Detecting hidden particles with MATHUSLA

NASA Astrophysics Data System (ADS)

Evans, Jared A.

2018-03-01

A hidden sector containing light long-lived particles provides a well-motivated place to find new physics. The recently proposed MATHUSLA experiment has the potential to be extremely sensitive to light particles originating from rare meson decays in the very long lifetime region. In this work, we illustrate this strength with the specific example of a light scalar mixed with the standard model-like Higgs boson, a model where MATHUSLA can further probe unexplored parameter space from exotic Higgs decays. Design augmentations should be considered in order to maximize the ability of MATHUSLA to discover very light hidden sector particles.

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.

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.

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

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.

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

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

Exploiting Mirrors in 3d Reconstruction of Small Artefacts

NASA Astrophysics Data System (ADS)

Kontogianni, G.; Thomaidis, A. T.; Chliverou, R.; Georgopoulos, A.

2018-05-01

3D reconstruction of small artefacts is very significant in order to capture the details of the whole object irrespective of the documentation method which is used (Ranged Based or Image Based). Sometimes it is very difficult to achieve it because of hidden parts, occlusions, and obstructions which the object has. Hence, more data are necessary in order to 3D digitise the whole of the artefact leading to increased time for collecting and consequently processing the data. A methodology is necessary in order to reduce the collection of the data and therefore their processing time especially in cases of mass digitisation. So in this paper, the use of mirrors in particular high-quality mirrors in the data acquisition phase for the 3D reconstruction of small artefacts is investigated. Two case studies of 3D reconstruction are presented: the first one concerns Range-Based modelling especially a Time of Flight laser scanner is utilised and in the second one Image-Based modelling technique is implemented.

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.

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…

Implications of the measured angular anisotropy at the hidden order transition of URu2Si2

NASA Astrophysics Data System (ADS)

Chandra, P.; Coleman, P.; Flint, R.; Trinh, J.; Ramirez, A. P.

2018-05-01

The heavy fermion compound URu2Si2 continues to attract great interest due to the long-unidentified nature of the hidden order that develops below 17.5 K. Here we discuss the implications of an angular survey of the linear and nonlinear susceptibility of URu2Si2 in the vicinity of the hidden order transition [1]. While the anisotropic nature of spin fluctuations and low-temperature quasiparticles was previously established, our recent results suggest that the order parameter itself has intrinsic Ising anisotropy, and that moreover this anisotropy extends far above the hidden order transition. Consistency checks and subsequent questions for future experimental and theoretical studies of hidden order are discussed.

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.

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.

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.

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.

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.

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

Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets

NASA Astrophysics Data System (ADS)

Suzuki, Tomoya; Ohkura, Yuushi

2016-01-01

In order to examine the predictability and profitability of financial markets, we introduce three ideas to improve the traditional technical analysis to detect investment timings more quickly. Firstly, a nonlinear prediction model is considered as an effective way to enhance this detection power by learning complex behavioral patterns hidden in financial markets. Secondly, the bagging algorithm can be applied to quantify the confidence in predictions and compose new technical indicators. Thirdly, we also introduce how to select more profitable stocks to improve investment performance by the two-step selection: the first step selects more predictable stocks during the learning period, and then the second step adaptively and dynamically selects the most confident stock showing the most significant technical signal in each investment. Finally, some investment simulations based on real financial data show that these ideas are successful in overcoming complex financial markets.

Revealing hidden antiferromagnetic correlations in doped Hubbard chains via string correlators

NASA Astrophysics Data System (ADS)

Hilker, Timon A.; Salomon, Guillaume; Grusdt, Fabian; Omran, Ahmed; Boll, Martin; Demler, Eugene; Bloch, Immanuel; Gross, Christian

2017-08-01

Topological phases, like the Haldane phase in spin-1 chains, defy characterization through local order parameters. Instead, nonlocal string order parameters can be employed to reveal their hidden order. Similar diluted magnetic correlations appear in doped one-dimensional lattice systems owing to the phenomenon of spin-charge separation. Here we report on the direct observation of such hidden magnetic correlations via quantum gas microscopy of hole-doped ultracold Fermi-Hubbard chains. The measurement of nonlocal spin-density correlation functions reveals a hidden finite-range antiferromagnetic order, a direct consequence of spin-charge separation. Our technique, which measures nonlocal order directly, can be readily extended to higher dimensions to study the complex interplay between magnetic order and density fluctuations.

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.

Non-Equilibrium Effects on the Hidden Order of Microstructured URu2Si2

NASA Astrophysics Data System (ADS)

Winter, Laurel E.; Moll, Philip J. W.; Ramshaw, B. J.; Shekhter, Arkady; Harrison, N.; Bauer, Eric D.; McDonald, Ross D.

Despite extensive studies on the heavy-fermion URu2Si2, the order parameter associated with the hidden order state has yet to be established. It is known, however that the hidden order can be suppressed with pressure and high magnetic fields, which results in the development of antiferromagnetism, and the realization of a polarized state respectively. Focused Ion Beam lithography (FIB) of URu2Si2 has enabled high magnetic field observation of quantum oscillations in the resistance, indicating the preservation of sample quality to micron scale structures. These recent advances in FIB lithography have enabled the application of unprecedented electric fields while minimizing the effects of Joule heating in highly conductive metals at cryogenic temperatures. To this end, we have been able to create the necessary sample geometry to study the effect of an electric field upon hidden order in magnetic fields up to 15 T. Preliminary results suggest that above a characteristic threshold electric field, hidden order is suppressed revealing a state with similar magnetoresistive properties to the Kondo lattice in the absence of hidden order. Work supported by US Dept. of Energy through LANL/LDRD Program and G.T. Seaborg Institute, as well as NSF DMR-1157490 and the State of Florida.

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

More Than Meets the Eye: Split-Second Social Perception

PubMed Central

Freeman, Jonathan B.; Johnson, Kerri L.

2017-01-01

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. PMID:27050834

Field-induced spin-density wave beyond hidden order in URu2Si2

NASA Astrophysics Data System (ADS)

Knafo, W.; Duc, F.; Bourdarot, F.; Kuwahara, K.; Nojiri, H.; Aoki, D.; Billette, J.; Frings, P.; Tonon, X.; Lelièvre-Berna, E.; Flouquet, J.; Regnault, L.-P.

2016-10-01

URu2Si2 is one of the most enigmatic strongly correlated electron systems and offers a fertile testing ground for new concepts in condensed matter science. In spite of >30 years of intense research, no consensus on the order parameter of its low-temperature hidden-order phase exists. A strong magnetic field transforms the hidden order into magnetically ordered phases, whose order parameter has also been defying experimental observation. Here, thanks to neutron diffraction under pulsed magnetic fields up to 40 T, we identify the field-induced phases of URu2Si2 as a spin-density-wave state. The transition to the spin-density wave represents a unique touchstone for understanding the hidden-order phase. An intimate relationship between this magnetic structure, the magnetic fluctuations and the Fermi surface is emphasized, calling for dedicated band-structure calculations.

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…

A hybrid wavelet transform based short-term wind speed forecasting approach.

PubMed

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

A Hybrid Wavelet Transform Based Short-Term Wind Speed Forecasting Approach

PubMed Central

Wang, Jujie

2014-01-01

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy. PMID:25136699

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.

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

You've got to know the rules to play the game: how medical students negotiate the hidden curriculum of surgical careers.

PubMed

Hill, Elspeth; Bowman, Katherine; Stalmeijer, Renée; Hart, Jo

2014-09-01

The hidden curriculum may be framed as the culture, beliefs and behaviours of a community that are passed to students outside formal course offerings. Medical careers involve diverse specialties, each with a different culture, yet how medical students negotiate these cultures has not been fully explored. Using surgery as a case study, we aimed to establish, first, whether a specialty-specific hidden curriculum existed for students, and second, how students encountered and negotiated surgical career options. Using a constructivist grounded theory approach, we explored students' thoughts, beliefs and experiences regarding career decisions and surgery. An exploratory questionnaire informed the discussion schedule for semi-structured individual interviews. Medical students were purposively sampled by year group, gender and career intentions in surgery. Data collection and analysis were iterative: analysis followed each interview and guided the adaptation of our discussion schedule to further our evolving model. Students held a clear sense of a hidden curriculum in surgery. To successfully negotiate a surgical career, students perceived that they must first build networks because careers information flows through relationships. They subsequently enacted what they learned by accruing the accolades ('ticking the boxes') and appropriating the dispositions ('walking the talk') of 'future surgeons'. This allowed them to identify themselves and to be identified by others as 'future surgeons' and to gain access to participation in the surgical world. Participation then enabled further network building and access to careers information in a positive feedback loop. For some, negotiating the hidden curriculum was more difficult, which, for them, rendered a surgical career unattractive or unattainable. Students perceive a clear surgery-specific hidden curriculum. Using a constructivist grounded theory approach, we have developed a model of how students encounter, uncover and enact this hidden curriculum to succeed. Drawing on concepts of Bourdieu, we discuss unequal access to the hidden curriculum, which was found to exclude many from the possibility of a surgical career. © 2014 John Wiley & Sons Ltd.

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Magnetic Correlations in URu2Si2 under Chemical and Hydrostatic Pressure

NASA Astrophysics Data System (ADS)

Williams, Travis; Aczel, Adam; Broholm, Collin; Buyers, William; Leao, Juscelino; Luke, Graeme; Rodriguez-Riviera, Jose; Stone, Matthew; Wilson, Murray; Yamani, Zahra

URu2Si2 has been an intense area of study for the last 30 years due to a mysterious hidden order phase that appears below T0 = 17.5 K. The hidden order phase has been shown to be extremely sensitive to perturbations, being destroyed quickly by the application of a magnetic field, hydrostatic or uniaxial pressure, and chemical doping. While attempting to understand the properties of URu2Si2, neutron scattering has found spin correlations that are intimately related to this hidden order phase and which are also suppressed with these perturbations. Here, I will outline some recent neutron scattering work to study these correlations in two exceptional cases where the hidden order phase is enhanced: hydrostatic pressure and chemical pressure using Fe- and Os-doping. In both of these cases, T0 increases before an antiferromagnetic phase emerges. By performing a careful analysis of the neutron data, we show that these two phases are much more related than had been previously appreciated. This implies that the hidden order is likely compatible with an antiferromagnetic ground state, placing constraints on the nature of the missing order parameter.

Laser projection positioning of spatial contour curves via a galvanometric scanner

NASA Astrophysics Data System (ADS)

Tu, Junchao; Zhang, Liyan

2018-04-01

The technology of laser projection positioning is widely applied in advanced manufacturing fields (e.g. composite plying, parts location and installation). In order to use it better, a laser projection positioning (LPP) system is designed and implemented. Firstly, the LPP system is built by a laser galvanometric scanning (LGS) system and a binocular vision system. Applying Single-hidden Layer Feed-forward Neural Network (SLFN), the system model is constructed next. Secondly, the LGS system and the binocular system, which are respectively independent, are integrated through a datadriven calibration method based on extreme learning machine (ELM) algorithm. Finally, a projection positioning method is proposed within the framework of the calibrated SLFN system model. A well-designed experiment is conducted to verify the viability and effectiveness of the proposed system. In addition, the accuracy of projection positioning are evaluated to show that the LPP system can achieves the good localization effect.

Teleparallel theories of gravity as analogue of nonlinear electrodynamics

NASA Astrophysics Data System (ADS)

Hohmann, Manuel; Järv, Laur; Krššák, Martin; Pfeifer, Christian

2018-05-01

The teleparallel formulation of gravity theories reveals close structural analogies to electrodynamics, which are more hidden in their usual formulation in terms of the curvature of spacetime. We show how every locally Lorentz invariant teleparallel theory of gravity with second-order field equations can be understood as built from a gravitational field strength and excitation tensor which are related to each other by a constitutive relation, analogous to the premetric construction of theories of electrodynamics. We demonstrate how the previously studied models of f (T ) and f (Tax,Tten,Tvec) gravity as well as teleparallel dark energy can be formulated in this language. The advantage of this approach to gravity is that the field equations for different models all take the same compact form and general results can be obtained. An important new such result we find is a constraint which relates the field equations of the tetrad and the spin connection.

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.

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

A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI

PubMed Central

Castillo-Barnes, Diego; Peis, Ignacio; Martínez-Murcia, Francisco J.; Segovia, Fermín; Illán, Ignacio A.; Górriz, Juan M.; Ramírez, Javier; Salas-Gonzalez, Diego

2017-01-01

A wide range of segmentation approaches assumes that intensity histograms extracted from magnetic resonance images (MRI) have a distribution for each brain tissue that can be modeled by a Gaussian distribution or a mixture of them. Nevertheless, intensity histograms of White Matter and Gray Matter are not symmetric and they exhibit heavy tails. In this work, we present a hidden Markov random field model with expectation maximization (EM-HMRF) modeling the components using the α-stable distribution. The proposed model is a generalization of the widely used EM-HMRF algorithm with Gaussian distributions. We test the α-stable EM-HMRF model in synthetic data and brain MRI data. The proposed methodology presents two main advantages: Firstly, it is more robust to outliers. Secondly, we obtain similar results than using Gaussian when the Gaussian assumption holds. This approach is able to model the spatial dependence between neighboring voxels in tomographic brain MRI. PMID:29209194

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

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.

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.

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.

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).

Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train.

PubMed

Bengua, Johann A; Phien, Ho N; Tuan, Hoang Duong; Do, Minh N

2017-05-01

This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.

