose any information, the geometric coordinate features are used by the coordinates of area and middle point lines in this paper, which are proved with the same information as the original interval data. The grey prediction model for interval grey number by the fractional-order accumulation calculus is proposed. Compared with integer-order model, the proposed method could have more freedom with better performance for modeling and prediction, which can be widely used in the modeling process and prediction for the small amount interval historical industry sequence samples. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A method of hidden Markov model optimization for use with geophysical data sets

NASA Technical Reports Server (NTRS)

Granat, R. A.

2003-01-01

Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models.

PubMed

Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I

2018-01-01

Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

A New Chaotic Flow with Hidden Attractor: The First Hyperjerk System with No Equilibrium

NASA Astrophysics Data System (ADS)

Ren, Shuili; Panahi, Shirin; Rajagopal, Karthikeyan; Akgul, Akif; Pham, Viet-Thanh; Jafari, Sajad

2018-02-01

Discovering unknown aspects of non-equilibrium systems with hidden strange attractors is an attractive research topic. A novel quadratic hyperjerk system is introduced in this paper. It is noteworthy that this non-equilibrium system can generate hidden chaotic attractors. The essential properties of such systems are investigated by means of equilibrium points, phase portrait, bifurcation diagram, and Lyapunov exponents. In addition, a fractional-order differential equation of this new system is presented. Moreover, an electronic circuit is also designed and implemented to verify the feasibility of the theoretical model.

Radio for hidden-photon dark matter detection

DOE PAGES

Chaudhuri, Saptarshi; Graham, Peter W.; Irwin, Kent; ...

2015-10-08

We propose a resonant electromagnetic detector to search for hidden-photon dark matter over an extensive range of masses. Hidden-photon dark matter can be described as a weakly coupled “hidden electric field,” oscillating at a frequency fixed by the mass, and able to penetrate any shielding. At low frequencies (compared to the inverse size of the shielding), we find that the observable effect of the hidden photon inside any shielding is a real, oscillating magnetic field. We outline experimental setups designed to search for hidden-photon dark matter, using a tunable, resonant LC circuit designed to couple to this magnetic field. Ourmore » “straw man” setups take into consideration resonator design, readout architecture and noise estimates. At high frequencies, there is an upper limit to the useful size of a single resonator set by 1/ν. However, many resonators may be multiplexed within a hidden-photon coherence length to increase the sensitivity in this regime. Hidden-photon dark matter has an enormous range of possible frequencies, but current experiments search only over a few narrow pieces of that range. As a result, we find the potential sensitivity of our proposal is many orders of magnitude beyond current limits over an extensive range of frequencies, from 100 Hz up to 700 GHz and potentially higher.« less

Hidden attractors in dynamical systems

NASA Astrophysics Data System (ADS)

Dudkowski, Dawid; Jafari, Sajad; Kapitaniak, Tomasz; Kuznetsov, Nikolay V.; Leonov, Gennady A.; Prasad, Awadhesh

2016-06-01

Complex dynamical systems, ranging from the climate, ecosystems to financial markets and engineering applications typically have many coexisting attractors. This property of the system is called multistability. The final state, i.e., the attractor on which the multistable system evolves strongly depends on the initial conditions. Additionally, such systems are very sensitive towards noise and system parameters so a sudden shift to a contrasting regime may occur. To understand the dynamics of these systems one has to identify all possible attractors and their basins of attraction. Recently, it has been shown that multistability is connected with the occurrence of unpredictable attractors which have been called hidden attractors. The basins of attraction of the hidden attractors do not touch unstable fixed points (if exists) and are located far away from such points. Numerical localization of the hidden attractors is not straightforward since there are no transient processes leading to them from the neighborhoods of unstable fixed points and one has to use the special analytical-numerical procedures. From the viewpoint of applications, the identification of hidden attractors is the major issue. The knowledge about the emergence and properties of hidden attractors can increase the likelihood that the system will remain on the most desirable attractor and reduce the risk of the sudden jump to undesired behavior. We review the most representative examples of hidden attractors, discuss their theoretical properties and experimental observations. We also describe numerical methods which allow identification of the hidden attractors.

Constraints on hidden photons from current and future observations of CMB spectral distortions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kunze, Kerstin E.; Vázquez-Mozo, Miguel Á., E-mail: kkunze@usal.es, E-mail: Miguel.Vazquez-Mozo@cern.ch

2015-12-01

A variety of beyond the standard model scenarios contain very light hidden sector U(1) gauge bosons undergoing kinetic mixing with the photon. The resulting oscillation between ordinary and hidden photons leads to spectral distortions of the cosmic microwave background. We update the bounds on the mixing parameter χ{sub 0} and the mass of the hidden photon m{sub γ'} for future experiments measuring CMB spectral distortions, such as PIXIE and PRISM/COrE. For 10{sup −14} eV∼< m{sub γ'}∼< 10{sup −13} eV, we find the kinetic mixing angle χ{sub 0} has to be less than 10{sup −8} at 95% CL. These bounds are more than an ordermore » of magnitude stronger than those derived from the COBE/FIRAS data.« less

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

Synchronization behaviors of coupled systems composed of hidden attractors

NASA Astrophysics Data System (ADS)

Zhang, Ge; Wu, Fuqiang; Wang, Chunni; Ma, Jun

2017-10-01

Based on a class of chaotic system composed of hidden attractors, in which the equilibrium points are described by a circular function, complete synchronization between two identical systems, pattern formation and synchronization of network is investigated, respectively. A statistical factor of synchronization is defined and calculated by using the mean field theory, the dependence of synchronization on bifurcation parameters discussed in numerical way. By setting a chain network, which local kinetic is described by hidden attractors, synchronization approach is investigated. It is found that the synchronization and pattern formation are dependent on the coupling intensity and also the selection of coupling variables. In the end, open problems are proposed for readers’ extensive guidance and investigation.

A New Look at the Eclipse Timing Variation Diagram Analysis of Selected 3-body W UMa Systems

NASA Astrophysics Data System (ADS)

Christopoulou, P.-E.; Papageorgiou, A.

2015-07-01

The light travel effect produced by the presence of tertiary components can reveal much about the origin and evolution of over-contact binaries. Monitoring of W UMa systems over the last decade and/or the use of publicly available photometric surveys (NSVS, ASAS, etc.) has uncovered or suggested the presence of many unseen companions, which calls for an in-depth investigation of the parameters derived from cyclic period variations in order to confirm or reject the assumption of hidden companion(s). Progress in the analysis of eclipse timing variations is summarized here both from the empirical and the theoretical points of view, and a more extensive investigation of the proposed orbital parameters of third bodies is proposed. The code we have developed for this, implemented in Python, is set up to handle heuristic scanning with parameter perturbation in parameter space, and to establish realistic uncertainties from the least squares fitting. A computational example is given for TZ Boo, a W UMa system with a spectroscopically detected third component. Future options to be implemented include MCMC and bootstrapping.

The perception of probability.

PubMed

Gallistel, C R; Krishan, Monika; Liu, Ye; Miller, Reilly; Latham, Peter E

2014-01-01

We present a computational model to explain the results from experiments in which subjects estimate the hidden probability parameter of a stepwise nonstationary Bernoulli process outcome by outcome. The model captures the following results qualitatively and quantitatively, with only 2 free parameters: (a) Subjects do not update their estimate after each outcome; they step from one estimate to another at irregular intervals. (b) The joint distribution of step widths and heights cannot be explained on the assumption that a threshold amount of change must be exceeded in order for them to indicate a change in their perception. (c) The mapping of observed probability to the median perceived probability is the identity function over the full range of probabilities. (d) Precision (how close estimates are to the best possible estimate) is good and constant over the full range. (e) Subjects quickly detect substantial changes in the hidden probability parameter. (f) The perceived probability sometimes changes dramatically from one observation to the next. (g) Subjects sometimes have second thoughts about a previous change perception, after observing further outcomes. (h) The frequency with which they perceive changes moves in the direction of the true frequency over sessions. (Explaining this finding requires 2 additional parametric assumptions.) The model treats the perception of the current probability as a by-product of the construction of a compact encoding of the experienced sequence in terms of its change points. It illustrates the why and the how of intermittent Bayesian belief updating and retrospective revision in simple perception. It suggests a reinterpretation of findings in the recent literature on the neurobiology of decision making. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

Post processing of optically recognized text via second order hidden Markov model

NASA Astrophysics Data System (ADS)

Poudel, Srijana

In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.

FIMP dark matter freeze-in gauge mediation and hidden sector

NASA Astrophysics Data System (ADS)

Tsao, Kuo-Hsing

2018-07-01

We explore the dark matter freeze-in mechanism within the gauge mediation framework, which involves a hidden feebly interacting massive particle (FIMP) coupling feebly with the messenger fields while the messengers are still in the thermal bath. The FIMP is the fermionic component of the pseudo-moduli in a generic metastable supersymmetry (SUSY) breaking model and resides in the hidden sector. The relic abundance and the mass of the FIMP are determined by the SUSY breaking scale and the feeble coupling. The gravitino, which is the canonical dark matter candidate in the gauge mediation framework, contributes to the dark matter relic abundance along with the freeze-in of the FIMP. The hidden sector thus becomes two-component with both the FIMP and gravitino lodging in the SUSY breaking hidden sector. We point out that the ratio between the FIMP and the gravitino is determined by how SUSY breaking is communicated to the messengers. In particular when the FIMP dominates the hidden sector, the gravitino becomes the minor contributor in the hidden sector. Meanwhile, the neutralino is assumed to be both the weakly interacting massive particle dark matter candidate in the freeze-out mechanism and the lightest observable SUSY particle. We further find out the neutralino has the sub-leading contribution to the current dark matter relic density in the parameter space of our freeze-in gauge mediation model. Our result links the SUSY breaking scale in the gauge mediation framework with the FIMP freeze-in production rate leading to a natural and predicting scenario for the studies of the dark matter in the hidden sector.

On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

PubMed

Yamazaki, Keisuke

2012-07-01

Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Weyl semimetals in optical lattices: moving and merging of Weyl points, and hidden symmetry at Weyl points

PubMed Central

Hou, Jing-Min; Chen, Wei

2016-01-01

We propose to realize Weyl semimetals in a cubic optical lattice. We find that there exist three distinct Weyl semimetal phases in the cubic optical lattice for different parameter ranges. One of them has two pairs of Weyl points and the other two have one pair of Weyl points in the Brillouin zone. For a slab geometry with (010) surfaces, the Fermi arcs connecting the projections of Weyl points with opposite topological charges on the surface Brillouin zone is presented. By adjusting the parameters, the Weyl points can move in the Brillouin zone. Interestingly, for two pairs of Weyl points, as one pair of them meet and annihilate, the originial two Fermi arcs coneect into one. As the remaining Weyl points annihilate further, the Fermi arc vanishes and a gap is opened. Furthermore, we find that there always exists a hidden symmetry at Weyl points, regardless of anywhere they located in the Brillouin zone. The hidden symmetry has an antiunitary operator with its square being −1. PMID:27644114

Semiparametric modeling: Correcting low-dimensional model error in parametric models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berry, Tyrus, E-mail: thb11@psu.edu; Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, 503 Walker Building, University Park, PA 16802-5013

2016-03-01

In this paper, a semiparametric modeling approach is introduced as a paradigm for addressing model error arising from unresolved physical phenomena. Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters. Practically, the proposed approach consists of the following steps. Given a physics-based model and a noisy data set of historical observations, a Bayesian filtering algorithm is used to extract a time-series of the parameter values. Subsequently, the diffusion forecast algorithm is applied to the retrieved time-series in order to construct the auxiliary model for the time evolving parameters. The semiparametric forecasting algorithm consistsmore » of integrating the existing physics-based model with an ensemble of parameters sampled from the probability density function of the diffusion forecast. To specify initial conditions for the diffusion forecast, a Bayesian semiparametric filtering method that extends the Kalman-based filtering framework is introduced. In difficult test examples, which introduce chaotically and stochastically evolving hidden parameters into the Lorenz-96 model, we show that our approach can effectively compensate for model error, with forecasting skill comparable to that of the perfect model.« less

Reconstructing the hidden states in time course data of stochastic models.

PubMed

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. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Hidden sector monopole, vector dark matter and dark radiation with Higgs portal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baek, Seungwon; Ko, P.; Park, Wan-Il, E-mail: sbaek1560@gmail.com, E-mail: pko@kias.re.kr, E-mail: wipark@kias.re.kr

2014-10-01

We show that the 't Hooft-Polyakov monopole model in the hidden sector with Higgs portal interaction makes a viable dark matter model, where monopole and massive vector dark matter (VDM) are stable due to topological conservation and the unbroken subgroup U(1 {sub X}. We show that, even though observed CMB data requires the dark gauge coupling to be quite small, a right amount of VDM thermal relic can be obtained via s-channel resonant annihilation for the mass of VDM close to or smaller than the half of SM higgs mass, thanks to Higgs portal interaction. Monopole relic density turns outmore » to be several orders of magnitude smaller than the observed dark matter relic density. Direct detection experiments, particularly, the projected XENON1T experiment, may probe the parameter space where the dark Higgs is lighter than ∼< 50 GeV. In addition, the dark photon associated with the unbroken U(1 {sub X} contributes to the radiation energy density at present, giving Δ N{sub eff}{sup ν} ∼ 0.1 as the extra relativistic neutrino species.« less

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

PubMed

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

Behavior of supercooled aqueous solutions stemming from hidden liquid–liquid transition in water

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biddle, John W.; Holten, Vincent; Anisimov, Mikhail A., E-mail: anisimov@umd.edu

2014-08-21

A popular hypothesis that explains the anomalies of supercooled water is the existence of a metastable liquid–liquid transition hidden below the line of homogeneous nucleation. If this transition exists and if it is terminated by a critical point, the addition of a solute should generate a line of liquid–liquid critical points emanating from the critical point of pure metastable water. We have analyzed thermodynamic consequences of this scenario. In particular, we consider the behavior of two systems, H{sub 2}O-NaCl and H{sub 2}O-glycerol. We find the behavior of the heat capacity in supercooled aqueous solutions of NaCl, as reported by Archermore » and Carter [J. Phys. Chem. B 104, 8563 (2000)], to be consistent with the presence of the metastable liquid–liquid transition. We elucidate the non-conserved nature of the order parameter (extent of “reaction” between two alternative structures of water) and the consequences of its coupling with conserved properties (density and concentration). We also show how the shape of the critical line in a solution controls the difference in concentration of the coexisting liquid phases.« less

Hidden SU ( N ) glueball dark matter

DOE PAGES

Soni, Amarjit; Zhang, Yue

2016-06-21

Here we investigate the possibility that the dark matter candidate is from a pure non-abelian gauge theory of the hidden sector, motivated in large part by its elegance and simplicity. The dark matter is the lightest bound state made of the confined gauge fields, the hidden glueball. We point out this simple setup is capable of providing rich and novel phenomena in the dark sector, especially in the parameter space of large N. They include self-interacting and warm dark matter scenarios, Bose-Einstein condensation leading to massive dark stars possibly millions of times heavier than our sun giving rise to gravitationalmore » lensing effects, and indirect detections through higher dimensional operators as well as interesting collider signatures.« less

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Das, Nandan K.; Kurmi, Indrajit; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-10-01

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

Quasifixed points from scalar sequestering and the little hierarchy problem in supersymmetry

NASA Astrophysics Data System (ADS)

Martin, Stephen P.

2018-02-01

In supersymmetric models with scalar sequestering, superconformal strong dynamics in the hidden sector suppresses the low-energy couplings of mass dimension 2, compared to the squares of the dimension-1 parameters. Taking into account restrictions on the anomalous dimensions in superconformal theories, I point out that the interplay between the hidden and visible sector renormalizations gives rise to quasifixed point running for the supersymmetric Standard Model squared mass parameters, rather than driving them to 0. The extent to which this dynamics can ameliorate the little hierarchy problem in supersymmetry is studied. Models of this type in which the gaugino masses do not unify are arguably more natural, and are certainly more likely to be accessible, eventually, to the Large Hadron Collider.

Emerging superconductivity hidden beneath charge-transfer insulators

PubMed Central

Krockenberger, Yoshiharu; Irie, Hiroshi; Matsumoto, Osamu; Yamagami, Keitaro; Mitsuhashi, Masaya; Tsukada, Akio; Naito, Michio; Yamamoto, Hideki

2013-01-01

In many of today's most interesting materials, strong interactions prevail upon the magnetic moments, the electrons, and the crystal lattice, forming strong links between these different aspects of the system. Particularly, in two-dimensional cuprates, where copper is either five- or six-fold coordinated, superconductivity is commonly induced by chemical doping which is deemed to be mandatory by destruction of long-range antiferromagnetic order of 3d9 Cu2+ moments. Here we show that superconductivity can be induced in Pr2CuO4, where copper is four-fold coordinated. We induced this novel quantum state of Pr2CuO4 by realizing pristine square-planar coordinated copper in the copper-oxygen planes, thus, resulting in critical superconducting temperatures even higher than by chemical doping. Our results demonstrate new degrees of freedom, i.e., coordination of copper, for the manipulation of magnetic and superconducting order parameters in quantum materials. PMID:23887134

Translational Symmetry Breaking and Gapping of Heavy-Quasiparticle Pocket in URu2Si2

PubMed Central

Yoshida, Rikiya; Tsubota, Koji; Ishiga, Toshihiko; Sunagawa, Masanori; Sonoyama, Jyunki; Aoki, Dai; Flouquet, Jacques; Wakita, Takanori; Muraoka, Yuji; Yokoya, Takayoshi

2013-01-01

URu2Si2 is a uranium compound that exhibits a so-called ‘hidden-order’ transition at ~17.5 K. However, the order parameter of this second-order transition as well as many of its microscopic properties remain unclarified despite considerable research. One of the key questions in this regard concerns the type of spontaneous symmetry breaking occurring at the transition; although rotational symmetry breaking has been detected, it is not clear whether another type of symmetry breaking also occurs. Another key question concerns the property of Fermi-surface gapping in the momentum space. Here we address these key questions by a momentum-dependent observation of electronic states at the transition employing ultrahigh-resolution three-dimensional angle-resolved photoemission spectroscopy. Our results provide compelling evidence of the spontaneous breaking of the lattice's translational symmetry and particle-hole asymmetric gapping of a heavy quasiparticle pocket at the transition. PMID:24084937

An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks

PubMed Central

Alanazi, Adwan; Elleithy, Khaled

2016-01-01

Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node– continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches. PMID:27618048

An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks.

PubMed

Alanazi, Adwan; Elleithy, Khaled

2016-09-07

Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node- continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches.

κ-deformed Dirac oscillator in an external magnetic field

NASA Astrophysics Data System (ADS)

Chargui, Y.; Dhahbi, A.; Cherif, B.

2018-04-01

We study the solutions of the (2 + 1)-dimensional κ-deformed Dirac oscillator in the presence of a constant transverse magnetic field. We demonstrate how the deformation parameter affects the energy eigenvalues of the system and the corresponding eigenfunctions. Our findings suggest that this system could be used to detect experimentally the effect of the deformation. We also show that the hidden supersymmetry of the non-deformed system reduces to a hidden pseudo-supersymmetry having the same algebraic structure as a result of the κ-deformation.

A neural network based computational model to predict the output power of different types of photovoltaic cells.

PubMed

Xiao, WenBo; Nazario, Gina; Wu, HuaMing; Zhang, HuaMing; Cheng, Feng

2017-01-01

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

Searching for confining hidden valleys at LHCb, ATLAS, and CMS

NASA Astrophysics Data System (ADS)

Pierce, Aaron; Shakya, Bibhushan; Tsai, Yuhsin; Zhao, Yue

2018-05-01

We explore strategies for probing hidden valley scenarios exhibiting confinement. Such scenarios lead to a moderate multiplicity of light hidden hadrons for generic showering and hadronization similar to QCD. Their decays are typically soft and displaced, making them challenging to probe with traditional LHC searches. We show that the low trigger requirements and excellent track and vertex reconstruction at LHCb provide a favorable environment to search for such signals. We propose novel search strategies in both muonic and hadronic channels. We also study existing ATLAS and CMS searches and compare them with our proposals at LHCb. We find that the reach at LHCb is generically better in the parameter space we consider here, even with optimistic background estimations for ATLAS and CMS searches. We discuss potential modifications at ATLAS and CMS that might make these experiments competitive with the LHCb reach. Our proposed searches can be applied to general hidden valley models as well as exotic Higgs boson decays, such as in twin Higgs models.

Nuclear magnetic resonance studies of pseudospin fluctuations in URu 2 Si 2

DOE PAGES

Shirer, K. R.; Haraldsen, J. T.; Dioguardi, A. P.; ...

2013-09-26

Here, we report 29Si nuclear magnetic resonance measurements in single crystals and aligned powders of URu 2Si 2 in the hidden order and paramagnetic phases. The spin-lattice relaxation data reveal evidence of pseudospin fluctuations of U moments in the paramagnetic phase. We find evidence for partial suppression of the density of states below 30 K and analyze the data in terms of a two-component spin-fermion model. We propose that this behavior is a realization of a pseudogap between the hidden-order transition T HO and 30 K. This behavior is then compared to other materials that demonstrate precursor fluctuations in amore » pseudogap regime above a ground state with long-range order.« less

Hidden order signatures in the antiferromagnetic phase of U (Ru1-xFex) 2Si2

NASA Astrophysics Data System (ADS)

Williams, T. J.; Aczel, A. A.; Stone, M. B.; Wilson, M. N.; Luke, G. M.

2017-03-01

We present a comprehensive set of elastic and inelastic neutron scattering measurements on a range of Fe-doped samples of U (Ru1-xFex) 2Si2 with 0.01 ≤x ≤0.15 . All of the samples measured exhibit long-range antiferromagnetic order, with the size of the magnetic moment quickly increasing to 0.51 μB at 2.5% doping and continuing to increase monotonically with doping, reaching 0.69 μB at 15% doping. Time-of-flight and inelastic triple-axis measurements show the existence of excitations at (1 0 0) and (1.4 0 0) in all samples, which are also observed in the parent compound. While the excitations in the 1% doping are quantitatively identical to the parent material, the gap and width of the excitations change rapidly at 2.5% Fe doping and above. The 1% doped sample shows evidence for a separation in temperature between the hidden order and antiferromagnetic transitions, suggesting that the antiferromagnetic state emerges at very low Fe dopings. The combined neutron scattering data suggest not only discontinuous changes in the magnetic moment and excitations between the hidden order and antiferromagnetic phases, but that these changes continue to evolve up to at least x =0.15 .

Hidden flows and waste processing--an analysis of illustrative futures.

PubMed

Schiller, F; Raffield, T; Angus, A; Herben, M; Young, P J; Longhurst, P J; Pollard, S J T

2010-12-14

An existing materials flow model is adapted (using Excel and AMBER model platforms) to account for waste and hidden material flows within a domestic environment. Supported by national waste data, the implications of legislative change, domestic resource depletion and waste technology advances are explored. The revised methodology offers additional functionality for economic parameters that influence waste generation and disposal. We explore this accounting system under hypothetical future waste and resource management scenarios, illustrating the utility of the model. A sensitivity analysis confirms that imports, domestic extraction and their associated hidden flows impact mostly on waste generation. The model offers enhanced utility for policy and decision makers with regard to economic mass balance and strategic waste flows, and may promote further discussion about waste technology choice in the context of reducing carbon budgets.

StegoWall: blind statistical detection of hidden data

NASA Astrophysics Data System (ADS)

Voloshynovskiy, Sviatoslav V.; Herrigel, Alexander; Rytsar, Yuri B.; Pun, Thierry

2002-04-01

Novel functional possibilities, provided by recent data hiding technologies, carry out the danger of uncontrolled (unauthorized) and unlimited information exchange that might be used by people with unfriendly interests. The multimedia industry as well as the research community recognize the urgent necessity for network security and copyright protection, or rather the lack of adequate law for digital multimedia protection. This paper advocates the need for detecting hidden data in digital and analog media as well as in electronic transmissions, and for attempting to identify the underlying hidden data. Solving this problem calls for the development of an architecture for blind stochastic hidden data detection in order to prevent unauthorized data exchange. The proposed architecture is called StegoWall; its key aspects are the solid investigation, the deep understanding, and the prediction of possible tendencies in the development of advanced data hiding technologies. The basic idea of our complex approach is to exploit all information about hidden data statistics to perform its detection based on a stochastic framework. The StegoWall system will be used for four main applications: robust watermarking, secret communications, integrity control and tamper proofing, and internet/network security.

Graviweak Unification, Invisible Universe and Dark Energy

NASA Astrophysics Data System (ADS)

Das, C. R.; Laperashvili, L. V.; Tureanu, A.

2013-07-01

We consider a graviweak unification model with the assumption of the existence of a hidden (invisible) sector of our Universe, parallel to the visible world. This Hidden World (HW) is assumed to be a Mirror World (MW) with broken mirror parity. We start with a diffeomorphism invariant theory of a gauge field valued in a Lie algebra g, which is broken spontaneously to the direct sum of the space-time Lorentz algebra and the Yang-Mills algebra: ˜ {g} = {{su}}(2) (grav)L ⊕ {{su}}(2)L — in the ordinary world, and ˜ {g}' = {{su}}(2){' (grav)}R ⊕ {{su}}(2)'R — in the hidden world. Using an extension of the Plebanski action for general relativity, we recover the actions for gravity, SU(2) Yang-Mills and Higgs fields in both (visible and invisible) sectors of the Universe, and also the total action. After symmetry breaking, all physical constants, including the Newton's constants, cosmological constants, Yang-Mills couplings, and other parameters, are determined by a single parameter g present in the initial action, and by the Higgs VEVs. The dark energy problem of this model predicts a too large supersymmetric breaking scale (MSUSY 1010GeV), which is not within the reach of the LHC experiments.

A Hidden Markov Model for Single Particle Tracks Quantifies Dynamic Interactions between LFA-1 and the Actin Cytoskeleton

PubMed Central

Das, Raibatak; Cairo, Christopher W.; Coombs, Daniel

2009-01-01

The extraction of hidden information from complex trajectories is a continuing problem in single-particle and single-molecule experiments. Particle trajectories are the result of multiple phenomena, and new methods for revealing changes in molecular processes are needed. We have developed a practical technique that is capable of identifying multiple states of diffusion within experimental trajectories. We model single particle tracks for a membrane-associated protein interacting with a homogeneously distributed binding partner and show that, with certain simplifying assumptions, particle trajectories can be regarded as the outcome of a two-state hidden Markov model. Using simulated trajectories, we demonstrate that this model can be used to identify the key biophysical parameters for such a system, namely the diffusion coefficients of the underlying states, and the rates of transition between them. We use a stochastic optimization scheme to compute maximum likelihood estimates of these parameters. We have applied this analysis to single-particle trajectories of the integrin receptor lymphocyte function-associated antigen-1 (LFA-1) on live T cells. Our analysis reveals that the diffusion of LFA-1 is indeed approximately two-state, and is characterized by large changes in cytoskeletal interactions upon cellular activation. PMID:19893741

Evidence for Spin Singlet Pairing with Strong Uniaxial Anisotropy in URu2Si2 Using Nuclear Magnetic Resonance

NASA Astrophysics Data System (ADS)

Hattori, T.; Sakai, H.; Tokunaga, Y.; Kambe, S.; Matsuda, T. D.; Haga, Y.

2018-01-01

In order to identify the spin contribution to superconducting pairing compatible with the so-called "hidden order", Si 29 nuclear magnetic resonance measurements have been performed using a high-quality single crystal of URu2 Si2 . A clear reduction of the Si 29 Knight shift in the superconducting state has been observed under a magnetic field applied along the crystalline c axis, corresponding to the magnetic easy axis. These results provide direct evidence for the formation of spin-singlet Cooper pairs. Consequently, results indicating a very tiny change of the in-plane Knight shift reported previously demonstrate extreme uniaxial anisotropy for the spin susceptibility in the hidden order state.

Adaptive super twisting vibration control of a flexible spacecraft with state rate estimation

NASA Astrophysics Data System (ADS)

Malekzadeh, Maryam; Karimpour, Hossein

2018-05-01

The robust attitude and vibration control of a flexible spacecraft trying to perform accurate maneuvers in spite of various sources of uncertainty is addressed here. Difficulties for achieving precise and stable pointing arise from noisy onboard sensors, parameters indeterminacy, outer disturbances as well as un-modeled or hidden dynamics interactions. Based on high-order sliding-mode methods, the non-minimum phase nature of the problem is dealt with through output redefinition. An adaptive super-twisting algorithm (ASTA) is incorporated with its observer counterpart on the system under consideration to get reliable attitude and vibration control in the presence of sensor noise and momentum coupling. The closed-loop efficiency is verified through simulations under various indeterminate situations and got compared to other methods.

Exotic quantum order in low-dimensional systems

NASA Astrophysics Data System (ADS)

Girvin, S. M.

1998-08-01

Strongly correlated quantum systems in low dimensions often exhibit novel quantum ordering. This ordering is sometimes hidden and can be revealed only by examining new "dual" types of correlations. Such ordering leads to novel collection modes and fractional quantum numbers. Examples will be presented from quantum spin chains and the quantum Hall effect.

Soft context clustering for F0 modeling in HMM-based speech synthesis

NASA Astrophysics Data System (ADS)

Khorram, Soheil; Sameti, Hossein; King, Simon

2015-12-01

This paper proposes the use of a new binary decision tree, which we call a soft decision tree, to improve generalization performance compared to the conventional `hard' decision tree method that is used to cluster context-dependent model parameters in statistical parametric speech synthesis. We apply the method to improve the modeling of fundamental frequency, which is an important factor in synthesizing natural-sounding high-quality speech. Conventionally, hard decision tree-clustered hidden Markov models (HMMs) are used, in which each model parameter is assigned to a single leaf node. However, this `divide-and-conquer' approach leads to data sparsity, with the consequence that it suffers from poor generalization, meaning that it is unable to accurately predict parameters for models of unseen contexts: the hard decision tree is a weak function approximator. To alleviate this, we propose the soft decision tree, which is a binary decision tree with soft decisions at the internal nodes. In this soft clustering method, internal nodes select both their children with certain membership degrees; therefore, each node can be viewed as a fuzzy set with a context-dependent membership function. The soft decision tree improves model generalization and provides a superior function approximator because it is able to assign each context to several overlapped leaves. In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples. Employing such a soft decision tree architecture with maximum entropy distributions, a novel speech synthesis system is trained using maximum likelihood (ML) parameter re-estimation and synthesis is achieved via maximum output probability parameter generation. In addition, a soft decision tree construction algorithm optimizing a log-likelihood measure is developed. Both subjective and objective evaluations were conducted and indicate a considerable improvement over the conventional method.

29 CFR 1205.3 - General Order No. 1.

Code of Federal Regulations, 2010 CFR

2010-07-01

... order prescribes that such notices are to be standard as to contents, dimensions of sheet, and size of... may be necessary to make them accessible to all employees. Such notices must not be hidden by other...

Utterance independent bimodal emotion recognition in spontaneous communication

NASA Astrophysics Data System (ADS)

Tao, Jianhua; Pan, Shifeng; Yang, Minghao; Li, Ya; Mu, Kaihui; Che, Jianfeng

2011-12-01

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MHMM). Then, the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model (GMM) mapping. Compared with traditional bimodal emotion recognition methods (e.g., SVM, CART, Boosting), the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment.

Hidden order signatures in the antiferromagnetic phase of U ( Ru 1 - x Fe x ) 2 Si 2

DOE PAGES

Williams, Travis J.; Aczel, Adam A.; Stone, Matthew B.; ...

2017-03-31

We present a comprehensive set of elastic and inelastic neutron scattering measurements on a range of Fe-doped samples of U(Ru 1–xFe x) 2Si 2 with 0.01 ≤ x ≤ 0.15. All of the samples measured exhibit long-range antiferromagnetic order, with the size of the magnetic moment quickly increasing to 0.51μB at 2.5% doping and continuing to increase monotonically with doping, reaching 0.69μB at 15% doping. Time-of-flight and inelastic triple-axis measurements show the existence of excitations at (1 0 0) and (1.4 0 0) in all samples, which are also observed in the parent compound. While the excitations in the 1%more » doping are quantitatively identical to the parent material, the gap and width of the excitations change rapidly at 2.5% Fe doping and above. The 1% doped sample shows evidence for a separation in temperature between the hidden order and antiferromagnetic transitions, suggesting that the antiferromagnetic state emerges at very low Fe dopings. Finally, the combined neutron scattering data suggest not only discontinuous changes in the magnetic moment and excitations between the hidden order and antiferromagnetic phases, but that these changes continue to evolve up to at least x = 0.15.« less

Exploring Partial Order of European Countries

ERIC Educational Resources Information Center

Annoni, Paola; Bruggemann, Rainer

2009-01-01

Partial Order Theory has been recently more and more employed in applied science to overcome the intrinsic disadvantage hidden in aggregation, if a multiple attribute system is available. Despite its numerous positive features, there are many practical cases where the interpretation of the partial order can be rather troublesome. In these cases…

Revealing Hidden Structural Order Controlling Both Fast and Slow Glassy Dynamics in Supercooled Liquids

NASA Astrophysics Data System (ADS)

Tong, Hua; Tanaka, Hajime

2018-01-01

The dynamics of a supercooled liquid near the glass transition is characterized by two-step relaxation, fast β and slow α relaxations. Because of the apparently disordered nature of glassy structures, there have been long debates over whether the origin of drastic slowing-down of the α relaxation accompanied by heterogeneous dynamics is thermodynamic or dynamic. Furthermore, it has been elusive whether there is any deep connection between fast β and slow α modes. To settle these issues, here we introduce a set of new structural order parameters characterizing sterically favored structures with high local packing capability, and then access structure-dynamics correlation by a novel nonlocal approach. We find that the particle mobility is under control of the static order parameter field. The fast β process is controlled by the instantaneous order parameter field locally, resulting in short-time particle-scale dynamics. Then the mobility field progressively develops with time t , following the initial order parameter field from disorder to more ordered regions. As is well known, the heterogeneity in the mobility field (dynamic heterogeneity) is maximized with a characteristic length ξ4, when t reaches the relaxation time τα. We discover that this mobility pattern can be predicted solely by a spatial coarse graining of the initial order parameter field at t =0 over a length ξ without any dynamical information. Furthermore, we find a relation ξ ˜ξ4, indicating that the static length ξ grows coherently with the dynamic one ξ4 upon cooling. This further suggests an intrinsic link between τα and ξ : the growth of the static length ξ is the origin of dynamical slowing-down. These we confirm for the first time in binary glass formers both in two and three spatial dimensions. Thus, a static structure has two intrinsic characteristic lengths, particle size and ξ , which control dynamics in local and nonlocal manners, resulting in the emergence of the two key relaxation modes, fast β and slow α processes, respectively. Because the two processes share a common structural origin, we can even predict a dynamic propensity pattern at long timescale from the fast β pattern. The presence of such intrinsic structure-dynamics correlation strongly indicates a thermodynamic nature of glass transition.

Cascade Error Projection: A Learning Algorithm for Hardware Implementation

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher

1996-01-01

In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters. Furthermore, CEP learning algorithm is operated only on one layer, whereas the other set of weights can be calculated deterministically. In association with the dynamical stepsize change concept to convert the weight update from infinite space into a finite space, the relation between the current stepsize and the previous energy level is also given and the estimation procedure for optimal stepsize is used for validation of our proposed technique. The weight values of zero are used for starting the learning for every layer, and a single hidden unit is applied instead of using a pool of candidate hidden units similar to cascade correlation scheme. Therefore, simplicity in hardware implementation is also obtained. Furthermore, this analysis allows us to select from other methods (such as the conjugate gradient descent or the Newton's second order) one of which will be a good candidate for the learning technique. The choice of learning technique depends on the constraints of the problem (e.g., speed, performance, and hardware implementation); one technique may be more suitable than others. Moreover, for a discrete weight space, the theoretical analysis presents the capability of learning with limited weight quantization. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.

Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model

NASA Astrophysics Data System (ADS)

Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.

2009-04-01

The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.

First-principles Theory of Magnetic Multipoles in Condensed Matter Systems

NASA Astrophysics Data System (ADS)

Suzuki, Michi-To; Ikeda, Hiroaki; Oppeneer, Peter M.

2018-04-01

The multipole concept, which characterizes the spacial distribution of scalar and vector objects by their angular dependence, has already become widely used in various areas of physics. In recent years it has become employed to systematically classify the anisotropic distribution of electrons and magnetization around atoms in solid state materials. This has been fuelled by the discovery of several physical phenomena that exhibit unusual higher rank multipole moments, beyond that of the conventional degrees of freedom as charge and magnetic dipole moment. Moreover, the higher rank electric/magnetic multipole moments have been suggested as promising order parameters in exotic hidden order phases. While the experimental investigations of such anomalous phases have provided encouraging observations of multipolar order, theoretical approaches have developed at a slower pace. In particular, a materials' specific theory has been missing. The multipole concept has furthermore been recognized as the key quantity which characterizes the resultant configuration of magnetic moments in a cluster of atomic moments. This cluster multipole moment has then been introduced as macroscopic order parameter for a noncollinear antiferromagnetic structure in crystals that can explain unusual physical phenomena whose appearance is determined by the magnetic point group symmetry. It is the purpose of this review to discuss the recent developments in the first-principles theory investigating multipolar degrees of freedom in condensed matter systems. These recent developments exemplify that ab initio electronic structure calculations can unveil detailed insight in the mechanism of physical phenomena caused by the unconventional, multipole degree of freedom.

A meta-cognitive learning algorithm for a Fully Complex-valued Relaxation Network.

PubMed

Savitha, R; Suresh, S; Sundararajan, N

2012-08-01

This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-valued neural network called "Meta-cognitive Fully Complex-valued Relaxation Network (McFCRN)". McFCRN has two components: a cognitive component and a meta-cognitive component. A Fully Complex-valued Relaxation Network (FCRN) with a fully complex-valued Gaussian like activation function (sech) in the hidden layer and an exponential activation function in the output layer forms the cognitive component. The meta-cognitive component contains a self-regulatory learning mechanism which controls the learning ability of FCRN by deciding what-to-learn, when-to-learn and how-to-learn from a sequence of training data. The input parameters of cognitive components are chosen randomly and the output parameters are estimated by minimizing a logarithmic error function. The problem of explicit minimization of magnitude and phase errors in the logarithmic error function is converted to system of linear equations and output parameters of FCRN are computed analytically. McFCRN starts with zero hidden neuron and builds the number of neurons required to approximate the target function. The meta-cognitive component selects the best learning strategy for FCRN to acquire the knowledge from training data and also adapts the learning strategies to implement best human learning components. Performance studies on a function approximation and real-valued classification problems show that proposed McFCRN performs better than the existing results reported in the literature. Copyright © 2012 Elsevier Ltd. All rights reserved.

Inelastic x-ray scattering measurements of phonon dynamics in URu 2Si 2

DOE PAGES

Gardner, D. R.; Bonnoit, C. J.; Chisnell, R.; ...

2016-02-11

In this paper, we study high-resolution inelastic x-ray scattering measurements of the acoustic phonons of URu 2Si 2. At all temperatures, the longitudinal acoustic phonon linewidths are anomalously broad at small wave vectors revealing a previously unknown anharmonicity. The phonon modes do not change significantly upon cooling into the hidden order phase. In addition, our data suggest that the increase in thermal conductivity in the hidden order phase cannot be driven by a change in phonon dispersions or lifetimes. Hence, the phonon contribution to the thermal conductivity is likely much less significant compared to that of the magnetic excitations inmore » the low temperature phase.« less

78 FR 15777 - Self-Regulatory Organizations; EDGA Exchange, Inc.; Notice of Filing and Immediate Effectiveness...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-12

... equitable because these rates represent a blended or hybrid rate between the rates the Exchange assesses for... traditional Non-Displayed Order, which is hidden on the order book. The Exchange notes that its proposal...