Limiting first-order phase transitions in dark gauge sectors from gravitational waves experiments

NASA Astrophysics Data System (ADS)

Addazi, Andrea

2017-03-01

We discuss the possibility to indirectly test first-order phase transitions of hidden sectors. We study the interesting example of a Dark Standard Model (D-SM) with a deformed parameter space in the Higgs potential. A dark electroweak phase transition can be limited from next future experiments like eLISA and DECIGO.

Precision Higgs Physics, Effective Field Theory, and Dark Matter

NASA Astrophysics Data System (ADS)

Henning, Brian Quinn

The recent discovery of the Higgs boson calls for detailed studies of its properties. As precision measurements are indirect probes of new physics, the appropriate theoretical framework is effective field theory. In the first part of this thesis, we present a practical three-step procedure of using the Standard Model effective field theory (SM EFT) to connect ultraviolet (UV) models of new physics with weak scale precision observables. With this procedure, one can interpret precision measurements as constraints on the UV model concerned. We give a detailed explanation for calculating the effective action up to one-loop order in a manifestly gauge covariant fashion. The covariant derivative expansion dramatically simplifies the process of matching a UV model with the SM EFT, and also makes available a universal formalism that is easy to use for a variety of UV models. A few general aspects of renormalization group running effects and choosing operator bases are discussed. Finally, we provide mapping results between the bosonic sector of the SM EFT and a complete set of precision electroweak and Higgs observables to which present and near future experiments are sensitive. With a detailed understanding of how to use the SM EFT, we then turn to applications and study in detail two well-motivated test cases. The first is singlet scalar field that enables the first-order electroweak phase transition for baryogenesis; the second example is due to scalar tops in the MSSM. We find both Higgs and electroweak measurements are sensitive probes of these cases. The second part of this thesis centers around dark matter, and consists of two studies. In the first, we examine the effects of relic dark matter annihilations on big bang nucleosynthesis (BBN). The magnitude of these effects scale simply with the dark matter mass and annihilation cross-section, which we derive. Estimates based on these scaling behaviors indicate that BBN severely constrains hadronic and radiative dark matter annihilation channels in the previously unconsidered dark matter mass region MeV <˜ m x <˜ 10 GeV. Interestingly, we find that BBN constraints on hadronic annihilation channels are competitive with similar bounds derived from the cosmic microwave background. Our second study of dark matter concerns a possible connection with supersymmetry and the keV scale. Various theoretical and experimental considerations motivate models with high scale supersymmetry breaking. While such models may be difficult to test in colliders, we propose looking for signatures at much lower energies. We show that a keV line in the X-ray spectrum of galaxy clusters (such as the recently disputed 3.5 keV observation) can have its origin in a universal string axion coupled to a hidden supersymmetry breaking sector. A linear combination of the string axion and an additional axion in the hidden sector remains light, obtaining a mass of order 10 keV through supersymmetry breaking dynamics. In order to explain the X-ray line, the scale of supersymmetry breaking must be about 1011-12 GeV. This motivates high scale supersymmetry as in pure gravity mediation or minimal split supersymmetry and is consistent with all current limits. Since the axion mass is controlled by a dynamical mass scale, this mass can be much higher during inflation, avoiding isocurvature (and domain wall) problems associated with high scale inflation. In an appendix E we present a mechanism for dilaton stabilization that additionally leads to O(1) modifications of the gaugino mass from anomaly mediation.

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.

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.

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.

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.

Generalization and capacity of extensively large two-layered perceptrons.

PubMed

Rosen-Zvi, Michal; Engel, Andreas; Kanter, Ido

2002-09-01

The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, alpha(c), at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID:28522969

Hybridization with a twist: Hidden (hastatic) order in URu2Si2

NASA Astrophysics Data System (ADS)

Flint, Rebecca

The hidden order developing below 17.5K in the heavy fermion material URu2Si2 has eluded identification for over thirty years. A number of recent experiments have shed new light on the nature of this phase. In particular, de Haas-van Alphen measurements indicate nearly perfectly Ising quasiparticles deep in the hidden order phase, and recent nonlinear susceptibility measurements show that this strong Ising anisotropy persists up to and above the hidden order transition itself. Along with other features, this Ising anisotropy implies that the conduction electrons hybridize with a local Ising moment - a 5f2 state of the uranium atom with integer spin. As the hybridization mixes states of integer and half-integer spin, it is itself a spinor and this ``hastatic'' (hasta: [Latin] spear) order parameter therefore breaks both time-reversal and double time-reversal symmetries. A microscopic theory of hastatic order naturally unites a number of disparate experimental results from the large entropy of condensation to the spin rotational symmetry breaking seen in torque magnetometry, and provides a number of experimental predictions. Moreover, this new spinorial order parameter provides a window into a number of new heavy fermion phases.

Familial or Sporadic Idiopathic Scoliosis – classification based on artificial neural network and GAPDH and ACTB transcription profile

PubMed Central

2013-01-01

Background Importance of hereditary factors in the etiology of Idiopathic Scoliosis is widely accepted. In clinical practice some of the IS patients present with positive familial history of the deformity and some do not. Traditionally about 90% of patients have been considered as sporadic cases without familial recurrence. However the exact proportion of Familial and Sporadic Idiopathic Scoliosis is still unknown. Housekeeping genes encode proteins that are usually essential for the maintenance of basic cellular functions. ACTB and GAPDH are two housekeeping genes encoding respectively a cytoskeletal protein β-actin, and glyceraldehyde-3-phosphate dehydrogenase, an enzyme of glycolysis. Although their expression levels can fluctuate between different tissues and persons, human housekeeping genes seem to exhibit a preserved tissue-wide expression ranking order. It was hypothesized that expression ranking order of two representative housekeeping genes ACTB and GAPDH might be disturbed in the tissues of patients with Familial Idiopathic Scoliosis (with positive family history of idiopathic scoliosis) opposed to the patients with no family members affected (Sporadic Idiopathic Scoliosis). An artificial neural network (ANN) was developed that could serve to differentiate between familial and sporadic cases of idiopathic scoliosis based on the expression levels of ACTB and GAPDH in different tissues of scoliotic patients. The aim of the study was to investigate whether the expression levels of ACTB and GAPDH in different tissues of idiopathic scoliosis patients could be used as a source of data for specially developed artificial neural network in order to predict the positive family history of index patient. Results The comparison of developed models showed, that the most satisfactory classification accuracy was achieved for ANN model with 18 nodes in the first hidden layer and 16 nodes in the second hidden layer. The classification accuracy for positive Idiopathic Scoliosis anamnesis only with the expression measurements of ACTB and GAPDH with the use of ANN based on 6-18-16-1 architecture was 8 of 9 (88%). Only in one case the prediction was ambiguous. Conclusions Specially designed artificial neural network model proved possible association between expression level of ACTB, GAPDH and positive familial history of Idiopathic Scoliosis. PMID:23289769

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.

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

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.

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.

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.

An Immunization Strategy for Hidden Populations.

PubMed

Chen, Saran; Lu, Xin

2017-06-12

Hidden populations, such as injecting drug users (IDUs), sex workers (SWs) and men who have sex with men (MSM), are considered at high risk of contracting and transmitting infectious diseases such as AIDS, gonorrhea, syphilis etc. However, public health interventions to such groups are prohibited due to strong privacy concerns and lack of global information, which is a necessity for traditional strategies such as targeted immunization and acquaintance immunization. In this study, we introduce an innovative intervention strategy to be used in combination with a sampling approach that is widely used for hidden populations, Respondent-driven Sampling (RDS). The RDS strategy is implemented in two steps: First, RDS is used to estimate the average degree (personal network size) and degree distribution of the target population with sample data. Second, a cut-off threshold is calculated and used to screen the respondents to be immunized. Simulations on model networks and real-world networks reveal that the efficiency of the RDS strategy is close to that of the targeted strategy. As the new strategy can be implemented with the RDS sampling process, it provides a cost-efficient and feasible approach for disease intervention and control for hidden populations.

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.

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.

Hidden order and flux attachment in symmetry-protected topological phases: A Laughlin-like approach

NASA Astrophysics Data System (ADS)

Ringel, Zohar; Simon, Steven H.

2015-05-01

Topological phases of matter are distinct from conventional ones by their lack of a local order parameter. Still in the quantum Hall effect, hidden order parameters exist and constitute the basis for the celebrated composite-particle approach. Whether similar hidden orders exist in 2D and 3D symmetry protected topological phases (SPTs) is a largely open question. Here, we introduce a new approach for generating SPT ground states, based on a generalization of the Laughlin wave function. This approach gives a simple and unifying picture of some classes of SPTs in 1D and 2D, and reveals their hidden order and flux attachment structures. For the 1D case, we derive exact relations between the wave functions obtained in this manner and group cohomology wave functions, as well as matrix product state classification. For the 2D Ising SPT, strong analytical and numerical evidence is given to show that the wave function obtained indeed describes the desired SPT. The Ising SPT then appears as a state with quasi-long-range order in composite degrees of freedom consisting of Ising-symmetry charges attached to Ising-symmetry fluxes.

Phase Transitions in Definite Total Spin States of Two-Component Fermi Gases.

PubMed

Yurovsky, Vladimir A

2017-05-19

Second-order phase transitions have no latent heat and are characterized by a change in symmetry. In addition to the conventional symmetric and antisymmetric states under permutations of bosons and fermions, mathematical group-representation theory allows for non-Abelian permutation symmetry. Such symmetry can be hidden in states with defined total spins of spinor gases, which can be formed in optical cavities. The present work shows that the symmetry reveals itself in spin-independent or coordinate-independent properties of these gases, namely as non-Abelian entropy in thermodynamic properties. In weakly interacting Fermi gases, two phases appear associated with fermionic and non-Abelian symmetry under permutations of particle states, respectively. The second-order transitions between the phases are characterized by discontinuities in specific heat. Unlike other phase transitions, the present ones are not caused by interactions and can appear even in ideal gases. Similar effects in Bose gases and strong interactions are discussed.

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

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.

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.

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.

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.

Citizenship, "Race" and the Hidden Curriculum.

ERIC Educational Resources Information Center

Gillborn, David

1992-01-01

Uses data from English comprehensive schools to suggest that schools teach the reality of second class citizenship for African-American people through a hidden curriculum. Argues that teachers have the opportunity to challenge stereotypes and empower students by putting antiracist principles into practice. Stresses the need to recognize and…

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

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

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.

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.

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).

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.

Accurate step-hold tracking of smoothly varying periodic and aperiodic probability.

PubMed

Ricci, Matthew; Gallistel, Randy

2017-07-01

Subjects observing many samples from a Bernoulli distribution are able to perceive an estimate of the generating parameter. A question of fundamental importance is how the current percept-what we think the probability now is-depends on the sequence of observed samples. Answers to this question are strongly constrained by the manner in which the current percept changes in response to changes in the hidden parameter. Subjects do not update their percept trial-by-trial when the hidden probability undergoes unpredictable and unsignaled step changes; instead, they update it only intermittently in a step-hold pattern. It could be that the step-hold pattern is not essential to the perception of probability and is only an artifact of step changes in the hidden parameter. However, we now report that the step-hold pattern obtains even when the parameter varies slowly and smoothly. It obtains even when the smooth variation is periodic (sinusoidal) and perceived as such. We elaborate on a previously published theory that accounts for: (i) the quantitative properties of the step-hold update pattern; (ii) subjects' quick and accurate reporting of changes; (iii) subjects' second thoughts about previously reported changes; (iv) subjects' detection of higher-order structure in patterns of change. We also call attention to the challenges these results pose for trial-by-trial updating theories.

The metrology of time.

PubMed

Arias, Elisa Felicitas

2005-09-15

Measuring time is a continuous activity, an international and restless enterprise hidden in time laboratories spread all over the planet. The Bureau International des Poids et Mesures is charged with coordinating activities for international timekeeping and it makes use of the world's capacity to produce a remarkably stable and accurate reference time-scale. Commercial atomic clocks beating the second in national laboratories can reach a stability of one part in 10(14) over a 5 day averaging time, compelling us to research the most highly performing methods of remote clock comparison. The unit of the international time-scale is the second of the International System of Units, realized with an uncertainty of the order 10(-15) by caesium fountains. Physicists in a few time laboratories are making efforts to gain one order of magnitude in the uncertainty of the realization of the second, and more refined techniques of time and frequency transfer are in development to accompany this progress. Femtosecond comb technology will most probably contribute in the near future to enhance the definition of the second with the incorporation of optical clocks. We will explain the evolution of the measuring of time, current state-of-the-art measures and future challenges.

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

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

PubMed Central

Zhao, Zhibiao

2011-01-01

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601

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

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.

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.

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.

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.

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

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...

On deception detection in multi-agent systems and deception intent

NASA Astrophysics Data System (ADS)

Santos, Eugene, Jr.; Li, Deqing; Yuan, Xiuqing

2008-04-01

Deception detection plays an important role in the military decision-making process, but detecting deception is a challenging task. The deception planning process involves a number of human factors. It is intent-driven where intentions are usually hidden or not easily observable. As a result, in order to detect deception, any adversary model must have the capability to capture the adversary's intent. This paper discusses deception detection in multi-agent systems and in adversary modeling. We examined psychological and cognitive science research on deception and implemented various theories of deception within our approach. First, in multi-agent expert systems, one detection method uses correlations between agents to predict reasonable opinions/responses of other agents (Santos & Johnson, 2004). We further explore this idea and present studies that show the impact of different factors on detection success rate. Second, from adversary modeling, our detection method focuses on inferring adversary intent. By combining deception "branches" with intent inference models, we can estimate an adversary's deceptive activities and at the same time enhance intent inference. Two major kinds of deceptions are developed in this approach in different fashions. Simulative deception attempts to find inconsistency in observables, while dissimulative deception emphasizes the inference of enemy intentions.

The hidden societal cost of antibiotic resistance per antibiotic prescribed in the United States: an exploratory analysis.

PubMed

Michaelidis, Constantinos I; Fine, Michael J; Lin, Chyongchiou Jeng; Linder, Jeffrey A; Nowalk, Mary Patricia; Shields, Ryan K; Zimmerman, Richard K; Smith, Kenneth J

2016-11-08

Ambulatory antibiotic prescribing contributes to the development of antibiotic resistance and increases societal costs. Here, we estimate the hidden societal cost of antibiotic resistance per antibiotic prescribed in the United States. In an exploratory analysis, we used published data to develop point and range estimates for the hidden societal cost of antibiotic resistance (SCAR) attributable to each ambulatory antibiotic prescription in the United States. We developed four estimation methods that focused on the antibiotic-resistance attributable costs of hospitalization, second-line inpatient antibiotic use, second-line outpatient antibiotic use, and antibiotic stewardship, then summed the estimates across all methods. The total SCAR attributable to each ambulatory antibiotic prescription was estimated to be $13 (range: $3-$95). The greatest contributor to the total SCAR was the cost of hospitalization ($9; 69 % of the total SCAR). The costs of second-line inpatient antibiotic use ($1; 8 % of the total SCAR), second-line outpatient antibiotic use ($2; 15 % of the total SCAR) and antibiotic stewardship ($1; 8 %). This apperars to be an error.; of the total SCAR) were modest contributors to the total SCAR. Assuming an average antibiotic cost of $20, the total SCAR attributable to each ambulatory antibiotic prescription would increase antibiotic costs by 65 % (range: 15-475 %) if incorporated into antibiotic costs paid by patients or payers. Each ambulatory antibiotic prescription is associated with a hidden SCAR that substantially increases the cost of an antibiotic prescription in the United States. This finding raises concerns regarding the magnitude of misalignment between individual and societal antibiotic costs.

Observer-Pattern Modeling and Slow-Scale Bifurcation Analysis of Two-Stage Boost Inverters

NASA Astrophysics Data System (ADS)

Zhang, Hao; Wan, Xiaojin; Li, Weijie; Ding, Honghui; Yi, Chuanzhi

2017-06-01

This paper deals with modeling and bifurcation analysis of two-stage Boost inverters. Since the effect of the nonlinear interactions between source-stage converter and load-stage inverter causes the “hidden” second-harmonic current at the input of the downstream H-bridge inverter, an observer-pattern modeling method is proposed by removing time variance originating from both fundamental frequency and hidden second harmonics in the derived averaged equations. Based on the proposed observer-pattern model, the underlying mechanism of slow-scale instability behavior is uncovered with the help of eigenvalue analysis method. Then eigenvalue sensitivity analysis is used to select some key system parameters of two-stage Boost inverter, and some behavior boundaries are given to provide some design-oriented information for optimizing the circuit. Finally, these theoretical results are verified by numerical simulations and circuit experiment.

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.

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.

Hand gesture recognition in confined spaces with partial observability and occultation constraints

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

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

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

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 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.

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.

Petri net-based modelling of human-automation conflicts in aviation.

PubMed

Pizziol, Sergio; Tessier, Catherine; Dehais, Frédéric

2014-01-01

Analyses of aviation safety reports reveal that human-machine conflicts induced by poor automation design are remarkable precursors of accidents. A review of different crew-automation conflicting scenarios shows that they have a common denominator: the autopilot behaviour interferes with the pilot's goal regarding the flight guidance via 'hidden' mode transitions. Considering both the human operator and the machine (i.e. the autopilot or the decision functions) as agents, we propose a Petri net model of those conflicting interactions, which allows them to be detected as deadlocks in the Petri net. In order to test our Petri net model, we designed an autoflight system that was formally analysed to detect conflicting situations. We identified three conflicting situations that were integrated in an experimental scenario in a flight simulator with 10 general aviation pilots. The results showed that the conflicts that we had a-priori identified as critical had impacted the pilots' performance. Indeed, the first conflict remained unnoticed by eight participants and led to a potential collision with another aircraft. The second conflict was detected by all the participants but three of them did not manage the situation correctly. The last conflict was also detected by all the participants but provoked typical automation surprise situation as only one declared that he had understood the autopilot behaviour. These behavioural results are discussed in terms of workload and number of fired 'hidden' transitions. Eventually, this study reveals that both formal and experimental approaches are complementary to identify and assess the criticality of human-automation conflicts. Practitioner Summary: We propose a Petri net model of human-automation conflicts. An experiment was conducted with general aviation pilots performing a scenario involving three conflicting situations to test the soundness of our formal approach. This study reveals that both formal and experimental approaches are complementary to identify and assess the criticality conflicts.

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.

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

Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

NASA Astrophysics Data System (ADS)

Wang, Xin; Li, Yan; Chen, Tongjun; Yan, Qiuyan; Ma, Li

2017-04-01

The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes. At first, we build an ELM prediction model using the PCA attributes of a synthetic seismic section. The results suggest that the ELM model can produce a reliable and accurate prediction of the TDC thickness for synthetic data, preferring Sigmoid activation function and 20 hidden nodes. Then, we analyze the applicability of the ELM model on the thickness prediction of the TDC with real application data. Through the cross validation of near-well traces, the results suggest that the ELM model can produce a reliable and accurate prediction of the TDC. After that, we use 250 near-well traces from 10 wells to build an ELM predicting model and use the model to forecast the TDC thickness of the No. 15 coal in the study area using the PCA attributes as the inputs. Comparing the predicted results, it is noted that the trained ELM model with two selected PCA attributes yields better predication results than those from the other combinations of the attributes. Finally, the trained ELM model with real seismic data have a different number of hidden nodes (10) than the trained ELM model with synthetic seismic data. In summary, it is feasible to use an ELM model to predict the TDC thickness using the calculated PCA attributes as the inputs. However, the input attributes, the activation function and the number of hidden nodes in the ELM model should be selected and tested carefully based on individual application.

Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

NASA Astrophysics Data System (ADS)

Boutros, Tony; Liang, Ming

2011-08-01

Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

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.

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

Modelling proteins' hidden conformations to predict antibiotic resistance

NASA Astrophysics Data System (ADS)

Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

2016-10-01

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM's specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models' prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

A fast hidden line algorithm for plotting finite element models

NASA Technical Reports Server (NTRS)

Jones, G. K.

1982-01-01

Effective plotting of finite element models requires the use of fast hidden line plot techniques that provide interactive response. A high speed hidden line technique was developed to facilitate the plotting of NASTRAN finite element models. Based on testing using 14 different models, the new hidden line algorithm (JONES-D) appears to be very fast: its speed equals that for normal (all lines visible) plotting and when compared to other existing methods it appears to be substantially faster. It also appears to be very reliable: no plot errors were observed using the new method to plot NASTRAN models. The new algorithm was made part of the NPLOT NASTRAN plot package and was used by structural analysts for normal production tasks.

Novel maximum-margin training algorithms for supervised neural networks.

PubMed

Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by MICI, MMGDX, and Levenberg-Marquard (LM), respectively. The resulting neural network was named assembled neural network (ASNN). Benchmark data sets of real-world problems have been used in experiments that enable a comparison with other state-of-the-art classifiers. The results provide evidence of the effectiveness of our methods regarding accuracy, AUC, and balanced error rate.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

NASA Astrophysics Data System (ADS)

Dragomir, Andrei; Akay, Yasemin M.; Akay, Metin

2010-10-01

In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10-4) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

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)…

Multilayer neural networks with extensively many hidden units.

PubMed

Rosen-Zvi, M; Engel, A; Kanter, I

2001-08-13

The information processing abilities of a multilayer neural network with a number of hidden units scaling as the input dimension are studied using statistical mechanics methods. The mapping from the input layer to the hidden units is performed by general symmetric Boolean functions, whereas the hidden layer is connected to the output by either discrete or continuous couplings. Introducing an overlap in the space of Boolean functions as order parameter, the storage capacity is found to scale with the logarithm of the number of implementable Boolean functions. The generalization behavior is smooth for continuous couplings and shows a discontinuous transition to perfect generalization for discrete ones.

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)

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

PubMed Central

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

2016-01-01

This paper proposes a joint model for longitudinal binary and count outcomes. We apply the model to a unique longitudinal study of teen driving where risky driving behavior and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and predicting crash and near crash outcomes. We propose a two-state mixed hidden Markov model whereby the hidden state characterizes the mean for the joint longitudinal crash/near crash outcomes and elevated g-force events which are a proxy for risky driving. Heterogeneity is introduced in both the conditional model for the count outcomes and the hidden process using a shared random effect. An estimation procedure is presented using the forward–backward algorithm along with adaptive Gaussian quadrature to perform numerical integration. The estimation procedure readily yields hidden state probabilities as well as providing for a broad class of predictors. PMID:27766124

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.

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.

Simulation of hydrodynamics using large eddy simulation-second-order moment model in circulating fluidized beds

NASA Astrophysics Data System (ADS)

Juhui, Chen; Yanjia, Tang; Dan, Li; Pengfei, Xu; Huilin, Lu

2013-07-01

Flow behavior of gas and particles is predicted by the large eddy simulation of gas-second order moment of solid model (LES-SOM model) in the simulation of flow behavior in CFB. This study shows that the simulated solid volume fractions along height using a two-dimensional model are in agreement with experiments. The velocity, volume fraction and second-order moments of particles are computed. The second-order moments of clusters are calculated. The solid volume fraction, velocity and second order moments are compared at the three different model constants.

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.

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.

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.

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Imaging of tissue using a NIR supercontinuum laser light source with wavelengths in the second and third NIR optical windows

NASA Astrophysics Data System (ADS)

Sordillo, Laura A.; Lindwasser, Lukas; Budansky, Yury; Leproux, Philippe; Alfano, R. R.

2015-03-01

Supercontinuum light (SC) at wavelengths in the second (1,100 nm to 1,350 nm) and third (1,600 nm to 1,870 nm) NIR optical windows can be used to improve penetration depths of light through tissue and produce clearer images. Image quality is increased due to a reduction in scattering (inverse wavelength power dependence 1/λn, n≥1). We report on the use of a compact Leukos supercontinuum laser (model STM-2000-IR), which utilizes the spectral range from 700 nm to 2,400 nm and offers between 200 - 500 microwatt/nm power in the second and third NIR windows, with an InGaAs detector to image abnormalities hidden beneath thick tissue.

Modelling proteins’ hidden conformations to predict antibiotic resistance

PubMed Central

Hart, Kathryn M.; Ho, Chris M. W.; Dutta, Supratik; Gross, Michael L.; Bowman, Gregory R.

2016-01-01

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design. PMID:27708258

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.

Understanding the Effects of One's Actions upon Hidden Objects and the Development of Search Behaviour in 7-Month-Old Infants

ERIC Educational Resources Information Center

O'Connor, Richard J.; Russell, James

2015-01-01

Infants' understanding of how their actions affect the visibility of hidden objects may be a crucial aspect of the development of search behaviour. To investigate this possibility, 7-month-old infants took part in a two-day training study. At the start of the first session, and at the end of the second, all infants performed a search task with a…

Filtering Using Nonlinear Expectations

DTIC Science & Technology

2016-04-16

gives a solution to estimating a Markov chain observed in Gaussian noise when the variance of the noise is unkown. This paper is accepted for the IEEE...Optimization, an A* journal. A short third paper discusses how to estimate a change in the transition dynamics of a noisily observed Markov chain ...The change point time is hidden in a hidden Markov chain , so a second level of discovery is involved. This paper is accepted for Communications in

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.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

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.

[Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

PubMed

Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

2015-10-01

To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

Gapped excitations in the high-pressure antiferromagnetic phase of URu 2 Si 2

DOE PAGES

Williams, Travis J.; Oak Ridge National Lab.; Barath, Harini; ...

2017-05-31

Here, we report a neutron scattering study of the magnetic excitation spectrum in each of the three temperature and pressure driven phases of URu 2Si 2. We also found qualitatively similar excitations throughout the (H0L) scattering plane in the hidden order and large moment phases, with no changes in the hbar-omega-widths of the excitations at the Sigma = (1.407,0,0) and Z = (1,0,0) points, within our experimental resolution. There is, however, an increase in the gap at the Sigma point and an increase in the first moment of both excitations. At 8 meV where the Q-dependence of magnetic scattering inmore » the hidden order phase is extended in Q-space, the excitations in the large moment phase are sharper. Furthermore, the expanded Q-hbar-omega coverage of this study suggest more complete nesting within the antiferromagnetic phase, an important property for future theoretical predictions of a hidden order parameter.« less

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.

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.

Development of a second order closure model for computation of turbulent diffusion flames

NASA Technical Reports Server (NTRS)

Varma, A. K.; Donaldson, C. D.

1974-01-01

A typical eddy box model for the second-order closure of turbulent, multispecies, reacting flows developed. The model structure was quite general and was valid for an arbitrary number of species. For the case of a reaction involving three species, the nine model parameters were determined from equations for nine independent first- and second-order correlations. The model enabled calculation of any higher-order correlation involving mass fractions, temperatures, and reaction rates in terms of first- and second-order correlations. Model predictions for the reaction rate were in very good agreement with exact solutions of the reaction rate equations for a number of assumed flow distributions.

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.

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.

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

Models of temporal enhanced ultrasound data for prostate cancer diagnosis: the impact of time-series order

NASA Astrophysics Data System (ADS)

Nahlawi, Layan; Goncalves, Caroline; Imani, Farhad; Gaed, Mena; Gomez, Jose A.; Moussa, Madeleine; Gibson, Eli; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin; Shatkay, Hagit

2017-03-01

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.