Gravitational waves from dark first order phase transitions and dark photons

NASA Astrophysics Data System (ADS)

Addazi, Andrea; Marcianò, Antonino

2018-01-01

Cold Dark Matter particles may interact with ordinary particles through a dark photon, which acquires a mass thanks to a spontaneous symmetry breaking mechanism. We discuss a dark photon model in which the scalar singlet associated to the spontaneous symmetry breaking has an effective potential that induces a first order phase transition in the early Universe. Such a scenario provides a rich phenomenology for electron-positron colliders and gravitational waves interferometers, and may be tested in several different channels. The hidden first order phase transition implies the emission of gravitational waves signals, which may constrain the dark photon’s space of parameters. Compared limits from electron-positron colliders, astrophysics, cosmology and future gravitational waves interferometers such as eLISA, U-DECIGO and BBO are discussed. This highly motivates a cross-checking strategy of data arising from experiments dedicated to gravitational waves, meson factories, the International Linear Collider (ILC), the Circular Electron Positron Collider (CEPC) and other underground direct detection experiments of cold dark matter candidates. Supported by the Shanghai Municipality (KBH1512299) and Fudan University (JJH1512105)

Hidden Broad-line Regions in Seyfert 2 Galaxies: From the Spectropolarimetric Perspective

NASA Astrophysics Data System (ADS)

Du, Pu; Wang, Jian-Min; Zhang, Zhi-Xiang

2017-05-01

The hidden broad-line regions (BLRs) in Seyfert 2 galaxies, which display broad emission lines (BELs) in their polarized spectra, are a key piece of evidence in support of the unified model for active galactic nuclei (AGNs). However, the detailed kinematics and geometry of hidden BLRs are still not fully understood. The virial factor obtained from reverberation mapping of type 1 AGNs may be a useful diagnostic of the nature of hidden BLRs in type 2 objects. In order to understand the hidden BLRs, we compile six type 2 objects from the literature with polarized BELs and dynamical measurements of black hole masses. All of them contain pseudobulges. We estimate their virial factors, and find the average value is 0.60 and the standard deviation is 0.69, which agree well with the value of type 1 AGNs with pseudobulges. This study demonstrates that (1) the geometry and kinematics of BLR are similar in type 1 and type 2 AGNs of the same bulge type (pseudobulges), and (2) the small values of virial factors in Seyfert 2 galaxies suggest that, similar to type 1 AGNs, BLRs tend to be very thick disks in type 2 objects.

Caries diagnosis using laser fluorescence

NASA Astrophysics Data System (ADS)

Zanin, Fatima A. A.; Pinheiro, Antonio L. B.; Souza-Campos, Dilma H.; Brugnera, Aldo, Jr.; Pecora, Jesus D.

2000-03-01

Caries prevention is a goal to be achieved by dentist in order to promote health. There are several methods used to detect dental caries each one presenting advantages and disadvantages, especially regarding hidden occlusal caries. The improvement of laser technology has permitted the use of laser fluorescence for early diagnosis of hidden occlusal caries. The aim of this study was to assess the efficacy of the use of 655 nm laser light on the detection of hidden occlusal caries. Forty molar teeth from patients of both sexes which ages ranging from 10 - 18 years old were used on this study. Following manufacture's instructions regarding the use of the equipment, the teeth had their occlusal surface examined with the DIAGNOdent. Twenty six of 40 teeth had hidden occlusal caries detected by the DIAGNOdent. However only 17 of these 26 teeth showed radiographic signs of caries the other 9 teeth showed no radiological signs of the lesion. Radiographic examination was able to identify 34,61% of false negative cases. This means that many caries would be left untreated due to the lack of diagnosis using both visual and radiographic examination. The use of the DIAGNOdent was effective in successfully detecting hidden occlusal caries.

Hidden Broad-line Regions in Seyfert 2 Galaxies: From the Spectropolarimetric Perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, Pu; Wang, Jian-Min; Zhang, Zhi-Xiang, E-mail: dupu@ihep.ac.cn

2017-05-01

The hidden broad-line regions (BLRs) in Seyfert 2 galaxies, which display broad emission lines (BELs) in their polarized spectra, are a key piece of evidence in support of the unified model for active galactic nuclei (AGNs). However, the detailed kinematics and geometry of hidden BLRs are still not fully understood. The virial factor obtained from reverberation mapping of type 1 AGNs may be a useful diagnostic of the nature of hidden BLRs in type 2 objects. In order to understand the hidden BLRs, we compile six type 2 objects from the literature with polarized BELs and dynamical measurements of blackmore » hole masses. All of them contain pseudobulges. We estimate their virial factors, and find the average value is 0.60 and the standard deviation is 0.69, which agree well with the value of type 1 AGNs with pseudobulges. This study demonstrates that (1) the geometry and kinematics of BLR are similar in type 1 and type 2 AGNs of the same bulge type (pseudobulges), and (2) the small values of virial factors in Seyfert 2 galaxies suggest that, similar to type 1 AGNs, BLRs tend to be very thick disks in type 2 objects.« less

Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

PubMed

Jiang, Yizhang; Chung, Fu-Lai; Ishibuchi, Hisao; Deng, Zhaohong; Wang, Shitong

2015-03-01

The classical fuzzy system modeling methods implicitly assume data generated from a single task, which is essentially not in accordance with many practical scenarios where data can be acquired from the perspective of multiple tasks. Although one can build an individual fuzzy system model for each task, the result indeed tells us that the individual modeling approach will get poor generalization ability due to ignoring the intertask hidden correlation. In order to circumvent this shortcoming, we consider a general framework for preserving the independent information among different tasks and mining hidden correlation information among all tasks in multitask fuzzy modeling. In this framework, a low-dimensional subspace (structure) is assumed to be shared among all tasks and hence be the hidden correlation information among all tasks. Under this framework, a multitask Takagi-Sugeno-Kang (TSK) fuzzy system model called MTCS-TSK-FS (TSK-FS for multiple tasks with common hidden structure), based on the classical L2-norm TSK fuzzy system, is proposed in this paper. The proposed model can not only take advantage of independent sample information from the original space for each task, but also effectively use the intertask common hidden structure among multiple tasks to enhance the generalization performance of the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multitask fuzzy system model in multitask regression learning scenarios.

Informatic analysis for hidden pulse attack exploiting spectral characteristics of optics in plug-and-play quantum key distribution system

NASA Astrophysics Data System (ADS)

Ko, Heasin; Lim, Kyongchun; Oh, Junsang; Rhee, June-Koo Kevin

2016-10-01

Quantum channel loopholes due to imperfect implementations of practical devices expose quantum key distribution (QKD) systems to potential eavesdropping attacks. Even though QKD systems are implemented with optical devices that are highly selective on spectral characteristics, information theory-based analysis about a pertinent attack strategy built with a reasonable framework exploiting it has never been clarified. This paper proposes a new type of trojan horse attack called hidden pulse attack that can be applied in a plug-and-play QKD system, using general and optimal attack strategies that can extract quantum information from phase-disturbed quantum states of eavesdropper's hidden pulses. It exploits spectral characteristics of a photodiode used in a plug-and-play QKD system in order to probe modulation states of photon qubits. We analyze the security performance of the decoy-state BB84 QKD system under the optimal hidden pulse attack model that shows enormous performance degradation in terms of both secret key rate and transmission distance.

The hidden ion population - Revisited. [in outer plasmasphere

NASA Technical Reports Server (NTRS)

Olsen, R. C.; Chappell, C. R.; Gallagher, D. L.; Green, J. L.; Gurnett, D. A.

1985-01-01

In an investigation conducted by Olsen (1982) on the basis of particle data taken with an electrostatic analyzer, it was found that a cold plasma population with a density between 10 and 100 per cu cm appeared suddenly when the satellite was eclipsed, but was hidden in sunlight. The present paper has the objective to show further measurements of ordinarily 'hidden' ion populations, in order to resolve some of the questions raised in connection with the Scatha satellite data reported by Olsen. It is found that the retarding ion mass spectrometer (RIMS) detector is capable of measuring the core of the plasma distribution in sunlight and eclipse, though the task is more easily done in eclipse. There are, however, limitations concerning the ability of the detector to measure all the plasma, all the time. It is, therefore, pointed out that continuous effective measurements of the 'hidden' ion population of the magnetosphere still awaits satellites with effective means of potential control.

Effectiveness of AODV Protocol under Hidden Node Environment

NASA Astrophysics Data System (ADS)

Garg, Ruchi; Sharma, Himanshu; Kumar, Sumit

IEEE 802.11 is a standard for mobile ad hoc networks (MANET), implemented with various different protocols. Ad Hoc on Demand Distance Vector Routing (AODV) is one of the several protocols of IEEE 802.11, intended to provide various Quality of Service (QOS) parameters under acceptable range. To avoid the collision and interference the MAC protocol has only two solutions, one, to sense the physical carrier and second, to use the RTS/CTS handshake mechanism. But with the help of these methods AODV is not free from the problem of hidden nodes like other several protocols. Under the hidden node environment, performance of AODV depends upon various factors. The position of receiver and sender among the other nodes is very crucial and it affects the performance. Under the various situations the AODV is simulated with the help of NS2 and the outcomes are discussed.

Dynamic extreme learning machine and its approximation capability.

PubMed

Zhang, Rui; Lan, Yuan; Huang, Guang-Bin; Xu, Zong-Ben; Soh, Yeng Chai

2013-12-01

Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron alike and perform well in both regression and classification applications. The problem of determining the suitable network architectures is recognized to be crucial in the successful application of ELMs. This paper first proposes a dynamic ELM (D-ELM) where the hidden nodes can be recruited or deleted dynamically according to their significance to network performance, so that not only the parameters can be adjusted but also the architecture can be self-adapted simultaneously. Then, this paper proves in theory that such D-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results obtained over various test problems demonstrate and verify that the proposed D-ELM does a good job reducing the network size while preserving good generalization performance.

Hidden correlations entailed by q-non additivity render the q-monoatomic gas highly non trivial

NASA Astrophysics Data System (ADS)

Plastino, A.; Rocca, M. C.

2018-01-01

It ts known that Tsallis' q-non-additivity entails hidden correlations. It has also been shown that even for a monoatomic gas, both the q-partition function Z and the mean energy 〈 U 〉 diverge and, in particular, exhibit poles for certain values of the Tsallis non additivity parameter q. This happens because Z and 〈 U 〉 both depend on a Γ-function. This Γ, in turn, depends upon the spatial dimension ν. We encounter three different regimes according to the argument A of the Γ-function. (1) A > 0, (2) A < 0 and Γ > 0 outside the poles. (3) A displays poles and the physics is obtained via dimensional regularization. In cases (2) and (3) one discovers gravitational effects and quartets of particles. Moreover, bound states and gravitational effects emerge as a consequence of the hidden q-correlations.

Hidden crossing theory of charge exchange in H+ + He+(1 s) collisions in vicinity of maximum of cross section

NASA Astrophysics Data System (ADS)

Grozdanov, Tasko P.; Solov'ev, Evgeni A.

2018-04-01

Within the framework of dynamical adiabatic approach the hidden crossing theory of inelastic transitions is applied to charge exchange in H+ + He+(1 s) collisions in the wide range of center of mass collision energies E cm = (1.6 -70) keV. The good agreement with experiment and molecular close coupling calculations is obtained. At low energies our 4-state results are closest to the experiment and correctly reproduce the shoulder in energy dependence of the cross section around E cm = 6 keV. The 2-state results correctly predict the position of the maximum of the cross section at E cm ≈ 40 keV, whereas 4-state results fail to correctly describe the region around the maximum. The reason for this is the fact that adiabatic approximation for a given two-state hidden crossing is applicable for values of the Schtueckelberg parameter >1. But with increase of principal quantum number N the Schtueckelberg parameter decreases as N -3. That is why the 4-state approach involving higher excited states fails at smaller collision energies E cm ≈ 15 keV, while the 2-state approximation which involves low lying states can be extended to higher collision energies.

High Magnetic Field Heat Capacity of URu_2Si_2

NASA Astrophysics Data System (ADS)

Jaime, Marcelo; Kim, Kee Hoon; McCall, Scott; Mydosh, John A.

2002-03-01

URu_2Si2 is a heavy-fermion in which superconductivity (T_C= 1.3 K) and antiferromagnetism (TN = 17.5 K) coexist. Transport, thermal and magnetic data suggest the opening of a gap in the magnetic excitation spectrum at TN possibly due to the formation of an itinerant spin density wave. Neutron-scattering experiment indicate an ordered moment (0.03 μ_B/U) that is too small to explain the entropy loss and the size of the gap which develops at T_N. The presence of a hidden primary order parameter has been suggested to explain these discrepancies. (1) Permanent magnetic fields up to 45 T have been used to completely suppress the magnetic transition, which was observed with measurements of the magneto-caloric effect during adiabatic magnetization of the sample, and specific heat vs. temperature at constant magnetic field. These experiments provide clues to understand the ground state of the system. (1) N. Sha et al., Phys. Rev. B61, 564 (2000).

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

PubMed

Omori, Toshiaki; Kuwatani, Tatsu; Okamoto, Atsushi; Hukushima, Koji

2016-09-01

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagation method and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.

Colossal thermomagnetic response in chiral d-wave superconductor URu2Si2

NASA Astrophysics Data System (ADS)

Matsuda, Yuji

The heavy-fermion compound URu2Si2 exhibits unconventional superconductivity at Tc = 1.45 K deep inside the so-called hidden order phase. An intriguing aspect is that this system has been suggested to be a candidate of a chiral d-wave superconductor, and possible Weyl-type topological superconducting states have been discussed recently. Here we report on the observation of a highly unusual Nernst signal due to the superconducting fluctuations above Tc. The Nernst coefficient is anomalously enhanced (by a factor of ~106) as compared with the theoretically expected value of the Gaussian fluctuations. This colossal Nernst effect intimately reflects the highly unusual superconducting state of URu2Si2. The results invoke possible chiral or Berry-phase fluctuations associated with the broken time-reversal symmetry of the superconducting order parameter. In collaboration with T. Yamashita, Y. Shimoyama, H. Sumiyoshi (Kyoto), S. Fujimoto (Osaka), T. Shibauchi (Tokyo), Y. Haga (JAEA), T. D. Matsuda (TMU) , Y. Onuki (Ryukyus), A. Levchenko (Wisconsin-Madison).

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Thermostatted molecular dynamics: How to avoid the Toda demon hidden in Nose-Hoover dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holian, B.L.; Voter, A.F.; Ravelo, R.

The Nose-Hoover thermostat, which is often used in the hope of modifying molecular dynamics trajectories in order to achieve canonical-ensemble averages, has hidden in it a Toda ``demon,`` which can give rise to unwanted, noncanonical undulations in the instantaneous kinetic temperature. We show how these long-lived oscillations arise from insufficient coupling of the thermostat to the atoms, and give straightforward, practical procedures for avoiding this weak-coupling pathology in isothermal molecular dynamics simulations.

Development of a neural network technique for KSTAR Thomson scattering diagnostics.

PubMed

Lee, Seung Hun; Lee, J H; Yamada, I; Park, Jae Sun

2016-11-01

Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ 2 method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ 2 method. The best results were obtained for 10 3 training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ 2 method and performs the calculation twenty times faster.

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Cosmic variance in inflation with two light scalars

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonga, Béatrice; Brahma, Suddhasattwa; Deutsch, Anne-Sylvie

We examine the squeezed limit of the bispectrum when a light scalar with arbitrary non-derivative self-interactions is coupled to the inflaton. We find that when the hidden sector scalar is sufficiently light ( m ∼< 0.1 H ), the coupling between long and short wavelength modes from the series of higher order correlation functions (from arbitrary order contact diagrams) causes the statistics of the fluctuations to vary in sub-volumes. This means that observations of primordial non-Gaussianity cannot be used to uniquely reconstruct the potential of the hidden field. However, the local bispectrum induced by mode-coupling from these diagrams always hasmore » the same squeezed limit, so the field's locally determined mass is not affected by this cosmic variance.« less

Estimating parameters of hidden Markov models based on marked individuals: use of robust design data

USGS Publications Warehouse

Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun

2012-01-01

Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).

Distinct magnetic spectra in the hidden order and antiferromagnetic phases in URu 2 - x Fe x Si 2

DOE PAGES

Butch, Nicholas P.; Ran, Sheng; Jeon, Inho; ...

2016-11-07

We use neutron scattering to compare the magnetic excitations in the hidden order (HO) and antiferromagnetic (AFM) phases in URu 2-xFe xSi 2 as a function of Fe concentration. The magnetic excitation spectra change significantly between x = 0.05 and x = 0.10, following the enhancement of the AFM ordered moment, in good analogy to the behavior of the parent compound under applied pressure. Prominent lattice-commensurate low-energy excitations characteristic of the HO phase vanish in the AFM phase. The magnetic scattering is dominated by strong excitations along the Brillouin zone edges, underscoring the important role of electron hybridization to bothmore » HO and AFM phases, and the similarity of the underlying electronic structure. The stability of the AFM phase is correlated with enhanced local-itinerant electron hybridization.« less

Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

PubMed

Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

2014-10-01

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

Generalised filtering and stochastic DCM for fMRI.

PubMed

Li, Baojuan; Daunizeau, Jean; Stephan, Klaas E; Penny, Will; Hu, Dewen; Friston, Karl

2011-09-15

This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and contrast deterministic and stochastic DCMs, which do and do not ignore random fluctuations or noise on hidden states. We then compare stochastic DCMs, which do and do not ignore conditional dependence between hidden states and model parameters (generalised filtering and dynamic expectation maximisation, respectively). We first characterise state-noise by comparing the log evidence of models with different a priori assumptions about its amplitude, form and smoothness. Face validity of the inversion scheme is then established using data simulated with and without state-noise to ensure that DCM can identify the parameters and model that generated the data. Finally, we address construct validity using real data from an fMRI study of internet addiction. Our analyses suggest the following. (i) The inversion of stochastic causal models is feasible, given typical fMRI data. (ii) State-noise has nontrivial amplitude and smoothness. (iii) Stochastic DCM has face validity, in the sense that Bayesian model comparison can distinguish between data that have been generated with high and low levels of physiological noise and model inversion provides veridical estimates of effective connectivity. (iv) Relaxing conditional independence assumptions can have greater construct validity, in terms of revealing group differences not disclosed by variational schemes. Finally, we note that the ability to model endogenous or random fluctuations on hidden neuronal (and physiological) states provides a new and possibly more plausible perspective on how regionally specific signals in fMRI are generated. Copyright © 2011. Published by Elsevier Inc.

The Robust Beauty of Ordinary Information

ERIC Educational Resources Information Center

Katsikopoulos, Konstantinos V.; Schooler, Lael J.; Hertwig, Ralph

2010-01-01

Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues…

Intuitive optics: what great apes infer from mirrors and shadows.

PubMed

Völter, Christoph J; Call, Josep

2018-05-02

There is ongoing debate about the extent to which nonhuman animals, like humans, can go beyond first-order perceptual information to abstract structural information from their environment. To provide more empirical evidence regarding this question, we examined what type of information great apes (chimpanzees, bonobos, and orangutans) gain from optical effects such as shadows and mirror images. In an initial experiment, we investigated whether apes would use mirror images and shadows to locate hidden food. We found that all examined ape species used these cues to find the food. Follow-up experiments showed that apes neither confused these optical effects with the food rewards nor did they merely associate cues with food. First, naïve chimpanzees used the shadow of the hidden food to locate it but they did not learn within the same number of trials to use a perceptually similar rubber patch as indicator of the hidden food reward. Second, apes made use of the mirror images to estimate the distance of the hidden food from their own body. Depending on the distance, apes either pointed into the direction of the food or tried to access the hidden food directly. Third, apes showed some sensitivity to the geometrical relation between mirror orientation and mirrored objects when searching hidden food. Fourth, apes tended to interpret mirror images and pictures of these mirror images differently depending on their prior knowledge. Together, these findings suggest that apes are sensitive to the optical relation between mirror images and shadows and their physical referents.

Bounds on the number of hidden neurons in three-layer binary neural networks.

PubMed

Zhang, Zhaozhi; Ma, Xiaomin; Yang, Yixian

2003-09-01

This paper investigates an important problem concerning the complexity of three-layer binary neural networks (BNNs) with one hidden layer. The neuron in the studied BNNs employs a hard limiter activation function with only integer weights and an integer threshold. The studies are focused on implementations of arbitrary Boolean functions which map from [0, 1]n into [0, 1]. A deterministic algorithm called set covering algorithm (SCA) is proposed for the construction of a three-layer BNN to implement an arbitrary Boolean function. The SCA is based on a unit sphere covering (USC) of the Hamming space (HS) which is chosen in advance. It is proved that for the implementation of an arbitrary Boolean function of n-variables (n > or = 3) by using SCA, [3L/2] hidden neurons are necessary and sufficient, where L is the number of unit spheres contained in the chosen USC of the n-dimensional HS. It is shown that by using SCA, the number of hidden neurons required is much less than that by using a two-parallel hyperplane method. In order to indicate the potential ability of three-layer BNNs, a lower bound on the required number of hidden neurons which is derived by using the method of estimating the Vapnik-Chervonenkis (VC) dimension is also given.

Hidden landscapes in thin film topological insulators: between order and disorder, 2D and 3D, normal and topological phases

NASA Astrophysics Data System (ADS)

Oh, Seongshik

Topological insulator (TI) is one of the rare systems in the history of condensed matter physics that is initiated by theories and followed by experiments. Although this theory-driven advance helped move the field quite fast despite its short history, apparently there exist significant gaps between theories and experiments. Many of these discrepancies originate from the very fact that the worlds readily accessible to theories are often far from the real worlds that are available in experiments. For example, the very paradigm of topological protection of the surface states on Z2 TIs such as Bi2Se3, Bi2Te3, Sb2Te3, etc, is in fact valid only if the sample size is infinite and the crystal momentum is well-defined in all three dimensions. On the other hand, many widely studied forms of TIs such as thin films and nano-wires have significant confinement in one or more of the dimensions with varying level of disorders. In other words, many of the real world topological systems have some important parameters that are not readily captured by theories, and thus it is often questionable how far the topological theories are valid to real systems. Interestingly, it turns out that this very uncertainty of the theories provides additional control knobs that allow us to explore hidden topological territories. In this talk, I will discuss how these additional knobs in thin film topological insulators reveal surprising, at times beautiful, landscapes at the boundaries between order and disorder, 2D and 3D, normal and topological phases. This work is supported by Gordon and Betty Moore Foundation's EPiQS Initiative (GBMF4418).

Hidden Symmetries in String Theory

NASA Astrophysics Data System (ADS)

Chervonyi, Iurii

In this thesis we study hidden symmetries within the framework of string theory. Symmetries play a very important role in physics: they lead to drastic simplifications, which allow one to compute various physical quantities without relying on perturbative techniques. There are two kinds of hidden symmetries investigated in this work: the first type is associated with dynamics of quantum fields and the second type is related to integrability of strings on various backgrounds. Integrability is a remarkable property of some theories that allows one to determine all dynamical properties of the system using purely analytical methods. The goals of this thesis are twofold: extension of hidden symmetries known in General Relativity to stringy backgrounds in higher dimensions and construction of new integrable string theories. In the context of the first goal we study hidden symmetries of stringy backgrounds, with and without supersymmetry. For supersymmetric geometries produced by D-branes we identify the backgrounds with solvable equations for geodesics, which can potentially give rise to integrable string theories. Relaxing the requirement of supersymmetry, we also study charged black holes in higher dimensions and identify their hidden symmetries encoded in so-called Killing(-Yano) tensors. We construct the explicit form of the Killing(-Yano) tensors for the charged rotating black hole in arbitrary number of dimensions, study behavior of such tensors under string dualities, and use the analysis of hidden symmetries to explain why exact solutions for black rings (black holes with non-spherical event horizons) in more than five dimensions remain elusive. As a byproduct we identify the standard parameterization of AdSp x Sq backgrounds with elliptic coordinates on a flat base. The second goal of this work is construction of new integrable string theories by applying continuous deformations of known examples. We use the recent developments called (generalized) lambda-deformation to construct new integrable backgrounds depending on several continuous parameters and study analytical properties of the such deformations.

Detect and exploit hidden structure in fatty acid signature data

USGS Publications Warehouse

Budge, Suzanne; Bromaghin, Jeffrey F.; Thiemann, Gregory

2017-01-01

Estimates of predator diet composition are essential to our understanding of their ecology. Although several methods of estimating diet are practiced, methods based on biomarkers have become increasingly common. Quantitative fatty acid signature analysis (QFASA) is a popular method that continues to be refined and extended. Quantitative fatty acid signature analysis is based on differences in the signatures of prey types, often species, which are recognized and designated by investigators. Similarly, predator signatures may be structured by known factors such as sex or age class, and the season or region of sample collection. The recognized structure in signature data inherently influences QFASA results in important and typically beneficial ways. However, predator and prey signatures may contain additional, hidden structure that investigators either choose not to incorporate into an analysis or of which they are unaware, being caused by unknown ecological mechanisms. Hidden structure also influences QFASA results, most often negatively. We developed a new method to explore signature data for hidden structure, called divisive magnetic clustering (DIMAC). Our DIMAC approach is based on the same distance measure used in diet estimation, closely linking methods of data exploration and parameter estimation, and it does not require data transformation or distributional assumptions, as do many multivariate ordination methods in common use. We investigated the potential benefits of the DIMAC method to detect and subsequently exploit hidden structure in signature data using two prey signature libraries with quite different characteristics. We found that the existence of hidden structure in prey signatures can increase the confusion between prey types and thereby reduce the accuracy and precision of QFASA diet estimates. Conversely, the detection and exploitation of hidden structure represent a potential opportunity to improve predator diet estimates and may lead to new insights into the ecology of either predator or prey. The DIMAC algorithm is implemented in the R diet estimation package qfasar.

Judging The Effectiveness Of Wool Combing By The Entropy Of The Images Of Wool Slivers

NASA Astrophysics Data System (ADS)

Rodrigues, F. Carvalho; Carvalho, Fernando D.; Peixoto, J. Pinto; Silva, M. Santos

1989-04-01

In general it can be said that the textile industry endeavours to render a bunch of fibers chaotically distributed in space into an ordered spatial distribution. This fact is independent of the nature the fibers, i.e., the aim of getting into higher order states in the spatial distribution of the fibers dictates different industrial processes depending on whether the fibers are wool, cotton or man made but the all effect is centred on obtaining at every step of any of the processes a more ordered state regarding the spatial distribution of the fibers. Thinking about the textile processes as a method of getting order out of chaos, the concept of entropy appears as the most appropriate judging parameter on the effectiveness of a step in the chain of an industrial process to produce a regular textile. In fact, entropy is the hidden parameter not only for the textile industry but also for the non woven and paper industrial processes. It happens that in these industries the state of order is linked with the spatial distribution of fibers and to obtain an image of a spatial distribution is an easy matter. To compute the image entropy from the grey level distribution requires only the use of the Shannon formula. In this paper to illustrate the usefulness of employing the entropy of an image concept to textiles the evolution of the entropy of wool slivers along the combing process is matched against the state of parallelization of the fibbers along the seven steps as measured by the existing method. The advantages of the entropy method over the previous method based on diffraction is also demonstrated.

Comment on ‘Information hidden in the velocity distribution of ions and the exact kinetic Bohm criterion’

NASA Astrophysics Data System (ADS)

Mustafaev, A. S.; Sukhomlinov, V. S.; Timofeev, N. A.

2018-03-01

This Comment is devoted to some mathematical inaccuracies made by the authors of the paper ‘Information hidden in the velocity distribution of ions and the exact kinetic Bohm criterion’ (Plasma Sources Science and Technology 26 055003). In the Comment, we show that the diapason of plasma parameters for the validity of the theoretical results obtained by the authors was defined incorrectly; we made a more accurate definition of this diapason. As a result, we show that it is impossible to confirm or refute the feasibility of the Bohm kinetic criterion on the basis of the data of the cited paper.

Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

NASA Astrophysics Data System (ADS)

Bui, Lam Thu; Barlow, Michael

We propose a methodology for employing memetics (local search) within the framework of evolutionary algorithms to optimize parameters of hidden markov models. With this proposal, the rate and frequency of using local search are automatically changed over time either at a population or individual level. At the population level, we allow the rate of using local search to decay over time to zero (at the final generation). At the individual level, each individual is equipped with information of when it will do local search and for how long. This information evolves over time alongside the main elements of the chromosome representing the individual.

Hidden Markov models of biological primary sequence information.

PubMed Central

Baldi, P; Chauvin, Y; Hunkapiller, T; McClure, M A

1994-01-01

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences. PMID:8302831

Under-reported data analysis with INAR-hidden Markov chains.

PubMed

Fernández-Fontelo, Amanda; Cabaña, Alejandra; Puig, Pedro; Moriña, David

2016-11-20

In this work, we deal with correlated under-reported data through INAR(1)-hidden Markov chain models. These models are very flexible and can be identified through its autocorrelation function, which has a very simple form. A naïve method of parameter estimation is proposed, jointly with the maximum likelihood method based on a revised version of the forward algorithm. The most-probable unobserved time series is reconstructed by means of the Viterbi algorithm. Several examples of application in the field of public health are discussed illustrating the utility of the models. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Ion behaviour in pulsed plasma regime by means of Time-resolved energy mass spectroscopy (TREMS) applied to an industrial radiofrequency Plasma Immersion Ion Implanter PULSION registered

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carrere, M.; Kaeppelin, V.; Torregrosa, F.

2006-11-13

In order to face the requirements for P+/N junctions requested for < 45 nm ITRS nodes, new doping techniques are studied. Among them Plasma Immersion Ion Implantation (PIII) has been largely studied. IBS has designed and developed its own PIII machine named PULSION registered . This machine is using a pulsed plasma. As other modem technological applications of low pressure plasma, PULSION registered needs a precise control over plasma parameters in order to optimise process characteristics. In order to improve pulsed plasma discharge devoted to PIII, a nitrogen pulsed plasma has been studied in the inductively coupled plasma (ICP) ofmore » PULSION registered and an argon pulsed plasma has been studied in the helicon discharge of the laboratory reactor of LPIIM (PHYSIS). Measurements of the Ion Energy Distribution Function (IEDF) with EQP300 (Hidden) have been performed in both pulsed plasma. This study has been done for different energies which allow to reconstruct the IEDF resolved in time (TREMS). By comparing these results, we found that the beginning of the plasma pulse, named ignition, exhaust at least three phases, or more. All these results allowed us to explain plasma dynamics during the pulse while observing transitions between capacitive and inductive coupling. This study leads in a better understanding of changes in discharge parameters as plasma potential, electron temperature, ion density.« less

Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services.

PubMed

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.

Quantitative metal magnetic memory reliability modeling for welded joints

NASA Astrophysics Data System (ADS)

Xing, Haiyan; Dang, Yongbin; Wang, Ben; Leng, Jiancheng

2016-03-01

Metal magnetic memory(MMM) testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K vs is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K vs statistical law is investigated, which shows that K vs obeys Gaussian distribution. So K vs is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R 1 and verification reliability degree R 2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.

Searching for Extragalactic Sources in the VISTA Variables in the Vía Láctea Survey

NASA Astrophysics Data System (ADS)

Baravalle, Laura D.; Alonso, M. Victoria; Nilo Castellón, José Luis; Beamín, Juan Carlos; Minniti, Dante

2018-01-01

We search for extragalactic sources in the VISTA Variables in the Vía Láctea survey that are hidden by the Galaxy. Herein, we describe our photometric procedure to find and characterize extragalactic objects using a combination of SExtractor and PSFEx. It was applied in two tiles of the survey: d010 and d115, without previous extragalactic IR detections, in order to obtain photometric parameters of the detected sources. The adopted criteria to define extragalactic candidates include CLASSSTAR< 0.3; 1.0 < R1/2< 5.0 arcsec; 2.1 < C < 5 and Φ > 0.002 and the colors: 0.5 < (J–K s ) < 2.0 mag; 0.0 < (J–H) < 1.0 mag; 0.0 < (H–K s ) < 2.0 mag and (J–H) + 0.9 (H–K s ) > 0.44 mag. We detected 345 and 185 extragalactic candidates in the d010 and d115 tiles, respectively. All of them were visually inspected and confirmed to be galaxies. In general, they are small and more circular objects, due to the near-IR sensitivity to select more compact objects with higher surface brightness. The procedure will be used to identify extragalactic objects in other tiles of the VVV disk, which will allow us to study the distribution of galaxies and filaments hidden by the Milky Way.

Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services

PubMed Central

Zare Hosseini, Zeinab; Mohammadzadeh, Mahdi

2016-01-01

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers. PMID:27610177

Nonlinear dynamical modes of climate variability: from curves to manifolds

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

The necessity of efficient dimensionality reduction methods capturing dynamical properties of the system from observed data is evident. Recent study shows that nonlinear dynamical mode (NDM) expansion is able to solve this problem and provide adequate phase variables in climate data analysis [1]. A single NDM is logical extension of linear spatio-temporal structure (like empirical orthogonal function pattern): it is constructed as nonlinear transformation of hidden scalar time series to the space of observed variables, i. e. projection of observed dataset onto a nonlinear curve. Both the hidden time series and the parameters of the curve are learned simultaneously using Bayesian approach. The only prior information about the hidden signal is the assumption of its smoothness. The optimal nonlinearity degree and smoothness are found using Bayesian evidence technique. In this work we do further extension and look for vector hidden signals instead of scalar with the same smoothness restriction. As a result we resolve multidimensional manifolds instead of sum of curves. The dimension of the hidden manifold is optimized using also Bayesian evidence. The efficiency of the extension is demonstrated on model examples. Results of application to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. http://doi.org/10.1038/srep15510

Astrophysics: Hidden chaos in cosmic order

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gnedin, Nickolay Y.; /Fermilab

'Galaxies, like elephants, have long memories,' says an influential article from the 1980s. Tapping into these memories has revealed some surprising facts about the history of our neighbouring Andromeda galaxy.

Astrophysics: Hidden chaos in cosmic order

NASA Astrophysics Data System (ADS)

Gnedin, Nickolay Y.

2009-09-01

``Galaxies, like elephants, have long memories,'' says an influential article from the 1980s. Tapping into these memories has revealed some surprising facts about the history of our neighbouring Andromeda galaxy.

GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays

PubMed Central

Li, Ao; Liu, Zongzhi; Lezon-Geyda, Kimberly; Sarkar, Sudipa; Lannin, Donald; Schulz, Vincent; Krop, Ian; Winer, Eric; Harris, Lyndsay; Tuck, David

2011-01-01

There is an increasing interest in using single nucleotide polymorphism (SNP) genotyping arrays for profiling chromosomal rearrangements in tumors, as they allow simultaneous detection of copy number and loss of heterozygosity with high resolution. Critical issues such as signal baseline shift due to aneuploidy, normal cell contamination, and the presence of GC content bias have been reported to dramatically alter SNP array signals and complicate accurate identification of aberrations in cancer genomes. To address these issues, we propose a novel Global Parameter Hidden Markov Model (GPHMM) to unravel tangled genotyping data generated from tumor samples. In contrast to other HMM methods, a distinct feature of GPHMM is that the issues mentioned above are quantitatively modeled by global parameters and integrated within the statistical framework. We developed an efficient EM algorithm for parameter estimation. We evaluated performance on three data sets and show that GPHMM can correctly identify chromosomal aberrations in tumor samples containing as few as 10% cancer cells. Furthermore, we demonstrated that the estimation of global parameters in GPHMM provides information about the biological characteristics of tumor samples and the quality of genotyping signal from SNP array experiments, which is helpful for data quality control and outlier detection in cohort studies. PMID:21398628

Development of a neural network technique for KSTAR Thomson scattering diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Seung Hun, E-mail: leesh81@nfri.re.kr; Lee, J. H.; Yamada, I.

Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ{sup 2} method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ{sup 2} method. The best results were obtained for 10{sup 3} training cyclesmore » and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ{sup 2} method and performs the calculation twenty times faster.« less

Phasic Triplet Markov Chains.

PubMed

El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar

2014-11-01

Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Adaptive partially hidden Markov models with application to bilevel image coding.

PubMed

Forchhammer, S; Rasmussen, T S

1999-01-01

Partially hidden Markov models (PHMMs) have previously been introduced. The transition and emission/output probabilities from hidden states, as known from the HMMs, are conditioned on the past. This way, the HMM may be applied to images introducing the dependencies of the second dimension by conditioning. In this paper, the PHMM is extended to multiple sequences with a multiple token version and adaptive versions of PHMM coding are presented. The different versions of the PHMM are applied to lossless bilevel image coding. To reduce and optimize the model cost and size, the contexts are organized in trees and effective quantization of the parameters is introduced. The new coding methods achieve results that are better than the JBIG standard on selected test images, although at the cost of increased complexity. By the minimum description length principle, the methods presented for optimizing the code length may apply as guidance for training (P)HMMs for, e.g., segmentation or recognition purposes. Thereby, the PHMM models provide a new approach to image modeling.

Evaluation of Bayesian estimation of a hidden continuous-time Markov chain model with application to threshold violation in water-quality indicators

USGS Publications Warehouse

Deviney, Frank A.; Rice, Karen; Brown, Donald E.

2012-01-01

Natural resource managers require information concerning  the frequency, duration, and long-term probability of occurrence of water-quality indicator (WQI) violations of defined thresholds. The timing of these threshold crossings often is hidden from the observer, who is restricted to relatively infrequent observations. Here, a model for the hidden process is linked with a model for the observations, and the parameters describing duration, return period, and long-term probability of occurrence are estimated using Bayesian methods. A simulation experiment is performed to evaluate the approach under scenarios based on the equivalent of a total monitoring period of 5-30 years and an observation frequency of 1-50 observations per year. Given constant threshold crossing rate, accuracy and precision of parameter estimates increased with longer total monitoring period and more-frequent observations. Given fixed monitoring period and observation frequency, accuracy and precision of parameter estimates increased with longer times between threshold crossings. For most cases where the long-term probability of being in violation is greater than 0.10, it was determined that at least 600 observations are needed to achieve precise estimates.  An application of the approach is presented using 22 years of quasi-weekly observations of acid-neutralizing capacity from Deep Run, a stream in Shenandoah National Park, Virginia. The time series also was sub-sampled to simulate monthly and semi-monthly sampling protocols. Estimates of the long-term probability of violation were unbiased despite sampling frequency; however, the expected duration and return period were over-estimated using the sub-sampled time series with respect to the full quasi-weekly time series.

Interpretation Difficulties of Serum Immunofixation Test in Immunoglobulin D Multiple Myeloma with Hidden Lambda Light Chains.

PubMed

Biaz, A; Uwingabiye, J; Rachid, A; Dami, A; Bouhsain, S; Ouzzif, Z; Idrissi, S El Machtani

2018-06-01

We report a case of immunoglobulin (Ig) D myeloma with hidden lambda light chains in a patient whose immunofixation test was very difficult to interpret: the IgD reacts with the anti-δ heavy chain antiserum but does not react with anti-lambda antiserum. The band in the D heavy chain lane is unmatched in light chain lanes and the band in lambda light chain lane migrates higher. To distinguish between heavy chain disease and immunoglobulin with "hidden" light chains, the sample was exposed to a very high concentration of anti-lambda and anti-kappa antisera for 48 hours. The serum immunofixation test of the sample treated with anti-lambda showed a decrease in the intensity of the band corresponding to D heavy chain lane as well as the modification of its mobility confirming the presence of IgD with the hidden lambda light chains. The IgD myeloma with hidden light chains remains a rare entity, hence the interest of sensitizing health professionals to be vigilant and ensure a good diagnosis. The proposed technique is useful, simple, reliable, and less laborious than those previous reported in the literature. Medical laboratories using Sebia-Hydrasys® system should be aware of the described phenomenon in order to avoid identifying an IgD myeloma as a delta heavy chain disease.

STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning

PubMed Central

Kappel, David; Nessler, Bernhard; Maass, Wolfgang

2014-01-01

In order to cross a street without being run over, we need to be able to extract very fast hidden causes of dynamically changing multi-modal sensory stimuli, and to predict their future evolution. We show here that a generic cortical microcircuit motif, pyramidal cells with lateral excitation and inhibition, provides the basis for this difficult but all-important information processing capability. This capability emerges in the presence of noise automatically through effects of STDP on connections between pyramidal cells in Winner-Take-All circuits with lateral excitation. In fact, one can show that these motifs endow cortical microcircuits with functional properties of a hidden Markov model, a generic model for solving such tasks through probabilistic inference. Whereas in engineering applications this model is adapted to specific tasks through offline learning, we show here that a major portion of the functionality of hidden Markov models arises already from online applications of STDP, without any supervision or rewards. We demonstrate the emergent computing capabilities of the model through several computer simulations. The full power of hidden Markov model learning can be attained through reward-gated STDP. This is due to the fact that these mechanisms enable a rejection sampling approximation to theoretically optimal learning. We investigate the possible performance gain that can be achieved with this more accurate learning method for an artificial grammar task. PMID:24675787

Maximum mutual information estimation of a simplified hidden MRF for offline handwritten Chinese character recognition

NASA Astrophysics Data System (ADS)

Xiong, Yan; Reichenbach, Stephen E.