Local suppression of the hidden-order phase by impurities in URu2Si2

NASA Astrophysics Data System (ADS)

Pezzoli, Maria E.; Graf, Matthias J.; Haule, Kristjan; Kotliar, Gabriel; Balatsky, Alexander V.

2011-06-01

We consider the effects of impurities on the enigmatic hidden order (HO) state of the heavy-fermion material URu2Si2. In particular, we focus on local effects of Rh impurities as a tool to probe the suppression of the HO state. To study local properties, we introduce a lattice free energy, where the time invariant HO order parameter Ψ and local antiferromagnetic (AFM) order parameter M are competing orders. Near each Rh atom, the HO order parameter is suppressed, creating a hole in which local AFM order emerges as a result of competition. These local holes are created in the fabric of the HO state like in a Swiss cheese and “filled” with droplets of AFM order. We compare our analysis with recent NMR results on U(RhxRu1-x)2Si2 and find good agreement with the data.

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.

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering.

PubMed

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-08-26

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell's nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR's original scenario is "steering", i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox.

p-adic stochastic hidden variable model

NASA Astrophysics Data System (ADS)

Khrennikov, Andrew

1998-03-01

We propose stochastic hidden variables model in which hidden variables have a p-adic probability distribution ρ(λ) and at the same time conditional probabilistic distributions P(U,λ), U=A,A',B,B', are ordinary probabilities defined on the basis of the Kolmogorov measure-theoretical axiomatics. A frequency definition of p-adic probability is quite similar to the ordinary frequency definition of probability. p-adic frequency probability is defined as the limit of relative frequencies νn but in the p-adic metric. We study a model with p-adic stochastics on the level of the hidden variables description. But, of course, responses of macroapparatuses have to be described by ordinary stochastics. Thus our model describes a mixture of p-adic stochastics of the microworld and ordinary stochastics of macroapparatuses. In this model probabilities for physical observables are the ordinary probabilities. At the same time Bell's inequality is violated.

A two particle hidden sector and the oscillations with photons

NASA Astrophysics Data System (ADS)

Alvarez, Pedro D.; Arias, Paola; Maldonado, Carlos

2018-01-01

We present a detailed study of the oscillations and optical properties for vacuum, in a model for the dark sector that contains axion-like particles and hidden photons. We provide bounds for the couplings versus the mass, using current results from ALPS-I and PVLAS. We also discuss the challenges for the detection of models with more than one hidden particle in light shining trough wall-like experiments.

Medical students' professionalism narratives: a window on the informal and hidden curriculum.

PubMed

Karnieli-Miller, Orit; Vu, T Robert; Holtman, Matthew C; Clyman, Stephen G; Inui, Thomas S

2010-01-01

The aim of this study was to use medical students' critical incident narratives to deepen understanding of the informal and hidden curricula. The authors conducted a thematic analysis of 272 stories of events recorded by 135 third-year medical students that "taught them something about professionalism and professional values." Students wrote these narratives in a "professionalism journal" during their internal medicine clerkships at Indiana University School of Medicine, June through November 2007. The majority of students' recorded experiences involved witnessing positive embodiment of professional values, rather than breaches. Attending physicians and residents were the central figures in the incidents. Analyses revealed two main thematic categories. The first focused on medical-clinical interactions, especially on persons who were role models interacting with patients, families, coworkers, and colleagues. The second focused on events in the teaching-and-learning environment, particularly on students' experiences as learners in the clinical setting. The findings strongly suggest that students' reflective narratives are a rich source of information about the elements of both the informal and hidden curricula, in which medical students learn to become physicians. Experiences with both positive and negative behaviors shaped the students' perceptions of the profession and its values. In particular, interactions that manifest respect and other qualities of good communication with patients, families, and colleagues taught powerfully.

Feature to prototype transition in neural networks

NASA Astrophysics Data System (ADS)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Killing and letting die: hidden value assumptions.

PubMed

Atkinson, G

1983-01-01

In this paper I argue for several related theses: first, that the distinction between killing and letting die, as it is drawn by ordinary persons in ordinary contexts, is more complex than is generally understood; second, that the key feature of this complexity lies in the presence of a hidden normative component in what appears to be a straightforwardly descriptive distinction; and, third, that this complexity renders the killing/letting die distinction an inadequate and hazardous guide for moral reasoning.

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.

Controlling flexible structures with second order actuator dynamics

NASA Technical Reports Server (NTRS)

Inman, Daniel J.; Umland, Jeffrey W.; Bellos, John

1989-01-01

The control of flexible structures for those systems with actuators that are modeled by second order dynamics is examined. Two modeling approaches are investigated. First a stability and performance analysis is performed using a low order finite dimensional model of the structure. Secondly, a continuum model of the flexible structure to be controlled, coupled with lumped parameter second order dynamic models of the actuators performing the control is used. This model is appropriate in the modeling of the control of a flexible panel by proof-mass actuators as well as other beam, plate and shell like structural numbers. The model is verified with experimental measurements.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

PubMed

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

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

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

Berlin, Asher; Gratia, Pierre; Hooper, Dan

2014-07-24

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

Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate).

PubMed

Shahsavari, Shadab; Rezaie Shirmard, Leila; Amini, Mohsen; Abedin Dokoosh, Farid

2017-01-01

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Singlet scalar top partners from accidental supersymmetry

NASA Astrophysics Data System (ADS)

Cheng, Hsin-Chia; Li, Lingfeng; Salvioni, Ennio; Verhaaren, Christopher B.

2018-05-01

We present a model wherein the Higgs mass is protected from the quadratic one-loop top quark corrections by scalar particles that are complete singlets under the Standard Model (SM) gauge group. While bearing some similarity to Folded Supersymmetry, the construction is purely four dimensional and enjoys more parametric freedom, allowing electroweak symmetry breaking to occur easily. The cancelation of the top loop quadratic divergence is ensured by a Z 3 symmetry that relates the SM top sector and two hidden top sectors, each charged under its own hidden color group. In addition to the singlet scalars, the hidden sectors contain electroweak-charged supermultiplets below the TeV scale, which provide the main access to this model at colliders. The phenomenology presents both differences and similarities with respect to other realizations of neutral naturalness. Generally, the glueballs of hidden color have longer decay lengths. The production of hidden sector particles results in quirk or squirk bound states, which later annihilate. We survey the possible signatures and corresponding experimental constraints.

Affective State Level Recognition in Naturalistic Facial and Vocal Expressions.

PubMed

Meng, Hongying; Bianchi-Berthouze, Nadia

2014-03-01

Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions. Using datasets of naturalistic affective expressions (AVEC 2011 audio and video dataset, PAINFUL video dataset) continuously labeled over time and over different dimensions, we analyze the transitions between levels of those dimensions (e.g., transitions in pain intensity level). We use an information theory approach to show that the transitions occur very slowly and hence suggest modeling them as first-order Markov models. The dimension levels are considered to be the hidden states in the Hidden Markov Model (HMM) framework. Their discrete transition and emission matrices are trained by using the labels provided with the training set. The recognition problem is converted into a best path-finding problem to obtain the best hidden states sequence in HMMs. This is a key difference from previous use of HMMs as classifiers. Modeling of the transitions between dimension levels is integrated in a multistage approach, where the first level performs a mapping between the affective expression features and a soft decision value (e.g., an affective dimension level), and further classification stages are modeled as HMMs that refine that mapping by taking into account the temporal relationships between the output decision labels. The experimental results for each of the unimodal datasets show overall performance to be significantly above that of a standard classification system that does not take into account temporal relationships. In particular, the results on the AVEC 2011 audio dataset outperform all other systems presented at the international competition.

A Bayesian model for estimating population means using a link-tracing sampling design.

PubMed

St Clair, Katherine; O'Connell, Daniel

2012-03-01

Link-tracing sampling designs can be used to study human populations that contain "hidden" groups who tend to be linked together by a common social trait. These links can be used to increase the sampling intensity of a hidden domain by tracing links from individuals selected in an initial wave of sampling to additional domain members. Chow and Thompson (2003, Survey Methodology 29, 197-205) derived a Bayesian model to estimate the size or proportion of individuals in the hidden population for certain link-tracing designs. We propose an addition to their model that will allow for the modeling of a quantitative response. We assess properties of our model using a constructed population and a real population of at-risk individuals, both of which contain two domains of hidden and nonhidden individuals. Our results show that our model can produce good point and interval estimates of the population mean and domain means when our population assumptions are satisfied. © 2011, The International Biometric Society.

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.

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

Sobottka, Marcelo, E-mail: sobottka@mtm.ufsc.br; Hart, Andrew G., E-mail: ahart@dim.uchile.cl

Highlights: {yields} We propose a simple stochastic model to construct primitive DNA sequences. {yields} The model provide an explanation for Chargaff's second parity rule in primitive DNA sequences. {yields} The model is also used to predict a novel type of strand symmetry in primitive DNA sequences. {yields} We extend the results for bacterial DNA sequences and compare distributional properties intrinsic to the model to statistical estimates from 1049 bacterial genomes. {yields} We find out statistical evidences that the novel type of strand symmetry holds for bacterial DNA sequences. -- Abstract: Chargaff's second parity rule for short oligonucleotides states that themore » frequency of any short nucleotide sequence on a strand is approximately equal to the frequency of its reverse complement on the same strand. Recent studies have shown that, with the exception of organellar DNA, this parity rule generally holds for double-stranded DNA genomes and fails to hold for single-stranded genomes. While Chargaff's first parity rule is fully explained by the Watson-Crick pairing in the DNA double helix, a definitive explanation for the second parity rule has not yet been determined. In this work, we propose a model based on a hidden Markov process for approximating the distributional structure of primitive DNA sequences. Then, we use the model to provide another possible theoretical explanation for Chargaff's second parity rule, and to predict novel distributional aspects of bacterial DNA sequences.« 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.

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.

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

Computational models of location-invariant orthographic processing

NASA Astrophysics Data System (ADS)

Dandurand, Frédéric; Hannagan, Thomas; Grainger, Jonathan

2013-03-01

We trained three topologies of backpropagation neural networks to discriminate 2000 words (lexical representations) presented at different positions of a horizontal letter array. The first topology (zero-deck) contains no hidden layer, the second (one-deck) has a single hidden layer, and for the last topology (two-deck), the task is divided in two subtasks implemented as two stacked neural networks, with explicit word-centred letters as intermediate representations. All topologies successfully simulated two key benchmark phenomena observed in skilled human reading: transposed-letter priming and relative-position priming. However, the two-deck topology most accurately simulated the ability to discriminate words from nonwords, while containing the fewest connection weights. We analysed the internal representations after training. Zero-deck networks implement a letter-based scheme with a position bias to differentiate anagrams. One-deck networks implement a holographic overlap coding in which representations are essentially letter-based and words are linear combinations of letters. Two-deck networks also implement holographic-coding.

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

PubMed

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to further investigate the disease mechanisms underlying each of these clusters. In summary, we show that a deep learning model can be trained to represent biologically and clinically meaningful abstractions of cancer gene expression data. Understanding what additional relationships these hidden layer abstractions have with the cancer cellular signaling system could have a significant impact on the understanding and treatment of cancer.

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.

Weighted projected networks: mapping hypergraphs to networks.

PubMed

López, Eduardo

2013-05-01

Many natural, technological, and social systems incorporate multiway interactions, yet are characterized and measured on the basis of weighted pairwise interactions. In this article, I propose a family of models in which pairwise interactions originate from multiway interactions, by starting from ensembles of hypergraphs and applying projections that generate ensembles of weighted projected networks. I calculate analytically the statistical properties of weighted projected networks, and suggest ways these could be used beyond theoretical studies. Weighted projected networks typically exhibit weight disorder along links even for very simple generating hypergraph ensembles. Also, as the size of a hypergraph changes, a signature of multiway interaction emerges on the link weights of weighted projected networks that distinguishes them from fundamentally weighted pairwise networks. This signature could be used to search for hidden multiway interactions in weighted network data. I find the percolation threshold and size of the largest component for hypergraphs of arbitrary uniform rank, translate the results into projected networks, and show that the transition is second order. This general approach to network formation has the potential to shed new light on our understanding of weighted networks.

DNA Base-Calling from a Nanopore Using a Viterbi Algorithm

PubMed Central

Timp, Winston; Comer, Jeffrey; Aksimentiev, Aleksei

2012-01-01

Nanopore-based DNA sequencing is the most promising third-generation sequencing method. It has superior read length, speed, and sample requirements compared with state-of-the-art second-generation methods. However, base-calling still presents substantial difficulty because the resolution of the technique is limited compared with the measured signal/noise ratio. Here we demonstrate a method to decode 3-bp-resolution nanopore electrical measurements into a DNA sequence using a Hidden Markov model. This method shows tremendous potential for accuracy (∼98%), even with a poor signal/noise ratio. PMID:22677395

Device Control Using Gestures Sensed from EMG

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.

2003-01-01

In this paper we present neuro-electric interfaces for virtual device control. The examples presented rely upon sampling Electromyogram data from a participants forearm. This data is then fed into pattern recognition software that has been trained to distinguish gestures from a given gesture set. The pattern recognition software consists of hidden Markov models which are used to recognize the gestures as they are being performed in real-time. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard.

A stochastic HMM-based forecasting model for fuzzy time series.

PubMed

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering

PubMed Central

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-01-01

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell’s nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR’s original scenario is “steering”, i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox. PMID:27562658

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering

NASA Astrophysics Data System (ADS)

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-08-01

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell’s nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR’s original scenario is “steering”, i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox.

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.

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.

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

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

Baldi, P.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

Intelligent data analysis to model and understand live cell time-lapse sequences.

PubMed

Paterson, Allan; Ashtari, M; Ribé, D; Stenbeck, G; Tucker, A

2012-01-01

One important aspect of cellular function, which is at the basis of tissue homeostasis, is the delivery of proteins to their correct destinations. Significant advances in live cell microscopy have allowed tracking of these pathways by following the dynamics of fluorescently labelled proteins in living cells. This paper explores intelligent data analysis techniques to model the dynamic behavior of proteins in living cells as well as to classify different experimental conditions. We use a combination of decision tree classification and hidden Markov models. In particular, we introduce a novel approach to "align" hidden Markov models so that hidden states from different models can be cross-compared. Our models capture the dynamics of two experimental conditions accurately with a stable hidden state for control data and multiple (less stable) states for the experimental data recapitulating the behaviour of particle trajectories within live cell time-lapse data. In addition to having successfully developed an automated framework for the classification of protein transport dynamics from live cell time-lapse data our model allows us to understand the dynamics of a complex trafficking pathway in living cells in culture.

On the use of hidden Markov models for gaze pattern modeling

NASA Astrophysics Data System (ADS)

Mannaru, Pujitha; Balasingam, Balakumar; Pattipati, Krishna; Sibley, Ciara; Coyne, Joseph

2016-05-01

Some of the conventional metrics derived from gaze patterns (on computer screens) to study visual attention, engagement and fatigue are saccade counts, nearest neighbor index (NNI) and duration of dwells/fixations. Each of these metrics has drawbacks in modeling the behavior of gaze patterns; one such drawback comes from the fact that some portions on the screen are not as important as some other portions on the screen. This is addressed by computing the eye gaze metrics corresponding to important areas of interest (AOI) on the screen. There are some challenges in developing accurate AOI based metrics: firstly, the definition of AOI is always fuzzy; secondly, it is possible that the AOI may change adaptively over time. Hence, there is a need to introduce eye-gaze metrics that are aware of the AOI in the field of view; at the same time, the new metrics should be able to automatically select the AOI based on the nature of the gazes. In this paper, we propose a novel way of computing NNI based on continuous hidden Markov models (HMM) that model the gazes as 2D Gaussian observations (x-y coordinates of the gaze) with the mean at the center of the AOI and covariance that is related to the concentration of gazes. The proposed modeling allows us to accurately compute the NNI metric in the presence of multiple, undefined AOI on the screen in the presence of intermittent casual gazing that is modeled as random gazes on the screen.

Evaluation of cracks with different hidden depths and shapes using surface magnetic field measurements based on semi-analytical modelling

NASA Astrophysics Data System (ADS)

Jiang, Feng; Liu, Shulin

2018-03-01

In this paper, we present a feasibility study for detecting cracks with different hidden depths and shapes using information contained in the magnetic field excited by a rectangular coil with a rectangular cross section. First, we solve for the eigenvalues and the unknown coefficients of the magnetic vector potential by imposing artificial and natural boundary conditions. Thus, a semi-analytical solution for the magnetic field distribution around the surface of a conducting plate that contains a long hidden crack is formulated. Next, based on the proposed modelling, the influences of the different hidden depth cracks on the surface magnetic field are analysed. The results show that the horizontal and vertical components of the magnetic field near the crack are becoming weaker and that the phase information of the magnetic field can be used to qualitatively determine the hidden depth of the crack. In addition, the model is optimised to improve its accuracy in classifying crack types. The relationship between signal features and crack shapes is subsequently established. The modified model is validated by using finite element simulations, visually indicating the change in the magnetic field near the crack.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

How transfer flights shape the structure of the airline network.

PubMed

Ryczkowski, Tomasz; Fronczak, Agata; Fronczak, Piotr

2017-07-17

In this paper, we analyse the gravity model in the global passenger air-transport network. We show that in the standard form, the model is inadequate for correctly describing the relationship between passenger flows and typical geo-economic variables that characterize connected countries. We propose a model for transfer flights that allows exploitation of these discrepancies in order to discover hidden subflows in the network. We illustrate its usefulness by retrieving the distance coefficient in the gravity model, which is one of the determinants of the globalization process. Finally, we discuss the correctness of the presented approach by comparing the distance coefficient to several well-known economic events.

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

PubMed Central

Tataru, Paula; Sand, Andreas; Hobolth, Asger; Mailund, Thomas; Pedersen, Christian N. S.

2013-01-01

Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model. PMID:24833225

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.

Modeling volatility using state space models.

PubMed

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

The basis function approach for modeling autocorrelation in ecological data

USGS Publications Warehouse

Hefley, Trevor J.; Broms, Kristin M.; Brost, Brian M.; Buderman, Frances E.; Kay, Shannon L.; Scharf, Henry; Tipton, John; Williams, Perry J.; Hooten, Mevin B.

2017-01-01

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.

Second-order singular pertubative theory for gravitational lenses

NASA Astrophysics Data System (ADS)

Alard, C.

2018-03-01

The extension of the singular perturbative approach to the second order is presented in this paper. The general expansion to the second order is derived. The second-order expansion is considered as a small correction to the first-order expansion. Using this approach, it is demonstrated that in practice the second-order expansion is reducible to a first order expansion via a re-definition of the first-order pertubative fields. Even if in usual applications the second-order correction is small the reducibility of the second-order expansion to the first-order expansion indicates a potential degeneracy issue. In general, this degeneracy is hard to break. A useful and simple second-order approximation is the thin source approximation, which offers a direct estimation of the correction. The practical application of the corrections derived in this paper is illustrated by using an elliptical NFW lens model. The second-order pertubative expansion provides a noticeable improvement, even for the simplest case of thin source approximation. To conclude, it is clear that for accurate modelization of gravitational lenses using the perturbative method the second-order perturbative expansion should be considered. In particular, an evaluation of the degeneracy due to the second-order term should be performed, for which the thin source approximation is particularly useful.

"On Second Thoughts…": Changes of Mind as an Indication of Competing Knowledge Structures

NASA Astrophysics Data System (ADS)

Wilson, Kate F.; Low, David J.

2015-09-01

A review of student answers to diagnostic questions concerned with Newton's Laws showed a tendency for some students to change their answer to a question when the following question caused them to think more about the situation. We investigate this behavior and interpret it in the framework of the resource model; in particular, a weak Newton's Third Law structure being dominated by an inconsistent Newton's Second Law (or "Net Force") structure, in the absence of a strong, consistent Newtonian structure. This observation highlights the hidden problem in instruction where the implicit use of Newton's Third Law is dominated by the explicit conceptual and mathematical application of Newton's Second Law, both within individual courses and across a degree program. To facilitate students' development of a consistent Newtonian knowledge structure, it is important that instructors highlight the interrelated nature of Newton's Laws in problem solving.

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.

Machine learning in sentiment reconstruction of the simulated stock market

NASA Astrophysics Data System (ADS)

Goykhman, Mikhail; Teimouri, Ali

2018-02-01

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Noise-induced cochlear synaptopathy: Past findings and future studies.

PubMed

Kobel, Megan; Le Prell, Colleen G; Liu, Jennifer; Hawks, John W; Bao, Jianxin

2017-06-01

For decades, we have presumed the death of hair cells and spiral ganglion neurons are the main cause of hearing loss and difficulties understanding speech in noise, but new findings suggest synapse loss may be the key contributor. Specifically, recent preclinical studies suggest that the synapses between inner hair cells and spiral ganglion neurons with low spontaneous rates and high thresholds are the most vulnerable subcellular structures, with respect to insults during aging and noise exposure. This cochlear synaptopathy can be "hidden" because this synaptic loss can occur without permanent hearing threshold shifts. This new discovery of synaptic loss opens doors to new research directions. Here, we review a number of recent studies and make suggestions in two critical future research directions. First, based on solid evidence of cochlear synaptopathy in animal models, it is time to apply molecular approaches to identify the underlying molecular mechanisms; improved understanding is necessary for developing rational, effective therapies against this cochlear synaptopathy. Second, in human studies, the data supporting cochlear synaptopathy are indirect although rapid progress has been made. To fully identify changes in function that are directly related this hidden synaptic damage, we argue that a battery of tests including both electrophysiological and behavior tests should be combined for diagnosis of "hidden hearing loss" in clinical studies. This new approach may provide a direct link between cochlear synaptopathy and perceptual difficulties. Copyright © 2016 Elsevier B.V. All rights reserved.

Percolation under noise: Detecting explosive percolation using the second-largest component

NASA Astrophysics Data System (ADS)

Viles, Wes; Ginestet, Cedric E.; Tang, Ariana; Kramer, Mark A.; Kolaczyk, Eric D.

2016-05-01

We consider the problem of distinguishing between different rates of percolation under noise. A statistical model of percolation is constructed allowing for the birth and death of edges as well as the presence of noise in the observations. This graph-valued stochastic process is composed of a latent and an observed nonstationary process, where the observed graph process is corrupted by type-I and type-II errors. This produces a hidden Markov graph model. We show that for certain choices of parameters controlling the noise, the classical (Erdős-Rényi) percolation is visually indistinguishable from a more rapid form of percolation. In this setting, we compare two different criteria for discriminating between these two percolation models, based on the interquartile range (IQR) of the first component's size, and on the maximal size of the second-largest component. We show through data simulations that this second criterion outperforms the IQR of the first component's size, in terms of discriminatory power. The maximal size of the second component therefore provides a useful statistic for distinguishing between different rates of percolation, under physically motivated conditions for the birth and death of edges, and under noise. The potential application of the proposed criteria for the detection of clinically relevant percolation in the context of applied neuroscience is also discussed.

Monitoring by forward scatter radar techniques: an improved second-order analytical model

NASA Astrophysics Data System (ADS)

Falconi, Marta Tecla; Comite, Davide; Galli, Alessandro; Marzano, Frank S.; Pastina, Debora; Lombardo, Pierfrancesco

2017-10-01

In this work, a second-order phase approximation is introduced to provide an improved analytical model of the signal received in forward scatter radar systems. A typical configuration with a rectangular metallic object illuminated while crossing the baseline, in far- or near-field conditions, is considered. An improved second-order model is compared with a simplified one already proposed by the authors and based on a paraxial approximation. A phase error analysis is carried out to investigate benefits and limitations of the second-order modeling. The results are validated by developing full-wave numerical simulations implementing the relevant scattering problem on a commercial tool.

A Third-Order Item Response Theory Model for Modeling the Effects of Domains and Subdomains in Large-Scale Educational Assessment Surveys

ERIC Educational Resources Information Center

Rijmen, Frank; Jeon, Minjeong; von Davier, Matthias; Rabe-Hesketh, Sophia

2014-01-01

Second-order item response theory models have been used for assessments consisting of several domains, such as content areas. We extend the second-order model to a third-order model for assessments that include subdomains nested in domains. Using a graphical model framework, it is shown how the model does not suffer from the curse of…

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 .

Machine Learning

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

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networksmore » and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.« less

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

NASA Astrophysics Data System (ADS)

Pfeiffer, Tim; Heinze, Nicolai; Frysch, Robert; Deouell, Leon Y.; Schoenfeld, Mircea A.; Knight, Robert T.; Rose, Georg

2016-04-01

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain-computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well-studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real-life BCI utilizations.

A substantial amount of hidden magnetic energy in the quiet Sun.

PubMed

Bueno, J Trujillo; Shchukina, N; Ramos, A Asensio

2004-07-15

Deciphering and understanding the small-scale magnetic activity of the quiet solar photosphere should help to solve many of the key problems of solar and stellar physics, such as the magnetic coupling to the outer atmosphere and the coronal heating. At present, we can see only approximately 1 per cent of the complex magnetism of the quiet Sun, which highlights the need to develop a reliable way to investigate the remaining 99 per cent. Here we report three-dimensional radiative transfer modelling of scattering polarization in atomic and molecular lines that indicates the presence of hidden, mixed-polarity fields on subresolution scales. Combining this modelling with recent observational data, we find a ubiquitous tangled magnetic field with an average strength of approximately 130 G, which is much stronger in the intergranular regions of solar surface convection than in the granular regions. So the average magnetic energy density in the quiet solar photosphere is at least two orders of magnitude greater than that derived from simplistic one-dimensional investigations, and sufficient to balance radiative energy losses from the solar chromosphere.