1999-01-01

Understanding of hand-written Chinese characters is at such a primitive stage that models include some assumptions about hand-written Chinese characters that are simply false. So Maximum Likelihood Estimation (MLE) may not be an optimal method for hand-written Chinese characters recognition. This concern motivates the research effort to consider alternative criteria. Maximum Mutual Information Estimation (MMIE) is an alternative method for parameter estimation that does not derive its rationale from presumed model correctness, but instead examines the pattern-modeling problem in automatic recognition system from an information- theoretic point of view. The objective of MMIE is to find a set of parameters in such that the resultant model allows the system to derive from the observed data as much information as possible about the class. We consider MMIE for recognition of hand-written Chinese characters using on a simplified hidden Markov Random Field. MMIE provides improved performance improvement over MLE in this application.

Hidden Quantum Processes, Quantum Ion Channels, and 1/ fθ-Type Noise.

PubMed

Paris, Alan; Vosoughi, Azadeh; Berman, Stephen A; Atia, George

2018-07-01

In this letter, we perform a complete and in-depth analysis of Lorentzian noises, such as those arising from [Formula: see text] and [Formula: see text] channel kinetics, in order to identify the source of [Formula: see text]-type noise in neurological membranes. We prove that the autocovariance of Lorentzian noise depends solely on the eigenvalues (time constants) of the kinetic matrix but that the Lorentzian weighting coefficients depend entirely on the eigenvectors of this matrix. We then show that there are rotations of the kinetic eigenvectors that send any initial weights to any target weights without altering the time constants. In particular, we show there are target weights for which the resulting Lorenztian noise has an approximately [Formula: see text]-type spectrum. We justify these kinetic rotations by introducing a quantum mechanical formulation of membrane stochastics, called hidden quantum activated-measurement models, and prove that these quantum models are probabilistically indistinguishable from the classical hidden Markov models typically used for ion channel stochastics. The quantum dividend obtained by replacing classical with quantum membranes is that rotations of the Lorentzian weights become simple readjustments of the quantum state without any change to the laboratory-determined kinetic and conductance parameters. Moreover, the quantum formalism allows us to model the activation energy of a membrane, and we show that maximizing entropy under constrained activation energy yields the previous [Formula: see text]-type Lorentzian weights, in which the spectral exponent [Formula: see text] is a Lagrange multiplier for the energy constraint. Thus, we provide a plausible neurophysical mechanism by which channel and membrane kinetics can give rise to [Formula: see text]-type noise (something that has been occasionally denied in the literature), as well as a realistic and experimentally testable explanation for the numerical values of the spectral exponents. We also discuss applications of quantum membranes beyond [Formula: see text]-type -noise, including applications to animal models and possible impact on quantum foundations.

Probing 5 f -state configurations in URu 2 Si 2 with U L III -edge resonant x-ray emission spectroscopy

DOE PAGES

Booth, Corwin H.; Medling, S. A.; Tobin, J. G.; ...

2016-07-15

Resonant x-ray emission spectroscopy (RXES) was employed at the U LIII absorption edge and the L α1 emission line to explore the 5f occupancy, nf, and the degree of 5f-orbital delocalization in the hidden-order compound URu 2Si 2. By comparing to suitable reference materials such as UF 4, UCd 11, and α-U, we conclude that the 5f orbital in URu 2Si 2 is at least partially delocalized with n f=2.87±0.08, and does not change with temperature down to 10 K within the estimated error. These results place further constraints on theoretical explanations of the hidden order, especially those requiring amore » localized f 2 ground state.« less

Exploration of metastability and hidden phases in correlated electron crystals visualized by femtosecond optical doping and electron crystallography

PubMed Central

Han, Tzong-Ru T.; Zhou, Faran; Malliakas, Christos D.; Duxbury, Phillip M.; Mahanti, Subhendra D.; Kanatzidis, Mercouri G.; Ruan, Chong-Yu

2015-01-01

Characterizing and understanding the emergence of multiple macroscopically ordered electronic phases through subtle tuning of temperature, pressure, and chemical doping has been a long-standing central issue for complex materials research. We report the first comprehensive studies of optical doping–induced emergence of stable phases and metastable hidden phases visualized in situ by femtosecond electron crystallography. The electronic phase transitions are triggered by femtosecond infrared pulses, and a temperature–optical density phase diagram is constructed and substantiated with the dynamics of metastable states, highlighting the cooperation and competition through which the macroscopic quantum orders emerge. These results elucidate key pathways of femtosecond electronic switching phenomena and provide an important new avenue to comprehensively investigate optical doping–induced transition states and phase diagrams of complex materials with wide-ranging applications. PMID:26601190

Reconstruction of pulse noisy images via stochastic resonance

PubMed Central

Han, Jing; Liu, Hongjun; Sun, Qibing; Huang, Nan

2015-01-01

We investigate a practical technology for reconstructing nanosecond pulse noisy images via stochastic resonance, which is based on the modulation instability. A theoretical model of this method for optical pulse signal is built to effectively recover the pulse image. The nanosecond noise-hidden images grow at the expense of noise during the stochastic resonance process in a photorefractive medium. The properties of output images are mainly determined by the input signal-to-noise intensity ratio, the applied voltage across the medium, and the correlation length of noise background. A high cross-correlation gain is obtained by optimizing these parameters. This provides a potential method for detecting low-level or hidden pulse images in various imaging applications. PMID:26067911

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

NASA Astrophysics Data System (ADS)

Turner, Sean; Galelli, Stefano; Wilcox, Karen

2015-04-01

Water reservoir systems are often affected by recurring large-scale ocean-atmospheric anomalies, known as teleconnections, that cause prolonged periods of climatological drought. Accurate forecasts of these events -- at lead times in the order of weeks and months -- may enable reservoir operators to take more effective release decisions to improve the performance of their systems. In practice this might mean a more reliable water supply system, a more profitable hydropower plant or a more sustainable environmental release policy. To this end, climate indices, which represent the oscillation of the ocean-atmospheric system, might be gainfully employed within reservoir operating models that adapt the reservoir operation as a function of the climate condition. This study develops a Stochastic Dynamic Programming (SDP) approach that can incorporate climate indices using a Hidden Markov Model. The model simulates the climatic regime as a hidden state following a Markov chain, with the state transitions driven by variation in climatic indices, such as the Southern Oscillation Index. Time series analysis of recorded streamflow data reveals the parameters of separate autoregressive models that describe the inflow to the reservoir under three representative climate states ("normal", "wet", "dry"). These models then define inflow transition probabilities for use in a classic SDP approach. The key advantage of the Hidden Markov Model is that it allows conditioning the operating policy not only on the reservoir storage and the antecedent inflow, but also on the climate condition, thus potentially allowing adaptability to a broader range of climate conditions. In practice, the reservoir operator would effect a water release tailored to a specific climate state based on available teleconnection data and forecasts. The approach is demonstrated on the operation of a realistic, stylised water reservoir with carry-over capacity in South-East Australia. Here teleconnections relating to both the El Niño Southern Oscillation and the Indian Ocean Dipole influence local hydro-meteorological processes; statistically significant lag correlations have already been established. Simulation of the derived operating policies, which are benchmarked against standard policies conditioned on the reservoir storage and the antecedent inflow, demonstrates the potential of the proposed approach. Future research will further develop the model for sensitivity analysis and regional studies examining the economic value of incorporating long range forecasts into reservoir operation.

Recovering a hidden polarization by ghost polarimetry.

PubMed

Janassek, Patrick; Blumenstein, Sébastien; Elsäßer, Wolfgang

2018-02-15

By exploiting polarization correlations of light from a broadband fiber-based amplified spontaneous emission source we succeed in reconstructing a hidden polarization in a ghost polarimetry experiment in close analogy to ghost imaging and ghost spectroscopy. Thereby, an original linear polarization state in the object arm of a Mach-Zehnder interferometer configuration which has been camouflaged by a subsequent depolarizer is recovered by correlating it with light from a reference beam. The variation of a linear polarizer placed inside the reference beam results in a Malus law type second-order intensity correlation with high contrast, thus measuring a ghost polarigram.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Multilayer perceptron architecture optimization using parallel computing techniques.

PubMed

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

Multilayer perceptron architecture optimization using parallel computing techniques

PubMed Central

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time. PMID:29236744

Analysis of Artificial Neural Network in Erosion Modeling: A Case Study of Serang Watershed

NASA Astrophysics Data System (ADS)

Arif, N.; Danoedoro, P.; Hartono

2017-12-01

Erosion modeling is an important measuring tool for both land users and decision makers to evaluate land cultivation and thus it is necessary to have a model to represent the actual reality. Erosion models are a complex model because of uncertainty data with different sources and processing procedures. Artificial neural networks can be relied on for complex and non-linear data processing such as erosion data. The main difficulty in artificial neural network training is the determination of the value of each network input parameters, i.e. hidden layer, momentum, learning rate, momentum, and RMS. This study tested the capability of artificial neural network application in the prediction of erosion risk with some input parameters through multiple simulations to get good classification results. The model was implemented in Serang Watershed, Kulonprogo, Yogyakarta which is one of the critical potential watersheds in Indonesia. The simulation results showed the number of iterations that gave a significant effect on the accuracy compared to other parameters. A small number of iterations can produce good accuracy if the combination of other parameters was right. In this case, one hidden layer was sufficient to produce good accuracy. The highest training accuracy achieved in this study was 99.32%, occurred in ANN 14 simulation with combination of network input parameters of 1 HL; LR 0.01; M 0.5; RMS 0.0001, and the number of iterations of 15000. The ANN training accuracy was not influenced by the number of channels, namely input dataset (erosion factors) as well as data dimensions, rather it was determined by changes in network parameters.

Design of double fuzzy clustering-driven context neural networks.

PubMed

Kim, Eun-Hu; Oh, Sung-Kwun; Pedrycz, Witold

2018-08-01

In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively. Cluster space refers to the local space positioned in the input space whereas context space concerns a local space formed in the output space. Through partitioning the output space into several local spaces, each context space is used as the desired (target) local output to construct local models. To complete this, the proposed network includes a new context layer for reasoning about context space in the output space. In this sense, Fuzzy C-Means (FCM) clustering is useful to form local spaces in both input and output spaces. The first one is used in order to form clusters and train weights positioned between the input and hidden layer, whereas the other one is applied to the output space to form context spaces. The key features of the proposed DFCCNNs can be enumerated as follows: (i) the parameters between the input layer and hidden layer are built through FCM clustering. The connections (weights) are specified as constant terms being in fact the centers of the clusters. The membership functions (represented through the partition matrix) produced by the FCM are used as activation functions located at the hidden layer of the "conventional" neural networks. (ii) Following the hidden layer, a context layer is formed to approximate the context space of the output variable and each node in context layer means individual local model. The outputs of the context layer are specified as a combination of both weights formed as linear function and the outputs of the hidden layer. The weights are updated using the least square estimation (LSE)-based method. (iii) At the output layer, the outputs of context layer are decoded to produce the corresponding numeric output. At this time, the weighted average is used and the weights are also adjusted with the use of the LSE scheme. From the viewpoint of performance improvement, the proposed design methodologies are discussed and experimented with the aid of benchmark machine learning datasets. Through the experiments, it is shown that the generalization abilities of the proposed DFCCNNs are better than those of the conventional FCNNs reported in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Influence of super-horizon modes on correlation functions during inflation

NASA Astrophysics Data System (ADS)

Deutsch, Anne-Sylvie

2018-05-01

Coupling between sub- and super-Hubble modes can affect the locally observed statistics of our universe. In the context of Quasi-Single Field Inflation, we can compute correlation functions and derive the influence of those unobservable modes on observed correlation functions as well as on the inferred cosmological parameters. We study how different classes of diagrams affect the bispectrum in the squeezed limit; in particular, while contact-like diagrams leave the scaling between the long and short modes unchanged, exchange-like diagrams do modify the shape of the bispectrum. We show that the mass of the hidden sector field can hence be biased by an unavoidable cosmic variance that can reach a 1-σ uncertainty of Script O(10%) for a weakly non-Gaussian universe. Finally, we go beyond the bispectrum and show how couplings between unobservable and observable modes can affect generic correlation functions with arbitrary order non-derivative self-interactions.

PeV-scale dark matter as a thermal relic of a decoupled sector

DOE PAGES

Berlin, Asher; Hooper, Dan; Krnjaic, Gordan

2016-06-21

We consider a class of scenarios in which the dark matter is part of a heavy hidden sector that is thermally decoupled from the Standard Model in the early universe. The dark matter freezes-out by annihilating to a lighter, metastable state, whose subsequent abundance can naturally come to dominate the energy density of the universe. Moreover, when this state decays, it reheats the visible sector and dilutes all relic abundances, thereby allowing the dark matter to be orders of magnitude heavier than the weak scale. For concreteness, we consider a simple realization with a Dirac fermion dark matter candidate coupledmore » to a massive gauge boson that decays to the Standard Model through its kinetic mixing with hypercharge. Finally, we identify viable parameter space in which the dark matter can be as heavy as ~1-100 PeV without being overproduced in the early universe.« less

Great apes (Pan paniscus, Pan troglodytes, Gorilla gorilla, Pongo abelii) follow visual trails to locate hidden food.

PubMed

Völter, Christoph J; Call, Josep

2014-05-01

Whether nonhuman primates understand causal relations beyond mere associations is still a matter of debate. We presented all four species of nonhuman great apes (N = 36) with a choice between 2 opaque, upside down cups after displacing them out of sight from their starting positions. Crucially, 1 of them had left a yogurt trail behind it. Great apes spontaneously used the trail to select the yogurt baited cup. Follow-up experiments demonstrated that chimpanzees distinguished trails based on the temporal order of cause and effect by ignoring trails that were already present before the reward was hidden. Additionally, chimpanzees did not select cups based on the amount of yogurt near them but instead preferred cups that signaled the endpoint of the trail. We conclude that apes' choices reveal sensitivity to a causal relation between cause (reward) and effect (trail) including their temporal order. ©2014 APA, all rights reserved.

Hidden Markov model tracking of continuous gravitational waves from young supernova remnants

NASA Astrophysics Data System (ADS)

Sun, L.; Melatos, A.; Suvorova, S.; Moran, W.; Evans, R. J.

2018-02-01

Searches for persistent gravitational radiation from nonpulsating neutron stars in young supernova remnants are computationally challenging because of rapid stellar braking. We describe a practical, efficient, semicoherent search based on a hidden Markov model tracking scheme, solved by the Viterbi algorithm, combined with a maximum likelihood matched filter, the F statistic. The scheme is well suited to analyzing data from advanced detectors like the Advanced Laser Interferometer Gravitational Wave Observatory (Advanced LIGO). It can track rapid phase evolution from secular stellar braking and stochastic timing noise torques simultaneously without searching second- and higher-order derivatives of the signal frequency, providing an economical alternative to stack-slide-based semicoherent algorithms. One implementation tracks the signal frequency alone. A second implementation tracks the signal frequency and its first time derivative. It improves the sensitivity by a factor of a few upon the first implementation, but the cost increases by 2 to 3 orders of magnitude.

Resources of learning through hidden curriculum: Iranian nursing students’ perspective

PubMed Central

Karimi, Zohreh; Ashktorab, Tahereh; Mohammadi, Eesa; Abedi, Heidarali; Zarea, Kourosh

2015-01-01

Background: Students tend to internalize and perpetuate the patterns of behavior and the values surrounding them. Review of literature showed that there are several student learning sources through the hidden curriculum, but they have not been identified in nursing yet. Hence, the purpose of this study is explanation of learning resources in the hidden curriculum in the view of baccalaureate nursing students. Materials and Methods: This qualitative study was carried out in 2012 with the participation of 32 baccalaureate nursing students in Nursing and Midwifery College of Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran by purposeful sampling strategies. Data were collected by semi-structured interviews and continued to the level of data saturation and themes’ emergence. Data analysis was performed through inductive content analysis method. Result: “Instructor as the unique learning element,” “various learning resources in the clinical setting,” and “instructive nature of the education environment” were extracted as the main themes, each of which incorporated some categories. Conclusion: Baccalaureate undergraduate nursing students learnt the hidden curriculum by the resources such as instructors, resources existing in the clinical setting, and the university campus. Therefore, more research is recommended for the identification of other resources. In order to promote positive messages and reduce the negative messages of the hidden curricula running at academic and clinical settings, nursing educators and nurses need to learn more about this issue in the nursing profession. PMID:26430684

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Hidden Markov model analysis of force/torque information in telemanipulation

NASA Technical Reports Server (NTRS)

Hannaford, Blake; Lee, Paul

1991-01-01

A model for the prediction and analysis of sensor information recorded during robotic performance of telemanipulation tasks is presented. The model uses the hidden Markov model to describe the task structure, the operator's or intelligent controller's goal structure, and the sensor signals. A methodology for constructing the model parameters based on engineering knowledge of the task is described. It is concluded that the model and its optimal state estimation algorithm, the Viterbi algorithm, are very succesful at the task of segmenting the data record into phases corresponding to subgoals of the task. The model provides a rich modeling structure within a statistical framework, which enables it to represent complex systems and be robust to real-world sensory signals.

The Development of Knowledge of an External Retrieval Cue Strategy.

ERIC Educational Resources Information Center

Ritter, Kenneth

1978-01-01

Investigated preschool and third grade children's metamnemonic knowledge that in order to serve as an efficient retrieval cue of the location of a hidden object, an external marker sign must differentiate it from other locations. (JMB)

Value Disciplines: A Lens for Successful Decision Making in IT

ERIC Educational Resources Information Center

Eichen, Marc

2006-01-01

To succeed professionally, a technology manager in higher education must align institutional goals with the skill set of campus technologists and the resources available for information technology (IT) support. Getting a firm grasp on any of these parameters is not easy. Resources are often committed to multiyear projects or hidden by opaque…

Improving Forecasts Through Realistic Uncertainty Estimates: A Novel Data Driven Method for Model Uncertainty Quantification in Data Assimilation

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; Moradkhani, H.; Marshall, L. A.; Sharma, A.; Geenens, G.

2016-12-01

Effective combination of model simulations and observations through Data Assimilation (DA) depends heavily on uncertainty characterisation. Many traditional methods for quantifying model uncertainty in DA require some level of subjectivity (by way of tuning parameters or by assuming Gaussian statistics). Furthermore, the focus is typically on only estimating the first and second moments. We propose a data-driven methodology to estimate the full distributional form of model uncertainty, i.e. the transition density p(xt|xt-1). All sources of uncertainty associated with the model simulations are considered collectively, without needing to devise stochastic perturbations for individual components (such as model input, parameter and structural uncertainty). A training period is used to derive the distribution of errors in observed variables conditioned on hidden states. Errors in hidden states are estimated from the conditional distribution of observed variables using non-linear optimization. The theory behind the framework and case study applications are discussed in detail. Results demonstrate improved predictions and more realistic uncertainty bounds compared to a standard perturbation approach.

Loss surface of XOR artificial neural networks

NASA Astrophysics Data System (ADS)

Mehta, Dhagash; Zhao, Xiaojun; Bernal, Edgar A.; Wales, David J.

2018-05-01

Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these landscapes using optimization tools developed for potential energy landscapes in molecular science. The number of local minima and transition states (saddle points of index one), as well as the ratio of transition states to minima, grow rapidly with the number of nodes in the network. There is also a strong dependence on the regularization parameter, with the landscape becoming more convex (fewer minima) as the regularization term increases. We demonstrate that in our formulation, stationary points for networks with Nh hidden nodes, including the minimal network required to fit the XOR data, are also stationary points for networks with Nh+1 hidden nodes when all the weights involving the additional node are zero. Hence, smaller networks trained on XOR data are embedded in the landscapes of larger networks. Our results clarify certain aspects of the classification and sensitivity (to perturbations in the input data) of minima and saddle points for this system, and may provide insight into dropout and network compression.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

A probabilistic union model with automatic order selection for noisy speech recognition.

PubMed

Jancovic, P; Ming, J

2001-09-01

A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.

Object permanence in the food-storing coal tit (Periparus ater) and the non-storing great tit (Parus major): Is the mental representation required?

PubMed

Marhounová, Lucie; Frynta, Daniel; Fuchs, Roman; Landová, Eva

2017-05-01

Object permanence is a cognitive ability that enables animals to mentally represent the continuous existence of temporarily hidden objects. Generally, it develops gradually through six qualitative stages, the evolution of which may be connected with some specific ecological and behavioral factors. In birds, the advanced object permanence skills were reported in several storing species of the Corvidae family. In order to test the association between food-storing and achieved performance within the stages, we compared food-storing coal tits (Periparus ater) and nonstoring great tits (Parus major) using an adapted version of Uzgiris & Hunt's Scale 1 tasks. The coal tits significantly outperformed the great tits in searching for completely hidden objects. Most of the great tits could not solve the task when the object disappeared completely. However, the upper limit for both species is likely to be Stage 4. The coal tits could solve problems with simply hidden objects, but they used alternative strategies rather than mental representation when searching for completely hidden objects, especially if choosing between two locations. Our results also suggest that neophobia did not affect the overall performance in the object permanence tasks. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

PubMed Central

Sánchez, Daniela; Melin, Patricia

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition. PMID:28894461

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

PubMed

Sánchez, Daniela; Melin, Patricia; Castillo, Oscar

2017-01-01

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition.

Dark forces in the sky: signals from Z{sup ′} and the dark Higgs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bell, Nicole F.; Cai, Yi; Leane, Rebecca K.

2016-08-01

We consider the indirect detection signals for a self-consistent hidden U(1) model containing a Majorana dark matter candidate, χ, a dark gauge boson, Z{sup ′}, and a dark Higgs, s. Compared with a model containing only a dark matter candidate and Z{sup ′} mediator, the addition of the scalar provides a mass generation mechanism for the dark sector particles and is required in order to avoid unitarity violation at high energies. We find that the inclusion of the two mediators opens up a new two-body s-wave annihilation channel, χχ→sZ{sup ′}. This new process, which is missed in the usual single-mediatormore » simplified model approach, can be the dominant annihilation channel. This provides rich phenomenology for indirect detection searches, allows indirect searches to explore regions of parameter space not accessible with other commonly considered s-wave annihilation processes, and enables both the Z{sup ′} and scalar couplings to be probed. We examine the phenomenology of the sector with a focus on this new process, and determine the limits on the model parameter space from Fermi data on dwarf spheriodal galaxies and other relevant experiments.« less

Time-delayed chameleon: Analysis, synchronization and FPGA implementation

NASA Astrophysics Data System (ADS)

Rajagopal, Karthikeyan; Jafari, Sajad; Laarem, Guessas

2017-12-01

In this paper we report a time-delayed chameleon-like chaotic system which can belong to different families of chaotic attractors depending on the choices of parameters. Such a characteristic of self-excited and hidden chaotic flows in a simple 3D system with time delay has not been reported earlier. Dynamic analysis of the proposed time-delayed systems are analysed in time-delay space and parameter space. A novel adaptive modified functional projective lag synchronization algorithm is derived for synchronizing identical time-delayed chameleon systems with uncertain parameters. The proposed time-delayed systems and the synchronization algorithm with controllers and parameter estimates are then implemented in FPGA using hardware-software co-simulation and the results are presented.

Reply to ''Comment on 'Mutually unbiased bases, orthogonal Latin squares, and hidden-variable models'''

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paterek, Tomasz; Dakic, Borivoje; Brukner, Caslav

In this Reply to the preceding Comment by Hall and Rao [Phys. Rev. A 83, 036101 (2011)], we motivate terminology of our original paper and point out that further research is needed in order to (dis)prove the claimed link between every orthogonal Latin square of order being a power of a prime and a mutually unbiased basis.

The hidden and implicit curricula in cultural context: new insights from Doha and New York.

PubMed

Fins, Joseph J; Rodríguez del Pozo, Pablo

2011-03-01

The authors report their longitudinal experience teaching a clerkship in clinical ethics and palliative care at the Weill Cornell Medical College campuses in New York and Doha. This course uses participant observation and reflective practice to counteract the hidden curriculum when learning about clinical ethics and end-of-life care. The authors consider how this formal element of the curriculum is influenced by the implicit and hidden curricula in different cultural contexts and how these differing venues affect communication and information exchange, using the anthropological concept of high- and low-context societies. The authors' analysis provides additional information on Weill Cornell's educational efforts in the medical humanities, bioethics, and palliative care across the curriculum and across cultural settings. By contrasting high-context Doha, where much information is culturally embedded and seemingly hidden, with low-context New York, where information is made overt, the authors theorize that in each setting, the proportion of implicit and explicit curricular elements is determined by the extramural cultural environment. They argue that there are many hidden and implicit curricula and that each is dependent on modes of communication in any given setting. They assert that these variations can be seen not only across differing societies but also, for example, among individual U.S. medical schools because of local custom, history, or mission. Because these contextual factors influence the relative importance of what is implicit and explicit in the student's educational experience, medical educators need to be aware of their local cultural contexts in order to engage in effective pedagogy.

CONSTRAINTS FROM ASYMMETRIC HEATING: INVESTIGATING THE EPSILON AURIGAE DISK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearson, Richard L. III; Stencel, Robert E., E-mail: richard.pearson@du.edu, E-mail: robert.stencel@du.edu

2015-01-01

Epsilon Aurigae is a long-period eclipsing binary that likely contains an F0Ia star and a circumstellar disk enshrouding a hidden companion, assumed to be a main-sequence B star. High uncertainty in its parallax has kept the evolutionary status of the system in question and, hence, the true nature of each component. This unknown, as well as the absence of solid state spectral features in the infrared, requires an investigation of a wide parameter space by means of both analytic and Monte Carlo radiative transfer (MCRT) methods. The first MCRT models of epsilon Aurigae that include all three system components aremore » presented here. We seek additional system parameter constraints by melding analytic approximations with MCRT outputs (e.g., dust temperatures) on a first-order level. The MCRT models investigate the effects of various parameters on the disk-edge temperatures; these include two distances, three particle size distributions, three compositions, and two disk masses, resulting in 36 independent models. Specifically, the MCRT temperatures permit analytic calculations of effective heating and cooling curves along the disk edge. These are used to calculate representative observed fluxes and corresponding temperatures. This novel application of thermal properties provides the basis for utilization of other binary systems containing disks. We find degeneracies in the model fits for the various parameter sets. However, the results show a preference for a carbon disk with particle size distributions ≥10 μm. Additionally, a linear correlation between the MCRT noon and basal temperatures serves as a tool for effectively eliminating portions of the parameter space.« less

Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yang, Fangfang; Tsui, Kwok-Leung

2017-09-01

Brownian motion with adaptive drift has attracted much attention in prognostics because its first hitting time is highly relevant to remaining useful life prediction and it follows the inverse Gaussian distribution. Besides linear degradation modeling, nonlinear-drifted Brownian motion has been developed to model nonlinear degradation. Moreover, the first hitting time distribution of the nonlinear-drifted Brownian motion has been approximated by time-space transformation. In the previous studies, the drift coefficient is the only hidden state used in state space modeling of the nonlinear-drifted Brownian motion. Besides the drift coefficient, parameters of a nonlinear function used in the nonlinear-drifted Brownian motion should be treated as additional hidden states of state space modeling to make the nonlinear-drifted Brownian motion more flexible. In this paper, a prognostic method based on nonlinear-drifted Brownian motion with multiple hidden states is proposed and then it is applied to predict remaining useful life of rechargeable batteries. 26 sets of rechargeable battery degradation samples are analyzed to validate the effectiveness of the proposed prognostic method. Moreover, some comparisons with a standard particle filter based prognostic method, a spherical cubature particle filter based prognostic method and two classic Bayesian prognostic methods are conducted to highlight the superiority of the proposed prognostic method. Results show that the proposed prognostic method has lower average prediction errors than the particle filter based prognostic methods and the classic Bayesian prognostic methods for battery remaining useful life prediction.

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance

PubMed Central

Ley-Bosch, Carlos; Alonso-González, Itziar; Sánchez-Rodríguez, David; Ramírez-Casañas, Carlos

2016-01-01

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results. PMID:26861352

Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance.

PubMed

Ley-Bosch, Carlos; Alonso-González, Itziar; Sánchez-Rodríguez, David; Ramírez-Casañas, Carlos

2016-02-06

In the last few years, the increasing use of LEDs in illumination systems has been conducted due to the emergence of Visible Light Communication (VLC) technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. In 2011, the Institute of Electrical and Electronics Engineers (IEEE) published the IEEE 802.15.7 standard for Wireless Personal Area Networks based on VLC. Due to limitations in the coverage of the transmitted signal, wireless networks can suffer from the hidden node problems, when there are nodes in the network whose transmissions are not detected by other nodes. This problem can cause an important degradation in communications when they are made by means of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access control method, which is used in IEEE 802.15.7 This research work evaluates the effects of the hidden node problem in the performance of the IEEE 802.15.7 standard We implement a simulator and analyze VLC performance in terms of parameters like end-to-end goodput and message loss rate. As part of this research work, a solution to the hidden node problem is proposed, based on the use of idle patterns defined in the standard. Idle patterns are sent by the network coordinator node to communicate to the other nodes that there is an ongoing transmission. The validity of the proposed solution is demonstrated with simulation results.

Classification of Arnold-Beltrami flows and their hidden symmetries

NASA Astrophysics Data System (ADS)

Fré, P.; Sorin, A. S.

2015-07-01

In the context of mathematical hydrodynamics, we consider the group theory structure which underlies the so named ABC flows introduced by Beltrami, Arnold and Childress. Main reference points are Arnold's theorem stating that, for flows taking place on compact three manifolds ℳ3, the only velocity fields able to produce chaotic streamlines are those satisfying Beltrami equation and the modern topological conception of contact structures, each of which admits a representative contact one-form also satisfying Beltrami equation. We advocate that Beltrami equation is nothing else but the eigenstate equation for the first order Laplace-Beltrami operator ★ g d, which can be solved by using time-honored harmonic analysis. Taking for ℳ3, a torus T 3 constructed as ℝ3/Λ, where Λ is a crystallographic lattice, we present a general algorithm to construct solutions of the Beltrami equation which utilizes as main ingredient the orbits under the action of the point group B A of three-vectors in the momentum lattice *Λ. Inspired by the crystallographic construction of space groups, we introduce the new notion of a Universal Classifying Group which contains all space groups as proper subgroups. We show that the ★ g d eigenfunctions are naturally arranged into irreducible representations of and by means of a systematic use of the branching rules with respect to various possible subgroups we search and find Beltrami fields with non trivial hidden symmetries. In the case of the cubic lattice the point group is the proper octahedral group O24 and the Universal Classifying Group is a finite group G1536 of order |G1536| = 1536 which we study in full detail deriving all of its 37 irreducible representations and the associated character table. We show that the O24 orbits in the cubic lattice are arranged into 48 equivalence classes, the parameters of the corresponding Beltrami vector fields filling all the 37 irreducible representations of G1536. In this way we obtain an exhaustive classification of all generalized ABC- flows and of their hidden symmetries. We make several conceptual comments about the need of a field-theory yielding Beltrami equation as a field equation and/or an instanton equation and on the possible relation of Arnold-Beltrami flows with (supersymmetric) Chern-Simons gauge theories. We also suggest linear generalizations of Beltrami equation to higher odd-dimensions that are different from the non-linear one proposed by Arnold and possibly make contact with M-theory and the geometry of flux-compactifications.

A modulation wave approach to the order hidden in disorder

PubMed Central

Withers, Ray

2015-01-01

The usefulness of a modulation wave approach to understanding and interpreting the highly structured continuous diffuse intensity distributions characteristic of the reciprocal spaces of the very large family of inherently flexible materials which exhibit ordered ‘disorder’ is pointed out. It is shown that both longer range order and truly short-range order are simultaneously encoded in highly structured diffuse intensity distributions. The long-range ordered crystal chemical rules giving rise to such diffuse distributions are highlighted, along with the existence and usefulness of systematic extinction conditions in these types of structured diffuse distributions. PMID:25610629

Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning

ERIC Educational Resources Information Center

MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R.

2015-01-01

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…

Electric transport of a single-crystal iron chalcogenide FeSe superconductor: Evidence of symmetry-breakdown nematicity and additional ultrafast Dirac cone-like carriers

NASA Astrophysics Data System (ADS)

Huynh, K. K.; Tanabe, Y.; Urata, T.; Oguro, H.; Heguri, S.; Watanabe, K.; Tanigaki, K.

2014-10-01

An SDW antiferromagnetic (SDW-AF) low-temperature phase transition is generally observed and the AF spin fluctuations are considered to play an important role for the superconductivity pairing mechanism in FeAs superconductors. However, a similar magnetic phase transition is not observed in FeSe superconductors, which has caused considerable discussion. We report on the intrinsic electronic states of FeSe as elucidated by electric transport measurements under magnetic fields using a high quality single crystal. A mobility spectrum analysis, an ab initio method that does not make assumptions on the transport parameters in a multicarrier system, provides very important and clear evidence that another hidden order, most likely the symmetry broken from the tetragonal C4 symmetry to the C2 symmetry nematicity associated with the selective d -orbital splitting, exists in the case of superconducting FeSe other than the AF magnetic order spin fluctuations. The intrinsic low-temperature phase in FeSe is in the almost compensated semimetallic states but is additionally accompanied by Dirac cone-like ultrafast electrons ˜104cm2(VS) -1 as minority carriers.

Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars

PubMed Central

Poon, Art F. Y.; Brouwer, Kimberly C.; Strathdee, Steffanie A.; Firestone-Cruz, Michelle; Lozada, Remedios M.; Kosakovsky Pond, Sergei L.; Heckathorn, Douglas D.; Frost, Simon D. W.

2009-01-01

Background Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Methodology/Principal Findings Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. Conclusions SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics. PMID:19738904

The Hidden Dimensions of Art.

ERIC Educational Resources Information Center

Klein, Bruce

1982-01-01

Describes an art program for preschool children that includes four social dimensions of art in order to heighten aesthetic perception, improve artistic creativity, and nurture self-esteem. The social dimensions are children having power, children acting on norms legitimate in their own eyes, children functioning "nonestrangedly," and children…

Bio-inspired passive actuator simulating an abalone shell mechanism for structural control

NASA Astrophysics Data System (ADS)

Yang, Henry T. Y.; Lin, Chun-Hung; Bridges, Daniel; Randall, Connor J.; Hansma, Paul K.

2010-10-01

An energy dispersion mechanism called 'sacrificial bonds and hidden length', which is found in some biological systems, such as abalone shells and bones, is the inspiration for new strategies for structural control. Sacrificial bonds and hidden length can substantially increase the stiffness and enhance energy dissipation in the constituent molecules of abalone shells and bone. Having been inspired by the usefulness and effectiveness of such a mechanism, which has evolved over millions of years and countless cycles of evolutions, the authors employ the conceptual underpinnings of this mechanism to develop a bio-inspired passive actuator. This paper presents a fundamental method for optimally designing such bio-inspired passive actuators for structural control. To optimize the bio-inspired passive actuator, a simple method utilizing the force-displacement-velocity (FDV) plots based on LQR control is proposed. A linear regression approach is adopted in this research to find the initial values of the desired parameters for the bio-inspired passive actuator. The illustrative examples, conducted by numerical simulation with experimental validation, suggest that the bio-inspired passive actuator based on sacrificial bonds and hidden length may be comparable in performance to state-of-the-art semi-active actuators.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

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

PubMed

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.

Analyzing Hidden Semantics in Social Bookmarking of Open Educational Resources

NASA Astrophysics Data System (ADS)

Minguillón, Julià

Web 2.0 services such as social bookmarking allow users to manage and share the links they find interesting, adding their own tags for describing them. This is especially interesting in the field of open educational resources, as delicious is a simple way to bridge the institutional point of view (i.e. learning object repositories) with the individual one (i.e. personal collections), thus promoting the discovering and sharing of such resources by other users. In this paper we propose a methodology for analyzing such tags in order to discover hidden semantics (i.e. taxonomies and vocabularies) that can be used to improve descriptions of learning objects and make learning object repositories more visible and discoverable. We propose the use of a simple statistical analysis tool such as principal component analysis to discover which tags create clusters that can be semantically interpreted. We will compare the obtained results with a collection of resources related to open educational resources, in order to better understand the real needs of people searching for open educational resources.

Intratumoral heterogeneity analysis reveals hidden associations between protein expression losses and patient survival in clear cell renal cell carcinoma

PubMed Central

Devarajan, Karthik; Parsons, Theodore; Wang, Qiong; O'Neill, Raymond; Solomides, Charalambos; Peiper, Stephen C.; Testa, Joseph R.; Uzzo, Robert; Yang, Haifeng

2017-01-01

Intratumoral heterogeneity (ITH) is a prominent feature of kidney cancer. It is not known whether it has utility in finding associations between protein expression and clinical parameters. We used ITH that is detected by immunohistochemistry (IHC) to aid the association analysis between the loss of SWI/SNF components and clinical parameters.160 ccRCC tumors (40 per tumor stage) were used to generate tissue microarray (TMA). Four foci from different regions of each tumor were selected. IHC was performed against PBRM1, ARID1A, SETD2, SMARCA4, and SMARCA2. Statistical analyses were performed to correlate biomarker losses with patho-clinical parameters. Categorical variables were compared between groups using Fisher's exact tests. Univariate and multivariable analyses were used to correlate biomarker changes and patient survivals. Multivariable analyses were performed by constructing decision trees using the classification and regression trees (CART) methodology. IHC detected widespread ITH in ccRCC tumors. The statistical analysis of the “Truncal loss” (root loss) found additional correlations between biomarker losses and tumor stages than the traditional “Loss in tumor (total)”. Losses of SMARCA4 or SMARCA2 significantly improved prognosis for overall survival (OS). Losses of PBRM1, ARID1A or SETD2 had the opposite effect. Thus “Truncal Loss” analysis revealed hidden links between protein losses and patient survival in ccRCC. PMID:28445125

A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-01-01

Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning Multivariate Time Series (MTS). However, in general, it is difficult to set the dimension of an LDS's hidden state space. A small number of hidden states may not be able to model the complexities of a MTS, while a large number of hidden states can lead to overfitting. In this paper, we study learning methods that impose various regularization penalties on the transition matrix of the LDS model and propose a regularized LDS learning framework (rLDS) which aims to (1) automatically shut down LDSs' spurious and unnecessary dimensions, and consequently, address the problem of choosing the optimal number of hidden states; (2) prevent the overfitting problem given a small amount of MTS data; and (3) support accurate MTS forecasting. To learn the regularized LDS from data we incorporate a second order cone program and a generalized gradient descent method into the Maximum a Posteriori framework and use Expectation Maximization to obtain a low-rank transition matrix of the LDS model. We propose two priors for modeling the matrix which lead to two instances of our rLDS. We show that our rLDS is able to recover well the intrinsic dimensionality of the time series dynamics and it improves the predictive performance when compared to baselines on both synthetic and real-world MTS datasets.

Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

PubMed

Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

2010-02-15

Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional neuroanatomy, and behavior, patients activated different task-related neural circuits, not associated with appropriate regional neuroanatomy. GLM analysis elucidated several comparable regions, with the exception of the hippocampus. Inefficient allocentric learning and memory in patients may be related to an inability to recruit appropriate task-dependent neural circuits. Copyright 2009 Elsevier Inc. All rights reserved.