A search for pair production of new light bosons decaying into muons

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Knünz, V.; König, A.; Krammer, M.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Ochesanu, S.; Rougny, R.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Keaveney, J.; Lowette, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Van Parijs, I.; Barria, P.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dobur, D.; Fasanella, G.; Favart, L.; Gay, A. P. R.; Grebenyuk, A.; Lenzi, T.; Léonard, A.; Maerschalk, T.; Mohammadi, A.; Perniè, L.; Randle-conde, A.; Reis, T.; Seva, T.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Wang, J.; Zenoni, F.; Zhang, F.; Beernaert, K.; Benucci, L.; Cimmino, A.; Crucy, S.; Fagot, A.; Garcia, G.; Gul, M.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva Diblen, S.; Sigamani, M.; Strobbe, N.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bondu, O.; Bruno, G.; Castello, R.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Mertens, A.; Nuttens, C.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Beliy, N.; Caebergs, T.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Dos Reis Martins, T.; Hensel, C.; Mora Herrera, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; De Souza Santos, A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Genchev, V.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Piperov, S.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Shaheen, S. M.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Zou, W.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Polic, D.; Puljak, I.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Bodlak, M.; Finger, M.; Finger, M.; Ali, A.; Aly, R.; Aly, S.; Assran, Y.; Ellithi Kamel, A.; Kuotb Awad, A. M.; Lotfy, A.; Masod, R.; Radi, A.; Calpas, B.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Pekkanen, J.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Dahms, T.; Davignon, O.; Filipovic, N.; Florent, A.; Granier de Cassagnac, R.; Lisniak, S.; Mastrolorenzo, L.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Brochet, S.; Carrillo Montoya, C. A.; Chasserat, J.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Xiao, H.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Edelhoff, M.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Sammet, J.; Schael, S.; Schulte, J. F.; Verlage, T.; Weber, H.; Wittmer, B.; Zhukov, V.; Ata, M.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Klingebiel, D.; Knutzen, S.; Kreuzer, P.; Merschmeyer, M.; Meyer, A.; Millet, P.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Geenen, H.; Geisler, M.; Haj Ahmad, W.; Hoehle, F.; Kargoll, B.; Kress, T.; Kuessel, Y.; Künsken, A.; Lingemann, J.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asin, I.; Bartosik, N.; Behnke, O.; Behrens, U.; Bell, A. J.; Borras, K.; Burgmeier, A.; Cakir, A.; Calligaris, L.; Campbell, A.; Choudhury, S.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Dooling, S.; Dorland, T.; Eckerlin, G.; Eckstein, D.; Eichhorn, T.; Flucke, G.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Gunnellini, P.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Katsas, P.; Kieseler, J.; Kleinwort, C.; Korol, I.; Lange, W.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Marfin, I.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Naumann-Emme, S.; Nayak, A.; Ntomari, E.; Perrey, H.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Ribeiro Cipriano, P. M.; Roland, B.; Sahin, M. Ö.; Salfeld-Nebgen, J.; Saxena, P.; Schoerner-Sadenius, T.; Schröder, M.; Seitz, C.; Spannagel, S.; Trippkewitz, K. D.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Erfle, J.; Garutti, E.; Goebel, K.; Gonzalez, D.; Görner, M.; Haller, J.; Hoffmann, M.; Höing, R. S.; Junkes, A.; Klanner, R.; Kogler, R.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Nowatschin, D.; Ott, J.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Pietsch, N.; Poehlsen, J.; Rathjens, D.; Sander, C.; Schettler, H.; Schleper, P.; Schlieckau, E.; Schmidt, A.; Schwandt, J.; Seidel, M.; Sola, V.; Stadie, H.; Steinbrück, G.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Akbiyik, M.; Barth, C.; Baus, C.; Berger, J.; Böser, C.; Butz, E.; Chwalek, T.; Colombo, F.; De Boer, W.; Descroix, A.; Dierlamm, A.; Feindt, M.; Frensch, F.; Giffels, M.; Gilbert, A.; Hartmann, F.; Husemann, U.; Kassel, F.; Katkov, I.; Kornmayer, A.; Lobelle Pardo, P.; Mozer, M. U.; Müller, T.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Simonis, H. J.; Stober, F. M.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weiler, T.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Markou, A.; Psallidas, A.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Bencze, G.; Hajdu, C.; Hazi, A.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Molnar, J.; Szillasi, Z.; Bartók, M.; Makovec, A.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Mal, P.; Mandal, K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Gupta, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Mehta, A.; Mittal, M.; Nishu, N.; Singh, J. B.; Walia, G.; Kumar, Ashok; Kumar, Arun; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Ranjan, K.; Sharma, R.; Sharma, V.; Banerjee, S.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutta, S.; Jain, Sa.; Jain, Sh.; Khurana, R.; Majumdar, N.; Modak, A.; Mondal, K.; Mukherjee, S.; Mukhopadhyay, S.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Abdulsalam, A.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Banerjee, S.; Bhowmik, S.; Chatterjee, R. M.; Dewanjee, R. K.; Dugad, S.; Ganguly, S.; Ghosh, S.; Guchait, M.; Gurtu, A.; Kole, G.; Kumar, S.; Mahakud, B.; Maity, M.; Majumder, G.; Mazumdar, K.; Mitra, S.; Mohanty, G. B.; Parida, B.; Sarkar, T.; Sudhakar, K.; Sur, N.; Sutar, B.; Wickramage, N.; Sharma, S.; Bakhshiansohi, H.; Behnamian, H.; Etesami, S. M.; Fahim, A.; Goldouzian, R.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Chhibra, S. S.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Benvenuti, A. C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Travaglini, R.; Cappello, G.; Chiorboli, M.; Costa, S.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gonzi, S.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Tropiano, A.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Calvelli, V.; Ferro, F.; Lo Vetere, M.; Robutti, E.; Tosi, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Gerosa, R.; Ghezzi, A.; Govoni, P.; Malvezzi, S.; Manzoni, R. A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Redaelli, N.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; Di Guida, S.; Esposito, M.; Fabozzi, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Merola, M.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Bisello, D.; Carlin, R.; Carvalho Antunes De Oliveira, A.; Checchia, P.; Dall'Osso, M.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pegoraro, M.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Tosi, M.; Vanini, S.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Braghieri, A.; Gabusi, M.; Magnani, A.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Biasini, M.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Spiezia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Broccolo, G.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Foà, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; Savoy-Navarro, A.; Serban, A. T.; Spagnolo, P.; Squillacioti, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; D'imperio, G.; Del Re, D.; Diemoz, M.; Gelli, S.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Micheli, F.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Soffi, L.; Traczyk, P.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bellan, R.; Biino, C.; Cartiglia, N.; Costa, M.; Covarelli, R.; Dattola, D.; Degano, A.; Demaria, N.; Finco, L.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Musich, M.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Solano, A.; Staiano, A.; Tamponi, U.; Belforte, S.; Candelise, V.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; Gobbo, B.; La Licata, C.; Marone, M.; Schizzi, A.; Umer, T.; Zanetti, A.; Chang, S.; Kropivnitskaya, A.; Nam, S. K.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Kong, D. J.; Lee, S.; Oh, Y. D.; Sakharov, A.; Son, D. C.; Kim, H.; Kim, T. J.; Ryu, M. S.; Song, S.; Choi, S.; Go, Y.; Gyun, D.; Hong, B.; Jo, M.; Kim, H.; Kim, Y.; Lee, B.; Lee, K.; Lee, K. S.; Lee, S.; Park, S. K.; Roh, Y.; Yoo, H. D.; Choi, M.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Choi, Y.; Choi, Y. K.; Goh, J.; Kim, D.; Kwon, E.; Lee, J.; Yu, I.; Juodagalvis, A.; Vaitkus, J.; Ibrahim, Z. A.; Komaragiri, J. R.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Casimiro Linares, E.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-de La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Ramirez Sanchez, G.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Vazquez Valencia, F.; Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Reucroft, S.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Khurshid, T.; Shoaib, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Brona, G.; Bunkowski, K.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Lloret Iglesias, L.; Nguyen, F.; Rodrigues Antunes, J.; Seixas, J.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Vischia, P.; Afanasiev, S.; Bunin, P.; Gavrilenko, M.; Golutvin, I.; Gorbunov, I.; Kamenev, A.; Karjavin, V.; Konoplyanikov, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Toriashvili, T.; Zarubin, A.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Levchenko, P.; Murzin, V.; Oreshkin, V.; Smirnov, I.; Sulimov, V.; Uvarov, L.; Vavilov, S.; Vorobyev, A.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Vlasov, E.; Zhokin, A.; Bylinkin, A.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Mesyats, G.; Rusakov, S. V.; Vinogradov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Myagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Tourtchanovitch, L.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Ekmedzic, M.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Domínguez Vázquez, D.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; Albajar, C.; de Trocóniz, J. F.; Missiroli, M.; Moran, D.; Brun, H.; Cuevas, J.; Fernandez Menendez, J.; Folgueras, S.; Gonzalez Caballero, I.; Palencia Cortezon, E.; Vizan Garcia, J. M.; Brochero Cifuentes, J. A.; Cabrillo, I. J.; Calderon, A.; Castiñeiras De Saa, J. R.; Duarte Campderros, J.; Fernandez, M.; Gomez, G.; Graziano, A.; Lopez Virto, A.; Marco, J.; Marco, R.; Martinez Rivero, C.; Matorras, F.; Munoz Sanchez, F. J.; Piedra Gomez, J.; Rodrigo, T.; Rodríguez-Marrero, A. Y.; Ruiz-Jimeno, A.; Scodellaro, L.; Vila, I.; Vilar Cortabitarte, R.; Abbaneo, D.; Auffray, E.; Auzinger, G.; Bachtis, M.; Baillon, P.; Ball, A. H.; Barney, D.; Benaglia, A.; Bendavid, J.; Benhabib, L.; Benitez, J. F.; Berruti, G. M.; Bianchi, G.; Bloch, P.; Bocci, A.; Bonato, A.; Botta, C.; Breuker, H.; Camporesi, T.; Cerminara, G.; Colafranceschi, S.; D'Alfonso, M.; d'Enterria, D.; Dabrowski, A.; Daponte, V.; David, A.; De Gruttola, M.; De Guio, F.; De Roeck, A.; De Visscher, S.; Di Marco, E.; Dobson, M.; Dordevic, M.; du Pree, T.; Dupont-Sagorin, N.; Elliott-Peisert, A.; Eugster, J.; Franzoni, G.; Funk, W.; Gigi, D.; Gill, K.; Giordano, D.; Girone, M.; Glege, F.; Guida, R.; Gundacker, S.; Guthoff, M.; Hammer, J.; Hansen, M.; Harris, P.; Hegeman, J.; Innocente, V.; Janot, P.; Kirschenmann, H.; Kortelainen, M. J.; Kousouris, K.; Krajczar, K.; Lecoq, P.; Lourenço, C.; Lucchini, M. T.; Magini, N.; Malgeri, L.; Mannelli, M.; Marrouche, J.; Martelli, A.; Masetti, L.; Meijers, F.; Mersi, S.; Meschi, E.; Moortgat, F.; Morovic, S.; Mulders, M.; Nemallapudi, M. V.; Neugebauer, H.; Orfanelli, S.; Orsini, L.; Pape, L.; Perez, E.; Petrilli, A.; Petrucciani, G.; Pfeiffer, A.; Piparo, D.; Racz, A.; Rolandi, G.; Rovere, M.; Ruan, M.; Sakulin, H.; Schäfer, C.; Schwick, C.; Sharma, A.; Silva, P.; Simon, M.; Sphicas, P.; Spiga, D.; Steggemann, J.; Stieger, B.; Stoye, M.; Takahashi, Y.; Treille, D.; Tsirou, A.; Veres, G. I.; Wardle, N.; Wöhri, H. K.; Zagozdzinska, A.; Zeuner, W. D.; Bertl, W.; Deiters, K.; Erdmann, W.; Horisberger, R.; Ingram, Q.; Kaestli, H. C.; Kotlinski, D.; Langenegger, U.; Rohe, T.; Bachmair, F.; Bäni, L.; Bianchini, L.; Buchmann, M. A.; Casal, B.; Dissertori, G.; Dittmar, M.; Donegà, M.; Dünser, M.; Eller, P.; Grab, C.; Heidegger, C.; Hits, D.; Hoss, J.; Kasieczka, G.; Lustermann, W.; Mangano, B.; Marini, A. C.; Marionneau, M.; Martinez Ruiz del Arbol, P.; Masciovecchio, M.; Meister, D.; Mohr, N.; Musella, P.; Nessi-Tedaldi, F.; Pandolfi, F.; Pata, J.; Pauss, F.; Perrozzi, L.; Peruzzi, M.; Quittnat, M.; Rossini, M.; Starodumov, A.; Takahashi, M.; Tavolaro, V. R.; Theofilatos, K.; Wallny, R.; Weber, H. A.; Aarrestad, T. K.; Amsler, C.; Canelli, M. F.; Chiochia, V.; De Cosa, A.; Galloni, C.; Hinzmann, A.; Hreus, T.; Kilminster, B.; Lange, C.; Ngadiuba, J.; Pinna, D.; Robmann, P.; Ronga, F. J.; Salerno, D.; Taroni, S.; Yang, Y.; Cardaci, M.; Chen, K. H.; Doan, T. H.; Ferro, C.; Konyushikhin, M.; Kuo, C. M.; Lin, W.; Lu, Y. J.; Volpe, R.; Yu, S. S.; Chang, P.; Chang, Y. H.; Chang, Y. W.; Chao, Y.; Chen, K. F.; Chen, P. H.; Dietz, C.; Fiori, F.; Grundler, U.; Hou, W.-S.; Hsiung, Y.; Liu, Y. F.; Lu, R.-S.; Miñano Moya, M.; Petrakou, E.; Tsai, J. f.; Tzeng, Y. M.; Wilken, R.; Asavapibhop, B.; Kovitanggoon, K.; Singh, G.; Srimanobhas, N.; Suwonjandee, N.; Adiguzel, A.; Cerci, S.; Dozen, C.; Girgis, S.; Gokbulut, G.; Guler, Y.; Gurpinar, E.; Hos, I.; Kangal, E. E.; Kayis Topaksu, A.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Tali, B.; Topakli, H.; Vergili, M.; Zorbilmez, C.; Akin, I. V.; Bilin, B.; Bilmis, S.; Isildak, B.; Karapinar, G.; Surat, U. E.; Yalvac, M.; Zeyrek, M.; Albayrak, E. A.; Gülmez, E.; Kaya, M.; Kaya, O.; Yetkin, T.; Cankocak, K.; Günaydin, Y. O.; Vardarlı, F. I.; Grynyov, B.; Levchuk, L.; Sorokin, P.; Aggleton, R.; Ball, F.; Beck, L.; Brooke, J. J.; Clement, E.; Cussans, D.; Flacher, H.; Goldstein, J.; Grimes, M.; Heath, G. P.; Heath, H. F.; Jacob, J.; Kreczko, L.; Lucas, C.; Meng, Z.; Newbold, D. M.; Paramesvaran, S.; Poll, A.; Sakuma, T.; Seif El Nasr-storey, S.; Senkin, S.; Smith, D.; Smith, V. J.; Bell, K. W.; Belyaev, A.; Brew, C.; Brown, R. M.; Cockerill, D. J. A.; Coughlan, J. A.; Harder, K.; Harper, S.; Olaiya, E.; Petyt, D.; Shepherd-Themistocleous, C. H.; Thea, A.; Tomalin, I. R.; Williams, T.; Womersley, W. J.; Worm, S. D.; Baber, M.; Bainbridge, R.; Buchmuller, O.; Bundock, A.; Burton, D.; Casasso, S.; Citron, M.; Colling, D.; Corpe, L.; Cripps, N.; Dauncey, P.; Davies, G.; De Wit, A.; Della Negra, M.; Dunne, P.; Elwood, A.; Ferguson, W.; Fulcher, J.; Futyan, D.; Hall, G.; Iles, G.; Karapostoli, G.; Kenzie, M.; Lane, R.; Lucas, R.; Lyons, L.; Magnan, A.-M.; Malik, S.; Nash, J.; Nikitenko, A.; Pela, J.; Pesaresi, M.; Petridis, K.; Raymond, D. M.; Richards, A.; Rose, A.; Seez, C.; Sharp, P.; Tapper, A.; Uchida, K.; Vazquez Acosta, M.; Virdee, T.; Zenz, S. C.; Cole, J. E.; Hobson, P. R.; Khan, A.; Kyberd, P.; Leggat, D.; Leslie, D.; Reid, I. D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Dittmann, J.; Hatakeyama, K.; Kasmi, A.; Liu, H.; Pastika, N.; Scarborough, T.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Avetisyan, A.; Bose, T.; Fantasia, C.; Gastler, D.; Lawson, P.; Rankin, D.; Richardson, C.; Rohlf, J.; St. John, J.; Sulak, L.; Zou, D.; Alimena, J.; Berry, E.; Bhattacharya, S.; Cutts, D.; Demiragli, Z.; Dhingra, N.; Ferapontov, A.; Garabedian, A.; Heintz, U.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Sagir, S.; Sinthuprasith, T.; Breedon, R.; Breto, G.; Calderon De La Barca Sanchez, M.; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Gardner, M.; Ko, W.; Lander, R.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Cousins, R.; Everaerts, P.; Farrell, C.; Hauser, J.; Ignatenko, M.; Rakness, G.; Saltzberg, D.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Rikova, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Luthra, A.; Malberti, M.; Olmedo Negrete, M.; Shrinivas, A.; Sumowidagdo, S.; Wei, H.; Wimpenny, S.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Holzner, A.; Kelley, R.; Klein, D.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Tu, Y.; Vartak, A.; Wasserbaech, S.; Welke, C.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Barge, D.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Incandela, J.; Justus, C.; Mccoll, N.; Mullin, S. D.; Richman, J.; Stuart, D.; To, W.; West, C.; Yoo, J.; Anderson, D.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Pierini, M.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.; Azzolini, V.; Calamba, A.; Carlson, B.; Ferguson, T.; Iiyama, Y.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Gaz, A.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Nauenberg, U.; Smith, J. G.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chaves, J.; Chu, J.; Dittmer, S.; Eggert, N.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Sun, W.; Tan, S. M.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Wittich, P.; Abdullin, S.; Albrow, M.; Anderson, J.; Apollinari, G.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hirschauer, J.; Hooberman, B.; Hu, Z.; Jindariani, S.; Johnson, M.; Joshi, U.; Jung, A. W.; Klima, B.; Kreis, B.; Kwan, S.; Lammel, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mishra, K.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Prokofyev, O.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Whitbeck, A.; Yang, F.; Yin, H.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Di Giovanni, G. P.; Field, R. D.; Fisher, M.; Furic, I. K.; Hugon, J.; Konigsberg, J.; Korytov, A.; Kypreos, T.; Low, J. F.; Ma, P.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Muniz, L.; Rank, D.; Shchutska, L.; Snowball, M.; Sperka, D.; Wang, S. J.; Yelton, J.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Veeraraghavan, V.; Weinberg, M.; Bhopatkar, V.; Hohlmann, M.; Kalakhety, H.; Mareskas-palcek, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, I. D.; Silkworth, C.; Turner, P.; Varelas, N.; Wu, Z.; Zakaria, M.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Sen, S.; Snyder, C.; Tan, P.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Nash, K.; Osherson, M.; Swartz, M.; Xiao, M.; Xin, Y.; Baringer, P.; Bean, A.; Benelli, G.; Bruner, C.; Gray, J.; Kenny, R. P., III; Majumder, D.; Malek, M.; Murray, M.; Noonan, D.; Sanders, S.; Stringer, R.; Wang, Q.; Wood, J. S.; Chakaberia, I.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Saini, L. K.; Skhirtladze, N.; Svintradze, I.; Toda, S.; Lange, D.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Pedro, K.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Baty, A.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Mcginn, C.; Niu, X.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Stephans, G. S. F.; Sumorok, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Dahmes, B.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Lazo-Flores, J.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Hahn, K. A.; Kubik, A.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Trovato, M.; Velasco, M.; Won, S.; Brinkerhoff, A.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Lynch, S.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Pearson, T.; Planer, M.; Ruchti, R.; Smith, G.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Kotov, K.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Quan, X.; Saka, H.; Stickland, D.; Tully, C.; Werner, J. S.; Zuranski, A.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, K.; Kress, M.; Leonardo, N.; Miller, D. H.; Neumeister, N.; Primavera, F.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Zablocki, J.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Goldenzweig, P.; Han, J.; Harel, A.; Hindrichs, O.; Khukhunaishvili, A.; Petrillo, G.; Verzetti, M.; Vishnevskiy, D.; Demortier, L.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Duggan, D.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Panwalkar, S.; Park, M.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; Riley, G.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Flanagan, W.; Gilmore, J.; Kamon, T.; Krutelyov, V.; Montalvo, R.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Roe, J.; Rose, A.; Safonov, A.; Suarez, I.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Boutle, S.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Christian, A.; Dasu, S.; Dodd, L.; Duric, S.; Friis, E.; Gomber, B.; Grothe, M.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ross, I.; Ruggles, T.; Sarangi, T.; Savin, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.

2016-01-01

A search for the pair production of new light bosons, each decaying into a pair of muons, is performed with the CMS experiment at the LHC, using a dataset corresponding to an integrated luminosity of 20.7 fb-1 collected in proton-proton collisions at center-of-mass energy of √{ s} = 8 TeV. No excess is observed in the data relative to standard model background expectation and a model independent upper limit on the product of the cross section, branching fraction, and acceptance is derived. The results are compared with two benchmark models, the first one in the context of the next-to-minimal supersymmetric standard model, and the second one in scenarios containing a hidden sector, including those predicting a nonnegligible light boson lifetime.

Modeling and Classifying Six-Dimensional Trajectories for Teleoperation Under a Time Delay

NASA Technical Reports Server (NTRS)

SunSpiral, Vytas; Wheeler, Kevin R.; Allan, Mark B.; Martin, Rodney

2006-01-01

Within the context of teleoperating the JSC Robonaut humanoid robot under 2-10 second time delays, this paper explores the technical problem of modeling and classifying human motions represented as six-dimensional (position and orientation) trajectories. A dual path research agenda is reviewed which explored both deterministic approaches and stochastic approaches using Hidden Markov Models. Finally, recent results are shown from a new model which represents the fusion of these two research paths. Questions are also raised about the possibility of automatically generating autonomous actions by reusing the same predictive models of human behavior to be the source of autonomous control. This approach changes the role of teleoperation from being a stand-in for autonomy into the first data collection step for developing generative models capable of autonomous control of the robot.

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.

Quantum mechanics and hidden superconformal symmetry

NASA Astrophysics Data System (ADS)

Bonezzi, R.; Corradini, O.; Latini, E.; Waldron, A.

2017-12-01

Solvability of the ubiquitous quantum harmonic oscillator relies on a spectrum generating osp (1 |2 ) superconformal symmetry. We study the problem of constructing all quantum mechanical models with a hidden osp (1 |2 ) symmetry on a given space of states. This problem stems from interacting higher spin models coupled to gravity. In one dimension, we show that the solution to this problem is the Vasiliev-Plyushchay family of quantum mechanical models with hidden superconformal symmetry obtained by viewing the harmonic oscillator as a one dimensional Dirac system, so that Grassmann parity equals wave function parity. These models—both oscillator and particlelike—realize all possible unitary irreducible representations of osp (1 |2 ).

Five-Year-Olds' Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study.

PubMed

Arslan, Burcu; Taatgen, Niels A; Verbrugge, Rineke

2017-01-01

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.

Five-Year-Olds’ Systematic Errors in Second-Order False Belief Tasks Are Due to First-Order Theory of Mind Strategy Selection: A Computational Modeling Study

PubMed Central

Arslan, Burcu; Taatgen, Niels A.; Verbrugge, Rineke

2017-01-01

The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy. PMID:28293206

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.

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

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.

NPLOT: an Interactive Plotting Program for NASTRAN Finite Element Models

NASA Technical Reports Server (NTRS)

Jones, G. K.; Mcentire, K. J.

1985-01-01

The NPLOT (NASTRAN Plot) is an interactive computer graphics program for plotting undeformed and deformed NASTRAN finite element models. Developed at NASA's Goddard Space Flight Center, the program provides flexible element selection and grid point, ASET and SPC degree of freedom labelling. It is easy to use and provides a combination menu and command driven user interface. NPLOT also provides very fast hidden line and haloed line algorithms. The hidden line algorithm in NPLOT proved to be both very accurate and several times faster than other existing hidden line algorithms. A fast spatial bucket sort and horizon edge computation are used to achieve this high level of performance. The hidden line and the haloed line algorithms are the primary features that make NPLOT different from other plotting programs.

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.

HIPPI: highly accurate protein family classification with ensembles of HMMs.

PubMed

Nguyen, Nam-Phuong; Nute, Michael; Mirarab, Siavash; Warnow, Tandy

2016-11-11

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .

Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth; Sivanesan, S

2006-08-25

Pseudo second order kinetic expressions of Ho, Sobkowsk and Czerwinski, Blanachard et al. and Ritchie were fitted to the experimental kinetic data of malachite green onto activated carbon by non-linear and linear method. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo second order model were the same. Non-linear regression analysis showed that both Blanachard et al. and Ho have similar ideas on the pseudo second order model but with different assumptions. The best fit of experimental data in Ho's pseudo second order expression by linear and non-linear regression method showed that Ho pseudo second order model was a better kinetic expression when compared to other pseudo second order kinetic expressions. The amount of dye adsorbed at equilibrium, q(e), was predicted from Ho pseudo second order expression and were fitted to the Langmuir, Freundlich and Redlich Peterson expressions by both linear and non-linear method to obtain the pseudo isotherms. The best fitting pseudo isotherm was found to be the Langmuir and Redlich Peterson isotherm. Redlich Peterson is a special case of Langmuir when the constant g equals unity.

Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

PubMed

Polur, Prasad D; Miller, Gerald E

2006-10-01

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

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.

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

PubMed Central

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons. PMID:26880875

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks.

PubMed

Maca, Petr; Pech, Pavel

2016-01-01

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948-2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Hidden Order and Symmetry Protected Topological States in Quantum Link Ladders

NASA Astrophysics Data System (ADS)

Cardarelli, L.; Greschner, S.; Santos, L.

2017-11-01

We show that, whereas spin-1 /2 one-dimensional U(1) quantum-link models (QLMs) are topologically trivial, when implemented in ladderlike lattices these models may present an intriguing ground-state phase diagram, which includes a symmetry protected topological (SPT) phase that may be readily revealed by analyzing long-range string spin correlations along the ladder legs. We propose a simple scheme for the realization of spin-1 /2 U(1) QLMs based on single-component fermions loaded in an optical lattice with s and p bands, showing that the SPT phase may be experimentally realized by adiabatic preparation.

Diagnosis and Management of Hidden Caries in a Primary Molar Tooth.

PubMed

Gera, Arwa; Zilberman, Uri

2017-01-01

Hidden caries is a dentinal lesion beneath the dentinoenamel junction, visible on radiographs. A single report described this lesion in primary dentition. This case report describes a case of hidden caries in a mandibular second primary molar, misdiagnosed as malignant swelling. A 3-year-old white girl was referred to the Department of Pediatric Dentistry with a chief complaint of pain and extraoral swelling on the right side of the mandible for the last 3 months. She was earlier referred to the surgical department for biopsy of the lesion. Radiographic and computed tomography scan examination showed a periapical lesion with buccal plate resorption and radiolucency beneath the enamel on the mesial part of tooth 85. The tooth was extracted, and follow-up of 2 years showed normal development of tooth 45. The main problem is early detection and treatment, since the outer surface of enamel may appear intact on tactile examination. Gera A, Zilberman U. Diagnosis and Management of Hidden Caries in a Primary Molar Tooth. Int J Clin Pediatr Dent 2017;10(1):99-102.

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.

Numerical Analysis of Modeling Based on Improved Elman Neural Network

PubMed Central

Jie, Shao

2014-01-01

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance. PMID:25054172

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.

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

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.

DNA base-calling from a nanopore using a Viterbi algorithm.