Looking for the WIMP next door

NASA Astrophysics Data System (ADS)

Evans, Jared A.; Gori, Stefania; Shelton, Jessie

2018-02-01

We comprehensively study experimental constraints and prospects for a class of minimal hidden sector dark matter (DM) models, highlighting how the cosmological history of these models informs the experimental signals. We study simple `secluded' models, where the DM freezes out into unstable dark mediator states, and consider the minimal cosmic history of this dark sector, where coupling of the dark mediator to the SM was sufficient to keep the two sectors in thermal equilibrium at early times. In the well-motivated case where the dark mediators couple to the Standard Model (SM) via renormalizable interactions, the requirement of thermal equilibrium provides a minimal, UV-insensitive, and predictive cosmology for hidden sector dark matter. We call DM that freezes out of a dark radiation bath in thermal equilibrium with the SM a WIMP next door, and demonstrate that the parameter space for such WIMPs next door is sharply defined, bounded, and in large part potentially accessible. This parameter space, and the corresponding signals, depend on the leading interaction between the SM and the dark mediator; we establish it for both Higgs and vector portal interactions. In particular, there is a cosmological lower bound on the portal coupling strength necessary to thermalize the two sectors in the early universe. We determine this thermalization floor as a function of equilibration temperature for the first time. We demonstrate that direct detection experiments are currently probing this cosmological lower bound in some regions of parameter space, while indirect detection signals and terrestrial searches for the mediator cut further into the viable parameter space. We present regions of interest for both direct detection and dark mediator searches, including motivated parameter space for the direct detection of sub-GeV DM.

Extended Friedberg-Lee hidden symmetries, quark masses, and CP violation with four generations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bar-Shalom, Shaouly; Oaknin, David; Soni, Amarjit

2009-07-01

Motivated in part by the several observed anomalies involving CP asymmetries of B and B{sub s} decays, we consider the standard model with a 4th sequential family (SM4) which seems to offer a rather simple resolution. We initially assume T-invariance by taking the up and down-quark 4x4 mass matrix to be real. Following Friedberg and Lee (FL), we then impose a hidden symmetry on the unobserved (hidden) up and down-quark SU(2) states. The hidden symmetry for four generations ensures the existence of two zero-mass eigenstates, which we take to be the (u,c) and (d,s) states in the up and down-quarkmore » sectors, respectively. Then, we simultaneously break T-invariance and the hidden symmetry by introducing two phase factors in each sector. This breaking mechanism generates the small quark masses m{sub u}, m{sub c} and m{sub d}, m{sub s}, which, along with the orientation of the hidden symmetry, determine the size of CP-violation in the SM4. For illustration we choose a specific physical picture for the hidden symmetry and the breaking mechanism that reproduces the observed quark masses, mixing angles and CP-violation, and at the same time allows us to further obtain very interesting relations/predictions for the mixing angles of t and t'. For example, with this choice we get V{sub td}{approx}(V{sub cb}/V{sub cd}-V{sub ts}/V{sub us})+O({lambda}{sup 2}) and V{sub t{sup '}}{sub b}{approx}V{sub t{sup '}}{sub d}{center_dot}(V{sub cb}/V{sub cd}), V{sub tb{sup '}}{approx}V{sub t{sup '}}{sub d}{center_dot}(V{sub ts}/V{sub us}), implying that V{sub t{sup '}}{sub d}>V{sub t{sup '}}{sub b}, V{sub tb{sup '}}. We furthermore find that the Cabibbo angle is related to the orientation of the hidden symmetry and that the key CP-violating quantity of our model at high energies, J{sub SM4}{identical_to}Im(V{sub tb}V{sub t{sup '}}{sub b}*V{sub t{sup '}}{sub b{sup '}}V{sub tb{sup '}}*), which is the high-energy analogue of the Jarlskog invariant of the SM, is proportional to the light-quark masses and the measured Cabibbo-Kobayashi-Maskawa quark-mixing matrix angles: |J{sub SM4}|{approx}A{sup 3}{lambda}{sup 5}x({radical}(m{sub u}/m{sub t})+{radical}(m{sub c}/m{sub t{sup '}})-{radical}(m{sub d}/m{sub b})+{radical}(m{sub s}/m{sub b{sup '}})){approx}10{sup -5}, where A{approx}0.81 and {lambda}=0.2257 are the Wolfenstein parameters. Other choices for the orientation of the hidden symmetry and/or the breaking mechanism may lead to different physical outcomes. A general solution, obtained numerically, will be presented in a forthcoming paper.« less

Extended Friedberg-Lee hidden symmetries, quark masses,and CP violation with four generations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bar-Shalom, S.; Soni, A.; Oaknin, D.

2009-07-16

Motivated in part by the several observed anomalies involving CP asymmetries of B and B{sub s} decays, we consider the standard model with a 4th sequential family (SM4) which seems to offer a rather simple resolution. We initially assume T-invariance by taking the up and down-quark 4 x 4 mass matrix to be real. Following Friedberg and Lee (FL), we then impose a hidden symmetry on the unobserved (hidden) up and down-quark SU(2) states. The hidden symmetry for four generations ensures the existence of two zero-mass eigenstates, which we take to be the (u,c) and (d,s) states in the upmore » and down-quark sectors, respectively. Then, we simultaneously break T-invariance and the hidden symmetry by introducing two phase factors in each sector. This breaking mechanism generates the small quark masses m{sub u}, m{sub c} and m{sub d}, m{sub s}, which, along with the orientation of the hidden symmetry, determine the size of CP-violation in the SM4. For illustration we choose a specific physical picture for the hidden symmetry and the breaking mechanism that reproduces the observed quark masses, mixing angles and CP-violation, and at the same time allows us to further obtain very interesting relations/predictions for the mixing angles of t and t'. For example, with this choice we get V{sub td} {approx} (V{sub cb}/V{sub cd}-V{sub ts}/V{sub us}) + O({lambda}{sup 2}) and V{sub t'b}{approx}V{sub t'd{sm_bullet}}(V{sub cb}/V{sub cd}), V{sub tb'}V{sub t'd{sm_bullet}}(V{sub ts}/V{sub us}), implying that V{sub t'd} > V{sub t'b}, V{sub tb'}. We furthermore find that the Cabibbo angle is related to the orientation of the hidden symmetry and that the key CP-violating quantity of our model at high energies, J{sub SM4} {triple_bond} Im(V{sub tb}V{sub t'b*}V{sub t'b{prime}}V{sub tb'*}), which is the high-energy analogue of the Jarlskog invariant of the SM, is proportional to the light-quark masses and the measured Cabibbo-Kobayashi-Maskawa quark-mixing matrix angles: |J{sub SM4}|A{sup 3}{lambda}{sup 5} x ({radical}(m{sub u}/m{sub t}) + {radical}m{sub c}/m{sub t'}-{radical}(m{sub d}/m{sub b}) + {radical}m{sub s}/m{sub b'}) {approx} 10{sup -5}, where A {approx} 0.81 and {lambda} = 0.2257 are the Wolfenstein parameters. Other choices for the orientation of the hidden symmetry and/or the breaking mechanism may lead to different physical outcomes. A general solution, obtained numerically, will be presented in a forthcoming paper.« less

Redefining Attack: Taking the Offensive Against Networks

DTIC Science & Technology

2003-03-01

for his insight, inspiration, and thought provoking discussion that stretched the limits of our imaginations. The thesis all began with owls, mice ......order of battle for these elusive networks is hidden from view. There are few tanks to count, barracks and command centers to reconnoiter or industrial

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

Minorities in Higher Education: Selected Papers from an Interdisciplinary Conference Held at Hofstra University (Hempstead, New York, March 9-11, 1989).

ERIC Educational Resources Information Center

Hofstra Univ., Hempstead, NY.

This report provides a selection of conference papers which discuss issues concerning minority participation in higher education, beginning with recognition of the many discrepancies between what are expressed as personal and organizational values and what parameters remain hidden. The papers consider the causes for limited minority participation…

Mode identification using stochastic hybrid models with applications to conflict detection and resolution

NASA Astrophysics Data System (ADS)

Naseri Kouzehgarani, Asal

2009-12-01

Most models of aircraft trajectories are non-linear and stochastic in nature; and their internal parameters are often poorly defined. The ability to model, simulate and analyze realistic air traffic management conflict detection scenarios in a scalable, composable, multi-aircraft fashion is an extremely difficult endeavor. Accurate techniques for aircraft mode detection are critical in order to enable the precise projection of aircraft conflicts, and for the enactment of altitude separation resolution strategies. Conflict detection is an inherently probabilistic endeavor; our ability to detect conflicts in a timely and accurate manner over a fixed time horizon is traded off against the increased human workload created by false alarms---that is, situations that would not develop into an actual conflict, or would resolve naturally in the appropriate time horizon-thereby introducing a measure of probabilistic uncertainty in any decision aid fashioned to assist air traffic controllers. The interaction of the continuous dynamics of the aircraft, used for prediction purposes, with the discrete conflict detection logic gives rise to the hybrid nature of the overall system. The introduction of the probabilistic element, common to decision alerting and aiding devices, places the conflict detection and resolution problem in the domain of probabilistic hybrid phenomena. A hidden Markov model (HMM) has two stochastic components: a finite-state Markov chain and a finite set of output probability distributions. In other words an unobservable stochastic process (hidden) that can only be observed through another set of stochastic processes that generate the sequence of observations. The problem of self separation in distributed air traffic management reduces to the ability of aircraft to communicate state information to neighboring aircraft, as well as model the evolution of aircraft trajectories between communications, in the presence of probabilistic uncertain dynamics as well as partially observable and uncertain data. We introduce the Hybrid Hidden Markov Modeling (HHMM) formalism to enable the prediction of the stochastic aircraft states (and thus, potential conflicts), by combining elements of the probabilistic timed input output automaton and the partially observable Markov decision process frameworks, along with the novel addition of a Markovian scheduler to remove the non-deterministic elements arising from the enabling of several actions simultaneously. Comparisons of aircraft in level, climbing/descending and turning flight are performed, and unknown flight track data is evaluated probabilistically against the tuned model in order to assess the effectiveness of the model in detecting the switch between multiple flight modes for a given aircraft. This also allows for the generation of probabilistic distribution over the execution traces of the hybrid hidden Markov model, which then enables the prediction of the states of aircraft based on partially observable and uncertain data. Based on the composition properties of the HHMM, we study a decentralized air traffic system where aircraft are moving along streams and can perform cruise, accelerate, climb and turn maneuvers. We develop a common decentralized policy for conflict avoidance with spatially distributed agents (aircraft in the sky) and assure its safety properties via correctness proofs.

Light Higgs channel of the resonant decay of magnon condensate in superfluid (3)He-B.

PubMed

Zavjalov, V V; Autti, S; Eltsov, V B; Heikkinen, P J; Volovik, G E

2016-01-08

In superfluids the order parameter, which describes spontaneous symmetry breaking, is an analogue of the Higgs field in the Standard Model of particle physics. Oscillations of the field amplitude are massive Higgs bosons, while oscillations of the orientation are massless Nambu-Goldstone bosons. The 125 GeV Higgs boson, discovered at Large Hadron Collider, is light compared with electroweak energy scale. Here, we show that such light Higgs exists in superfluid (3)He-B, where one of three Nambu-Goldstone spin-wave modes acquires small mass due to the spin-orbit interaction. Other modes become optical and acoustic magnons. We observe parametric decay of Bose-Einstein condensate of optical magnons to light Higgs modes and decay of optical to acoustic magnons. Formation of a light Higgs from a Nambu-Goldstone mode observed in (3)He-B opens a possibility that such scenario can be realized in other systems, where violation of some hidden symmetry is possible, including the Standard Model.

Light Higgs channel of the resonant decay of magnon condensate in superfluid 3He-B

PubMed Central

Zavjalov, V. V.; Autti, S.; Eltsov, V. B.; Heikkinen, P. J.; Volovik, G. E.

2016-01-01

In superfluids the order parameter, which describes spontaneous symmetry breaking, is an analogue of the Higgs field in the Standard Model of particle physics. Oscillations of the field amplitude are massive Higgs bosons, while oscillations of the orientation are massless Nambu-Goldstone bosons. The 125 GeV Higgs boson, discovered at Large Hadron Collider, is light compared with electroweak energy scale. Here, we show that such light Higgs exists in superfluid 3He-B, where one of three Nambu-Goldstone spin-wave modes acquires small mass due to the spin–orbit interaction. Other modes become optical and acoustic magnons. We observe parametric decay of Bose-Einstein condensate of optical magnons to light Higgs modes and decay of optical to acoustic magnons. Formation of a light Higgs from a Nambu-Goldstone mode observed in 3He-B opens a possibility that such scenario can be realized in other systems, where violation of some hidden symmetry is possible, including the Standard Model. PMID:26743951

Dimensional feature weighting utilizing multiple kernel learning for single-channel talker location discrimination using the acoustic transfer function.

PubMed

Takashima, Ryoichi; Takiguchi, Tetsuya; Ariki, Yasuo

2013-02-01

This paper presents a method for discriminating the location of the sound source (talker) using only a single microphone. In a previous work, the single-channel approach for discriminating the location of the sound source was discussed, where the acoustic transfer function from a user's position is estimated by using a hidden Markov model of clean speech in the cepstral domain. In this paper, each cepstral dimension of the acoustic transfer function is newly weighted, in order to obtain the cepstral dimensions having information that is useful for classifying the user's position. Then, this paper proposes a feature-weighting method for the cepstral parameter using multiple kernel learning, defining the base kernels for each cepstral dimension of the acoustic transfer function. The user's position is trained and classified by support vector machine. The effectiveness of this method has been confirmed by sound source (talker) localization experiments performed in different room environments.

A recurrent self-organizing neural fuzzy inference network.

PubMed

Juang, C F; Lin, C T

1999-01-01

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.

Phenomenology of pure-gauge hidden valleys at hadron colliders

NASA Astrophysics Data System (ADS)

Juknevich, Jose E.

Expectations for new physics at the LHC have been greatly influenced by the Hierarchy problem of electroweak symmetry breaking. However, there are reasons to believe that the LHC may still discover new physics, but not directly related to the resolution of the Hierarchy problem. To ensure that such a physics does not go undiscovered requires precise understanding of how new phenomena will reveal themselves in the current and future generation of particle-physics experiments. Given this fact it seems sensible to explore other approaches to this problem; we study three alternatives here. In this thesis I argue for the plausibility that the standard model is coupled, through new massive charged or colored particles, to a hidden sector whose low energy dynamics is controlled by a pure Yang-Mills theory, with no light matter. Such a sector would have numerous metastable "hidden glueballs" built from the hidden gluons. These states would decay to particles of the standard model. I consider the phenomenology of this scenario, and find formulas for the lifetimes and branching ratios of the most important of these states. The dominant decays are to two standard model gauge bosons or to fermion-antifermion pairs, or by radiative decays with photon or Higgs emission, leading to jet- and photon-rich signals, and some occasional leptons. The presence of effective operators of different mass dimensions, often competing with each other, together with a great diversity of states, leads to a great variability in the lifetimes and decay modes of the hidden glueballs. I find that most of the operators considered in this work are not heavily constrained by precision electroweak physics, therefore leaving plenty of room in the parameter space to be explored by the future experiments at the LHC. Finally, I discuss several issues on the phenomenology of the new massive particles as well as an outlook for experimental searches.

Games Children Play: An Exercise Illustrating Agents of Socialization.

ERIC Educational Resources Information Center

Glasberg, Davita Silfen; Maatita, Florence; Nangle, Barbara; Schauer, Tracy

1998-01-01

Argues that children's toys and games contribute to representing and reinforcing dominant conceptions of appropriate social identities. Invites students to play a number of children's games in order to experience the "hidden agendas" concerning race, class, gender, and political socialization conveyed to them while they are playing. (DSK)

Proximal versus distal cue utilization in spatial navigation: the role of visual acuity?

PubMed

Carman, Heidi M; Mactutus, Charles F

2002-09-01

Proximal versus distal cue use in the Morris water maze is a widely accepted strategy for the dissociation of various problems affecting spatial navigation in rats such as aging, head trauma, lesions, and pharmacological or hormonal agents. Of the limited number of ontogenetic rat studies conducted, the majority have approached the problem of preweanling spatial navigation through a similar proximal-distal dissociation. An implicit assumption among all of these studies has been that the animal's visual system is sufficient to permit robust spatial navigation. We challenged this assumption and have addressed the role of visual acuity in spatial navigation in the preweanling Fischer 344-N rat by training animals to locate a visible (proximal) or hidden (distal) platform using double or null extramaze cues within the testing environment. All pups demonstrated improved performance across training, but animals presented with a visible platform, regardless of extramaze cues, simultaneously reached asymptotic performance levels; animals presented with a hidden platform, dependent upon location of extramaze cues, differentially reached asymptotic performance levels. Probe trial performance, defined by quadrant time and platform crossings, revealed that distal-double-cue pups demonstrated spatial navigational ability superior to that of the remaining groups. These results suggest that a pup's ability to spatially navigate a hidden platform is dependent on not only its response repertoire and task parameters, but also its visual acuity, as determined by the extramaze cue location within the testing environment. The standard hidden versus visible platform dissociation may not be a satisfactory strategy for the control of potential sensory deficits.

Spectral reflectance inversion with high accuracy on green target

NASA Astrophysics Data System (ADS)

Jiang, Le; Yuan, Jinping; Li, Yong; Bai, Tingzhu; Liu, Shuoqiong; Jin, Jianzhou; Shen, Jiyun

2016-09-01

Using Landsat-7 ETM remote sensing data, the inversion of spectral reflectance of green wheat in visible and near infrared waveband in Yingke, China is studied. In order to solve the problem of lower inversion accuracy, custom atmospheric conditions method based on moderate resolution transmission model (MODTRAN) is put forward. Real atmospheric parameters are considered when adopting this method. The atmospheric radiative transfer theory to calculate atmospheric parameters is introduced first and then the inversion process of spectral reflectance is illustrated in detail. At last the inversion result is compared with simulated atmospheric conditions method which was a widely used method by previous researchers. The comparison shows that the inversion accuracy of this paper's method is higher in all inversion bands; the inversed spectral reflectance curve by this paper's method is more similar to the measured reflectance curve of wheat and better reflects the spectral reflectance characteristics of green plant which is very different from green artificial target. Thus, whether a green target is a plant or artificial target can be judged by reflectance inversion based on remote sensing image. This paper's research is helpful for the judgment of green artificial target hidden in the greenery, which has a great significance on the precise strike of green camouflaged weapons in military field.

Intelligent complementary sliding-mode control for LUSMS-based X-Y-theta motion control stage.

PubMed

Lin, Faa-Jeng; Chen, Syuan-Yi; Shyu, Kuo-Kai; Liu, Yen-Hung

2010-07-01

An intelligent complementary sliding-mode control (ICSMC) system using a recurrent wavelet-based Elman neural network (RWENN) estimator is proposed in this study to control the mover position of a linear ultrasonic motors (LUSMs)-based X-Y-theta motion control stage for the tracking of various contours. By the addition of a complementary generalized error transformation, the complementary sliding-mode control (CSMC) can efficiently reduce the guaranteed ultimate bound of the tracking error by half compared with the slidingmode control (SMC) while using the saturation function. To estimate a lumped uncertainty on-line and replace the hitting control of the CSMC directly, the RWENN estimator is adopted in the proposed ICSMC system. In the RWENN, each hidden neuron employs a different wavelet function as an activation function to improve both the convergent precision and the convergent time compared with the conventional Elman neural network (ENN). The estimation laws of the RWENN are derived using the Lyapunov stability theorem to train the network parameters on-line. A robust compensator is also proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher-order terms in Taylor series. Finally, some experimental results of various contours tracking show that the tracking performance of the ICSMC system is significantly improved compared with the SMC and CSMC systems.

Parameter diagnostics of phases and phase transition learning by neural networks

NASA Astrophysics Data System (ADS)

Suchsland, Philippe; Wessel, Stefan

2018-05-01

We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

Chen, B.; Creze, M.; Robin, A.; Bienayme, O.

1992-01-01

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

PubMed

Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

2013-01-01

There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P < 0.05) and no significant difference between Cox and the neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer). Probabilities of survival were calculated using three neural network models with 3, 5, and 7 nodes in the hidden layer, and it has been observed that none of the predictions was significantly different from results with the Kaplan-Meier method and they appeared more comparable towards the last months (fifth year). However, we observed better accuracy using the neural network with 5 nodes in the hidden layer. Using the Cox proportional hazards and a neural network with 3 nodes in the hidden layer, we found enhanced accuracy with the neural network model. Neural networks can provide more accurate predictions for survival probabilities compared to the Cox proportional hazards mode, especially now that advances in computer sciences have eliminated limitations associated with complex computations. It is not recommended in order to adding too many hidden layer nodes because sample size related effects can reduce the accuracy. We recommend increasing the number of nodes to a point that increased accuracy continues (decrease in mean standard error), however increasing nodes should cease when a change in this trend is observed.

Light and compressed gluinos at the LHC via string theory.

PubMed

AbdusSalam, S S

2017-01-01

In this article, we show that making global fits of string theory model parameters to data is an interesting mechanism for probing, mapping and forecasting connections of the theory to real world physics. We considered a large volume scenario (LVS) with D3-brane matter fields and supersymmetry breaking. A global fit of the parameters to low-energy data shows that the set of LVS models are associated with light gluinos which are quasi-degenerate with the neutralinos and charginos they can promptly decay into, and thus they are possibly hidden to current LHC gluino search strategies.

Troublesome aspects of the Renyi-MaxEnt treatment.

PubMed

Plastino, A; Rocca, M C; Pennini, F

2016-07-01

We study in great detail the possible existence of a Renyi-associated thermodynamics, with negative results. In particular, we uncover a hidden relation in Renyi's variational problem (MaxEnt). This relation connects the two associated Lagrange multipliers (canonical ensemble) with the mean energy 〈U〉 and the Renyi parameter α. As a consequence of such relation, we obtain anomalous Renyi-MaxEnt thermodynamic results.

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

PubMed

Belciug, Smaranda; Gorunescu, Florin

2018-06-08

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models. Copyright © 2018. Published by Elsevier Inc.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

The Hidden Diversity of Flagellated Protists in Soil.

PubMed

Venter, Paul Christiaan; Nitsche, Frank; Arndt, Hartmut

2018-07-01

Protists are among the most diverse and abundant eukaryotes in soil. However, gaps between described and sequenced protist morphospecies still present a pending problem when surveying environmental samples for known species using molecular methods. The number of sequences in the molecular PR 2 database (∼130,000) is limited compared to the species richness expected (>1 million protist species) - limiting the recovery rate. This is important, since high throughput sequencing (HTS) methods are used to find associative patterns between functional traits, taxa and environmental parameters. We performed HTS to survey soil flagellates in 150 grasslands of central Europe, and tested the recovery rate of ten previously isolated and cultivated cercomonad species, among locally found diversity. We recovered sequences for reference soil flagellate species, but also a great number of their phylogenetically evaluated genetic variants, among rare and dominant taxa with presumably own biogeography. This was recorded among dominant (cercozoans, Sandona), rare (apusozoans) and a large hidden diversity of predominantly aquatic protists in soil (choanoflagellates, bicosoecids) often forming novel clades associated with uncultured environmental sequences. Evaluating the reads, instead of the OTUs that individual reads are usually clustered into, we discovered that much of this hidden diversity may be lost due to clustering. Copyright © 2018 Elsevier GmbH. All rights reserved.

New axion and hidden photon constraints from a solar data global fit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vinyoles, N.; Serenelli, A.; Isern, J.

2015-10-01

We present a new statistical analysis that combines helioseismology (sound speed, surface helium and convective radius) and solar neutrino observations (the {sup 8}B and {sup 7}Be fluxes) to place upper limits to the properties of non standard weakly interacting particles. Our analysis includes theoretical and observational errors, accounts for tensions between input parameters of solar models and can be easily extended to include other observational constraints. We present two applications to test the method: the well studied case of axions and axion-like particles and the more novel case of low mass hidden photons. For axions we obtain an upper limitmore » at 3σ for the axion-photon coupling constant of g{sub aγ} < 4.1 · 10{sup −10} GeV{sup −1}. For hidden photons we obtain the most restrictive upper limit available accross a wide range of masses for the product of the kinetic mixing and mass of χ m < 1.8 ⋅ 10{sup −12} eV at 3σ. Both cases improve the previous solar constraints based on the Standard Solar Models showing the power of using a global statistical approach.« less

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Hidden scale invariance of metals

NASA Astrophysics Data System (ADS)

Hummel, Felix; Kresse, Georg; Dyre, Jeppe C.; Pedersen, Ulf R.

2015-11-01

Density functional theory (DFT) calculations of 58 liquid elements at their triple point show that most metals exhibit near proportionality between the thermal fluctuations of the virial and the potential energy in the isochoric ensemble. This demonstrates a general "hidden" scale invariance of metals making the condensed part of the thermodynamic phase diagram effectively one dimensional with respect to structure and dynamics. DFT computed density scaling exponents, related to the Grüneisen parameter, are in good agreement with experimental values for the 16 elements where reliable data were available. Hidden scale invariance is demonstrated in detail for magnesium by showing invariance of structure and dynamics. Computed melting curves of period three metals follow curves with invariance (isomorphs). The experimental structure factor of magnesium is predicted by assuming scale invariant inverse power-law (IPL) pair interactions. However, crystal packings of several transition metals (V, Cr, Mn, Fe, Nb, Mo, Ta, W, and Hg), most post-transition metals (Ga, In, Sn, and Tl), and the metalloids Si and Ge cannot be explained by the IPL assumption. The virial-energy correlation coefficients of iron and phosphorous are shown to increase at elevated pressures. Finally, we discuss how scale invariance explains the Grüneisen equation of state and a number of well-known empirical melting and freezing rules.

Pion decay constant and the {rho}-meson mass at finite temperature in hidden local symmetry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harada, M.; Shibata, A.

1997-06-01

We study the temperature dependence of the pion decay constant and {rho}-meson mass in the hidden local symmetry model at one loop. Using the standard imaginary time formalism, we include the thermal effect of the {rho} meson as well as that of the pion. We show that the pion gives a dominant contribution to the pion decay constant and the {rho}-meson contribution slightly decreases the critical temperature. The {rho}-meson pole mass increases as T{sup 4}/m{sub {rho}}{sup 2} at low temperature, dominated by the pion-loop effect. At high temperature, although the pion-loop effect decreases the {rho}-meson mass, the {rho}-loop contribution overcomesmore » the pion-loop contribution and the {rho}-meson mass increases with temperature. We also show that the conventional parameter a is stable as the temperature increases. {copyright} {ital 1997} {ital The American Physical Society}« less

Hidden Markov Item Response Theory Models for Responses and Response Times.

PubMed

Molenaar, Dylan; Oberski, Daniel; Vermunt, Jeroen; De Boeck, Paul

2016-01-01

Current approaches to model responses and response times to psychometric tests solely focus on between-subject differences in speed and ability. Within subjects, speed and ability are assumed to be constants. Violations of this assumption are generally absorbed in the residual of the model. As a result, within-subject departures from the between-subject speed and ability level remain undetected. These departures may be of interest to the researcher as they reflect differences in the response processes adopted on the items of a test. In this article, we propose a dynamic approach for responses and response times based on hidden Markov modeling to account for within-subject differences in responses and response times. A simulation study is conducted to demonstrate acceptable parameter recovery and acceptable performance of various fit indices in distinguishing between different models. In addition, both a confirmatory and an exploratory application are presented to demonstrate the practical value of the modeling approach.

Adaptive distributed source coding.

PubMed

Varodayan, David; Lin, Yao-Chung; Girod, Bernd

2012-05-01

We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.

Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment.

PubMed

Bidargaddi, Niranjan P; Chetty, Madhu; Kamruzzaman, Joarder

2008-06-01

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 forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm 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.

Using $$X(3823)\\to J/\\psi\\pi^+\\pi^-$$ to Identify Coupled-Channel Effects

DOE PAGES

Wang, Bo; Xu, Hao; Liu, Xiang; ...

2016-03-17

Very recently, a new charmonium-like state X(3823) was observed by the Belle and BESIII experiments, which is a good candidate of D-wave charmonium ψ(13D2). Since the X(3872) is just below the DD¯ * threshold, the decay X(3823) → J/ψπ +π - can be a golden channel to test the significance of coupled-channel effects. In this work, this decay is considered including both the hidden-charm dipion and the usual QCDME contributions. The partial decay width, the dipion invariant mass spectrum distribution dΓ[X(3823) → J/ψπ +π - ]/dmπ +π - , and the corresponding dΓ[X(3823) → J/ψπ +π - ]/d cos θmore » distribution are computed. Many parameters are determined from existing experimental data, leaving the results mainly dependent on only one unknown phase between the QCDME and hidden-charm dipion amplitudes.« less

Global-constrained hidden Markov model applied on wireless capsule endoscopy video segmentation

NASA Astrophysics Data System (ADS)

Wan, Yiwen; Duraisamy, Prakash; Alam, Mohammad S.; Buckles, Bill

2012-06-01

Accurate analysis of wireless capsule endoscopy (WCE) videos is vital but tedious. Automatic image analysis can expedite this task. Video segmentation of WCE into the four parts of the gastrointestinal tract is one way to assist a physician. The segmentation approach described in this paper integrates pattern recognition with statiscal analysis. Iniatially, a support vector machine is applied to classify video frames into four classes using a combination of multiple color and texture features as the feature vector. A Poisson cumulative distribution, for which the parameter depends on the length of segments, models a prior knowledge. A priori knowledge together with inter-frame difference serves as the global constraints driven by the underlying observation of each WCE video, which is fitted by Gaussian distribution to constrain the transition probability of hidden Markov model.Experimental results demonstrated effectiveness of the approach.

A Self-Organizing State-Space-Model Approach for Parameter Estimation in Hodgkin-Huxley-Type Models of Single Neurons

PubMed Central

Vavoulis, Dimitrios V.; Straub, Volko A.; Aston, John A. D.; Feng, Jianfeng

2012-01-01

Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in the construction of biophysical neuron models. PMID:22396632

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

Dialogue in the Relationships between Principals and Teachers: A Qualitative Study

ERIC Educational Resources Information Center

Prichard, Tracie Shelley

2013-01-01

This qualitative case study examines dialogue and discourse patterns between principals and teachers. It analyzes daily verbal interactions in order to identify shared meanings, hidden messages, and the dynamics of power. This study is also based on the belief that democracy in education is vital to maintaining a collaborative, people friendly…

Fibonacci Numbers Revisited: Technology-Motivated Inquiry into a Two-Parametric Difference Equation

ERIC Educational Resources Information Center

Abramovich, Sergei; Leonov, Gennady A.

2008-01-01

This article demonstrates how within an educational context, supported by the notion of hidden mathematics curriculum and enhanced by the use of technology, new mathematical knowledge can be discovered. More specifically, proceeding from the well-known representation of Fibonacci numbers through a second-order difference equation, this article…

FIRE ALARM SYSTEM OUTDATED.

ERIC Educational Resources Information Center

CHANDLER, L.T.

AN EFFICIENT FIRE ALARM SYSTEM SHOULD--(1) PROVIDE WARNING OF FIRES THAT START IN HIDDEN OR UNOCCUPIED LOCATIONS, (2) INDICATE WHERE THE FIRE IS, (3) GIVE ADVANCE WARNING TO FACULTY AND ADMINISTRATION SO THAT PANIC AND CONFUSION CAN BE AVOIDED AND ORDERLY EVACUATION OCCUR, (4) AUTOMATICALLY NOTIFY CITY FIRE HEADQUARTERS OF THE FIRE, (5) OPERATE BY…

Naming Is Taming: Outlining Psycho-Social Hypotheses with Regard to the Present Global Situation

ERIC Educational Resources Information Center

Rauch, Herbert

2014-01-01

Purpose: The purpose of this paper is to encourage a discourse aiming to better understand the "psycho-social situation" of many people--from a global perspective. The following "first hypotheses" are formulated; pointing to crucial "hidden agendas" which shall be "named" in order to focus attention towards…

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Differential effects of massed and spaced training on place and response learning: A memory systems perspective.

PubMed

Wingard, Jeffrey C; Goodman, Jarid; Leong, Kah-Chung; Packard, Mark G

2015-09-01

Studies employing brain lesion or intracerebral drug infusions in rats have demonstrated a double dissociation between the roles of the hippocampus and dorsolateral striatum in place and response learning. The hippocampus mediates a rapid cognitive learning process underlying place learning, whereas the dorsolateral striatum mediates a relatively slower learning process in which stimulus-response habits underlying response learning are acquired in an incremental fashion. One potential implication of these findings is that hippocampus-dependent learning may benefit from a relative massing of training trials, whereas dorsal striatum-dependent learning may benefit from a relative distribution of training trials. In order to examine this hypothesis, the present study compared the effects of massed (30s inter-trial interval; ITI) or spaced (30min ITI) training on acquisition of a hippocampus-dependent place learning task, and a dorsolateral striatum-dependent response task in a plus-maze. In the place task rats swam from varying start points (N or S) to a hidden escape platform located in a consistent spatial location (W). In the response task rats swam from varying start points (N or S) to a hidden escape platform located in the maze arm consistent with a body-turn response (left). In the place task, rats trained with the massed trial schedule acquired the task quicker than rats trained with the spaced trial schedule. In the response task, rats trained with the spaced trial schedule acquired the task quicker than rats trained with the massed trial schedule. The double dissociation observed suggests that the reinforcement parameters most conducive to effective learning in hippocampus-dependent and dorsolateral striatum-dependent learning may have differential temporal characteristics. Copyright © 2015 Elsevier B.V. All rights reserved.

Directed Hidden-Code Extractor for Environment-Sensitive Malwares

NASA Astrophysics Data System (ADS)

Jia, Chunfu; Wang, Zhi; Lu, Kai; Liu, Xinhai; Liu, Xin

Malware writers often use packing technique to hide malicious payload. A number of dynamic unpacking tools are.designed in order to identify and extract the hidden code in the packed malware. However, such unpacking methods.are all based on a highly controlled environment that is vulnerable to various anti-unpacking techniques. If execution.environment is suspicious, malwares may stay inactive for a long time or stop execution immediately to evade.detection. In this paper, we proposed a novel approach that automatically reasons about the environment requirements.imposed by malware, then directs a unpacking tool to change the controlled environment to extract the hide code at.the new environment. The experimental results show that our approach significantly increases the resilience of the.traditional unpacking tools to environment-sensitive malware.

More Than Meets the Eye: Split-Second Social Perception.

PubMed

Freeman, Jonathan B; Johnson, Kerri L

2016-05-01

Recent research suggests that visual perception of social categories is shaped not only by facial features but also by higher-order social cognitive processes (e.g., stereotypes, attitudes, goals). Building on neural computational models of social perception, we outline a perspective of how multiple bottom-up visual cues are flexibly integrated with a range of top-down processes to form perceptions, and we identify a set of key brain regions involved. During this integration, 'hidden' social category activations are often triggered which temporarily impact perception without manifesting in explicit perceptual judgments. Importantly, these hidden impacts and other aspects of the perceptual process predict downstream social consequences - from politicians' electoral success to several evaluative biases - independently of the outcomes of that process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Factor Analysis of Traffic Safety in Urban Roads Based on FTA-LEC

NASA Astrophysics Data System (ADS)

Shuicheng, TIAN; Xingbo, YANG; Xiaoqing, SHEN; Detao, ZHANG

2018-05-01

In order to reduce the number and the loss of urban road traffic accidents in our country, improve the safety of road traffic, a statistical analysis of the research report on major road traffic accidents in 2016 was conducted. The risk factors affecting urban road traffic in China were analyzed by using FTA to find the basic hidden events. Secondly, the risk value of the identified hidden danger events were calculated and classified into four levels I, II, III and IV through the LEC evaluation method. Finally, the graded results of risk factors are verified through a case of specific accidents in Beijing. The results show that: the case verified the scientificalness and effectiveness of hazard classification and provided guidance for urban road traffic management.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

PubMed

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Accelerating Convergence in Molecular Dynamics Simulations of Solutes in Lipid Membranes by Conducting a Random Walk along the Bilayer Normal.

PubMed

Neale, Chris; Madill, Chris; Rauscher, Sarah; Pomès, Régis

2013-08-13

All molecular dynamics simulations are susceptible to sampling errors, which degrade the accuracy and precision of observed values. The statistical convergence of simulations containing atomistic lipid bilayers is limited by the slow relaxation of the lipid phase, which can exceed hundreds of nanoseconds. These long conformational autocorrelation times are exacerbated in the presence of charged solutes, which can induce significant distortions of the bilayer structure. Such long relaxation times represent hidden barriers that induce systematic sampling errors in simulations of solute insertion. To identify optimal methods for enhancing sampling efficiency, we quantitatively evaluate convergence rates using generalized ensemble sampling algorithms in calculations of the potential of mean force for the insertion of the ionic side chain analog of arginine in a lipid bilayer. Umbrella sampling (US) is used to restrain solute insertion depth along the bilayer normal, the order parameter commonly used in simulations of molecular solutes in lipid bilayers. When US simulations are modified to conduct random walks along the bilayer normal using a Hamiltonian exchange algorithm, systematic sampling errors are eliminated more rapidly and the rate of statistical convergence of the standard free energy of binding of the solute to the lipid bilayer is increased 3-fold. We compute the ratio of the replica flux transmitted across a defined region of the order parameter to the replica flux that entered that region in Hamiltonian exchange simulations. We show that this quantity, the transmission factor, identifies sampling barriers in degrees of freedom orthogonal to the order parameter. The transmission factor is used to estimate the depth-dependent conformational autocorrelation times of the simulation system, some of which exceed the simulation time, and thereby identify solute insertion depths that are prone to systematic sampling errors and estimate the lower bound of the amount of sampling that is required to resolve these sampling errors. Finally, we extend our simulations and verify that the conformational autocorrelation times estimated by the transmission factor accurately predict correlation times that exceed the simulation time scale-something that, to our knowledge, has never before been achieved.

Nonequilibrium optical control of dynamical states in superconducting nanowire circuits.

PubMed

Madan, Ivan; Buh, Jože; Baranov, Vladimir V; Kabanov, Viktor V; Mrzel, Aleš; Mihailovic, Dragan

2018-03-01

Optical control of states exhibiting macroscopic phase coherence in condensed matter systems opens intriguing possibilities for materials and device engineering, including optically controlled qubits and photoinduced superconductivity. Metastable states, which in bulk materials are often associated with the formation of topological defects, are of more practical interest. Scaling to nanosize leads to reduced dimensionality, fundamentally changing the system's properties. In one-dimensional superconducting nanowires, vortices that are present in three-dimensional systems are replaced by fluctuating topological defects of the phase. These drastically change the dynamical behavior of the superconductor and introduce dynamical periodic long-range ordered states when the current is driven through the wire. We report the control and manipulation of transitions between different dynamically stable states in superconducting δ 3 -MoN nanowire circuits by ultrashort laser pulses. Not only can the transitions between different dynamically stable states be precisely controlled by light, but we also discovered new photoinduced hidden states that cannot be reached under near-equilibrium conditions, created while laser photoexcited quasi-particles are outside the equilibrium condition. The observed switching behavior can be understood in terms of dynamical stabilization of various spatiotemporal periodic trajectories of the order parameter in the superconductor nanowire, providing means for the optical control of the superconducting phase with subpicosecond control of timing.

Nonequilibrium optical control of dynamical states in superconducting nanowire circuits

PubMed Central

Madan, Ivan; Baranov, Vladimir V.

2018-01-01

Optical control of states exhibiting macroscopic phase coherence in condensed matter systems opens intriguing possibilities for materials and device engineering, including optically controlled qubits and photoinduced superconductivity. Metastable states, which in bulk materials are often associated with the formation of topological defects, are of more practical interest. Scaling to nanosize leads to reduced dimensionality, fundamentally changing the system’s properties. In one-dimensional superconducting nanowires, vortices that are present in three-dimensional systems are replaced by fluctuating topological defects of the phase. These drastically change the dynamical behavior of the superconductor and introduce dynamical periodic long-range ordered states when the current is driven through the wire. We report the control and manipulation of transitions between different dynamically stable states in superconducting δ3-MoN nanowire circuits by ultrashort laser pulses. Not only can the transitions between different dynamically stable states be precisely controlled by light, but we also discovered new photoinduced hidden states that cannot be reached under near-equilibrium conditions, created while laser photoexcited quasi-particles are outside the equilibrium condition. The observed switching behavior can be understood in terms of dynamical stabilization of various spatiotemporal periodic trajectories of the order parameter in the superconductor nanowire, providing means for the optical control of the superconducting phase with subpicosecond control of timing. PMID:29670935

Hidden momentum of electrons, nuclei, atoms, and molecules

NASA Astrophysics Data System (ADS)

Cameron, Robert P.; Cotter, J. P.

2018-04-01

We consider the positions and velocities of electrons and spinning nuclei and demonstrate that these particles harbour hidden momentum when located in an electromagnetic field. This hidden momentum is present in all atoms and molecules, however it is ultimately canceled by the momentum of the electromagnetic field. We point out that an electron vortex in an electric field might harbour a comparatively large hidden momentum and recognize the phenomenon of hidden hidden momentum.

Another convex combination of product states for the separable Werner state

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azuma, Hiroo; Ban, Masashi; CREST, Japan Science and Technology Agency, 1-1-9 Yaesu, Chuo-ku, Tokyo 103-0028

2006-03-15

In this paper, we write down the separable Werner state in a two-qubit system explicitly as a convex combination of product states, which is different from the convex combination obtained by Wootters' method. The Werner state in a two-qubit system has a single real parameter and varies from inseparable to separable according to the value of its parameter. We derive a hidden variable model that is induced by our decomposed form for the separable Werner state. From our explicit form of the convex combination of product states, we understand the following: The critical point of the parameter for separability ofmore » the Werner state comes from positivity of local density operators of the qubits.« less

Topology versus Anderson localization: Nonperturbative solutions in one dimension

NASA Astrophysics Data System (ADS)

Altland, Alexander; Bagrets, Dmitry; Kamenev, Alex

2015-02-01

We present an analytic theory of quantum criticality in quasi-one-dimensional topological Anderson insulators. We describe these systems in terms of two parameters (g ,χ ) representing localization and topological properties, respectively. Certain critical values of χ (half-integer for Z classes, or zero for Z2 classes) define phase boundaries between distinct topological sectors. Upon increasing system size, the two parameters exhibit flow similar to the celebrated two-parameter flow of the integer quantum Hall insulator. However, unlike the quantum Hall system, an exact analytical description of the entire phase diagram can be given in terms of the transfer-matrix solution of corresponding supersymmetric nonlinear sigma models. In Z2 classes we uncover a hidden supersymmetry, present at the quantum critical point.

Double Higgs mechanisms, supermassive stable particles and the vacuum energy

NASA Astrophysics Data System (ADS)

Santillán, Osvaldo P.; Gabbanelli, Luciano

2016-07-01

In the present work, a hidden scenario which cast a long-lived superheavy particle A0 and simultaneously an extremely light particle a with mass ma ˜ 10-32-10-33 eV is presented. The potential energy V (a) of the particle a models the vacuum energy density of the universe ρc ≃ 10-47GeV4. On the other hand, the A0 particle may act as superheavy dark matter at present times and the products of its decay may be observed in high energy cosmic ray events. The hidden sector proposed here include light fermions with masses near the neutrino mass mν ˜ 10-2 eV and superheavy ones with masses of the order of the GUT scale, interacting through a hidden SU(2)L interaction which also affects the ordinary sector. The construction of such combined scenario is nontrivial since the presence of light particles may spoil the stability of the heavy particle A0. However, double Higgs mechanisms may be helpful for overcoming this problem. In this context, the stability of the superheavy particle A0 is ensured due to chiral symmetry arguments elaborated in the text.

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Relativistic properties of a molecule: energy, linear momentum, angular momentum and boost momentum to order 1/c 2

NASA Astrophysics Data System (ADS)

Cameron, Robert P.; Cotter, J. P.

2018-05-01

We give an explicit and general description of the energy, linear momentum, angular momentum and boost momentum of a molecule to order 1/c 2, where it necessary to take account of kinetic contributions made by the electrons and nuclei as well as electromagnetic contributions made by the intramolecular field. A wealth of interesting subtleties are encountered that are not seen at order 1/c 0, including relativistic Hall shifts, anomalous velocities and hidden momenta. Some of these have well known analogues in solid state physics.

Information Hiding: an Annotated Bibliography

DTIC Science & Technology

1999-04-13

parameters needed for reconstruction are enciphered using DES . The encrypted image is hidden in a cover image . [153] 074115, ‘Watermarking algorithm ...authors present a block based watermarking algorithm for digital images . The D.C.T. of the block is increased by a certain value. Quality control is...includes evaluation of the watermark robustness and the subjec- tive visual image quality. Two algorithms use the frequency domain while the two others use

Proposed Test of Relative Phase as Hidden Variable in Quantum Mechanics

DTIC Science & Technology

2012-01-01

implicitly due to its ubiquity in quantum theory , but searches for dependence of measurement outcome on other parameters have been lacking. For a two -state...implemen- tation for the specific case of an atomic two -state system with laser-induced fluores- cence for measurement. Keywords Quantum measurement...Measurement postulate · Born rule 1 Introduction 1.1 Problems with Quantum Measurement Quantum theory prescribes probabilities for outcomes of measurements

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

PubMed Central

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

Investigation of hidden periodic structures on SEM images of opal-like materials using FFT and IFFT.

PubMed

Stephant, Nicolas; Rondeau, Benjamin; Gauthier, Jean-Pierre; Cody, Jason A; Fritsch, Emmanuel

2014-01-01

We have developed a method to use fast Fourier transformation (FFT) and inverse fast Fourier transformation (IFFT) to investigate hidden periodic structures on SEM images. We focused on samples of natural, play-of-color opals that diffract visible light and hence are periodically structured. Conventional sample preparation by hydrofluoric acid etch was not used; untreated, freshly broken surfaces were examined at low magnification relative to the expected period of the structural features, and, the SEM was adjusted to get a very high number of pixels in the images. These SEM images were treated by software to calculate autocorrelation, FFT, and IFFT. We present how we adjusted SEM acquisition parameters for best results. We first applied our procedure on an SEM image on which the structure was obvious. Then, we applied the same procedure on a sample that must contain a periodic structure because it diffracts visible light, but on which no structure was visible on the SEM image. In both cases, we obtained clearly periodic patterns that allowed measurements of structural parameters. We also investigated how the irregularly broken surface interfered with the periodic structure to produce additional periodicity. We tested the limits of our methodology with the help of simulated images. © 2014 Wiley Periodicals, Inc.

Nature's Mechanisms for Tough, Self-healing Polymers and Polymer Adhesives

NASA Astrophysics Data System (ADS)

Hansma, Paul

2007-03-01

Spider silk^2 and the natural polymer adhesives in abalone shells^3 and bone^4,5 can give us insights into nature's mechanisms for tough, self-healing polymers and polymer adhesives. The natural polymer adhesives in biomaterials have been optimized by evolution. An optimized polymer adhesive has five characteristics. 1) It holds together the strong elements of the composite. 2) It yields just before the strong elements would otherwise break. 3) It dissipates large amounts of energy as it yields. 4) It self heals after it yields. 5) It takes just a few percent by weight. Both natural polymer adhesives and silk rely on sacrificial bonds and hidden length for toughness and self-healing.^6 A relatively large energy, of order 100eV, is required to stretch a polymer molecule after a weak bond, a sacrificial bond, breaks and liberates hidden length, which was previously hidden, typically in a loop or folded domain, from whatever was stretching the polymer. The bond is called sacrificial if it breaks at forces well below the forces that could otherwise break the polymer backbone, typically greater than 1nN. In many biological cases, the breaking of sacrificial bonds has been found to be reversible, thereby also providing a ``self-healing'' property to the material.^2-4 Individual polymer adhesive molecules based on sacrificial bonds and hidden length can supply forces of order 300pN over distances of 100s of nanometers. Model calculations show that a few percent by weight of adhesives based on these principles could be optimized adhesives for high performance composite materials including nanotube and graphene sheet composites. ^2N. Becker, E. Oroudjev, S. Mutz et al., Nature Materials 2 (4), 278 (2003). ^3B. L. Smith, T. E. Schaffer, M. Viani et al., Nature 399 (6738), 761 (1999). ^4J. B. Thompson, J. H. Kindt, B. Drake et al., Nature 414 (6865), 773 (2001). ^5G. E. Fantner, T. Hassenkam, J. H. Kindt et al., Nature Materials 4, 612 (2005). ^6G. E. Fantner, E. Oroudjev, G. Schitter et al., Biophysical Journal 90 (4), 1411 (2006).

Hidden Stories: Uncovering the Visual Metaphor for Education and Communication

ERIC Educational Resources Information Center

Hube, Amy M.; Tremblay, Kenneth R., Jr.; Leigh, Katharine E.

2015-01-01

Design solutions have become increasingly complex and based on a rapidly growing body of knowledge. In order to articulate a design solution to a client, the graphic use of the design narrative can effectively communicate complex ideas. Two case study interventions were conducted in an interior design program in which students were introduced to…

The Pitch: How To Analyze Ads. 2nd Edition.

ERIC Educational Resources Information Center

Rank, Hugh

This book probes the ways ads persuade people to purchase, and attempts to teach individuals to become more discerning consumers. Critical thinking, when applied to analyzing ads, benefits consumers by helping them recognize patterns of persuasion and sort incoming information in order to get to the hidden message. The book s basic premise is that…

Boosting standard order sets utilization through clinical decision support.

PubMed

Li, Haomin; Zhang, Yinsheng; Cheng, Haixia; Lu, Xudong; Duan, Huilong

2013-01-01

Well-designed standard order sets have the potential to integrate and coordinate care by communicating best practices through multiple disciplines, levels of care, and services. However, there are several challenges which certainly affected the benefits expected from standard order sets. To boost standard order sets utilization, a problem-oriented knowledge delivery solution was proposed in this study to facilitate access of standard order sets and evaluation of its treatment effect. In this solution, standard order sets were created along with diagnostic rule sets which can trigger a CDS-based reminder to help clinician quickly discovery hidden clinical problems and corresponding standard order sets during ordering. Those rule set also provide indicators for targeted evaluation of standard order sets during treatment. A prototype system was developed based on this solution and will be presented at Medinfo 2013.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Correlation Effects and Hidden Spin-Orbit Entangled Electronic Order in Parent and Electron-Doped Iridates Sr2 IrO4

NASA Astrophysics Data System (ADS)

Zhou, Sen; Jiang, Kun; Chen, Hua; Wang, Ziqiang

2017-10-01

Analogs of the high-Tc cuprates have been long sought after in transition metal oxides. Because of the strong spin-orbit coupling, the 5 d perovskite iridates Sr2 IrO4 exhibit a low-energy electronic structure remarkably similar to the cuprates. Whether a superconducting state exists as in the cuprates requires understanding the correlated spin-orbit entangled electronic states. Recent experiments discovered hidden order in the parent and electron-doped iridates, some with striking analogies to the cuprates, including Fermi surface pockets, Fermi arcs, and pseudogap. Here, we study the correlation and disorder effects in a five-orbital model derived from the band theory. We find that the experimental observations are consistent with a d -wave spin-orbit density wave order that breaks the symmetry of a joint twofold spin-orbital rotation followed by a lattice translation. There is a Berry phase and a plaquette spin flux due to spin procession as electrons hop between Ir atoms, akin to the intersite spin-orbit coupling in quantum spin Hall insulators. The associated staggered circulating Jeff=1 /2 spin current can be probed by advanced techniques of spin-current detection in spintronics. This electronic order can emerge spontaneously from the intersite Coulomb interactions between the spatially extended iridium 5 d orbitals, turning the metallic state into an electron-doped quasi-2D Dirac semimetal with important implications on the possible superconducting state suggested by recent experiments.

Sub-seasonal-to-seasonal Reservoir Inflow Forecast using Bayesian Hierarchical Hidden Markov Model

NASA Astrophysics Data System (ADS)

Mukhopadhyay, S.; Arumugam, S.

2017-12-01

Sub-seasonal-to-seasonal (S2S) (15-90 days) streamflow forecasting is an emerging area of research that provides seamless information for reservoir operation from weather time scales to seasonal time scales. From an operational perspective, sub-seasonal inflow forecasts are highly valuable as these enable water managers to decide short-term releases (15-30 days), while holding water for seasonal needs (e.g., irrigation and municipal supply) and to meet end-of-the-season target storage at a desired level. We propose a Bayesian Hierarchical Hidden Markov Model (BHHMM) to develop S2S inflow forecasts for the Tennessee Valley Area (TVA) reservoir system. Here, the hidden states are predicted by relevant indices that influence the inflows at S2S time scale. The hidden Markov model also captures the both spatial and temporal hierarchy in predictors that operate at S2S time scale with model parameters being estimated as a posterior distribution using a Bayesian framework. We present our work in two steps, namely single site model and multi-site model. For proof of concept, we consider inflows to Douglas Dam, Tennessee, in the single site model. For multisite model we consider reservoirs in the upper Tennessee valley. Streamflow forecasts are issued and updated continuously every day at S2S time scale. We considered precipitation forecasts obtained from NOAA Climate Forecast System (CFSv2) GCM as predictors for developing S2S streamflow forecasts along with relevant indices for predicting hidden states. Spatial dependence of the inflow series of reservoirs are also preserved in the multi-site model. To circumvent the non-normality of the data, we consider the HMM in a Generalized Linear Model setting. Skill of the proposed approach is tested using split sample validation against a traditional multi-site canonical correlation model developed using the same set of predictors. From the posterior distribution of the inflow forecasts, we also highlight different system behavior under varied global and local scale climatic influences from the developed BHMM.

Semantic Context Detection Using Audio Event Fusion

NASA Astrophysics Data System (ADS)

Chu, Wei-Ta; Cheng, Wen-Huang; Wu, Ja-Ling

2006-12-01

Semantic-level content analysis is a crucial issue in achieving efficient content retrieval and management. We propose a hierarchical approach that models audio events over a time series in order to accomplish semantic context detection. Two levels of modeling, audio event and semantic context modeling, are devised to bridge the gap between physical audio features and semantic concepts. In this work, hidden Markov models (HMMs) are used to model four representative audio events, that is, gunshot, explosion, engine, and car braking, in action movies. At the semantic context level, generative (ergodic hidden Markov model) and discriminative (support vector machine (SVM)) approaches are investigated to fuse the characteristics and correlations among audio events, which provide cues for detecting gunplay and car-chasing scenes. The experimental results demonstrate the effectiveness of the proposed approaches and provide a preliminary framework for information mining by using audio characteristics.

Hidden Attractors in Dynamical Systems. From Hidden Oscillations in Hilbert-Kolmogorov Aizerman, and Kalman Problems to Hidden Chaotic Attractor in Chua Circuits

NASA Astrophysics Data System (ADS)

Leonov, G. A.; Kuznetsov, N. V.

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect with small neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure. For localization of hidden attractors it is necessary to develop special procedures, since there are no similar transient processes leading to such attractors. At first, the problem of investigating hidden oscillations arose in the second part of Hilbert's 16th problem (1900). The first nontrivial results were obtained in Bautin's works, which were devoted to constructing nested limit cycles in quadratic systems, that showed the necessity of studying hidden oscillations for solving this problem. Later, the problem of analyzing hidden oscillations arose from engineering problems in automatic control. In the 50-60s of the last century, the investigations of widely known Markus-Yamabe's, Aizerman's, and Kalman's conjectures on absolute stability have led to the finding of hidden oscillations in automatic control systems with a unique stable stationary point. In 1961, Gubar revealed a gap in Kapranov's work on phase locked-loops (PLL) and showed the possibility of the existence of hidden oscillations in PLL. At the end of the last century, the difficulties in analyzing hidden oscillations arose in simulations of drilling systems and aircraft's control systems (anti-windup) which caused crashes. Further investigations on hidden oscillations were greatly encouraged by the present authors' discovery, in 2010 (for the first time), of chaotic hidden attractor in Chua's circuit. This survey is dedicated to efficient analytical-numerical methods for the study of hidden oscillations. Here, an attempt is made to reflect the current trends in the synthesis of analytical and numerical methods.

Neural node network and model, and method of teaching same

DOEpatents

Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

1995-12-26

The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing. 21 figs.

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

PubMed Central

Nakamura, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

2017-01-01

Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods. PMID:29311889

THE DIFFERENCES IN THE TORUS GEOMETRY BETWEEN HIDDEN AND NON-HIDDEN BROAD LINE ACTIVE GALACTIC NUCLEI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ichikawa, Kohei; Ueda, Yoshihiro; Packham, Christopher

2015-04-20

We present results from the fitting of infrared (IR) spectral energy distributions of 21 active galactic nuclei (AGNs) with clumpy torus models. We compiled high spatial resolution (∼0.3–0.7 arcsec) mid-IR (MIR) N-band spectroscopy, Q-band imaging, and nuclear near- and MIR photometry from the literature. Combining these nuclear near- and MIR observations, far-IR photometry, and clumpy torus models enables us to put constraints on the torus properties and geometry. We divide the sample into three types according to the broad line region (BLR) properties: type-1s, type-2s with scattered or hidden broad line region (HBLR) previously observed, and type-2s without any publishedmore » HBLR signature (NHBLR). Comparing the torus model parameters gives us the first quantitative torus geometrical view for each subgroup. We find that NHBLR AGNs have smaller torus opening angles and larger covering factors than HBLR AGNs. This suggests that the chance to observe scattered (polarized) flux from the BLR in NHBLR could be reduced by the dual effects of (a) less scattering medium due to the reduced scattering volume given the small torus opening angle and (b) the increased torus obscuration between the observer and the scattering region. These effects give a reasonable explanation for the lack of observed HBLR in some type-2 AGNs.« less

Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models

PubMed Central

2014-01-01

Background Logos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position. Results We present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position. Conclusion Skylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign’s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org. PMID:24410852

Neural node network and model, and method of teaching same

DOEpatents

Parlos, Alexander G.; Atiya, Amir F.; Fernandez, Benito; Tsai, Wei K.; Chong, Kil T.

1995-01-01

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

Fractional Klein-Gordon equation composed of Jumarie fractional derivative and its interpretation by a smoothness parameter

NASA Astrophysics Data System (ADS)

Ghosh, Uttam; Banerjee, Joydip; Sarkar, Susmita; Das, Shantanu

2018-06-01

Klein-Gordon equation is one of the basic steps towards relativistic quantum mechanics. In this paper, we have formulated fractional Klein-Gordon equation via Jumarie fractional derivative and found two types of solutions. Zero-mass solution satisfies photon criteria and non-zero mass satisfies general theory of relativity. Further, we have developed rest mass condition which leads us to the concept of hidden wave. Classical Klein-Gordon equation fails to explain a chargeless system as well as a single-particle system. Using the fractional Klein-Gordon equation, we can overcome the problem. The fractional Klein-Gordon equation also leads to the smoothness parameter which is the measurement of the bumpiness of space. Here, by using this smoothness parameter, we have defined and interpreted the various cases.

The bright and choked gamma-ray burst contribution to the IceCube and ANTARES low-energy excess

NASA Astrophysics Data System (ADS)

Denton, Peter B.; Tamborra, Irene

2018-04-01

The increasing statistics of the high-energy neutrino flux observed by the IceCube Observatory points towards an excess of events above the atmospheric neutrino background in the 30–400 TeV energy range. Such an excess is compatible with the findings of the ANTARES Telescope and it would naturally imply the possibility that more than one source class contributes to the observed flux. Electromagnetically hidden sources have been invoked to interpret this excess of events at low energies. By adopting a unified model for the electromagnetically bright and choked gamma-ray bursts and taking into account particle acceleration at the internal and collimation shock radii, we discuss whether bright and choked bursts are viable candidates. Our findings suggest that, although producing a copious neutrino flux, choked and bright astrophysical jets cannot be the dominant sources of the excess of neutrino events. A fine tuning of the model parameters or distinct scenarios for choked jets should be invoked in order to explain the low-energy neutrino data of IceCube and ANTARES.

Dynamics of a parametrically excited simple pendulum

NASA Astrophysics Data System (ADS)

Depetri, Gabriela I.; Pereira, Felipe A. C.; Marin, Boris; Baptista, Murilo S.; Sartorelli, J. C.

2018-03-01

The dynamics of a parametric simple pendulum submitted to an arbitrary angle of excitation ϕ was investigated experimentally by simulations and analytically. Analytical calculations for the loci of saddle-node bifurcations corresponding to the creation of resonant orbits were performed by applying Melnikov's method. However, this powerful perturbative method cannot be used to predict the existence of odd resonances for a vertical excitation within first order corrections. Yet, we showed that period-3 resonances indeed exist in such a configuration. Two degenerate attractors of different phases, associated with the same loci of saddle-node bifurcations in parameter space, are reported. For tilted excitation, the degeneracy is broken due to an extra torque, which was confirmed by the calculation of two distinct loci of saddle-node bifurcations for each attractor. This behavior persists up to ϕ≈7 π/180 , and for inclinations larger than this, only one attractor is observed. Bifurcation diagrams were constructed experimentally for ϕ=π/8 to demonstrate the existence of self-excited resonances (periods smaller than three) and hidden oscillations (for periods greater than three).

Dynamics of a parametrically excited simple pendulum.

PubMed

Depetri, Gabriela I; Pereira, Felipe A C; Marin, Boris; Baptista, Murilo S; Sartorelli, J C

2018-03-01

The dynamics of a parametric simple pendulum submitted to an arbitrary angle of excitation ϕ was investigated experimentally by simulations and analytically. Analytical calculations for the loci of saddle-node bifurcations corresponding to the creation of resonant orbits were performed by applying Melnikov's method. However, this powerful perturbative method cannot be used to predict the existence of odd resonances for a vertical excitation within first order corrections. Yet, we showed that period-3 resonances indeed exist in such a configuration. Two degenerate attractors of different phases, associated with the same loci of saddle-node bifurcations in parameter space, are reported. For tilted excitation, the degeneracy is broken due to an extra torque, which was confirmed by the calculation of two distinct loci of saddle-node bifurcations for each attractor. This behavior persists up to ϕ≈7π/180, and for inclinations larger than this, only one attractor is observed. Bifurcation diagrams were constructed experimentally for ϕ=π/8 to demonstrate the existence of self-excited resonances (periods smaller than three) and hidden oscillations (for periods greater than three).

Residual Symmetries Applied to Neutrino Oscillations at NO ν A and T2K

DOE PAGES

Hanlon, Andrew D.; Repko, Wayne W.; Dicus, Duane A.

2014-01-01

Tmore » he results previously obtained from the model-independent application of a generalized hidden horizontal Z 2 symmetry to the neutrino mass matrix are updated using the latest global fits for the neutrino oscillation parameters. he resulting prediction for the Dirac CP phase δ D is in agreement with recent results from 2K. he distribution for the Jarlskog invariant J ν has become sharper and appears to be approaching a particular region. he approximate effects of matter on long-baseline neutrino experiments are explored, and it is shown how the weak interactions between the neutrinos and the particles that make up the Earth can help to determine the mass hierarchy. A similar strategy is employed to show how NO ν A and 2K could determine the octant of θ a ( ≡ θ 23 ) . Finally, the exact effects of matter are obtained numerically in order to make comparisons with the form of the approximate solutions. From this analysis there emerge some interesting features of the effective mass eigenvalues.« less

Cryptoachneliths: Hidden glassy ash in composite spheroidal lapilli

NASA Astrophysics Data System (ADS)

Carracedo Sánchez, M.; Arostegui, J.; Sarrionandia, F.; Larrondo, E.; Gil Ibarguchi, J. I.

2010-09-01

Cryptoachneliths, perceptible by means of electron microscopy but unresolved under the optical microscope, occur unnoticed inside spheroidal lapilli of ultrabasic composition of the Cabezo Segura volcano (Calatrava volcanic province, Spain). The cryptoachneliths are glassy spherical particles that have compositions of Al-rich silicate with minor amounts of Fe, Ca and other elements. The smallest cryptoachneliths of < 1 μm in diameter (nanoachneliths) joined by coalescence to form microspheres > 1 μm (microachneliths) and homogeneous less regular masses of similar composition. Nano and microachneliths welded each other or to other types of volcanic particles (crystals, crystal fragments, spinning droplets, cognate lithic clasts, etc.) to form spheroidal lapilli and even bomb size clasts within proximal fall deposits of the Cabezo Segura volcano. The welding processes took place inside the eruptive column, previous to the fall of the spheroidal lapilli on top of the volcanic cone. The presence of the cryptoachneliths implies that lapilli and even bomb size tephra within deposits formed during explosive eruptions of low-viscosity basic to ultrabasic magmas should be carefully examined in order to establish key parameters of eruption dynamics, like size, amount and distribution of juvenile fine particles.

Implementing a Bayes Filter in a Neural Circuit: The Case of Unknown Stimulus Dynamics.

PubMed

Sokoloski, Sacha

2017-09-01

In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli that caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the predictions of an internal model. The internal model of the Bayes filter is based on the true stimulus dynamics, and in this note, we present a method for training a theoretical neural circuit to approximately implement a Bayes filter when the stimulus dynamics are unknown. To do this we use the inferential properties of linear probabilistic population codes to compute Bayes' rule and train a neural network to compute approximate predictions by the method of maximum likelihood. In particular, we perform stochastic gradient descent on the negative log-likelihood of the neural network parameters with a novel approximation of the gradient. We demonstrate our methods on a finite-state, a linear, and a nonlinear filtering problem and show how the hidden layer of the neural network develops tuning curves consistent with findings in experimental neuroscience.

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations.

PubMed

León Blanco, José M; González-R, Pedro L; Arroyo García, Carmen Martina; Cózar-Bernal, María José; Calle Suárez, Marcos; Canca Ortiz, David; Rabasco Álvarez, Antonio María; González Rodríguez, María Luisa

2018-01-01

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

Femtosecond x rays link melting of charge-density wave correlations and light-enhanced coherent transport in YB a 2 C u 3 O 6.6

DOE Office of Scientific and Technical Information (OSTI.GOV)

Först, M.; Frano, A.; Kaiser, S.

2014-11-17

In this study, we use femtosecond resonant soft x-ray diffraction to measure the optically stimulated ultrafast changes of charge density wave correlations in underdoped YBa₂Cu₃O₆.₆. We find that when coherent interlayer transport is enhanced by optical excitation of the apical oxygen distortions, at least 50% of the in-plane charge density wave order is melted. These results indicate that charge ordering and superconductivity may be competing up to the charge ordering transition temperature, with the latter becoming a hidden phase that is accessible only by nonlinear phonon excitation.

Baseball and Canadian Identity

ERIC Educational Resources Information Center

Humber, William

2005-01-01

Baseball research generally acts as a window into the game--a means as it were to understand its underlying order and disorder, its hidden beauty and historic complexity. Less common is the view from the other side of the window in which the patterns of the game are a lens as it were into the outside world, a channel for making sense of a…

Are There Hidden Supernovae?

NASA Technical Reports Server (NTRS)

Bregman, Jesse; Harker, David; Dunham, E.; Rank, David; Temi, Pasquale

1997-01-01

Ames Research Center and UCSC have been working on the development of a Mid IR Camera for the KAO in order to search for extra galactic supernovae. The development of the camera and its associated data reduction software have been successfully completed. Spectral Imaging of the Orion Bar at 6.2 and 7.8 microns demonstrates the derotation and data reduction software which was developed.

The Hidden Role of Fraternal Organizations in the Education of Black Adults: Prince Hall Freemasonry as a Case Study

ERIC Educational Resources Information Center

Muraskin, William

1976-01-01

Prince Hall Freemasonry is the black branch of the international Freemasonic Order. Its role in the educational development of black Masons over the last 200 years is discussed. The following areas of teaching are the focal points: business administration, community leadership, middle class values, self government, and responsibilities of manhood.…

Development of Innovative Nondestructive Evaluation Technologies for the Inspection of Cracking and Corrosion Under Coatings

NASA Astrophysics Data System (ADS)

Lipetzky, Kirsten G.; Novack, Michele R.; Perez, Ignacio; Davis, William R.

2001-11-01

Three different innovative nondestructive evaluation technologies were developed and evaluated for the ability to detect fatigue cracks and corrosion hidden under painted aluminum panels. The three technologies included real-time ultrasound imaging, thermal imaging, and near-field microwave imaging. With each of these nondestructive inspection methods, subtasks were performed in order to optimize each methodology.

Using Educational Data Mining Methods to Assess Field-Dependent and Field-Independent Learners' Complex Problem Solving

ERIC Educational Resources Information Center

Angeli, Charoula; Valanides, Nicos

2013-01-01

The present study investigated the problem-solving performance of 101 university students and their interactions with a computer modeling tool in order to solve a complex problem. Based on their performance on the hidden figures test, students were assigned to three groups of field-dependent (FD), field-mixed (FM), and field-independent (FI)…

Competing Spin Liquids and Hidden Spin-Nematic Order in Spin Ice with Frustrated Transverse Exchange

NASA Astrophysics Data System (ADS)

Taillefumier, Mathieu; Benton, Owen; Yan, Han; Jaubert, L. D. C.; Shannon, Nic

2017-10-01

Frustration in magnetic interactions can give rise to disordered ground states with subtle and beautiful properties. The spin ices Ho2 Ti2 O7 and Dy2 Ti2 O7 exemplify this phenomenon, displaying a classical spin-liquid state, with fractionalized magnetic-monopole excitations. Recently, there has been great interest in closely related "quantum spin-ice" materials, following the realization that anisotropic exchange interactions could convert spin ice into a massively entangled, quantum spin liquid, where magnetic monopoles become the charges of an emergent quantum electrodynamics. Here we show that even the simplest model of a quantum spin ice, the XXZ model on the pyrochlore lattice, can realize a still-richer scenario. Using a combination of classical Monte Carlo simulation, semiclassical molecular-dynamics simulation, and analytic field theory, we explore the properties of this model for frustrated transverse exchange. We find not one, but three competing forms of spin liquid, as well as a phase with hidden, spin-nematic order. We explore the experimental signatures of each of these different states, making explicit predictions for inelastic neutron scattering. These results show an intriguing similarity to experiments on a range of pyrochlore oxides.

The secret art of managing healthcare expenses: investigating implicit rationing and autonomy in public healthcare systems.

PubMed

Lauridsen, S M R; Norup, M S; Rossel, P J H

2007-12-01

Rationing healthcare is a difficult task, which includes preventing patients from accessing potentially beneficial treatments. Proponents of implicit rationing argue that politicians cannot resist pressure from strong patient groups for treatments and conclude that physicians should ration without informing patients or the public. The authors subdivide this specific programme of implicit rationing, or "hidden rationing", into local hidden rationing, unsophisticated global hidden rationing and sophisticated global hidden rationing. They evaluate the appropriateness of these methods of rationing from the perspectives of individual and political autonomy and conclude that local hidden rationing and unsophisticated global hidden rationing clearly violate patients' individual autonomy, that is, their right to participate in medical decision-making. While sophisticated global hidden rationing avoids this charge, the authors point out that it nonetheless violates the political autonomy of patients, that is, their right to engage in public affairs as citizens. A defence of any of the forms of hidden rationing is therefore considered to be incompatible with a defence of autonomy.

An integrated system for identifying the hidden assassins in traditional medicines containing aristolochic acids

NASA Astrophysics Data System (ADS)

Wu, Lan; Sun, Wei; Wang, Bo; Zhao, Haiyu; Li, Yaoli; Cai, Shaoqing; Xiang, Li; Zhu, Yingjie; Yao, Hui; Song, Jingyuan; Cheng, Yung-Chi; Chen, Shilin

2015-08-01

Traditional herbal medicines adulterated and contaminated with plant materials from the Aristolochiaceae family, which contain aristolochic acids (AAs), cause aristolochic acid nephropathy. Approximately 256 traditional Chinese patent medicines, containing Aristolochiaceous materials, are still being sold in Chinese markets today. In order to protect consumers from health risks due to AAs, the hidden assassins, efficient methods to differentiate Aristolochiaceous herbs from their putative substitutes need to be established. In this study, 158 Aristolochiaceous samples representing 46 species and four genera as well as 131 non-Aristolochiaceous samples representing 33 species, 20 genera and 12 families were analyzed using DNA barcodes based on the ITS2 and psbA-trnH sequences. Aristolochiaceous materials and their non-Aristolochiaceous substitutes were successfully identified using BLAST1, the nearest distance method and the neighbor-joining (NJ) tree. In addition, based on sequence information of ITS2, we developed a Real-Time PCR assay which successfully identified herbal material from the Aristolochiaceae family. Using Ultra High Performance Liquid Chromatography-Mass Spectrometer (UHPLC-HR-MS), we demonstrated that most representatives from the Aristolochiaceae family contain toxic AAs. Therefore, integrated DNA barcodes, Real-Time PCR assays using TaqMan probes and UHPLC-HR-MS system provides an efficient and reliable authentication system to protect consumers from health risks due to the hidden assassins (AAs).

A Bayesian approach to estimating hidden variables as well as missing and wrong molecular interactions in ordinary differential equation-based mathematical models.

PubMed

Engelhardt, Benjamin; Kschischo, Maik; Fröhlich, Holger

2017-06-01

Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α -Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data. © 2017 The Author(s).

A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.

PubMed

Wang, Xiaomeng; Peng, Ling; Chi, Tianhe; Li, Mengzhu; Yao, Xiaojing; Shao, Jing

2015-01-01

Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.

Sensitivity Analysis of the Land Surface Model NOAH-MP for Different Model Fluxes

NASA Astrophysics Data System (ADS)

Mai, Juliane; Thober, Stephan; Samaniego, Luis; Branch, Oliver; Wulfmeyer, Volker; Clark, Martyn; Attinger, Sabine; Kumar, Rohini; Cuntz, Matthias

2015-04-01

Land Surface Models (LSMs) use a plenitude of process descriptions to represent the carbon, energy and water cycles. They are highly complex and computationally expensive. Practitioners, however, are often only interested in specific outputs of the model such as latent heat or surface runoff. In model applications like parameter estimation, the most important parameters are then chosen by experience or expert knowledge. Hydrologists interested in surface runoff therefore chose mostly soil parameters while biogeochemists interested in carbon fluxes focus on vegetation parameters. However, this might lead to the omission of parameters that are important, for example, through strong interactions with the parameters chosen. It also happens during model development that some process descriptions contain fixed values, which are supposedly unimportant parameters. However, these hidden parameters remain normally undetected although they might be highly relevant during model calibration. Sensitivity analyses are used to identify informative model parameters for a specific model output. Standard methods for sensitivity analysis such as Sobol indexes require large amounts of model evaluations, specifically in case of many model parameters. We hence propose to first use a recently developed inexpensive sequential screening method based on Elementary Effects that has proven to identify the relevant informative parameters. This reduces the number parameters and therefore model evaluations for subsequent analyses such as sensitivity analysis or model calibration. In this study, we quantify parametric sensitivities of the land surface model NOAH-MP that is a state-of-the-art LSM and used at regional scale as the land surface scheme of the atmospheric Weather Research and Forecasting Model (WRF). NOAH-MP contains multiple process parameterizations yielding a considerable amount of parameters (˜ 100). Sensitivities for the three model outputs (a) surface runoff, (b) soil drainage and (c) latent heat are calculated on twelve Model Parameter Estimation Experiment (MOPEX) catchments ranging in size from 1020 to 4421 km2. This allows investigation of parametric sensitivities for distinct hydro-climatic characteristics, emphasizing different land-surface processes. The sequential screening identifies the most informative parameters of NOAH-MP for different model output variables. The number of parameters is reduced substantially for all of the three model outputs to approximately 25. The subsequent Sobol method quantifies the sensitivities of these informative parameters. The study demonstrates the existence of sensitive, important parameters in almost all parts of the model irrespective of the considered output. Soil parameters, e.g., are informative for all three output variables whereas plant parameters are not only informative for latent heat but also for soil drainage because soil drainage is strongly coupled to transpiration through the soil water balance. These results contrast to the choice of only soil parameters in hydrological studies and only plant parameters in biogeochemical ones. The sequential screening identified several important hidden parameters that carry large sensitivities and have hence to be included during model calibration.

Revealing Stellar Surface Structure Behind Transiting Exoplanets

NASA Astrophysics Data System (ADS)

Dravins, Dainis

2018-04-01

During exoplanet transits, successive stellar surface portions become hidden and differential spectroscopy between various transit phases provide spectra of small surface segments temporarily hidden behind the planet. Line profile changes across the stellar disk offer diagnostics for hydrodynamic modeling, while exoplanet analyses require stellar background spectra to be known along the transit path. Since even giant planets cover only a small fraction of any main-sequence star, very precise observations are required, as well as averaging over numerous spectral lines with similar parameters. Spatially resolved Fe I line profiles across stellar disks have now been retrieved for HD209458 (G0V) and HD189733A (K1V), using data from the UVES and HARPS spectrometers. Free from rotational broadening, spatially resolved profiles are narrower and deeper than in integrated starlight. During transit, the profiles shift towards longer wavelengths, illustrating both stellar rotation at the latitude of transit and the prograde orbital motion of the exoplanets. This method will soon become applicable to more stars, once additional bright exoplanet hosts have been found.

Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

NASA Astrophysics Data System (ADS)

Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

2018-06-01

Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

LHC searches for dark sector showers

NASA Astrophysics Data System (ADS)

Cohen, Timothy; Lisanti, Mariangela; Lou, Hou Keong; Mishra-Sharma, Siddharth

2017-11-01

This paper proposes a new search program for dark sector parton showers at the Large Hadron Collider (LHC). These signatures arise in theories characterized by strong dynamics in a hidden sector, such as Hidden Valley models. A dark parton shower can be composed of both invisible dark matter particles as well as dark sector states that decay to Standard Model particles via a portal. The focus here is on the specific case of `semi-visible jets,' jet-like collider objects where the visible states in the shower are Standard Model hadrons. We present a Simplified Model-like parametrization for the LHC observables and propose targeted search strategies for regions of parameter space that are not covered by existing analyses. Following the `mono- X' literature, the portal is modeled using either an effective field theoretic contact operator approach or with one of two ultraviolet completions; sensitivity projections are provided for all three cases. We additionally highlight that the LHC has a unique advantage over direct detection experiments in the search for this class of dark matter theories.

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

NASA Technical Reports Server (NTRS)

Korsmeyer, David (Technical Monitor); Rajkumar, T.; Bardina, Jorge

2003-01-01

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Modeling T-cell activation using gene expression profiling and state-space models.

PubMed

Rangel, Claudia; Angus, John; Ghahramani, Zoubin; Lioumi, Maria; Sotheran, Elizabeth; Gaiba, Alessia; Wild, David L; Falciani, Francesco

2004-06-12

We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. Supplementary data and Matlab computer source code will be made available on the web at the URL given below. http://public.kgi.edu/~wild/LDS/index.htm

Implications of hidden gauged U (1 ) model for B anomalies

NASA Astrophysics Data System (ADS)

Fuyuto, Kaori; Li, Hao-Lin; Yu, Jiang-Hao

2018-06-01

We propose a hidden gauged U (1 )H Z' model to explain deviations from the standard model (SM) values in lepton flavor universality known as RK and RD anomalies. The Z' only interacts with the SM fermions via their mixing with vectorlike doublet fermions after the U (1 )H symmetry breaking, which leads to b →s μ μ transition through the Z' at tree level. Moreover, introducing an additional mediator, inert-Higgs doublet, yields b →c τ ν process via charged scalar contribution at tree level. Using flavio package, we scrutinize adequate sizes of the relevant Wilson coefficients to these two processes by taking various flavor observables into account. It is found that significant mixing between the vectorlike and the second generation leptons is needed for the RK anomaly. A possible explanation of the RD anomaly can also be simultaneously addressed in a motivated situation, where a single scalar operator plays a dominant role, by the successful model parameters for the RK anomaly.

The Hidden Curriculum as Emancipatory and Non-Emancipatory Tools.

ERIC Educational Resources Information Center

Kanpol, Barry

Moral values implied in school practices and policies constitute the "hidden curriculum." Because the hidden curriculum may promote certain moral values to students, teachers are partially responsible for the moral education of students. A component of the hidden curriculum, institutional political resistance, concerns teacher opposition to…

The growth crisis--and how to escape it.

PubMed

Slywotzky, Adrian J; Wise, Richard

2002-07-01

At a time when companies are poised to seize the growth opportunities of a rebounding economy, many of them, whether they know it or not, face a growth crisis. Even during the boom years of the past decade, only a small fraction of companies enjoyed consistent double-digit revenue growth. And those that did often achieved it through short-term measures--such as mergers and inflated price increases--that don't provide the foundation for growth over the long term. But there is a way out of this predicament. The authors claim that companies can achieve sustained growth by leveraging their "hidden assets," a wide array of underused, intangible capabilities and advantages that most established companies already hold. To date, much of the research on intangible assets has centered on intellectual property and brand recognition. But in this article, the authors uncover a host of other assets that can help spark growth. They identify four major categories of hidden assets: customer relationships, strategic real estate, networks, and information. And they illustrate each with an example of a company that has creatively used its hidden assets to produce new sources of revenue. Executives have spent years learning to create growth using products, facilities, and working capital. But they should really focus on mobilizing their hidden assets to serve their customers' higher-order needs--in other words, create offerings that make customers' lives easier, better, or less expensive. Making that shift in mind-set isn't easy, admit the authors, but companies that do it may not only create meaningful new value for their customers but also produce double-digit revenue and earnings growth for investors.

Use of a Parabolic Microphone to Detect Hidden Subjects in Search and Rescue.

PubMed

Bowditch, Nathaniel L; Searing, Stanley K; Thomas, Jeffrey A; Thompson, Peggy K; Tubis, Jacqueline N; Bowditch, Sylvia P

2018-03-01

This study compares a parabolic microphone to unaided hearing in detecting and comprehending hidden callers at ranges of 322 to 2510 m. Eight subjects were placed 322 to 2510 m away from a central listening point. The subjects were concealed, and their calling volume was calibrated. In random order, subjects were asked to call the name of a state for 5 minutes. Listeners with parabolic microphones and others with unaided hearing recorded the direction of the call (detection) and name of the state (comprehension). The parabolic microphone was superior to unaided hearing in both detecting subjects and comprehending their calls, with an effect size (Cohen's d) of 1.58 for detection and 1.55 for comprehension. For each of the 8 hidden subjects, there were 24 detection attempts with the parabolic microphone and 54 to 60 attempts by unaided listeners. At the longer distances (1529-2510 m), the parabolic microphone was better at detecting callers (83% vs 51%; P<0.00001 by χ 2 ) and comprehension (57% vs 12%; P<0.00001). At the shorter distances (322-1190 m), the parabolic microphone offered advantages in detection (100% vs 83%; P=0.000023) and comprehension (86% vs 51%; P<0.00001), although not as pronounced as at the longer distances. Use of a 66-cm (26-inch) parabolic microphone significantly improved detection and comprehension of hidden calling subjects at distances between 322 and 2510 m when compared with unaided hearing. This study supports the use of a parabolic microphone in search and rescue to locate responsive subjects in favorable weather and terrain. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Adaptation of hidden Markov models for recognizing speech of reduced frame rate.

PubMed

Lee, Lee-Min; Jean, Fu-Rong

2013-12-01

The frame rate of the observation sequence in distributed speech recognition applications may be reduced to suit a resource-limited front-end device. In order to use models trained using full-frame-rate data in the recognition of reduced frame-rate (RFR) data, we propose a method for adapting the transition probabilities of hidden Markov models (HMMs) to match the frame rate of the observation. Experiments on the recognition of clean and noisy connected digits are conducted to evaluate the proposed method. Experimental results show that the proposed method can effectively compensate for the frame-rate mismatch between the training and the test data. Using our adapted model to recognize the RFR speech data, one can significantly reduce the computation time and achieve the same level of accuracy as that of a method, which restores the frame rate using data interpolation.

Design strategies from sexual exploitation and sex work studies among women and girls: Methodological considerations in a hidden and vulnerable population.

PubMed

Gerassi, Lara; Edmond, Tonya; Nichols, Andrea

2017-06-01

The study of sex trafficking, prostitution, sex work, and sexual exploitation is associated with many methodological issues and challenges. Researchers' study designs must consider the many safety issues related to this vulnerable and hidden population. Community advisory boards and key stakeholder involvement are essential to study design to increase safety of participants, usefulness of study aims, and meaningfulness of conclusions. Nonrandomized sampling strategies are most often utilized when studying exploited women and girls, which have the capacity to provide rich data and require complex sampling and recruitment methods. This article reviews the current methodological issues when studying this marginalized population as well as strategies to address challenges while working with the community in order to bring about social change. The authors also discuss their own experiences in collaborating with community organizations to conduct research in this field.

Design strategies from sexual exploitation and sex work studies among women and girls: Methodological considerations in a hidden and vulnerable population

PubMed Central

Gerassi, Lara; Edmond, Tonya; Nichols, Andrea

2016-01-01

The study of sex trafficking, prostitution, sex work, and sexual exploitation is associated with many methodological issues and challenges. Researchers’ study designs must consider the many safety issues related to this vulnerable and hidden population. Community advisory boards and key stakeholder involvement are essential to study design to increase safety of participants, usefulness of study aims, and meaningfulness of conclusions. Nonrandomized sampling strategies are most often utilized when studying exploited women and girls, which have the capacity to provide rich data and require complex sampling and recruitment methods. This article reviews the current methodological issues when studying this marginalized population as well as strategies to address challenges while working with the community in order to bring about social change. The authors also discuss their own experiences in collaborating with community organizations to conduct research in this field. PMID:28824337

Automated Cough Assessment on a Mobile Platform

PubMed Central

2014-01-01

The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test. PMID:25506590

Hidden profiles and concealed information: strategic information sharing and use in group decision making.

PubMed

Toma, Claudia; Butera, Fabrizio

2009-06-01

Two experiments investigated the differential impact of cooperation and competition on strategic information sharing and use in a three-person group decision-making task. Information was distributed in order to create a hidden profile so that disconfirmation of group members' initial preferences was required to solve the task. Experiment 1 revealed that competition, compared to cooperation, led group members to withhold unshared information, a difference that was not significant for shared information. In competition, compared to cooperation, group members were also more reluctant to disconfirm their initial preferences. Decision quality was lower in competition than in cooperation, this effect being mediated by disconfirmation use and not by information sharing. Experiment 2 replicated these findings and revealed the role of mistrust in predicting strategic information sharing and use in competition. These results support a motivated information processing approach of group decision making.

Failure monitoring in dynamic systems: Model construction without fault training data

NASA Technical Reports Server (NTRS)

Smyth, P.; Mellstrom, J.

1993-01-01

Advances in the use of autoregressive models, pattern recognition methods, and hidden Markov models for on-line health monitoring of dynamic systems (such as DSN antennas) have recently been reported. However, the algorithms described in previous work have the significant drawback that data acquired under fault conditions are assumed to be available in order to train the model used for monitoring the system under observation. This article reports that this assumption can be relaxed and that hidden Markov monitoring models can be constructed using only data acquired under normal conditions and prior knowledge of the system characteristics being measured. The method is described and evaluated on data from the DSS 13 34-m beam wave guide antenna. The primary conclusion from the experimental results is that the method is indeed practical and holds considerable promise for application at the 70-m antenna sites where acquisition of fault data under controlled conditions is not realistic.

Experimentally observed conformation-dependent geometry and hidden strain in proteins.

PubMed Central

Karplus, P. A.

1996-01-01

A database has been compiled documenting the peptide conformations and geometries from 70 diverse proteins refined at 1.75 A or better. Analysis of the well-ordered residues within the database shows phi, psi-distributions that have more fine structure than is generally observed. Also, clear evidence is presented that the peptide covalent geometry depends on conformation, with the interpeptide N-C alpha-C bond angle varying by nearly +/-5 degrees from its standard value. The observed deviations from standard peptide geometry are greatest near the edges of well-populated regions, consistent with strain occurring in these conformations. Minimization of such hidden strain could be an important factor in thermostability of proteins. These empirical data describing how equilibrium peptide geometry varies as a function of conformation confirm and extend quantum mechanics calculations, and have predictive value that will aid both theoretical and experimental analyses of protein structure. PMID:8819173

Short range, ultra-wideband radar with high resolution swept range gate

DOEpatents

McEwan, T.E.

1998-05-26

A radar range finder and hidden object locator is based on ultra-wide band radar with a high resolution swept range gate. The device generates an equivalent time amplitude scan with a typical range of 4 inches to 20 feet, and an analog range resolution as limited by a jitter of on the order of 0.01 inches. A differential sampling receiver is employed to effectively eliminate ringing and other aberrations induced in the receiver by the near proximity of the transmit antenna, so a background subtraction is not needed, simplifying the circuitry while improving performance. Uses of the invention include a replacement of ultrasound devices for fluid level sensing, automotive radar, such as cruise control and parking assistance, hidden object location, such as stud and rebar finding. Also, this technology can be used when positioned over a highway lane to collect vehicle count and speed data for traffic control. 14 figs.

Steganography on multiple MP3 files using spread spectrum and Shamir's secret sharing

NASA Astrophysics Data System (ADS)

Yoeseph, N. M.; Purnomo, F. A.; Riasti, B. K.; Safiie, M. A.; Hidayat, T. N.

2016-11-01

The purpose of steganography is how to hide data into another media. In order to increase security of data, steganography technique is often combined with cryptography. The weakness of this combination technique is the data was centralized. Therefore, a steganography technique is develop by using combination of spread spectrum and secret sharing technique. In steganography with secret sharing, shares of data is created and hidden in several medium. Medium used to concealed shares were MP3 files. Hiding technique used was Spread Spectrum. Secret sharing scheme used was Shamir's Secret Sharing. The result showed that steganography with spread spectrum combined with Shamir's Secret Share using MP3 files as medium produce a technique that could hid data into several cover. To extract and reconstruct the data hidden in stego object, it is needed the amount of stego object which more or equal to its threshold. Furthermore, stego objects were imperceptible and robust.

Search for Hidden-Sector Bosons in B(0)→K(*0)μ(+)μ(-) Decays.

PubMed

Aaij, R; Adeva, B; Adinolfi, M; Affolder, A; Ajaltouni, Z; Akar, S; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves, A A; Amato, S; Amerio, S; Amhis, Y; An, L; Anderlini, L; Anderson, J; Andreassi, G; Andreotti, M; Andrews, J E; Appleby, R B; Aquines Gutierrez, O; Archilli, F; d'Argent, P; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Baalouch, M; Bachmann, S; Back, J J; Badalov, A; Baesso, C; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Batozskaya, V; Battista, V; Bay, A; Beaucourt, L; Beddow, J; Bedeschi, F; Bediaga, I; Bel, L J; Bellee, V; Belloli, N; Belyaev, I; Ben-Haim, E; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bertolin, A; Bettler, M-O; van Beuzekom, M; Bien, A; Bifani, S; Billoir, P; Bird, T; Birnkraut, A; Bizzeti, A; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borsato, M; Bowcock, T J V; Bowen, E; Bozzi, C; Braun, S; Britsch, M; Britton, T; Brodzicka, J; Brook, N H; Buchanan, E; Bursche, A; Buytaert, J; Cadeddu, S; Calabrese, R; Calvi, M; Calvo Gomez, M; Campana, P; Campora Perez, D; Capriotti, L; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carniti, P; Carson, L; Carvalho Akiba, K; Casse, G; Cassina, L; Castillo Garcia, L; Cattaneo, M; Cauet, Ch; Cavallero, G; Cenci, R; Charles, M; Charpentier, Ph; Chefdeville, M; Chen, S; Cheung, S-F; Chiapolini, N; Chrzaszcz, M; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coco, V; Cogan, J; Cogneras, E; Cogoni, V; Cojocariu, L; Collazuol, G; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Coquereau, S; Corti, G; Corvo, M; Couturier, B; Cowan, G A; Craik, D C; Crocombe, A; Cruz Torres, M; Cunliffe, S; Currie, R; D'Ambrosio, C; Dall'Occo, E; Dalseno, J; David, P N Y; Davis, A; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Paula, L; De Simone, P; Dean, C-T; Decamp, D; Deckenhoff, M; Del Buono, L; Déléage, N; Demmer, M; Derkach, D; Deschamps, O; Dettori, F; Dey, B; Di Canto, A; Di Ruscio, F; Dijkstra, H; Donleavy, S; Dordei, F; Dorigo, M; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dreimanis, K; Dufour, L; Dujany, G; Dupertuis, F; Durante, P; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; El Rifai, I; Elsasser, Ch; Ely, S; Esen, S; Evans, H M; Evans, T; Falabella, A; Färber, C; Farley, N; Farry, S; Fay, R; Ferguson, D; Fernandez Albor, V; Ferrari, F; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fiore, M; Fiorini, M; Firlej, M; Fitzpatrick, C; Fiutowski, T; Fohl, K; Fol, P; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Fu, J; Furfaro, E; Gallas Torreira, A; Galli, D; Gallorini, S; Gambetta, S; Gandelman, M; Gandini, P; Gao, Y; García Pardiñas, J; Garra Tico, J; Garrido, L; Gascon, D; Gaspar, C; Gauld, R; Gavardi, L; Gazzoni, G; Gerick, D; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gianelle, A; Gianì, S; Gibson, V; Girard, O G; Giubega, L; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gotti, C; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graverini, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Griffith, P; Grillo, L; Grünberg, O; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadavizadeh, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hamilton, B; Han, X; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; He, J; Head, T; Heijne, V; Hennessy, K; Henrard, P; Henry, L; Hernando Morata, J A; van Herwijnen, E; Heß, M; Hicheur, A; Hill, D; Hoballah, M; Hombach, C; Hulsbergen, W; Humair, T; Hussain, N; Hutchcroft, D; Hynds, D; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jalocha, J; Jans, E; Jawahery, A; Jing, F; John, M; Johnson, D; Jones, C R; Joram, C; Jost, B; Jurik, N; Kandybei, S; Kanso, W; Karacson, M; Karbach, T M; Karodia, S; Kecke, M; Kelsey, M; Kenyon, I R; Kenzie, M; Ketel, T; Khairullin, E; Khanji, B; Khurewathanakul, C; Klaver, S; Klimaszewski, K; Kochebina, O; Kolpin, M; Komarov, I; Koopman, R F; Koppenburg, P; Kozeiha, M; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Krzemien, W; Kucewicz, W; Kucharczyk, M; Kudryavtsev, V; Kuonen, A K; Kurek, K; Kvaratskheliya, T; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lanfranchi, G; Langenbruch, C; Langhans, B; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J-P; Lefèvre, R; Leflat, A; Lefrançois, J; Lemos Cid, E; Leroy, O; Lesiak, T; Leverington, B; Li, Y; Likhomanenko, T; Liles, M; Lindner, R; Linn, C; Lionetto, F; Liu, B; Liu, X; Loh, D; Longstaff, I; Lopes, J H; Lucchesi, D; Lucio Martinez, M; Luo, H; Lupato, A; Luppi, E; Lupton, O; Lusiani, A; Machefert, F; Maciuc, F; Maev, O; Maguire, K; Malde, S; Malinin, A; Manca, G; Mancinelli, G; Manning, P; Mapelli, A; Maratas, J; Marchand, J F; Marconi, U; Marin Benito, C; Marino, P; Marks, J; Martellotti, G; Martin, M; Martinelli, M; Martinez Santos, D; Martinez Vidal, F; Martins Tostes, D; Massafferri, A; Matev, R; Mathad, A; Mathe, Z; Matteuzzi, C; Mauri, A; Maurin, B; Mazurov, A; McCann, M; McCarthy, J; McNab, A; McNulty, R; Meadows, B; Meier, F; Meissner, M; Melnychuk, D; Merk, M; Michielin, E; Milanes, D A; Minard, M-N; Mitzel, D S; Molina Rodriguez, J; Monroy, I A; Monteil, S; Morandin, M; Morawski, P; Mordà, A; Morello, M J; Moron, J; Morris, A B; Mountain, R; Muheim, F; Müller, D; Müller, J; Müller, K; Müller, V; Mussini, M; Muster, B; Naik, P; Nakada, T; Nandakumar, R; Nandi, A; Nasteva, I; Needham, M; Neri, N; Neubert, S; Neufeld, N; Neuner, M; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Niess, V; Niet, R; Nikitin, N; Nikodem, T; Ninci, D; Novoselov, A; O'Hanlon, D P; Oblakowska-Mucha, A; Obraztsov, V; Ogilvy, S; Okhrimenko, O; Oldeman, R; Onderwater, C J G; Osorio Rodrigues, B; Otalora Goicochea, J M; Otto, A; Owen, P; Oyanguren, A; Palano, A; Palombo, F; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Pappalardo, L L; Pappenheimer, C; Parkes, C; Passaleva, G; Patel, G D; Patel, M; Patrignani, C; Pearce, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perret, P; Pescatore, L; Petridis, K; Petrolini, A; Petruzzo, M; Picatoste Olloqui, E; Pietrzyk, B; Pilař, T; Pinci, D; Pistone, A; Piucci, A; Playfer, S; Plo Casasus, M; Poikela, T; Polci, F; Poluektov, A; Polyakov, I; Polycarpo, E; Popov, A; Popov, D; Popovici, B; Potterat, C; Price, E; Price, J D; Prisciandaro, J; Pritchard, A; Prouve, C; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Quagliani, R; Rachwal, B; Rademacker, J H; Rama, M; Rangel, M S; Raniuk, I; Rauschmayr, N; Raven, G; Redi, F; Reichert, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, S; Rihl, M; Rinnert, K; Rives Molina, V; Robbe, P; Rodrigues, A B; Rodrigues, E; Rodriguez Lopez, J A; Rodriguez Perez, P; Roiser, S; Romanovsky, V; Romero Vidal, A; Ronayne, J W; Rotondo, M; Rouvinet, J; Ruf, T; Ruiz Valls, P; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salustino Guimaraes, V; Sanchez Mayordomo, C; Sanmartin Sedes, B; Santacesaria, R; Santamarina Rios, C; Santimaria, M; Santovetti, E; Sarti, A; Satriano, C; Satta, A; Saunders, D M; Savrina, D; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmelzer, T; Schmidt, B; Schneider, O; Schopper, A; Schubiger, M; Schune, M-H; Schwemmer, R; Sciascia, B; Sciubba, A; Semennikov, A; Serra, N; Serrano, J; Sestini, L; Seyfert, P; Shapkin, M; Shapoval, I; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, V; Shires, A; Siddi, B G; Silva Coutinho, R; Silva de Oliveira, L; Simi, G; Sirendi, M; Skidmore, N; Skillicorn, I; Skwarnicki, T; Smith, E; Smith, E; Smith, I T; Smith, J; Smith, M; Snoek, H; Sokoloff, M D; Soler, F J P; Soomro, F; Souza, D; Souza De Paula, B; Spaan, B; Spradlin, P; Sridharan, S; Stagni, F; Stahl, M; Stahl, S; Stefkova, S; Steinkamp, O; Stenyakin, O; Stevenson, S; Stoica, S; Stone, S; Storaci, B; Stracka, S; Straticiuc, M; Straumann, U; Sun, L; Sutcliffe, W; Swientek, K; Swientek, S; Syropoulos, V; Szczekowski, M; Szumlak, T; T'Jampens, S; Tayduganov, A; Tekampe, T; Teklishyn, M; Tellarini, G; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Todd, J; Tolk, S; Tomassetti, L; Tonelli, D; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Trabelsi, K; Tran, M T; Tresch, M; Trisovic, A; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ukleja, A; Ustyuzhanin, A; Uwer, U; Vacca, C; Vagnoni, V; Valenti, G; Vallier, A; Vazquez Gomez, R; Vazquez Regueiro, P; Vázquez Sierra, C; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vieites Diaz, M; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; de Vries, J A; Waldi, R; Wallace, C; Wallace, R; Walsh, J; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Websdale, D; Weiden, A; Whitehead, M; Wilkinson, G; Wilkinson, M; Williams, M; Williams, M P; Williams, M; Williams, T; Wilson, F F; Wimberley, J; Wishahi, J; Wislicki, W; Witek, M; Wormser, G; Wotton, S A; Wright, S; Wyllie, K; Xie, Y; Xu, Z; Yang, Z; Yu, J; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, L; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zucchelli, S

2015-10-16

A search is presented for hidden-sector bosons, χ, produced in the decay B(0)→K*(892)(0)χ, with K*(892)(0)→K(+)π(-) and χ→μ(+)μ(-). The search is performed using pp-collision data corresponding to 3.0  fb(-1) collected with the LHCb detector. No significant signal is observed in the accessible mass range 214≤m(χ)≤4350  MeV, and upper limits are placed on the branching fraction product B(B(0)→K*(892)(0)χ)×B(χ→μ(+)μ(-)) as a function of the mass and lifetime of the χ boson. These limits are of the order of 10(-9) for χ lifetimes less than 100 ps over most of the m(χ) range, and place the most stringent constraints to date on many theories that predict the existence of additional low-mass bosons.

Short range, ultra-wideband radar with high resolution swept range gate

DOEpatents

McEwan, Thomas E.

1998-05-26

A radar range finder and hidden object locator is based on ultra-wide band radar with a high resolution swept range gate. The device generates an equivalent time amplitude scan with a typical range of 4 inches to 20 feet, and an analog range resolution as limited by a jitter of on the order of 0.01 inches. A differential sampling receiver is employed to effectively eliminate ringing and other aberrations induced in the receiver by the near proximity of the transmit antenna, so a background subtraction is not needed, simplifying the circuitry while improving performance. Uses of the invention include a replacement of ultrasound devices for fluid level sensing, automotive radar, such as cruise control and parking assistance, hidden object location, such as stud and rebar finding. Also, this technology can be used when positioned over a highway lane to collect vehicle count and speed data for traffic control.

Exact solution of the hidden Markov processes.

PubMed

Saakian, David B

2017-11-01

We write a master equation for the distributions related to hidden Markov processes (HMPs) and solve it using a functional equation. Thus the solution of HMPs is mapped exactly to the solution of the functional equation. For a general case the latter can be solved only numerically. We derive an exact expression for the entropy of HMPs. Our expression for the entropy is an alternative to the ones given before by the solution of integral equations. The exact solution is possible because actually the model can be considered as a generalized random walk on a one-dimensional strip. While we give the solution for the two second-order matrices, our solution can be easily generalized for the L values of the Markov process and M values of observables: We should be able to solve a system of L functional equations in the space of dimension M-1.

Exact solution of the hidden Markov processes

NASA Astrophysics Data System (ADS)

Saakian, David B.

2017-11-01

We write a master equation for the distributions related to hidden Markov processes (HMPs) and solve it using a functional equation. Thus the solution of HMPs is mapped exactly to the solution of the functional equation. For a general case the latter can be solved only numerically. We derive an exact expression for the entropy of HMPs. Our expression for the entropy is an alternative to the ones given before by the solution of integral equations. The exact solution is possible because actually the model can be considered as a generalized random walk on a one-dimensional strip. While we give the solution for the two second-order matrices, our solution can be easily generalized for the L values of the Markov process and M values of observables: We should be able to solve a system of L functional equations in the space of dimension M -1 .

Photoacoustic imaging of hidden dental caries by using a fiber-based probing system

NASA Astrophysics Data System (ADS)

Koyama, Takuya; Kakino, Satoko; Matsuura, Yuji

2017-04-01

Photoacoustic method to detect hidden dental caries is proposed. It was found that high frequency ultrasonic waves are generated from hidden carious part when radiating laser light to occlusal surface of model tooth. By making a map of intensity of these high frequency components, photoacoustic images of hidden caries were successfully obtained. A photoacoustic imaging system using a bundle of hollow optical fiber was fabricated for using clinical application, and clear photoacoustic image of hidden caries was also obtained by this system.

Modulation of Kekulé adatom ordering due to strain in graphene

NASA Astrophysics Data System (ADS)

González-Árraga, L.; Guinea, F.; San-Jose, P.

2018-04-01

Intervalley scattering of carriers in graphene at "top" adatoms may give rise to a hidden Kekulé ordering pattern in the adatom positions. This ordering is the result of a rapid modulation in the electron-mediated interaction between adatoms at the wave vector K -K' , which has been shown experimentally and theoretically to dominate their spatial distribution. Here we show that the adatom interaction is extremely sensitive to strain in the supporting graphene, which leads to a characteristic spatial modulation of the Kekulé order as a function of adatom distance. Our results suggest that the spatial distributions of adatoms could provide a way to measure the type and magnitude of strain in graphene and the associated pseudogauge field with high accuracy.

Revealing hidden spectral information of chlorine and sulfur in data of a mobile Laser-induced Breakdown Spectroscopy system using chemometrics

NASA Astrophysics Data System (ADS)

Gottlieb, C.; Millar, S.; Günther, T.; Wilsch, G.

2017-06-01

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (λ = 1064 nm, Epulse ≤ 3 mJ, τ = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation.

Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM.

PubMed

Davari Dolatabadi, Azam; Khadem, Siamak Esmael Zadeh; Asl, Babak Mohammadzadeh

2017-01-01

Currently Coronary Artery Disease (CAD) is one of the most prevalent diseases, and also can lead to death, disability and economic loss in patients who suffer from cardiovascular disease. Diagnostic procedures of this disease by medical teams are typically invasive, although they do not satisfy the required accuracy. In this study, we have proposed a methodology for the automatic diagnosis of normal and Coronary Artery Disease conditions using Heart Rate Variability (HRV) signal extracted from electrocardiogram (ECG). The features are extracted from HRV signal in time, frequency and nonlinear domains. The Principal Component Analysis (PCA) is applied to reduce the dimension of the extracted features in order to reduce computational complexity and to reveal the hidden information underlaid in the data. Finally, Support Vector Machine (SVM) classifier has been utilized to classify two classes of data using the extracted distinguishing features. In this paper, parameters of the SVM have been optimized in order to improve the accuracy. Provided reports in this paper indicate that the detection of CAD class from normal class using the proposed algorithm was performed with accuracy of 99.2%, sensitivity of 98.43%, and specificity of 100%. This study has shown that methods which are based on the feature extraction of the biomedical signals are an appropriate approach to predict the health situation of the patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

--No Title--

Science.gov Websites

;height:auto;overflow:hidden}.poc_table .top_row{background-color:#eee;height:auto;overflow:hidden}.poc_table ;background-color:#FFF;height:auto;overflow:hidden;border-top:1px solid #ccc}.poc_table .main_row .name :200px;padding:5px;height:auto;overflow:hidden}.tli_grey_box{background-color:#eaeaea;text-align:center

Natural hidden antibodies reacting with DNA or cardiolipin bind to thymocytes and evoke their death.

PubMed

Zamulaeva, I A; Lekakh, I V; Kiseleva, V I; Gabai, V L; Saenko, A S; Shevchenko, A S; Poverenny, A M

1997-08-18

Both free and hidden natural antibodies to DNA or cardiolipin were obtained from immunoglobulins of a normal donor. The free antibodies reacting with DNA or cardiolipin were isolated by means of affinity chromatography. Antibodies occurring in an hidden state were disengaged from the depleted immunoglobulins by ion-exchange chromatography and were then affinity-isolated on DNA or cardiolipin sorbents. We used flow cytometry to study the ability of free and hidden antibodies to bind to rat thymocytes. Simultaneously, plasma membrane integrity was tested by propidium iodide (PI) exclusion. The hidden antibodies reacted with 65.2 +/- 10.9% of the thymocytes and caused a fast plasma membrane disruption. Cells (28.7 +/- 7.1%) were stained with PI after incubation with the hidden antibodies for 1 h. The free antibodies bound to a very small fraction of the thymocytes and did not evoke death as compared to control without antibodies. The possible reason for the observed effects is difference in reactivity of the free and hidden antibodies to phospholipids. While free antibodies reacted preferentially with phosphotidylcholine, hidden antibodies reacted with cardiolipin and phosphotidylserine.

Raising awareness of the hidden curriculum in veterinary medical education: a review and call for research.

PubMed

Whitcomb, Tiffany L

2014-01-01

The hidden curriculum is characterized by information that is tacitly conveyed to and among students about the cultural and moral environment in which they find themselves. Although the hidden curriculum is often defined as a distinct entity, tacit information is conveyed to students throughout all aspects of formal and informal curricula. This unconsciously communicated knowledge has been identified across a wide spectrum of educational environments and is known to have lasting and powerful impacts, both positive and negative. Recently, medical education research on the hidden curriculum of becoming a doctor has come to the forefront as institutions struggle with inconsistencies between formal and hidden curricula that hinder the practice of patient-centered medicine. Similarly, the complex ethical questions that arise during the practice and teaching of veterinary medicine have the potential to cause disagreement between what the institution sets out to teach and what is actually learned. However, the hidden curriculum remains largely unexplored for this field. Because the hidden curriculum is retained effectively by students, elucidating its underlying messages can be a key component of program refinement. A review of recent literature about the hidden curriculum in a variety of fields, including medical education, will be used to explore potential hidden curricula in veterinary medicine and draw attention to the need for further investigation.

Structural colours of nickel bioreplicas of butterfly wings

NASA Astrophysics Data System (ADS)

Tolenis, Tomas; Swiontek, Stephen E.; Lakhtakia, Akhlesh

2017-04-01

The two-angle conformally evaporated-film-by-rotation technique (TA-CEFR) was devised to coat the wings of the monarch butterfly with nickel in order to form a 500-nm thick bioreplica thereof. The bioreplica exhibits structural colours that are completely obscured in actual wings by pigmental colours. Thus, the TA-CEFR technique provides a way to replicate, study and exploit hidden morphologies of biological surfaces.

One of the Hidden Diversities in Schools: Families with Parents Who Are Lesbian or Gay

ERIC Educational Resources Information Center

Fox, Robin K.

2007-01-01

This article offers a number of ideas that teachers and administrators can implement in order to help make educational settings more welcoming for parents who are LGBT (lesbian/gay/bisexual or transgender). However, the real work is beyond that of putting up posters or dolls in the dollhouse or changing the words to fingerplays to reflect the…

Emotional Congruence in Learning and Health Encounters in Medicine: Addressing an Aspect of the Hidden Curriculum

ERIC Educational Resources Information Center

O'Callaghan, Anne

2013-01-01

This paper aims to draw attention to and provide insights into an area that is of educational significance for clinical teachers, namely the need to acknowledge and respond appropriately to the emotional context of both learning and health encounters in order to improve the outcomes of both. This need has been highlighted by recent calls for more…

Fermi surface in the hidden-order state of URu2Si2 under intense pulsed magnetic fields up to 81 T

NASA Astrophysics Data System (ADS)

Scheerer, G. W.; Knafo, W.; Aoki, D.; Nardone, M.; Zitouni, A.; Béard, J.; Billette, J.; Barata, J.; Jaudet, C.; Suleiman, M.; Frings, P.; Drigo, L.; Audouard, A.; Matsuda, T. D.; Pourret, A.; Knebel, G.; Flouquet, J.

2014-04-01

We present measurements of the resistivity ρx ,x of URu2Si2 high-quality single crystals in pulsed high magnetic fields up to 81 T at a temperature of 1.4 K and up to 60 T at temperatures down to 100 mK. For a field H applied along the magnetic easy axis c, a strong sample dependence of the low-temperature resistivity in the hidden-order phase is attributed to a high carrier mobility. The interplay between the magnetic and orbital properties is emphasized by the angle dependence of the phase diagram, where magnetic transition fields and crossover fields related to the Fermi surface properties follow a 1/cosθ law, θ being the angle between H and c. For H ∥c, a crossover defined at a kink of ρx ,x, as initially reported in [Shishido, Phys. Rev. Lett. 102, 156403 (2009), 10.1103/PhysRevLett.102.156403], is found to be strongly sample dependent: its characteristic field μ0H* varies from ≃20 T in our best sample with a residual resistivity ratio RRR = ρx ,x(300K)/ ρx ,x(2K) of 225 to ≃25 T in a sample with a RRR of 90. A second crossover is defined at the maximum of ρx ,x at the sample-independent low-temperature (LT) characteristic field μ0Hρ,maxLT≃30 T. Fourier analyses of Shubnikov-de Haas oscillations show that Hρ,maxLT coincides with a sudden modification of the Fermi surface, while H* lies in a regime where the Fermi surface is smoothly modified. For H ∥a, (i) no phase transition is observed at low temperature and the system remains in the hidden-order phase up to 81 T, (ii) quantum oscillations surviving up to 7 K are related to a new orbit observed at the frequency Fλ≃1350 T and associated with a low effective mass mλ*=(1±0.5)m0, where m0 is the free electron mass, and (iii) no Fermi surface modification occurs up to 81 T.

Managing uncertainty in flood protection planning with climate projections

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Schoppa, Lukas; Straub, Daniel

2018-04-01

Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure lifetime, which is typically many decades long. Flood protection requirements should account for multiple future uncertain factors: socioeconomic, e.g., whether the population and with it the damage potential grows or falls; technological, e.g., possible advancements in flood protection; and climatic, e.g., whether extreme discharge will become more frequent or not. This paper focuses on climatic uncertainty. Specifically, we devise methodology to account for uncertainty associated with the use of discharge projections, ultimately leading to planning implications. For planning purposes, we categorize uncertainties as either visible, if they can be quantified from available catchment data, or hidden, if they cannot be quantified from catchment data and must be estimated, e.g., from the literature. It is vital to consider the hidden uncertainty, since in practical applications only a limited amount of information (e.g., a finite projection ensemble) is available. We use a Bayesian approach to quantify the visible uncertainties and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection. The results show that hidden uncertainty ought to be considered in planning, but the larger the uncertainty already present, the smaller the impact of adding more. The recommended planning is robust to moderate changes in uncertainty as well as in trend. In contrast, planning without consideration of bias and dependencies in and between uncertainty components leads to strongly suboptimal planning recommendations.

Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network.

PubMed

Poernomo, Alvin; Kang, Dae-Ki

2018-08-01

Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been thoroughly explored. Moreover, training a Dropout model will significantly increase the training time as it takes longer time to converge than a non-Dropout model with the same architecture. To deal with these issues, we address Biased Dropout and Crossmap Dropout, two novel approaches of Dropout extension based on the behavior of hidden units in CNN model. Biased Dropout divides the hidden units in a certain layer into two groups based on their magnitude and applies different Dropout rate to each group appropriately. Hidden units with higher activation value, which give more contributions to the network final performance, will be retained by a lower Dropout rate, while units with lower activation value will be exposed to a higher Dropout rate to compensate the previous part. The second approach is Crossmap Dropout, which is an extension of the regular Dropout in convolution layer. Each feature map in a convolution layer has a strong correlation between each other, particularly in every identical pixel location in each feature map. Crossmap Dropout tries to maintain this important correlation yet at the same time break the correlation between each adjacent pixel with respect to all feature maps by applying the same Dropout mask to all feature maps, so that all pixels or units in equivalent positions in each feature map will be either dropped or active during training. Our experiment with various benchmark datasets shows that our approaches provide better generalization than the regular Dropout. Moreover, our Biased Dropout takes faster time to converge during training phase, suggesting that assigning noise appropriately in hidden units can lead to an effective regularization. Copyright © 2018 Elsevier Ltd. All rights reserved.

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

PubMed

Zhou, Deyu; Zhong, Dayou

2015-05-01

Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely described by hidden topics and structures of the sentences. Copyright © 2015 Elsevier B.V. All rights reserved.

Geophysical Investigations at Hidden Dam, Raymond, California Flow Simulations

USGS Publications Warehouse

Minsley, Burke J.; Ikard, Scott

2010-01-01

Numerical flow modeling and analysis of observation-well data at Hidden Dam are carried out to supplement recent geophysical field investigations at the site (Minsley and others, 2010). This work also is complementary to earlier seepage-related studies at Hidden Dam documented by Cedergren (1980a, b). Known seepage areas on the northwest right abutment area of the downstream side of the dam was documented by Cedergren (1980a, b). Subsequent to the 1980 seepage study, a drainage blanket with a sub-drain system was installed to mitigate downstream seepage. Flow net analysis provided by Cedergren (1980a, b) suggests that the primary seepage mechanism involves flow through the dam foundation due to normal reservoir pool elevations, which results in upflow that intersects the ground surface in several areas on the downstream side of the dam. In addition to the reservoir pool elevations and downstream surface topography, flow is also controlled by the existing foundation geology as well as the presence or absence of a horizontal drain in the downstream portion of the dam. The current modeling study is aimed at quantifying how variability in dam and foundation hydrologic properties influences seepage as a function of reservoir stage. Flow modeling is implemented using the COMSOL Multiphysics software package, which solves the partially saturated flow equations in a two-dimensional (2D) cross-section of Hidden Dam that also incorporates true downstream topography. Use of the COMSOL software package provides a more quantitative approach than the flow net analysis by Cedergren (1980a, b), and allows for rapid evaluation of the influence of various parameters such as reservoir level, dam structure and geometry, and hydrogeologic properties of the dam and foundation materials. Historical observation-well data are used to help validate the flow simulations by comparing observed and predicted water levels for a range of reservoir elevations. The flow models are guided by, and discussed in the context of, the geophysical work (Minsley and others, 2010) where appropriate.

Zero velocity interval detection based on a continuous hidden Markov model in micro inertial pedestrian navigation

NASA Astrophysics Data System (ADS)

Sun, Wei; Ding, Wei; Yan, Huifang; Duan, Shunli

2018-06-01

Shoe-mounted pedestrian navigation systems based on micro inertial sensors rely on zero velocity updates to correct their positioning errors in time, which effectively makes determining the zero velocity interval play a key role during normal walking. However, as walking gaits are complicated, and vary from person to person, it is difficult to detect walking gaits with a fixed threshold method. This paper proposes a pedestrian gait classification method based on a hidden Markov model. Pedestrian gait data are collected with a micro inertial measurement unit installed at the instep. On the basis of analyzing the characteristics of the pedestrian walk, a single direction angular rate gyro output is used to classify gait features. The angular rate data are modeled into a univariate Gaussian mixture model with three components, and a four-state left–right continuous hidden Markov model (CHMM) is designed to classify the normal walking gait. The model parameters are trained and optimized using the Baum–Welch algorithm and then the sliding window Viterbi algorithm is used to decode the gait. Walking data are collected through eight subjects walking along the same route at three different speeds; the leave-one-subject-out cross validation method is conducted to test the model. Experimental results show that the proposed algorithm can accurately detect different walking gaits of zero velocity interval. The location experiment shows that the precision of CHMM-based pedestrian navigation improved by 40% when compared to the angular rate threshold method.

3.55 keV line from exciting dark matter without a hidden sector

DOE PAGES

Berlin, Asher; DiFranzo, Anthony; Hooper, Dan

2015-04-24

In this study, models in which dark matter particles can scatter into a slightly heavier state which promptly decays to the lighter state and a photon (known as eXciting Dark Matter, or XDM) have been shown to be capable of generating the 3.55 keV line observed from galaxy clusters, while suppressing the flux of such a line from smaller halos, including dwarf galaxies. In most of the XDM models discussed in the literature, this up-scattering is mediated by a new light particle, and dark matter annihilations proceed into pairs of this same light state. In these models, the dark matter andmore » the mediator effectively reside within a hidden sector, without sizable couplings to the Standard Model. In this paper, we explore a model of XDM that does not include a hidden sector. Instead, the dark matter both up-scatters and annihilates through the near resonant exchange of an O(10 2) GeV pseudoscalar with large Yukawa couplings to the dark matter and smaller, but non-neglibile, couplings to Standard Model fermions. The dark matter and the mediator are each mixtures of Standard Model singlets and SU(2) W doublets. We identify parameter space in which this model can simultaneously generate the 3.55 keV line and the gamma-ray excess observed from the Galactic center, without conflicting with constraints from colliders, direct detection experiments, or observations of dwarf galaxies.« less

Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

PubMed

Tu, Junchao; Zhang, Liyan

2018-01-12

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

More on the hidden symmetries of 11D supergravity

NASA Astrophysics Data System (ADS)

Andrianopoli, Laura; D'Auria, Riccardo; Ravera, Lucrezia

2017-09-01

In this paper we clarify the relations occurring among the osp (1 | 32) algebra, the M-algebra and the hidden superalgebra underlying the Free Differential Algebra of D=11 supergravity (to which we will refer as DF-algebra) that was introduced in the literature by D'Auria and Frè in 1981 and is actually a (Lorentz valued) central extension of the M-algebra including a nilpotent spinor generator, Q‧. We focus in particular on the 4-form cohomology in 11D superspace of the supergravity theory, strictly related to the presence in the theory of a 3-form A (3). Once formulated in terms of its hidden superalgebra of 1-forms, we find that A (3) can be decomposed into the sum of two parts having different group-theoretical meaning: One of them allows to reproduce the FDA of the 11D Supergravity due to non-trivial contributions to the 4-form cohomology in superspace, while the second one does not contribute to the 4-form cohomology, being a closed 3-form in the vacuum, defining however a one parameter family of trilinear forms invariant under a symmetry algebra related to osp (1 | 32) by redefining the spin connection and adding a new Maurer-Cartan equation. We further discuss about the crucial role played by the 1-form spinor η (dual to the nilpotent generator Q‧) for the 4-form cohomology of the eleven dimensional theory on superspace.

Recognizing visual focus of attention from head pose in natural meetings.

PubMed

Ba, Sileye O; Odobez, Jean-Marc

2009-02-01

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters.

Search for hidden high-Z materials inside containers with the Muon Portal Project

NASA Astrophysics Data System (ADS)

La Rocca, P.; Antonuccio, V.; Bandieramonte, M.; Becciani, U.; Belluomo, F.; Belluso, M.; Billotta, S.; Blancato, A. A.; Bonanno, D.; Bonanno, G.; Costa, A.; Fallica, G.; Garozzo, S.; Indelicato, V.; Leonora, E.; Longhitano, F.; Longo, S.; Lo Presti, D.; Massimino, P.; Petta, C.; Pistagna, C.; Pugliatti, C.; Puglisi, M.; Randazzo, N.; Riggi, F.; Riggi, S.; Romeo, G.; Russo, G. V.; Santagati, G.; Valvo, G.; Vitello, F.; Zaia, A.; Zappalà, G.

2014-01-01

The Muon Portal is a recently born project that plans to build a large area muon detector for a noninvasive inspection of shipping containers in the ports, searching for the presence of potential fissile (U, Pu) threats. The technique employed by the project is the well-known muon tomography, based on cosmic muon scattering from high-Z materials. The design and operational parameters of the muon portal under construction will be described in this paper, together with preliminary simulation and test results.

Effect of design selection on response surface performance

NASA Technical Reports Server (NTRS)

Carpenter, William C.

1993-01-01

Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net and the number of designs needed to train an approximation is discussed.

Collective purchase behavior toward retail price changes

NASA Astrophysics Data System (ADS)

Ueno, Hiromichi; Watanabe, Tsutomu; Takayasu, Hideki; Takayasu, Misako

2011-02-01

By analyzing a huge amount of point-of-sale data collected from Japanese supermarkets, we find power law relationships between price and sales numbers. The estimated values of the exponents of these power laws depend on the category of products; however, they are independent of the stores, thereby implying the existence of universal human purchase behavior. The rate of sales numbers around these power laws are generally approximated by log-normal distributions implying that there are hidden random parameters, which might proportionally affect the purchase activity.

"It's Not Always What It Seems": Exploring the Hidden Curriculum within a Doctoral Program

ERIC Educational Resources Information Center

Foot, Rachel Elizabeth

2017-01-01

The purpose of this qualitative, naturalistic study was to explore the ways in which hidden curriculum might influence doctoral student success. Two questions guided the study: (a) How do doctoral students experience the hidden curriculum? (b) What forms of hidden curricula can be identified in a PhD program? Data were collected from twelve…

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-04-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

The hidden and informal curriculum across the continuum of training: A cross-sectional qualitative study.

PubMed

Doja, Asif; Bould, M Dylan; Clarkin, Chantalle; Eady, Kaylee; Sutherland, Stephanie; Writer, Hilary

2016-01-01

The hidden and informal curricula refer to learning in response to unarticulated processes and constraints, falling outside the formal medical curriculum. The hidden curriculum has been identified as requiring attention across all levels of learning. We sought to assess the knowledge and perceptions of the hidden and informal curricula across the continuum of learning at a single institution. Focus groups were held with undergraduate and postgraduate learners and faculty to explore knowledge and perceptions relating to the hidden and informal curricula. Thematic analysis was conducted both inductively by research team members and deductively using questions structured by the existing literature. Participants highlighted several themes related to the presence of the hidden and informal curricula in medical training and practice, including: the privileging of some specialties over others; the reinforcement of hierarchies within medicine; and a culture of tolerance towards unprofessional behaviors. Participants acknowledged the importance of role modeling in the development of professional identities and discussed the deterioration in idealism that occurs. Common issues pertaining to the hidden curriculum exist across all levels of learners, including faculty. Increased awareness of these issues could allow for the further development of methods to address learning within the hidden curriculum.

Hidden Farmworker Labor Camps in North Carolina: An Indicator of Structural Vulnerability

PubMed Central

Summers, Phillip; Quandt, Sara A.; Talton, Jennifer W.; Galván, Leonardo

2015-01-01

Objectives. We used geographic information systems (GIS) to delineate whether farmworker labor camps were hidden and to determine whether hidden camps differed from visible camps in terms of physical and resident characteristics. Methods. We collected data using observation, interview, and public domain GIS data for 180 farmworker labor camps in east central North Carolina. A hidden camp was defined as one that was at least 0.15 miles from an all-weather road or located behind natural or manufactured objects. Hidden camps were compared with visible camps in terms of physical and resident characteristics. Results. More than one third (37.8%) of the farmworker labor camps were hidden. Hidden camps were significantly larger (42.7% vs 17.0% with 21 or more residents; P ≤ .001; and 29.4% vs 13.5% with 3 or more dwellings; P = .002) and were more likely to include barracks (50% vs 19.6%; P ≤ .001) than were visible camps. Conclusions. Poor housing conditions in farmworker labor camps often go unnoticed because they are hidden in the rural landscape, increasing farmworker vulnerability. Policies that promote greater community engagement with farmworker labor camp residents to reduce structural vulnerability should be considered. PMID:26469658

Cryptographic Securities Exchanges

NASA Astrophysics Data System (ADS)

Thorpe, Christopher; Parkes, David C.

While transparency in financial markets should enhance liquidity, its exploitation by unethical and parasitic traders discourages others from fully embracing disclosure of their own information. Traders exploit both the private information in upstairs markets used to trade large orders outside traditional exchanges and the public information present in exchanges' quoted limit order books. Using homomorphic cryptographic protocols, market designers can create "partially transparent" markets in which every matched trade is provably correct and only beneficial information is revealed. In a cryptographic securities exchange, market operators can hide information to prevent its exploitation, and still prove facts about the hidden information such as bid/ask spread or market depth.

Zipf exponent of trajectory distribution in the hidden Markov model

NASA Astrophysics Data System (ADS)

Bochkarev, V. V.; Lerner, E. Yu

2014-03-01

This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.

Hidden disorder in the α '→δ transformation of Pu-1.9 at.% Ga

DOE PAGES

Jeffries, J. R.; Manley, M. E.; Wall, M. A.; ...

2012-06-06

Enthalpy and entropy are thermodynamic quantities critical to determining how and at what temperature a phase transition occurs. At a phase transition, the enthalpy and temperature-weighted entropy differences between two phases are equal (ΔH=TΔS), but there are materials where this balance has not been experimentally or theoretically realized, leading to the idea of hidden order and disorder. In a Pu-1.9 at. % Ga alloy, the δ phase is retained as a metastable state at room temperature, but at low temperatures, the δ phase yields to a mixed-phase microstructure of δ- and α'-Pu. The previously measured sources of entropy associated withmore » the α'→δ transformation fail to sum to the entropy predicted theoretically. We report an experimental measurement of the entropy of the α'→δ transformation that corroborates the theoretical prediction, and implies that only about 65% of the entropy stabilizing the δ phase is accounted for, leaving a missing entropy of about 0.5 k B/atom. Some previously proposed mechanisms for generating entropy are discussed, but none seem capable of providing the necessary disorder to stabilize the δ phase. This hidden disorder represents multiple accessible states per atom within the δ phase of Pu that may not be included in our current understanding of the properties and phase stability of δ-Pu.« less

Comparison of the amount of bioaccessible fumonisin B1 and B2 in maize and rice inoculated with Fusarium verticillioides (MRC 826) and determined by in vitro digestion-preliminary results.

PubMed

Szabó-Fodor, J; Bors, I; Szabó, A; Kovács, M

2016-08-01

In this study the occurrence of hidden fumonisin B1 (FB1) and fumonisin B2 (FB2) was analysed, on two cereal substrates (maize and rice), inoculated with Fusarium verticillioides (MRC 826), in order to determine the ratio of hidden FB1 and FB2. Two parallel methods were applied: an in vitro human digestion sample pre-treatment and the routine extraction procedure, in both cases with subsequent LC-MS analysis. It was found that all samples showed higher concentration of total fumonisin B1 after digestion, as compared to that of free fumonisin analysed only after extraction. The percentage of the hidden form by maize was 18.8 % (±2.4) for FB1 and 36.8 % (±3.8) for FB2, while for rice it was 32.3 % (±11.3) and 58.0 (±6.8), respectively, expressed as the proportion to total fumonisin B1, for the total dataset. Significant differences were found in the FB1 and FB2 concentration measured after the different digestion phases (saliva, gastric and duodenal) in case of both matrixes. The results are useful for human risk assessment, since both humans and animals may be exposed to markedly higher toxin load, as determined merely by conventional analytical methods.

Liquidity Dynamics in the Xetra Order Book

NASA Astrophysics Data System (ADS)

Schmidinger, Christoph

2010-09-01

In this paper we show how to reconstruct the limit order book of the 30 stocks constituting the DAX30 index based on the trading protocol of the Xetra Trading System at the Frankfurt Stock Exchange. The algorithm used is innovative as it captures all trading phases, including auctions, and delivers a reconstruction of the orderbook either from a trader's view or a supervisory view including hidden volume as well. Based on the rebuilt order book, liquidity dynamics are examined. In contrats to findings for dealer markets, past market returns play a minor role in the determination of liquidity and liquidity commonality in Xetra, a pure limit order book market. Consequently, we provide evidence that liquidity provision by multiple sources in Xetra mitigates systemic liquidity risk introduced by the interrelation of return and liquidity.

Uncovering the Hidden Molecular Signatures of Breast Cancer

DTIC Science & Technology

2013-05-01

that! synergy! between! the! oncogene!MET!and!loss!of!p53!(tumor! protein !p53)!lead!to!a!tumor!phenotype!that! reflects! the! human! claudinDlow...membrane! protein ! levels! increase! continuously! in! accordance.! This! increase! has! been! directly! linked! to! a! corresponding! change! in...the!ESR! protein ! (Figure!1).!Alternatively,!a!signature!may! order!patients!in!such!a!way!that!associations!can!be!made!with!a!variety!of!other

Attentional Selection in Object Recognition

DTIC Science & Technology

1993-02-01

order. It also affects the choice of strategies in both the 24 A Computational Model of Attentional Selection filtering and arbiter stages. The set...such processing. In Treisman’s model this was hidden in the concept of the selection filter . Later computational models of attention tried to...This thesis presents a novel approach to the selection problem by propos. ing a computational model of visual attentional selection as a paradigm for

Hybridization in Kondo lattice heavy fermions via quasiparticle scattering spectroscopy (QPS)

NASA Astrophysics Data System (ADS)

Narasiwodeyar, Sanjay; Dwyer, Matt; Greene, Laura; Park, Wan Kyu; Bauer, Eric; Tobash, Paul; Baumbach, Ryan; Ronning, Filip; Sarrao, John; Thompson, Joe; Canfield, Paul

2014-03-01

Band renormalization in a Kondo lattice via hybridization of the conduction band with localized states has been a hot topic over the last several years. In part, this has to do with recently reignited interest in the hidden order problem in URu2Si2. Despite recent developments regarding the electronic structure in this compound, it remains to be resolved whether the hidden order phase transition is related to the opening of a hybridization gap. Our quasiparticle scattering spectroscopy (QPS) has shown they are not related directly. This can be understood naturally since in principle band renormalization does not involve symmetry breaking. To deepen our understanding, we extend to other Kondo lattice compounds. For instance, when applied to YbAl3, a vegetable heavy-fermion system, QPS reveals conductance signatures for hybridization in a Kondo lattice such as asymmetric Fano background along with characteristic energy scales. Presenting new results on these materials, we will discuss a broader picture. The work at UIUC is supported by the NSF DMR 12-06766, the work at LANL is carried out under the auspices of the U.S. DOE, Office of Science, and the work done at Ames Lab. was supported under Contract No. DE-AC02-07CH11358.

Spatio-Temporal Pattern Mining on Trajectory Data Using Arm

NASA Astrophysics Data System (ADS)

Khoshahval, S.; Farnaghi, M.; Taleai, M.

2017-09-01

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user's visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users' behaviour in a system and can be utilized in various location-based applications.

Hidden regularity and universal classification of fast side chain motions in proteins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rajeshwar, Rajitha; Smith, Jeremy C.; Krishnam, Marimuthu

Proteins display characteristic dynamical signatures that appear to be universal across all proteins regardless of topology and size. Here, we systematically characterize the universal features of fast side chain motions in proteins by examining the conformational energy surfaces of individual residues obtained using enhanced sampling molecular dynamics simulation (618 free energy surfaces obtained from 0.94 s MD simulation). The side chain conformational free energy surfaces obtained using the adaptive biasing force (ABF) method for a set of eight proteins with different molecular weights and secondary structures are used to determine the methyl axial NMR order parameters (O axis 2), populationsmore » of side chain rotamer states (ρ), conformational entropies (S conf), probability fluxes, and activation energies for side chain inter-rotameric transitions. The free energy barriers separating side chain rotamer states range from 0.3 to 12 kcal/mol in all proteins and follow a trimodal distribution with an intense peak at ~5 kcal/mol and two shoulders at ~3 and ~7.5 kcal/mol, indicating that some barriers are more favored than others by proteins to maintain a balance between their conformational stability and flexibility. The origin and the influences of the trimodal barrier distribution on the distribution of O axis 2 and the side chain conformational entropy are discussed. A hierarchical grading of rotamer states based on the conformational free energy barriers, entropy, and probability flux reveals three distinct classes of side chains in proteins. A unique nonlinear correlation is established between O axis 2 and the side chain rotamer populations (ρ). In conclusion, the apparent universality in O axis 2 versus correlation, trimodal barrier distribution, and distinct characteristics of three classes of side chains observed among all proteins indicates a hidden regularity (or commonality) in the dynamical heterogeneity of fast side chain motions in proteins.« less

Application of artificial neural network to predict clay sensitivity in a high landslide prone area using CPTu data- A case study in Southwest of Sweden

NASA Astrophysics Data System (ADS)

Shahri, Abbas; Mousavinaseri, Mahsasadat; Naderi, Shima; Espersson, Maria

2015-04-01

Application of Artificial Neural Networks (ANNs) in many areas of engineering, in particular to geotechnical engineering problems such as site characterization has demonstrated some degree of success. The present paper aims to evaluate the feasibility of several various types of ANN models to predict the clay sensitivity of soft clays form piezocone penetration test data (CPTu). To get the aim, a research database of CPTu data of 70 test points around the Göta River near the Lilli Edet in the southwest of Sweden which is a high prone land slide area were collected and considered as input for ANNs. For training algorithms the quick propagation, conjugate gradient descent, quasi-Newton, limited memory quasi-Newton and Levenberg-Marquardt were developed tested and trained using the CPTu data to provide a comparison between the results of field investigation and ANN models to estimate the clay sensitivity. The reason of using the clay sensitivity parameter in this study is due to its relation to landslides in Sweden.A special high sensitive clay namely quick clay is considered as the main responsible for experienced landslides in Sweden which has high sensitivity and prone to slide. The training and testing program was started with 3-2-1 ANN architecture structure. By testing and trying several various architecture structures and changing the hidden layer in order to have a higher output resolution the 3-4-4-3-1 architecture structure for ANN in this study was confirmed. The tested algorithm showed that increasing the hidden layers up to 4 layers in ANN can improve the results and the 3-4-4-3-1 architecture structure ANNs for prediction of clay sensitivity represent reliable and reasonable response. The obtained results showed that the conjugate gradient descent algorithm with R2=0.897 has the best performance among the tested algorithms. Keywords: clay sensitivity, landslide, Artificial Neural Network

A composite model for the 750 GeV diphoton excess

DOE PAGES

Harigaya, Keisuke; Nomura, Yasunori

2016-03-14

We study a simple model in which the recently reported 750 GeV diphoton excess arises from a composite pseudo Nambu-Goldstone boson — hidden pion — produced by gluon fusion and decaying into two photons. The model only introduces an extra hidden gauge group at the TeV scale with a vectorlike quark in the bifundamental representation of the hidden and standard model gauge groups. We calculate the masses of all the hidden pions and analyze their experimental signatures and constraints. We find that two colored hidden pions must be near the current experimental limits, and hence are probed in the nearmore » future. We study physics of would-be stable particles — the composite states that do not decay purely by the hidden and standard model gauge dynamics — in detail, including constraints from cosmology. We discuss possible theoretical structures above the TeV scale, e.g. conformal dynamics and supersymmetry, and their phenomenological implications. We also discuss an extension of the minimal model in which there is an extra hidden quark that is singlet under the standard model and has a mass smaller than the hidden dynamical scale. This provides two standard model singlet hidden pions that can both be viewed as diphoton/diboson resonances produced by gluon fusion. We discuss several scenarios in which these (and other) resonances can be used to explain various excesses seen in the LHC data.« less

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Temperature based Restricted Boltzmann Machines

NASA Astrophysics Data System (ADS)

Li, Guoqi; Deng, Lei; Xu, Yi; Wen, Changyun; Wang, Wei; Pei, Jing; Shi, Luping

2016-01-01

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

PCSYS: The optimal design integration system picture drawing system with hidden line algorithm capability for aerospace vehicle configurations

NASA Technical Reports Server (NTRS)

Hague, D. S.; Vanderburg, J. D.

1977-01-01

A vehicle geometric definition based upon quadrilateral surface elements to produce realistic pictures of an aerospace vehicle. The PCSYS programs can be used to visually check geometric data input, monitor geometric perturbations, and to visualize the complex spatial inter-relationships between the internal and external vehicle components. PCSYS has two major component programs. The between program, IMAGE, draws a complex aerospace vehicle pictorial representation based on either an approximate but rapid hidden line algorithm or without any hidden line algorithm. The second program, HIDDEN, draws a vehicle representation using an accurate but time consuming hidden line algorithm.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector ismore » cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.« less

Phases of cannibal dark matter

NASA Astrophysics Data System (ADS)

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.; Trevisan, Gabriele

2016-12-01

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector is cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.

Phases of cannibal dark matter

DOE PAGES

Farina, Marco; Pappadopulo, Duccio; Ruderman, Joshua T.; ...

2016-12-13

A hidden sector with a mass gap undergoes an epoch of cannibalism if number changing interactions are active when the temperature drops below the mass of the lightest hidden particle. During cannibalism, the hidden sector temperature decreases only logarithmically with the scale factor. We consider the possibility that dark matter resides in a hidden sector that underwent cannibalism, and has relic density set by the freeze-out of two-to-two annihilations. We identify three novel phases, depending on the behavior of the hidden sector when dark matter freezes out. During the cannibal phase, dark matter annihilations decouple while the hidden sector ismore » cannibalizing. During the chemical phase, only two-to-two interactions are active and the total number of hidden particles is conserved. During the one way phase, the dark matter annihilation products decay out of equilibrium, suppressing the production of dark matter from inverse annihilations. We map out the distinct phenomenology of each phase, which includes a boosted dark matter annihilation rate, new relativistic degrees of freedom, warm dark matter, and observable distortions to the spectrum of the cosmic microwave background.« less

A shared synapse architecture for efficient FPGA implementation of autoencoders.

PubMed

Suzuki, Akihiro; Morie, Takashi; Tamukoh, Hakaru

2018-01-01

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers' units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks.

Spatio-Temporal Regression Based Clustering of Precipitation Extremes in a Presence of Systematically Missing Covariates

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Martius, Olivia; Horenko, Illia

2017-04-01

Regression based Generalized Pareto Distribution (GPD) models are often used to describe the dynamics of hydrological threshold excesses relying on the explicit availability of all of the relevant covariates. But, in real application the complete set of relevant covariates might be not available. In this context, it was shown that under weak assumptions the influence coming from systematically missing covariates can be reflected by a nonstationary and nonhomogenous dynamics. We present a data-driven, semiparametric and an adaptive approach for spatio-temporal regression based clustering of threshold excesses in a presence of systematically missing covariates. The nonstationary and nonhomogenous behavior of threshold excesses is describes by a set of local stationary GPD models, where the parameters are expressed as regression models, and a non-parametric spatio-temporal hidden switching process. Exploiting nonparametric Finite Element time-series analysis Methodology (FEM) with Bounded Variation of the model parameters (BV) for resolving the spatio-temporal switching process, the approach goes beyond strong a priori assumptions made is standard latent class models like Mixture Models and Hidden Markov Models. Additionally, the presented FEM-BV-GPD provides a pragmatic description of the corresponding spatial dependence structure by grouping together all locations that exhibit similar behavior of the switching process. The performance of the framework is demonstrated on daily accumulated precipitation series over 17 different locations in Switzerland from 1981 till 2013 - showing that the introduced approach allows for a better description of the historical data.

A shared synapse architecture for efficient FPGA implementation of autoencoders

PubMed Central

Morie, Takashi; Tamukoh, Hakaru

2018-01-01

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half. For the proposed circuit to be implemented into various types of AEs, it utilizes three kinds of parameters; one to change the number of layers’ units, one to change the bit width of an internal value, and a learning rate. By altering a network configuration using these parameters, the proposed architecture can be used to construct a stacked AE. The proposed circuits are logically synthesized, and the number of their resources is determined. Our experimental results show that single and stacked AE circuits utilizing the proposed shared synapse architecture operate as regular AEs and as regular stacked AEs. The scalability of the proposed circuit and the relationship between the bit widths and the learning results are also determined. The clock cycles of the proposed circuits are formulated, and this formula is used to estimate the theoretical performance of the circuit when the circuit is used to construct arbitrary networks. PMID:29543909

Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

PubMed

Hatt, M; Lamare, F; Boussion, N; Turzo, A; Collet, C; Salzenstein, F; Roux, C; Jarritt, P; Carson, K; Cheze-Le Rest, C; Visvikis, D

2007-06-21

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

Guided wave energy trapping to detect hidden multilayer delamination damage

NASA Astrophysics Data System (ADS)

Leckey, Cara A. C.; Seebo, Jeffrey P.

2015-03-01

Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM) simulation tools capable of modeling three-dimensional (3D) realistic energy-damage interactions are needed for aerospace composites. Current practice in NDE/SHM simulation for composites commonly involves over-simplification of the material parameters and/or a simplified two-dimensional (2D) approach. The unique damage types that occur in composite materials (delamination, microcracking, etc) develop as complex 3D geometry features. This paper discusses the application of 3D custom ultrasonic simulation tools to study wave interaction with multilayer delamination damage in carbon-fiber reinforced polymer (CFRP) composites. In particular, simulation based studies of ultrasonic guided wave energy trapping due to multilayer delamination damage were performed. The simulation results show changes in energy trapping at the composite surface as additional delaminations are added through the composite thickness. The results demonstrate a potential approach for identifying the presence of hidden multilayer delamination damage in applications where only single-sided access to a component is available. The paper also describes recent advancements in optimizing the custom ultrasonic simulation code for increases in computation speed.

Illusion induced overlapped optics.

PubMed

Zang, XiaoFei; Shi, Cheng; Li, Zhou; Chen, Lin; Cai, Bin; Zhu, YiMing; Zhu, HaiBin

2014-01-13

The traditional transformation-based cloak seems like it can only hide objects by bending the incident electromagnetic waves around the hidden region. In this paper, we prove that invisible cloaks can be applied to realize the overlapped optics. No matter how many in-phase point sources are located in the hidden region, all of them can overlap each other (this can be considered as illusion effect), leading to the perfect optical interference effect. In addition, a singular parameter-independent cloak is also designed to obtain quasi-overlapped optics. Even more amazing of overlapped optics is that if N identical separated in-phase point sources covered with the illusion media, the total power outside the transformation region is N2I0 (not NI0) (I0 is the power of just one point source, and N is the number point sources), which seems violating the law of conservation of energy. A theoretical model based on interference effect is proposed to interpret the total power of these two kinds of overlapped optics effects. Our investigation may have wide applications in high power coherent laser beams, and multiple laser diodes, and so on.

Direct detection signatures of self-interacting dark matter with a light mediator

DOE PAGES

Nobile, Eugenio Del; Kaplinghat, Manoj; Yu, Hai-Bo

2015-10-27

Self-interacting dark matter (SIDM) is a simple and well-motivated scenario that could explain long-standing puzzles in structure formation on small scales. If the required self-interaction arises through a light mediator (with mass ~ 10 MeV) in the dark sector, this new particle must be unstable to avoid overclosing the universe. The decay of the light mediator could happen due to a weak coupling of the hidden and visible sectors, providing new signatures for direct detection experiments. The SIDM nuclear recoil spectrum is more peaked towards low energies compared to the usual case of contact interactions, because the mediator mass ismore » comparable to the momentum transfer of nuclear recoils. We show that the SIDM signal could be distinguished from that of DM particles with contact interactions by considering the time-average energy spectrum in experiments employing different target materials, or the average and modulated spectra in a single experiment. Using current limits from LUX and SuperCDMS, we also derive strong bounds on the mixing parameter between hidden and visible sector.« less

Short-term prediction of chaotic time series by using RBF network with regression weights.

PubMed

Rojas, I; Gonzalez, J; Cañas, A; Diaz, A F; Rojas, F J; Rodriguez, M

2000-10-01

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

Gauging hidden symmetries in two dimensions

NASA Astrophysics Data System (ADS)

Samtleben, Henning; Weidner, Martin

2007-08-01

We initiate the systematic construction of gauged matter-coupled supergravity theories in two dimensions. Subgroups of the affine global symmetry group of toroidally compactified supergravity can be gauged by coupling vector fields with minimal couplings and a particular topological term. The gauge groups typically include hidden symmetries that are not among the target-space isometries of the ungauged theory. The gaugings constructed in this paper are described group-theoretically in terms of a constant embedding tensor subject to a number of constraints which parametrizes the different theories and entirely encodes the gauged Lagrangian. The prime example is the bosonic sector of the maximally supersymmetric theory whose ungauged version admits an affine fraktur e9 global symmetry algebra. The various parameters (related to higher-dimensional p-form fluxes, geometric and non-geometric fluxes, etc.) which characterize the possible gaugings, combine into an embedding tensor transforming in the basic representation of fraktur e9. This yields an infinite-dimensional class of maximally supersymmetric theories in two dimensions. We work out and discuss several examples of higher-dimensional origin which can be systematically analyzed using the different gradings of fraktur e9.

Hidden Markov model for dependent mark loss and survival estimation

USGS Publications Warehouse

Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.

2014-01-01

Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

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

PubMed

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.

Markov Chain Monte Carlo in the Analysis of Single-Molecule Experimental Data

NASA Astrophysics Data System (ADS)

Kou, S. C.; Xie, X. Sunney; Liu, Jun S.

2003-11-01

This article provides a Bayesian analysis of the single-molecule fluorescence lifetime experiment designed to probe the conformational dynamics of a single DNA hairpin molecule. The DNA hairpin's conformational change is initially modeled as a two-state Markov chain, which is not observable and has to be indirectly inferred. The Brownian diffusion of the single molecule, in addition to the hidden Markov structure, further complicates the matter. We show that the analytical form of the likelihood function can be obtained in the simplest case and a Metropolis-Hastings algorithm can be designed to sample from the posterior distribution of the parameters of interest and to compute desired estiamtes. To cope with the molecular diffusion process and the potentially oscillating energy barrier between the two states of the DNA hairpin, we introduce a data augmentation technique to handle both the Brownian diffusion and the hidden Ornstein-Uhlenbeck process associated with the fluctuating energy barrier, and design a more sophisticated Metropolis-type algorithm. Our method not only increases the estimating resolution by several folds but also proves to be successful for model discrimination.

Forecasting daily streamflow using online sequential extreme learning machines

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

2016-06-01

While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

Out of Reach, Out of Mind? Infants' Comprehension of References to Hidden Inaccessible Objects.

PubMed

Osina, Maria A; Saylor, Megan M; Ganea, Patricia A

2017-09-01

This study investigated the nature of infants' difficulty understanding references to hidden inaccessible objects. Twelve-month-old infants (N = 32) responded to the mention of objects by looking at, pointing at, or approaching them when the referents were visible or accessible, but not when they were hidden and inaccessible (Experiment I). Twelve-month-olds (N = 16) responded robustly when a container with the hidden referent was moved from a previously inaccessible position to an accessible position before the request, but failed to respond when the reverse occurred (Experiment II). This suggests that infants might be able to track the hidden object's dislocations and update its accessibility as it changes. Knowing the hidden object is currently inaccessible inhibits their responding. Older, 16-month-old (N = 17) infants' performance was not affected by object accessibility. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Severely Constraining Dark Matter Interpretations of the 21-cm Anomaly

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berlin, Asher; Hooper, Dan; Krnjaic, Gordan

The EDGES Collaboration has recently reported the detection of a stronger-than-expected absorption feature in the global 21-cm spectrum, centered at a frequency corresponding to a redshift of z ~ 17. This observation has been interpreted as evidence that the gas was cooled during this era as a result of scattering with dark matter. In this study, we explore this possibility, applying constraints from the cosmic microwave background, light element abundances, Supernova 1987A, and a variety of laboratory experiments. After taking these constraints into account, we find that the vast majority of the parameter space capable of generating the observed 21-cmmore » signal is ruled out. The only range of models that remains viable is that in which a small fraction, ~ 0.3-2%, of the dark matter consists of particles with a mass of ~ 10-80 MeV and which couple to the photon through a small electric charge, epsilon ~ 10^{-6}-10^{-4}. Furthermore, in order to avoid being overproduced in the early universe, such models must be supplemented with an additional depletion mechanism, such as annihilations through a L_{\\mu}-L_{\\tau} gauge boson or annihilations to a pair of rapidly decaying hidden sector scalars.« less

Learning and inference in a nonequilibrium Ising model with hidden nodes.

PubMed

Dunn, Benjamin; Roudi, Yasser

2013-02-01

We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.

More about solar g modes

NASA Astrophysics Data System (ADS)

Fossat, E.; Schmider, F. X.

2018-04-01

Context. The detection of asymptotic solar g-mode parameters was the main goal of the GOLF instrument onboard the SOHO space observatory. This detection has recently been reported and has identified a rapid mean rotation of the solar core, with a one-week period, nearly four times faster than all the rest of the solar body, from the surface to the bottom of the radiative zone. Aim. We present here the detection of more g modes of higher degree, and a more precise estimation of all their parameters, which will have to be exploited as additional constraints in modeling the solar core. Methods: Having identified the period equidistance and the splitting of a large number of asymptotic g modes of degrees 1 and 2, we test a model of frequencies of these modes by a cross-correlation with the power spectrum from which they have been detected. It shows a high correlation peak at lag zero, showing that the model is hidden but present in the real spectrum. The model parameters can then be adjusted to optimize the position (at exactly zero lag) and the height of this correlation peak. The same method is then extended to the search for modes of degrees 3 and 4, which were not detected in the previous analysis. Results: g-mode parameters are optimally measured in similar-frequency bandwidths, ranging from 7 to 8 μHz at one end and all close to 30 μHz at the other end, for the degrees 1 to 4. They include the four asymptotic period equidistances, the slight departure from equidistance of the detected periods for l = 1 and l = 2, the measured amplitudes, functions of the degree and the tesseral order, and the splittings that will possibly constrain the estimated sharpness of the transition between the one-week mean rotation of the core and the almost four-week rotation of the radiative envelope. The g-mode periods themselves are crucial inputs in the solar core structure helioseismic investigation.

Multi-scale chromatin state annotation using a hierarchical hidden Markov model

NASA Astrophysics Data System (ADS)

Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang; Glass, Kimberly; Pinello, Luca; Wang, Jianrong; Kellis, Manolis; Yuan, Guo-Cheng

2017-04-01

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.

Hamiltonian dynamics of a quantum of space: hidden symmetries and spectrum of the volume operator, and discrete orthogonal polynomials

NASA Astrophysics Data System (ADS)

Aquilanti, Vincenzo; Marinelli, Dimitri; Marzuoli, Annalisa

2013-05-01

The action of the quantum mechanical volume operator, introduced in connection with a symmetric representation of the three-body problem and recently recognized to play a fundamental role in discretized quantum gravity models, can be given as a second-order difference equation which, by a complex phase change, we turn into a discrete Schrödinger-like equation. The introduction of discrete potential-like functions reveals the surprising crucial role here of hidden symmetries, first discovered by Regge for the quantum mechanical 6j symbols; insight is provided into the underlying geometric features. The spectrum and wavefunctions of the volume operator are discussed from the viewpoint of the Hamiltonian evolution of an elementary ‘quantum of space’, and a transparent asymptotic picture of the semiclassical and classical regimes emerges. The definition of coordinates adapted to the Regge symmetry is exploited for the construction of a novel set of discrete orthogonal polynomials, characterizing the oscillatory components of torsion-like modes.

Cross-modal learning to rank via latent joint representation.

PubMed

Wu, Fei; Jiang, Xinyang; Li, Xi; Tang, Siliang; Lu, Weiming; Zhang, Zhongfei; Zhuang, Yueting

2015-05-01

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.

Identifying influential user communities on the social network

NASA Astrophysics Data System (ADS)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

Doppler Radar Vital Signs Detection Method Based on Higher Order Cyclostationary.

PubMed

Yu, Zhibin; Zhao, Duo; Zhang, Zhiqiang

2017-12-26

Due to the non-contact nature, using Doppler radar sensors to detect vital signs such as heart and respiration rates of a human subject is getting more and more attention. However, the related detection-method research meets lots of challenges due to electromagnetic interferences, clutter and random motion interferences. In this paper, a novel third-order cyclic cummulant (TOCC) detection method, which is insensitive to Gaussian interference and non-cyclic signals, is proposed to investigate the heart and respiration rate based on continuous wave Doppler radars. The k -th order cyclostationary properties of the radar signal with hidden periodicities and random motions are analyzed. The third-order cyclostationary detection theory of the heart and respiration rate is studied. Experimental results show that the third-order cyclostationary approach has better estimation accuracy for detecting the vital signs from the received radar signal under low SNR, strong clutter noise and random motion interferences.

Folksonomies and clustering in the collaborative system CiteULike

NASA Astrophysics Data System (ADS)

Capocci, Andrea; Caldarelli, Guido

2008-06-01

We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tri-partite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient can be used to analyze the semantical patterns among tags.

Electronic health record analysis via deep poisson factor models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henao, Ricardo; Lu, James T.; Lucas, Joseph E.

Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. In-formation from different modalities is shared via a deep hierarchy of common hidden units. Activationmore » of these binary units occurs with probability characterized as Bernoulli-Poisson link functions, instead of more traditional logistic link functions. In addition, we demon-strate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 12,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.« less

Electronic health record analysis via deep poisson factor models

DOE PAGES

Henao, Ricardo; Lu, James T.; Lucas, Joseph E.; ...

2016-01-01

Electronic Health Record (EHR) phenotyping utilizes patient data captured through normal medical practice, to identify features that may represent computational medical phenotypes. These features may be used to identify at-risk patients and improve prediction of patient morbidity and mortality. We present a novel deep multi-modality architecture for EHR analysis (applicable to joint analysis of multiple forms of EHR data), based on Poisson Factor Analysis (PFA) modules. Each modality, composed of observed counts, is represented as a Poisson distribution, parameterized in terms of hidden binary units. In-formation from different modalities is shared via a deep hierarchy of common hidden units. Activationmore » of these binary units occurs with probability characterized as Bernoulli-Poisson link functions, instead of more traditional logistic link functions. In addition, we demon-strate that PFA modules can be adapted to discriminative modalities. To compute model parameters, we derive efficient Markov Chain Monte Carlo (MCMC) inference that scales efficiently, with significant computational gains when compared to related models based on logistic link functions. To explore the utility of these models, we apply them to a subset of patients from the Duke-Durham patient cohort. We identified a cohort of over 12,000 patients with Type 2 Diabetes Mellitus (T2DM) based on diagnosis codes and laboratory tests out of our patient population of over 240,000. Examining the common hidden units uniting the PFA modules, we identify patient features that represent medical concepts. Experiments indicate that our learned features are better able to predict mortality and morbidity than clinical features identified previously in a large-scale clinical trial.« less

Wigner flow reveals topological order in quantum phase space dynamics.

PubMed

Steuernagel, Ole; Kakofengitis, Dimitris; Ritter, Georg

2013-01-18

The behavior of classical mechanical systems is characterized by their phase portraits, the collections of their trajectories. Heisenberg's uncertainty principle precludes the existence of sharply defined trajectories, which is why traditionally only the time evolution of wave functions is studied in quantum dynamics. These studies are quite insensitive to the underlying structure of quantum phase space dynamics. We identify the flow that is the quantum analog of classical particle flow along phase portrait lines. It reveals hidden features of quantum dynamics and extra complexity. Being constrained by conserved flow winding numbers, it also reveals fundamental topological order in quantum dynamics that has so far gone unnoticed.

Cooper pair induced frustration and nematicity of two-dimensional magnetic adatom lattices

NASA Astrophysics Data System (ADS)

Schecter, Michael; Syljuâsen, Olav F.; Paaske, Jens

2018-05-01

We propose utilizing the Cooper pair to induce magnetic frustration in systems of two-dimensional (2D) magnetic adatom lattices on s -wave superconducting surfaces. The competition between singlet electron correlations and the RKKY coupling is shown to lead to a variety of hidden-order states that break the point-group symmetry of the 2D adatom lattice at finite temperature. The phase diagram is constructed using a newly developed effective bond theory [M. Schecter et al., Phys. Rev. Lett. 119, 157202 (2017), 10.1103/PhysRevLett.119.157202], and exhibits broad regions of long-range vestigial nematic order.

Life imitating art: depictions of the hidden curriculum in medical television programs.

PubMed

Stanek, Agatha; Clarkin, Chantalle; Bould, M Dylan; Writer, Hilary; Doja, Asif

2015-09-26

The hidden curriculum represents influences occurring within the culture of medicine that indirectly alter medical professionals' interactions, beliefs and clinical practices throughout their training. One approach to increase medical student awareness of the hidden curriculum is to provide them with readily available examples of how it is enacted in medicine; as such the purpose of this study was to examine depictions of the hidden curriculum in popular medical television programs. One full season of ER, Grey's Anatomy and Scrubs were selected for review. A summative content analysis was performed to ascertain the presence of depictions of the hidden curriculum, as well as to record the type, frequency and quality of examples. A second reviewer also viewed a random selection of episodes from each series to establish coding reliability. The most prevalent themes across all television programs were: the hierarchical nature of medicine; challenges during transitional stages in medicine; the importance of role modeling; patient dehumanization; faking or overstating one's capabilities; unprofessionalism; the loss of idealism; and difficulties with work-life balance. The hidden curriculum is frequently depicted in popular medical television shows. These examples of the hidden curriculum could serve as a valuable teaching resource in undergraduate medical programs.

Uncovering hidden nodes in complex networks in the presence of noise

PubMed Central

Su, Ri-Qi; Lai, Ying-Cheng; Wang, Xiao; Do, Younghae

2014-01-01

Ascertaining the existence of hidden objects in a complex system, objects that cannot be observed from the external world, not only is curiosity-driven but also has significant practical applications. Generally, uncovering a hidden node in a complex network requires successful identification of its neighboring nodes, but a challenge is to differentiate its effects from those of noise. We develop a completely data-driven, compressive-sensing based method to address this issue by utilizing complex weighted networks with continuous-time oscillatory or discrete-time evolutionary-game dynamics. For any node, compressive sensing enables accurate reconstruction of the dynamical equations and coupling functions, provided that time series from this node and all its neighbors are available. For a neighboring node of the hidden node, this condition cannot be met, resulting in abnormally large prediction errors that, counterintuitively, can be used to infer the existence of the hidden node. Based on the principle of differential signal, we demonstrate that, when strong noise is present, insofar as at least two neighboring nodes of the hidden node are subject to weak background noise only, unequivocal identification of the hidden node can be achieved. PMID:24487720

Apparent impact: the hidden cost of one-shot trades

NASA Astrophysics Data System (ADS)

Mastromatteo, Iacopo

2015-06-01

We study the problem of the execution of a moderate size order in an illiquid market within the framework of a solvable Markovian model. We suppose that in order to avoid impact costs, a trader decides to execute her order through a unique trade, waiting for enough liquidity to accumulate at the best quote. We find that despite the absence of a proper price impact, such trader faces an execution cost arising from a non-vanishing correlation among volume at the best quotes and price changes. We characterize analytically the statistics of the execution time and its cost by mapping the problem to the simpler one of calculating a set of first-passage probabilities on a semi-infinite strip. We finally argue that price impact cannot be completely avoided by conditioning the execution of an order to a more favorable liquidity scenario.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PubMed Central

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Machine learning topological states

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Convex clustering: an attractive alternative to hierarchical clustering.

PubMed

Chen, Gary K; Chi, Eric C; Ranola, John Michael O; Lange, Kenneth

2015-05-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/.

The Politics of the Hidden Curriculum

ERIC Educational Resources Information Center

Giroux, Henry A.

1977-01-01

Schools teach much more than the traditional curriculum. They also teach a "hidden curriculum"--those unstated norms, values, and beliefs promoting hierarchic and authoritarian social relations that are transmitted to students through the underlying educational structure. Discusses the effects of the "hidden curriculum" on the…

Development of adapted GMR-probes for automated detection of hidden defects in thin steel sheets

NASA Astrophysics Data System (ADS)

Pelkner, Matthias; Pohl, Rainer; Kreutzbruck, Marc; Commandeur, Colin

2016-02-01

Thin steel sheets with a thickness of 0.3 mm and less are the base materials of many everyday life products (cans, batteries, etc.). Potential inhomogeneities such as non-metallic inclusions inside the steel can lead to a rupture of the sheets when it is formed into a product such as a beverage can. Therefore, there is a need to develop automated NDT techniques to detect hidden defects and inclusions in thin sheets during production. For this purpose Tata Steel Europe and BAM, the Federal Institute for Materials Research and Testing (Germany), collaborate in order to develop an automated NDT-system. Defect detection systems have to be robust against external influences, especially when used in an industrial environment. In addition, such a facility has to achieve a high sensitivity and a high spatial resolution in terms of detecting small inclusions in the μm-regime. In a first step, we carried out a feasibility study to determine which testing method is promising for detecting hidden defects and inclusions inside ferrous thin steel sheets. Therefore, two methods were investigated in more detail - magnetic flux leakage testing (MFL) using giant magneto resistance sensor arrays (GMR) as receivers [1,2] and eddy current testing (ET). The capabilities of both methods were tested with 0.2 mm-thick steel samples containing small defects with depths ranging from 5 µm up to 60 µm. Only in case of GMR-MFL-testing, we were able to detect parts of the hidden defects with a depth of 10 µm trustworthily with a SNR better than 10 dB. Here, the lift off between sensor and surface was 250 µm. On this basis, we investigated different testing scenarios including velocity tests and different lift offs. In this contribution we present the results of the feasibility study leading to first prototypes of GMR-probes which are now installed as part of a demonstrator inside a production line.

Experimental and Computational Studies on the Scattering of an Edge-Guided Wave by a Hidden Crack on a Racecourse Shaped Hole.

PubMed

Vien, Benjamin Steven; Rose, Louis Raymond Francis; Chiu, Wing Kong

2017-07-01

Reliable and quantitative non-destructive evaluation for small fatigue cracks, in particular those in hard-to-inspect locations, is a challenging problem. Guided waves are advantageous for structural health monitoring due to their slow geometrical decay of amplitude with propagating distance, which is ideal for rapid wide-area inspection. This paper presents a 3D laser vibrometry experimental and finite element analysis of the interaction between an edge-guided wave and a small through-thickness hidden edge crack on a racecourse shaped hole that occurs, in practice, as a fuel vent hole. A piezoelectric transducer is bonded on the straight edge of the hole to generate the incident wave. The excitation signal consists of a 5.5 cycle Hann-windowed tone burst of centre frequency 220 kHz, which is below the cut-off frequency for the first order Lamb wave modes (SH1). Two-dimensional fast Fourier transformation (2D FFT) is applied to the incident and scattered wave field along radial lines emanating from the crack mouth, so as to identify the wave modes and determine their angular variation and amplitude. It is shown experimentally and computationally that mid-plane symmetric edge waves can travel around the hole's edge to detect a hidden crack. Furthermore, the scattered wave field due to a small crack length, a , (compared to the wavelength λ of the incident wave) is shown to be equivalent to a point source consisting of a particular combination of body-force doublets. It is found that the amplitude of the scattered field increases quadratically as a function of a/λ , whereas the scattered wave pattern is independent of crack length for small cracks a < λ . This study of the forward scattering problem from a known crack size provides a useful guide for the inverse problem of hidden crack detection and sizing.

Damage modeling and statistical analysis of optics damage performance in MJ-class laser systems.

PubMed

Liao, Zhi M; Raymond, B; Gaylord, J; Fallejo, R; Bude, J; Wegner, P

2014-11-17

Modeling the lifetime of a fused silica optic is described for a multiple beam, MJ-class laser system. This entails combining optic processing data along with laser shot data to account for complete history of optic processing and shot exposure. Integrating with online inspection data allows for the construction of a performance metric to describe how an optic performs with respect to the model. This methodology helps to validate the damage model as well as allows strategic planning and identifying potential hidden parameters that are affecting the optic's performance.

A comparison of polynomial approximations and artificial neural nets as response surfaces

NASA Technical Reports Server (NTRS)

Carpenter, William C.; Barthelemy, Jean-Francois M.

1992-01-01

Artificial neural nets and polynomial approximations were used to develop response surfaces for several test problems. Based on the number of functional evaluations required to build the approximations and the number of undetermined parameters associated with the approximations, the performance of the two types of approximations was found to be comparable. A rule of thumb is developed for determining the number of nodes to be used on a hidden layer of an artificial neural net, and the number of designs needed to train an approximation is discussed.

Highly Productive Tools For Turning And Milling

NASA Astrophysics Data System (ADS)

Vasilko, Karol

2015-12-01

Beside cutting speed, shift is another important parameter of machining. Its considerable influence is shown mainly in the workpiece machined surface microgeometry. In practice, mainly its combination with the radius of cutting tool tip rounding is used. Options to further increase machining productivity and machined surface quality are hidden in this approach. The paper presents variations of the design of productive cutting tools for lathe work and milling on the base of the use of the laws of the relationship among the highest reached uneveness of machined surface, tool tip radius and shift.

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

PubMed Central

2016-01-01

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods. PMID:27891146

Hidden discriminative features extraction for supervised high-order time series modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mean-field theory of baryonic matter for QCD in the large Nc and heavy quark mass limits

NASA Astrophysics Data System (ADS)

Adhikari, Prabal; Cohen, Thomas D.

2013-11-01

We discuss theoretical issues pertaining to baryonic matter in the combined heavy-quark and large Nc limits of QCD. Witten's classic argument that baryons and interacting systems of baryons can be described in a mean-field approximation with each of the quarks moving in an average potential due to the remaining quarks is heuristic. It is important to justify this heuristic description for the case of baryonic matter since systems of interacting baryons are intrinsically more complicated than single baryons due to the possibility of hidden color states—states in which the subsystems making up the entire baryon crystal are not color-singlet nucleons but rather colorful states coupled together to make a color-singlet state. In this work, we provide a formal justification of this heuristic prescription. In order to do this, we start by taking the heavy quark limit, thus effectively reducing the problem to a many-body quantum mechanical system. This problem can be formulated in terms of integrals over coherent states, which for this problem are simple Slater determinants. We show that for the many-body problem, the support region for these integrals becomes narrow at large Nc, yielding an energy which is well approximated by a single coherent state—that is a mean-field description. Corrections to the energy are of relative order 1/Nc. While hidden color states are present in the exact state of the heavy quark system, they only influence the interaction energy below leading order in 1/Nc.

Infinite hidden conditional random fields for human behavior analysis.

PubMed

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

2013-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models that have been shown to successfully learn the hidden structure of a given classification problem (provided an appropriate validation of the number of hidden states). In this brief, we present the infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task. We show how we learn the model hyperparameters with an effective Markov-chain Monte Carlo sampling technique, and we explain the process that underlines our iHCRF model with the Restaurant Franchise Rating Agencies analogy. We show that the iHCRF is able to converge to a correct number of represented hidden states, and outperforms the best finite HCRFs--chosen via cross-validation--for the difficult tasks of recognizing instances of agreement, disagreement, and pain. Moreover, the iHCRF manages to achieve this performance in significantly less total training, validation, and testing time.

A new look at the Y tetraquarks and Ω _c baryons in the diquark model

NASA Astrophysics Data System (ADS)

Ali, Ahmed; Maiani, Luciano; Borisov, Anatoly V.; Ahmed, Ishtiaq; Aslam, M. Jamil; Parkhomenko, Alexander Ya.; Polosa, Antonio D.; Rehman, Abdur

2018-01-01

We analyze the hidden charm P-wave tetraquarks in the diquark model, using an effective Hamiltonian incorporating the dominant spin-spin, spin-orbit and tensor interactions. We compare with other P-wave systems such as P-wave charmonia and the newly discovered Ω _c baryons, analysed recently in this framework. Given the uncertain experimental situation on the Y states, we allow for different spectra and discuss the related parameters in the diquark model. In addition to the presently observed ones, we expect many more states in the supermultiplet of L=1 diquarkonia, whose J^{PC} quantum numbers and masses are worked out, using the parameters from the currently preferred Y-states pattern. The existence of these new resonances would be a decisive footprint of the underlying diquark dynamics.

Towards parameter-free classification of sound effects in movies

NASA Astrophysics Data System (ADS)

Chu, Selina; Narayanan, Shrikanth; Kuo, C.-C. J.

2005-08-01

The problem of identifying intense events via multimedia data mining in films is investigated in this work. Movies are mainly characterized by dialog, music, and sound effects. We begin our investigation with detecting interesting events through sound effects. Sound effects are neither speech nor music, but are closely associated with interesting events such as car chases and gun shots. In this work, we utilize low-level audio features including MFCC and energy to identify sound effects. It was shown in previous work that the Hidden Markov model (HMM) works well for speech/audio signals. However, this technique requires a careful choice in designing the model and choosing correct parameters. In this work, we introduce a framework that will avoid such necessity and works well with semi- and non-parametric learning algorithms.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Investigating Dueling Scenarios in NGC 7582 with Broadband X-ray Spectroscopy

NASA Astrophysics Data System (ADS)

Rivers, E.

2015-09-01

NGC 7582 is a well-studied X-ray bright Seyfert 2 with moderately heavy (NH = 10^{23} - 10^{24} cm^{-2}), highly variable absorption and unusually strong reflection spectral features. The spectral shape changed around the year 2000, dropping in observed flux and becoming much more highly absorbed. Two scenarios have been put forth to explain this spectral change: 1) the source "shut off" around this time, decreasing in intrinsic luminosity, with a delayed decrease in reflection features due to the light crossing time of the Compton-thick material or 2) the source is a "hidden nucleus" which has recently become more heavily obscured, with only a portion of the power law continuum leaking through. NuSTAR observed NGC 7582 twice in 2012 two weeks apart in order to quantify the reflection using high-quality data above 10 keV. We analyze both NuSTAR observations placing them in the context of historical X-ray, infrared and optical observations, including re-analysis of RXTE data from 2003-2005. We find that the most plausible scenario is that NGC 7582 has a hidden nucleus which has recently become more heavily absorbed by a patchy torus with a covering fraction of 80-90% and a column density of 3.6 x 10^{24} cm^{-2}. We find the need for an additional highly variable full-covering absorber with NH= 4-6 x 10^{23} cm^{-2}, possibly associated with a hidden broad line region or a dust lane in the host galaxy.

The Hidden Curriculum in Distance Education: An Updated View.

ERIC Educational Resources Information Center

Anderson, Terry

2001-01-01

Addressing recent criticism of distance education, explores the distinctive hidden curriculum (supposed "real" agenda) of distance education, focusing on both its positive and negative expressions. Also offers an updated view of the hidden curriculum of traditional, campus-based education, grounded in an emerging worldwide context of broadening…

77 FR 8253 - Notice of Proposed Settlement Agreement and Opportunity for Public Comment: Hidden Lane Landfill...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-14

... ENVIRONMENTAL PROTECTION AGENCY [FRL-9631-3] Notice of Proposed Settlement Agreement and Opportunity for Public Comment: Hidden Lane Landfill Superfund Site ACTION: Notice. SUMMARY: In accordance... (``DOJ'') on behalf of EPA, in connection with the Hidden Lane Landfill Superfund Site, Sterling, Loudoun...

Hidden Curriculum as One of Current Issue of Curriculum

ERIC Educational Resources Information Center

Alsubaie, Merfat Ayesh

2015-01-01

There are several issues in the education system, especially in the curriculum field that affect education. Hidden curriculum is one of current controversial curriculum issues. Many hidden curricular issues are the result of assumptions and expectations that are not formally communicated, established, or conveyed within the learning environment.…

Hidden Variable Theories and Quantum Nonlocality

ERIC Educational Resources Information Center

Boozer, A. D.

2009-01-01

We clarify the meaning of Bell's theorem and its implications for the construction of hidden variable theories by considering an example system consisting of two entangled spin-1/2 particles. Using this example, we present a simplified version of Bell's theorem and describe several hidden variable theories that agree with the predictions of…

Building Simple Hidden Markov Models. Classroom Notes

ERIC Educational Resources Information Center

Ching, Wai-Ki; Ng, Michael K.

2004-01-01

Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

Seuss's Butter Battle Book: Is There Hidden Harm?

ERIC Educational Resources Information Center

Van Cleaf, David W.; Martin, Rita J.

1986-01-01

Examines whether elementary school children relate to the "harmful hidden message" about nuclear war in Dr. Seuss's THE BUTTER BATTLE BOOK. After ascertaining the children's cognitive level, they participated in activities to find hidden meanings in stories, including Seuss's book. Students failed to identify the nuclear war message in…

Comment on 'All quantum observables in a hidden-variable model must commute simultaneously'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nagata, Koji

Malley discussed [Phys. Rev. A 69, 022118 (2004)] that all quantum observables in a hidden-variable model for quantum events must commute simultaneously. In this comment, we discuss that Malley's theorem is indeed valid for the hidden-variable theoretical assumptions, which were introduced by Kochen and Specker. However, we give an example that the local hidden-variable (LHV) model for quantum events preserves noncommutativity of quantum observables. It turns out that Malley's theorem is not related to the LHV model for quantum events, in general.

Inference for dynamics of continuous variables: the extended Plefka expansion with hidden nodes

NASA Astrophysics Data System (ADS)

Bravi, B.; Sollich, P.

2017-06-01

We consider the problem of a subnetwork of observed nodes embedded into a larger bulk of unknown (i.e. hidden) nodes, where the aim is to infer these hidden states given information about the subnetwork dynamics. The biochemical networks underlying many cellular and metabolic processes are important realizations of such a scenario as typically one is interested in reconstructing the time evolution of unobserved chemical concentrations starting from the experimentally more accessible ones. We present an application to this problem of a novel dynamical mean field approximation, the extended Plefka expansion, which is based on a path integral description of the stochastic dynamics. As a paradigmatic model we study the stochastic linear dynamics of continuous degrees of freedom interacting via random Gaussian couplings. The resulting joint distribution is known to be Gaussian and this allows us to fully characterize the posterior statistics of the hidden nodes. In particular the equal-time hidden-to-hidden variance—conditioned on observations—gives the expected error at each node when the hidden time courses are predicted based on the observations. We assess the accuracy of the extended Plefka expansion in predicting these single node variances as well as error correlations over time, focussing on the role of the system size and the number of observed nodes.

Predictive Rate-Distortion for Infinite-Order Markov Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2016-06-01

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

Unraveling hidden order in the dynamics of developed and emerging markets.

PubMed

Berman, Yonatan; Shapira, Yoash; Ben-Jacob, Eshel

2014-01-01

The characterization of asset price returns is an important subject in modern finance. Traditionally, the dynamics of stock returns are assumed to lack any temporal order. Here we present an analysis of the autocovariance of stock market indices and unravel temporal order in several major stock markets. We also demonstrate a fundamental difference between developed and emerging markets in the past decade - emerging markets are marked by positive order in contrast to developed markets whose dynamics are marked by weakly negative order. In addition, the reaction to financial crises was found to be reversed among developed and emerging markets, presenting large positive/negative autocovariance spikes following the onset of these crises. Notably, the Chinese market shows neutral or no order while being regarded as an emerging market. These findings show that despite the coupling between international markets and global trading, major differences exist between different markets, and demonstrate that the autocovariance of markets is correlated with their stability, as well as with their state of development.

Unraveling Hidden Order in the Dynamics of Developed and Emerging Markets

PubMed Central

Berman, Yonatan; Shapira, Yoash; Ben-Jacob, Eshel

2014-01-01

The characterization of asset price returns is an important subject in modern finance. Traditionally, the dynamics of stock returns are assumed to lack any temporal order. Here we present an analysis of the autocovariance of stock market indices and unravel temporal order in several major stock markets. We also demonstrate a fundamental difference between developed and emerging markets in the past decade - emerging markets are marked by positive order in contrast to developed markets whose dynamics are marked by weakly negative order. In addition, the reaction to financial crises was found to be reversed among developed and emerging markets, presenting large positive/negative autocovariance spikes following the onset of these crises. Notably, the Chinese market shows neutral or no order while being regarded as an emerging market. These findings show that despite the coupling between international markets and global trading, major differences exist between different markets, and demonstrate that the autocovariance of markets is correlated with their stability, as well as with their state of development. PMID:25383630

A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling.

PubMed

Huda, Shamsul; Yearwood, John; Togneri, Roberto

2009-02-01

This paper attempts to overcome the tendency of the expectation-maximization (EM) algorithm to locate a local rather than global maximum when applied to estimate the hidden Markov model (HMM) parameters in speech signal modeling. We propose a hybrid algorithm for estimation of the HMM in automatic speech recognition (ASR) using a constraint-based evolutionary algorithm (EA) and EM, the CEL-EM. The novelty of our hybrid algorithm (CEL-EM) is that it is applicable for estimation of the constraint-based models with many constraints and large numbers of parameters (which use EM) like HMM. Two constraint-based versions of the CEL-EM with different fusion strategies have been proposed using a constraint-based EA and the EM for better estimation of HMM in ASR. The first one uses a traditional constraint-handling mechanism of EA. The other version transforms a constrained optimization problem into an unconstrained problem using Lagrange multipliers. Fusion strategies for the CEL-EM use a staged-fusion approach where EM has been plugged with the EA periodically after the execution of EA for a specific period of time to maintain the global sampling capabilities of EA in the hybrid algorithm. A variable initialization approach (VIA) has been proposed using a variable segmentation to provide a better initialization for EA in the CEL-EM. Experimental results on the TIMIT speech corpus show that CEL-EM obtains higher recognition accuracies than the traditional EM algorithm as well as a top-standard EM (VIA-EM, constructed by applying the VIA to EM).

Topological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system.

PubMed

Savic, Aleksandar; Toth, Gergely; Duponchel, Ludovic

2017-05-15

Recent developments in applied mathematics are bringing new tools that are capable to synthesize knowledge in various disciplines, and help in finding hidden relationships between variables. One such technique is topological data analysis (TDA), a fusion of classical exploration techniques such as principal component analysis (PCA), and a topological point of view applied to clustering of results. Various phenomena have already received new interpretations thanks to TDA, from the proper choice of sport teams to cancer treatments. For the first time, this technique has been applied in soil science, to show the interaction between physical and chemical soil attributes and main soil-forming factors, such as climate and land use. The topsoil data set of the Land Use/Land Cover Area Frame survey (LUCAS) was used as a comprehensive database that consists of approximately 20,000 samples, each described by 12 physical and chemical parameters. After the application of TDA, results obtained were cross-checked against known grouping parameters including five types of land cover, nine types of climate and the organic carbon content of soil. Some of the grouping characteristics observed using standard approaches were confirmed by TDA (e.g., organic carbon content) but novel subtle relationships (e.g., magnitude of anthropogenic effect in soil formation), were discovered as well. The importance of this finding is that TDA is a unique mathematical technique capable of extracting complex relations hidden in soil science data sets, giving the opportunity to see the influence of physicochemical, biotic and abiotic factors on topsoil formation through fresh eyes. Copyright © 2017 Elsevier B.V. All rights reserved.

Increased taxon sampling reveals thousands of hidden orthologs in flatworms

PubMed Central

2017-01-01

Gains and losses shape the gene complement of animal lineages and are a fundamental aspect of genomic evolution. Acquiring a comprehensive view of the evolution of gene repertoires is limited by the intrinsic limitations of common sequence similarity searches and available databases. Thus, a subset of the gene complement of an organism consists of hidden orthologs, i.e., those with no apparent homology to sequenced animal lineages—mistakenly considered new genes—but actually representing rapidly evolving orthologs or undetected paralogs. Here, we describe Leapfrog, a simple automated BLAST pipeline that leverages increased taxon sampling to overcome long evolutionary distances and identify putative hidden orthologs in large transcriptomic databases by transitive homology. As a case study, we used 35 transcriptomes of 29 flatworm lineages to recover 3427 putative hidden orthologs, some unidentified by OrthoFinder and HaMStR, two common orthogroup inference algorithms. Unexpectedly, we do not observe a correlation between the number of putative hidden orthologs in a lineage and its “average” evolutionary rate. Hidden orthologs do not show unusual sequence composition biases that might account for systematic errors in sequence similarity searches. Instead, gene duplication with divergence of one paralog and weak positive selection appear to underlie hidden orthology in Platyhelminthes. By using Leapfrog, we identify key centrosome-related genes and homeodomain classes previously reported as absent in free-living flatworms, e.g., planarians. Altogether, our findings demonstrate that hidden orthologs comprise a significant proportion of the gene repertoire in flatworms, qualifying the impact of gene losses and gains in gene complement evolution. PMID:28400424

A Rose By Other Names: Some General Musings on Lawrence and Colleagues' Hidden Curriculum Scoping Review.

PubMed

Hafferty, Frederic W; Martimianakis, Maria Athina

2017-11-07

In this Commentary, the authors explore the scoping review by Lawrence and colleagues by challenging their conclusion that with over 25 years' worth of "ambiguous and seemingly ubiquitous use" of the hidden curriculum construct in health professions education scholarship, it is time to either move to a more uniform definitional foundation or abandon the term altogether. The commentary authors counter these remedial propositions by foregrounding the importance of theoretical diversity and the conceptual richness afforded when the hidden curriculum construct is used as an entry point for studying the interstitial space between the formal and a range of other-than-formal domains of learning. Further, they document how tightly-delimited scoping strategies fail to capture the wealth of educational scholarship that operates within a hidden curriculum framework, including "hidden" hidden curriculum articles, studies that employ alternative constructs, and investigations that target important tacit socio-cultural influences on learners and faculty without formally deploying the term. They offer examples of how the hidden curriculum construct, while undergoing significant transformation in its application within the field of health professions education, has created the conceptual foundation for the application of a number of critical perspectives that make visible the field's political investments in particular forms of knowing and associated practices. Finally, the commentary authors invite readers to consider the methodological promise afforded by conceptual heterogeneity, particularly strands of scholarship that resituate the hidden curriculum concept within the magically expansive dance of social relationships, social learning, and social life that form the learning environments of health professions education.

What Should We Do With a Hidden Curriculum When We Fine One?

ERIC Educational Resources Information Center

Martin, Jane R.

1976-01-01

A hidden curriculum consists of those learning states of a setting that are either unintended or intended but not openly acknowledged to the learners in the setting unless the learners are aware of them. Consciousness-raising may be the best weapon of individuals who are subject to hidden curricula. (Author/MLF)

Manifestations of Hidden Curriculum in a Community College Online Opticianry Program: An Ecological Approach

ERIC Educational Resources Information Center

Hubbard, Barry

2010-01-01

Understanding the influential factors at work within an online learning environment is a growing area of interest. Hidden or implicit expectations, skill sets, knowledge, and social process can help or hinder student achievement, belief systems, and persistence. This qualitative study investigated how hidden curricular issues transpired in an…

The Hidden Reason Behind Children's Misbehavior.

ERIC Educational Resources Information Center

Nystul, Michael S.

1986-01-01

Discusses hidden reason theory based on the assumptions that: (1) the nature of people is positive; (2) a child's most basic psychological need is involvement; and (3) a child has four possible choices in life (good somebody, good nobody, bad somebody, or severely mentally ill.) A three step approach for implementing hidden reason theory is…

Student Teaching: A Hidden Wholeness

ERIC Educational Resources Information Center

Bowman, Richard F.

2007-01-01

Productive student teachers lead learning by emergently sensing and honoring the hidden wholeness of life in classrooms. That hidden wholeness mirrors seven contextual concerns which learners reflect upon in the everydayness of classroom life: What are we going to do in class today? What am I going to have to do in class? What counts in today's…

Environmental Assessment for the Expansion of Permitted Land and Operations at the 9940 Complex and Thunder Range at Sandia National Laboratories/New Mexico

DTIC Science & Technology

2008-03-01

vivipara Hidden flower Cryptantha crassisepala Hidden flower Cryptantha fulvocanescens James’s hidden flower Cryptantha jamesii Buffalo gourd...pumila Bigbract verbena ta Verbena bractea Banana yucca ta Yucca bacca Soapweed yucca Yucca glauca Rocky Mountain zinnia Zinnia grandiflora A-9

Hidden Agendas in Marriage: Affective and Longitudinal Dimensions.

ERIC Educational Resources Information Center

Krokoff, Lowell J.

1990-01-01

Examines how couples' discussions of troublesome problems reveal hidden agendas (issues not directly discussed or explored). Finds disgust and contempt are at the core of both love and respect agendas for husbands and wives. Finds that wives' more than husbands' hidden agendas are directly predictive of how negatively they argue at home. (SR)

Reputation and Competition in a Hidden Action Model

PubMed Central

Fedele, Alessandro; Tedeschi, Piero

2014-01-01

The economics models of reputation and quality in markets can be classified in three categories. (i) Pure hidden action, where only one type of seller is present who can provide goods of different quality. (ii) Pure hidden information, where sellers of different types have no control over product quality. (iii) Mixed frameworks, which include both hidden action and hidden information. In this paper we develop a pure hidden action model of reputation and Bertrand competition, where consumers and firms interact repeatedly in a market with free entry. The price of the good produced by the firms is contractible, whilst the quality is noncontractible, hence it is promised by the firms when a contract is signed. Consumers infer future quality from all available information, i.e., both from what they know about past quality and from current prices. According to early contributions, competition should make reputation unable to induce the production of high-quality goods. We provide a simple solution to this problem by showing that high quality levels are sustained as an outcome of a stationary symmetric equilibrium. PMID:25329387

Reputation and competition in a hidden action model.

PubMed

Fedele, Alessandro; Tedeschi, Piero

2014-01-01

The economics models of reputation and quality in markets can be classified in three categories. (i) Pure hidden action, where only one type of seller is present who can provide goods of different quality. (ii) Pure hidden information, where sellers of different types have no control over product quality. (iii) Mixed frameworks, which include both hidden action and hidden information. In this paper we develop a pure hidden action model of reputation and Bertrand competition, where consumers and firms interact repeatedly in a market with free entry. The price of the good produced by the firms is contractible, whilst the quality is noncontractible, hence it is promised by the firms when a contract is signed. Consumers infer future quality from all available information, i.e., both from what they know about past quality and from current prices. According to early contributions, competition should make reputation unable to induce the production of high-quality goods. We provide a simple solution to this problem by showing that high quality levels are sustained as an outcome of a stationary symmetric equilibrium.

Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2017-05-01

Thanks to some recent research works, dynamic Bayesian wavelet transform as new methodology for extraction of repetitive transients is proposed in this short communication to reveal fault signatures hidden in rotating machine. The main idea of the dynamic Bayesian wavelet transform is to iteratively estimate posterior parameters of wavelet transform via artificial observations and dynamic Bayesian inference. First, a prior wavelet parameter distribution can be established by one of many fast detection algorithms, such as the fast kurtogram, the improved kurtogram, the enhanced kurtogram, the sparsogram, the infogram, continuous wavelet transform, discrete wavelet transform, wavelet packets, multiwavelets, empirical wavelet transform, empirical mode decomposition, local mean decomposition, etc.. Second, artificial observations can be constructed based on one of many metrics, such as kurtosis, the sparsity measurement, entropy, approximate entropy, the smoothness index, a synthesized criterion, etc., which are able to quantify repetitive transients. Finally, given artificial observations, the prior wavelet parameter distribution can be posteriorly updated over iterations by using dynamic Bayesian inference. More importantly, the proposed new methodology can be extended to establish the optimal parameters required by many other signal processing methods for extraction of repetitive transients.

Photoacoustic imaging of hidden dental caries by using a bundle of hollow optical fibers

NASA Astrophysics Data System (ADS)

Koyama, Takuya; Kakino, Satoko; Matsuura, Yuji

2018-02-01

Photoacoustic imaging system using a bundle of hollow-optical fibers to detect hidden dental caries is proposed. Firstly, we fabricated a hidden caries model with a brown pigment simulating a common color of caries lesion. It was found that high frequency ultrasonic waves are generated from hidden carious part when radiating Nd:YAG laser light with a 532 nm wavelength to occlusal surface of model tooth. We calculated by Fourier transform and found that the waveform from the carious part provides frequency components of approximately from 0.5 to 1.2 MHz. Then a photoacoustic imaging system using a bundle of hollow optical fiber was fabricated for clinical applications. From intensity map of frequency components in 0.5-1.2 MHz, photoacoustic images of hidden caries in the simulated samples were successfully obtained.

On the LHC sensitivity for non-thermalised hidden sectors

NASA Astrophysics Data System (ADS)

Kahlhoefer, Felix

2018-04-01

We show under rather general assumptions that hidden sectors that never reach thermal equilibrium in the early Universe are also inaccessible for the LHC. In other words, any particle that can be produced at the LHC must either have been in thermal equilibrium with the Standard Model at some point or must be produced via the decays of another hidden sector particle that has been in thermal equilibrium. To reach this conclusion, we parametrise the cross section connecting the Standard Model to the hidden sector in a very general way and use methods from linear programming to calculate the largest possible number of LHC events compatible with the requirement of non-thermalisation. We find that even the HL-LHC cannot possibly produce more than a few events with energy above 10 GeV involving states from a non-thermalised hidden sector.

Hidden charged dark matter and chiral dark radiation

NASA Astrophysics Data System (ADS)

Ko, P.; Nagata, Natsumi; Tang, Yong

2017-10-01

In the light of recent possible tensions in the Hubble constant H0 and the structure growth rate σ8 between the Planck and other measurements, we investigate a hidden-charged dark matter (DM) model where DM interacts with hidden chiral fermions, which are charged under the hidden SU(N) and U(1) gauge interactions. The symmetries in this model assure these fermions to be massless. The DM in this model, which is a Dirac fermion and singlet under the hidden SU(N), is also assumed to be charged under the U(1) gauge symmetry, through which it can interact with the chiral fermions. Below the confinement scale of SU(N), the hidden quark condensate spontaneously breaks the U(1) gauge symmetry such that there remains a discrete symmetry, which accounts for the stability of DM. This condensate also breaks a flavor symmetry in this model and Nambu-Goldstone bosons associated with this flavor symmetry appear below the confinement scale. The hidden U(1) gauge boson and hidden quarks/Nambu-Goldstone bosons are components of dark radiation (DR) above/below the confinement scale. These light fields increase the effective number of neutrinos by δNeff ≃ 0.59 above the confinement scale for N = 2, resolving the tension in the measurements of the Hubble constant by Planck and Hubble Space Telescope if the confinement scale is ≲1 eV. DM and DR continuously scatter with each other via the hidden U(1) gauge interaction, which suppresses the matter power spectrum and results in a smaller structure growth rate. The DM sector couples to the Standard Model sector through the exchange of a real singlet scalar mixing with the Higgs boson, which makes it possible to probe our model in DM direct detection experiments. Variants of this model are also discussed, which may offer alternative ways to investigate this scenario.

Diagnosis of hidden bronchial obstruction using computer-assessed tracheal forced expiratory noise time.

PubMed

Pochekutova, Irina A; Korenbaum, Vladimir I

2013-04-01

Increased forced expiratory time was first recognized as a marker of obstruction half a century ago. However, the reported diagnostic capabilities of both auscultated forced expiratory time (FET(as)) and spirometric forced expiratory time are contradictory. Computer analysis of respiratory noises provides a precise estimation of acoustic forced expiratory noise time (FET(a)) being the object-measured analogue of FET(as). The aim of this study was to analyse FET(a) diagnostic capabilities in patients with asthma based on the hypothesis that FET(a) could reveal hidden bronchial obstruction. A group of asthma patients involved 149 males aged 16-25 years. In this group, 71 subjects had spirometry features of bronchial obstruction, meanwhile, the remaining 78 had normal spirometry. A control group involved 77 healthy subjects. Spirometry and forced expiratory tracheal noise recording were sequentially measured for each participant. FET(a) values were estimated by means of a developed computer procedure, including bandpass filtration (200-2000 Hz), waveform envelope calculation with accumulation period of 0.01 s, automated measurement of FET(a) at 0.5% level from the peak amplitude. Specificity, sensitivity and area under Receiver Operating Characteristic curve of FET(a) and its ratios to squared chest circumference, height, weight were indistinguishable with baseline spirometry index FEV1 /forced vital capacity. Meanwhile, acoustic features of obstruction were revealed in 41%-49% of subgroup of patients with asthma but normal spirometry. FET(a) of tracheal noise and its ratio to anthropometric parameters seem to be sensitive and specific tests of hidden bronchial obstruction in young male asthma patients. © 2012 The Authors. Respirology © 2012 Asian Pacific Society of Respirology.

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

PubMed

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.

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.

PubMed

Broniec, A

2016-12-01

Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain-computer interface.

A Novel Higher Order Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Xu, Shuxiang

2010-05-01

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

State Space Model with hidden variables for reconstruction of gene regulatory networks.

PubMed

Wu, Xi; Li, Peng; Wang, Nan; Gong, Ping; Perkins, Edward J; Deng, Youping; Zhang, Chaoyang

2011-01-01

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN. True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks. Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN. This study provides useful information in handling the hidden variables and improving the inference precision.

Broadband unidirectional invisibility for airborne sound

NASA Astrophysics Data System (ADS)

Kan, Weiwei; Guo, Mengping; Shen, Zhonghua

2018-05-01

We present a metafluid-based broadband cloak capable of guiding acoustic waves around obstacles along given directions while maintaining the wavefront undisturbed. The required parameter distribution of the proposed cloak is derived by coordinate transformation and practically implemented by employing the acoustic metafluid formed with periodically arranged slabs in acoustic chambers. The method for independently modulating the effective mass density and bulk modulus of the metafluid is developed by tuning the geometry parameters and the temperature of the acoustic chamber in a specific process. By virtue of this free-modulated method, the range of realizable effective parameters is substantially broadened, and the acoustic impedance of the anisotropic structures can be well matched to the background. The performance of the designed structure is quantitatively evaluated in the frequency range of 3-4 kHz by the averaged invisibility factor. The results show that the proposed cloak is effective in manipulating the acoustic field along the given direction and suppressing the wave scattering from the hidden object.

Geophysical techniques in detection to river embankments - A case study: To locate sites of potential leaks using surface-wave and electrical methods

USGS Publications Warehouse

Chen, C.; Liu, J.; Xu, S.; Xia, J.; ,

2004-01-01

Geophysical technologies are very effective in environmental, engineering and groundwater applications. Parameters of delineating nature of near-surface materials such as compressional-wave velocity, shear-wave velocity can be obtained using shallow seismic methods. Electric methods are primary approaches for investigating groundwater and detecting leakage. Both of methods are applied to detect embankment in hope of obtaining evidences of the strength and moisture inside the body. A technological experiment has done for detecting and discovering the hidden troubles in the embankment of Yangtze River, Songzi, Hubei, China in 2003. Surface-wave and DC multi-channel array resistivity sounding techniques were used to detect hidden trouble inside and under dike like pipe-seeps. This paper discusses the exploration strategy and the effect of geological characteristics. A practical approach of combining seismic and electric resistivity measurements was applied to locate potential pipe-seeps in embankment in the experiment. The method presents a potential leak factor based on the shear-wave velocity and the resistivity of the medium to evaluate anomalies. An anomaly found in a segment of embankment detected was verified, where occurred a pipe-seep during the 98' flooding.

Evaluation of extra virgin olive oil stability by artificial neural network.

PubMed

Silva, Simone Faria; Anjos, Carlos Alberto Rodrigues; Cavalcanti, Rodrigo Nunes; Celeghini, Renata Maria dos Santos

2015-07-15

The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data.

PubMed

Lu, Ji; Pan, Junhao; Zhang, Qiang; Dubé, Laurette; Ip, Edward H

2015-01-01

With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not generally well equipped to analyze a system of variables that contain feedback loops, this paper proposes the utility of an extended hidden Markov model to model reciprocal the relationship between momentary emotion and eating behavior. This paper revisited an ESM data set (Lu, Huet, & Dube, 2011) that observed 160 participants' food consumption and momentary emotions 6 times per day in 10 days. Focusing on the analyses on feedback loop between mood and meal-healthiness decision, the proposed reciprocal Markov model (RMM) can accommodate both hidden ("general" emotional states: positive vs. negative state) and observed states (meal: healthier, same or less healthy than usual) without presuming independence between observations and smooth trajectories of mood or behavior changes. The results of RMM analyses illustrated the reciprocal chains of meal consumption and mood as well as the effect of contextual factors that moderate the interrelationship between eating and emotion. A simulation experiment that generated data consistent with the empirical study further demonstrated that the procedure is promising in terms of recovering the parameters.

Adaptive quantification and longitudinal analysis of pulmonary emphysema with a hidden Markov measure field model.

PubMed

Hame, Yrjo; Angelini, Elsa D; Hoffman, Eric A; Barr, R Graham; Laine, Andrew F

2014-07-01

The extent of pulmonary emphysema is commonly estimated from CT scans by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols, and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the presented model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was applied on a longitudinal data set with 87 subjects and a total of 365 scans acquired with varying imaging protocols. The resulting emphysema estimates had very high intra-subject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. The generated emphysema delineations promise advantages for regional analysis of emphysema extent and progression.

Implications of two-component dark matter induced by forbidden channels and thermal freeze-out

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aoki, Mayumi; Toma, Takashi, E-mail: mayumi@hep.s.kanazawa-u.ac.jp, E-mail: takashi.toma@tum.de

2017-01-01

We consider a model of two-component dark matter based on a hidden U(1) {sub D} symmetry, in which relic densities of the dark matter are determined by forbidden channels and thermal freeze-out. The hidden U(1) {sub D} symmetry is spontaneously broken to a residual Z{sub 4} symmetry, and the lightest Z{sub 4} charged particle can be a dark matter candidate. Moreover, depending on the mass hierarchy in the dark sector, we have two-component dark matter. We show that the relic density of the lighter dark matter component can be determined by forbidden annihilation channels which require larger couplings compared tomore » the normal freeze-out mechanism. As a result, a large self-interaction of the lighter dark matter component can be induced, which may solve small scale problems of ΛCDM model. On the other hand, the heavier dark matter component is produced by normal freeze-out mechanism. We find that interesting implications emerge between the two dark matter components in this framework. We explore detectabilities of these dark matter particles and show some parameter space can be tested by the SHiP experiment.« less

COACH: profile-profile alignment of protein families using hidden Markov models.

PubMed

Edgar, Robert C; Sjölander, Kimmen

2004-05-22

Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profile-sequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM. We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. COACH is freely available from www.drive5.com/lobster

Pancytopenia in the first trimester: An indicator of hidden hyperthyroidism.

PubMed

Imai, Ken; Ohkuchi, Akihide; Nagayama, Shiho; Saito, Shinsuke; Matsubara, Shigeki; Suzuki, Mitsuaki

2015-12-01

Pancytopenia in the first trimester is very rare. A 33-year-old multiparous woman presented with nausea, loss of appetite, and bodyweight loss of 7.4 kg at 9(1/7) weeks of gestation due to hyperemesis gravidarum. Her laboratory data demonstrated pancytopenia involving white blood cell count of 3500/μL, a hemoglobin level of 9.8 g/dL, and a platelet count of 10.5 × 10(4)/μL. An extensive investigation into the causes of the pancytopenia detected true hyperthyroidism: thyroid-stimulating hormone, <0.02 μU/mL; free triiodothyronine, 11.25 pg/mL; free thyroxine, 4.74 ng/dL; and anti-thyroid-stimulating hormone receptor antibodies, 12.2 IU/L. Propylthiouracil was started at a dose of 300 mg/day at 10(5/7) weeks of gestation, which resulted in the normalization of her blood parameters and concomitant improvements in her free triiodothyronine and free thyroxine levels at 12(0/7) weeks of gestation. Pancytopenia in the first trimester might be indicative of hidden hyperthyroidism. © 2015 Japan Society of Obstetrics and Gynecology.