PubMed

Timp, Winston; Comer, Jeffrey; Aksimentiev, Aleksei

2012-05-16

Nanopore-based DNA sequencing is the most promising third-generation sequencing method. It has superior read length, speed, and sample requirements compared with state-of-the-art second-generation methods. However, base-calling still presents substantial difficulty because the resolution of the technique is limited compared with the measured signal/noise ratio. Here we demonstrate a method to decode 3-bp-resolution nanopore electrical measurements into a DNA sequence using a Hidden Markov model. This method shows tremendous potential for accuracy (~98%), even with a poor signal/noise ratio. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

The basis function approach for modeling autocorrelation in ecological data.

PubMed

Hefley, Trevor J; Broms, Kristin M; Brost, Brian M; Buderman, Frances E; Kay, Shannon L; Scharf, Henry R; Tipton, John R; Williams, Perry J; Hooten, Mevin B

2017-03-01

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data. © 2016 by the Ecological Society of America.

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.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

PubMed

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

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.

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.

Extracting volatility signal using maximum a posteriori estimation

NASA Astrophysics Data System (ADS)

Neto, David

2016-11-01

This paper outlines a methodology to estimate a denoised volatility signal for foreign exchange rates using a hidden Markov model (HMM). For this purpose a maximum a posteriori (MAP) estimation is performed. A double exponential prior is used for the state variable (the log-volatility) in order to allow sharp jumps in realizations and then log-returns marginal distributions with heavy tails. We consider two routes to choose the regularization and we compare our MAP estimate to realized volatility measure for three exchange rates.

Hidden order in crackling noise during peeling of an adhesive tape.

PubMed

Kumar, Jagadish; Ciccotti, M; Ananthakrishna, G

2008-04-01

We address the longstanding problem of recovering dynamical information from noisy acoustic emission signals arising from peeling of an adhesive tape subject to constant traction velocity. Using the phase space reconstruction procedure we demonstrate the deterministic chaotic dynamics by establishing the existence of correlation dimension as also a positive Lyapunov exponent in a midrange of traction velocities. The results are explained on the basis of the model that also emphasizes the deterministic origin of acoustic emission by clarifying its connection to stick-slip dynamics.

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...

Contesting the Violence of Tylerism: Toward a Cosmopolitan Approach to the Curriculum of Second Language Teacher Education

ERIC Educational Resources Information Center

Anwaruddin, Sardar M.

2016-01-01

While the field of second language teacher education (SLTE) has expanded noticeably over the past decade, little attention has been paid to specific approaches to its curriculum. Taking a hidden-curriculum approach, I show that the curriculum of SLTE is focused predominantly on the content, method and assessment of teaching, which I describe as…

Behavioral and Temporal Pattern Detection Within Financial Data With Hidden Information

DTIC Science & Technology

2012-02-01

probabilistic pattern detector to monitor the pattern. 15. SUBJECT TERMS Runtime verification, Hidden data, Hidden Markov models, Formal specifications...sequences in many other fields besides financial systems [L, TV, LC, LZ ]. Rather, the technique suggested in this paper is positioned as a hybrid...operation of the pattern detector . Section 7 describes the operation of the probabilistic pattern-matching monitor, and section 8 describes three

2015 Cataloging Hidden Special Collections and Archives Unconference and Symposium: Innovation, Collaboration, and Models. Proceedings of the CLIR Cataloging Hidden Special Collections and Archives Symposium (Philadelphia, Pennsylvania, March 12-13, 2015)

ERIC Educational Resources Information Center

Oestreicher, Cheryl, Ed.

2015-01-01

The 2015 CLIR Unconference & Symposium was the capstone event to seven years of grant funding through CLIR's Cataloging Hidden Special Collections and Archives program. These proceedings group presentations by theme. Collaborations provides examples of multi-institutional projects, including one international collaboration; Student and Faculty…

An alternative assessment of second-order closure models in turbulent shear flows

NASA Technical Reports Server (NTRS)

Speziale, Charles G.; Gatski, Thomas B.

1994-01-01

The performance of three recently proposed second-order closure models is tested in benchmark turbulent shear flows. Both homogeneous shear flow and the log-layer of an equilibrium turbulent boundary layer are considered for this purpose. An objective analysis of the results leads to an assessment of these models that stands in contrast to that recently published by other authors. A variety of pitfalls in the formulation and testing of second-order closure models are uncovered by this analysis.

Using chemical organization theory for model checking

PubMed Central

Kaleta, Christoph; Richter, Stephan; Dittrich, Peter

2009-01-01

Motivation: The increasing number and complexity of biomodels makes automatic procedures for checking the models' properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws. Results: First, we introduce an algorithm that uncovers stoichiometric information that might be hidden in the kinetic laws of a reaction network. This allows us to apply OT to SBML models using modifiers. Second, using the new algorithm, we performed a large-scale analysis of the 185 models contained in the manually curated BioModels Database. We found that for 41 models (22%) the set of organizations changes when modifiers are considered correctly. We discuss one of these models in detail (BIOMD149, a combined model of the ERK- and Wnt-signaling pathways), whose set of organizations drastically changes when modifiers are considered. Third, we found inconsistencies in 5 models (3%) and identified their characteristics. Compared with flux-based methods, OT is able to identify those species and reactions more accurately [in 26 cases (14%)] that can be present in a long-term simulation of the model. We conclude that our approach is a valuable tool that helps to improve the consistency of biomodels and their repositories. Availability: All data and a JAVA applet to check SBML-models is available from http://www.minet.uni-jena.de/csb/prj/ot/tools Contact: dittrich@minet.uni-jena.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19468053

Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

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.

Hidden complexity of free energy surfaces for peptide (protein) folding.

PubMed

Krivov, Sergei V; Karplus, Martin

2004-10-12

An understanding of the thermodynamics and kinetics of protein folding requires a knowledge of the free energy surface governing the motion of the polypeptide chain. Because of the many degrees of freedom involved, surfaces projected on only one or two progress variables are generally used in descriptions of the folding reaction. Such projections result in relatively smooth surfaces, but they could mask the complexity of the unprojected surface. Here we introduce an approach to determine the actual (unprojected) free energy surface and apply it to the second beta-hairpin of protein G, which has been used as a model system for protein folding. The surface is represented by a disconnectivity graph calculated from a long equilibrium folding-unfolding trajectory. The denatured state is found to have multiple low free energy basins. Nevertheless, the peptide shows exponential kinetics in folding to the native basin. Projected surfaces obtained from the present analysis have a simple form in agreement with other studies of the beta-hairpin. The hidden complexity found for the beta-hairpin surface suggests that the standard funnel picture of protein folding should be revisited.

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.

Estimating Density and Temperature Dependence of Juvenile Vital Rates Using a Hidden Markov Model

PubMed Central

McElderry, Robert M.

2017-01-01

Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I have included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the preferred model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild. PMID:28505138

Multiple Detector Optimization for Hidden Radiation Source Detection

DTIC Science & Technology

2015-03-26

important in achieving operationally useful methods for optimizing detector emplacement, the 2-D attenuation model approach promises to speed up the...process of hidden source detection significantly. The model focused on detection of the full energy peak of a radiation source. Methods to optimize... radioisotope identification is possible without using a computationally intensive stochastic model such as the Monte Carlo n-Particle (MCNP) code

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

ERIC Educational Resources Information Center

Stifter, Cynthia A.; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…

Low-lying 1/2- hidden strange pentaquark states in the constituent quark model

NASA Astrophysics Data System (ADS)

Li, Hui; Wu, Zong-Xiu; An, Chun-Sheng; Chen, Hong

2017-12-01

We investigate the spectrum of the low-lying 1/2- hidden strange pentaquark states, employing the constituent quark model, and looking at two ways within that model of mediating the hyperfine interaction between quarks - Goldstone boson exchange and one gluon exchange. Numerical results show that the lowest 1/2- hidden strange pentaquark state in the Goldstone boson exchange model lies at ˜1570 MeV, so this pentaquark configuration may form a notable component in S 11(1535) if the Goldstone boson exchange model is applied. This is consistent with the prediction that S 11(1535) couples very strongly to strangeness channels. Supported by National Natural Science Foundation of China (11675131, 11645002), Chongqing Natural Science Foundation (cstc2015jcyjA00032) and Fundamental Research Funds for the Central Universities (SWU115020)

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

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

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.

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

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…

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.

Two-dimensional hidden semantic information model for target saliency detection and eyetracking identification

NASA Astrophysics Data System (ADS)

Wan, Weibing; Yuan, Lingfeng; Zhao, Qunfei; Fang, Tao

2018-01-01

Saliency detection has been applied to the target acquisition case. This paper proposes a two-dimensional hidden Markov model (2D-HMM) that exploits the hidden semantic information of an image to detect its salient regions. A spatial pyramid histogram of oriented gradient descriptors is used to extract features. After encoding the image by a learned dictionary, the 2D-Viterbi algorithm is applied to infer the saliency map. This model can predict fixation of the targets and further creates robust and effective depictions of the targets' change in posture and viewpoint. To validate the model with a human visual search mechanism, two eyetrack experiments are employed to train our model directly from eye movement data. The results show that our model achieves better performance than visual attention. Moreover, it indicates the plausibility of utilizing visual track data to identify targets.

A search for pair production of new light bosons decaying into muons

DOE PAGES

Khachatryan, Vardan

2015-11-03

In this study, a search for the pair production of new light bosons, each decaying into a pair of muons, is performed with the CMS experiment at the LHC, using a dataset corresponding to an integrated luminosity of 20.7 fb –1 collected in proton–proton collisions at center-of-mass energy of √s = 8 TeV. No excess is observed in the data relative to standard model background expectation and a model independent upper limit on the product of the cross section, branching fraction, and acceptance is derived. The results are compared with two benchmark models, the first one in the context ofmore » the next-to-minimal supersymmetric standard model, and the second one in scenarios containing a hidden sector, including those predicting a nonnegligible light boson lifetime.« less

Prediction of temperature and HAZ in thermal-based processes with Gaussian heat source by a hybrid GA-ANN model

NASA Astrophysics Data System (ADS)

Fazli Shahri, Hamid Reza; Mahdavinejad, Ramezanali

2018-02-01

Thermal-based processes with Gaussian heat source often produce excessive temperature which can impose thermally-affected layers in specimens. Therefore, the temperature distribution and Heat Affected Zone (HAZ) of materials are two critical factors which are influenced by different process parameters. Measurement of the HAZ thickness and temperature distribution within the processes are not only difficult but also expensive. This research aims at finding a valuable knowledge on these factors by prediction of the process through a novel combinatory model. In this study, an integrated Artificial Neural Network (ANN) and genetic algorithm (GA) was used to predict the HAZ and temperature distribution of the specimens. To end this, a series of full factorial design of experiments were conducted by applying a Gaussian heat flux on Ti-6Al-4 V at first, then the temperature of the specimen was measured by Infrared thermography. The HAZ width of each sample was investigated through measuring the microhardness. Secondly, the experimental data was used to create a GA-ANN model. The efficiency of GA in design and optimization of the architecture of ANN was investigated. The GA was used to determine the optimal number of neurons in hidden layer, learning rate and momentum coefficient of both output and hidden layers of ANN. Finally, the reliability of models was assessed according to the experimental results and statistical indicators. The results demonstrated that the combinatory model predicted the HAZ and temperature more effective than a trial-and-error ANN model.

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.

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.

Experimental Design for the Evaluation of Detection Techniques of Hidden Corrosion Beneath the Thermal Protective System of the Space Shuttle Orbiter

NASA Technical Reports Server (NTRS)

Kemmerer, Catherine C.; Jacoby, Joseph A.; Lomness, Janice K.; Hintze, Paul E.; Russell, Richard W.

2007-01-01

The detection of corrosion beneath Space Shuttle Orbiter thermal protective system is traditionally accomplished by removing the Reusable Surface Insulation tiles and performing a visual inspection of the aluminum substrate and corrosion protection system. This process is time consuming and has the potential to damage high cost tiles. To evaluate non-intrusive NDE methods, a Proof of Concept (PoC) experiment was designed and test panels were manufactured. The objective of the test plan was three-fold: establish the ability to detect corrosion hidden from view by tiles; determine the key factor affecting detectability; roughly quantify the detection threshold. The plan consisted of artificially inducing dimensionally controlled corrosion spots in two panels and rebonding tile over the spots to model the thermal protective system of the orbiter. The corrosion spot diameter ranged from 0.100" to 0.600" inches and the depth ranged from 0.003" to 0.020". One panel consisted of a complete factorial array of corrosion spots with and without tile coverage. The second panel consisted of randomized factorial points replicated and hidden by tile. Conventional methods such as ultrasonics, infrared, eddy current and microwave methods have shortcomings. Ultrasonics and IR cannot sufficiently penetrate the tiles, while eddy current and microwaves have inadequate resolution. As such, the panels were interrogated using Backscatter Radiography and Terahertz Imaging. The terahertz system successfully detected artificially induced corrosion spots under orbiter tile and functional testing is in-work in preparation for implementation.

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

Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods.

PubMed

Ho, Yuh-Shan

2006-01-01

A comparison was made of the linear least-squares method and a trial-and-error non-linear method of the widely used pseudo-second-order kinetic model for the sorption of cadmium onto ground-up tree fern. Four pseudo-second-order kinetic linear equations are discussed. Kinetic parameters obtained from the four kinetic linear equations using the linear method differed but they were the same when using the non-linear method. A type 1 pseudo-second-order linear kinetic model has the highest coefficient of determination. Results show that the non-linear method may be a better way to obtain the desired parameters.

Orangutans (Pongo abelii) seek information about tool functionality in a metacognition tubes task.

PubMed

Mulcahy, Nicholas J

2016-11-01

Nonhuman primates appear to engage in metacognition by knowing when they need to search for relevant information for solving the tubes task. The task involves presenting subjects with a number of tubes with only 1 having food hidden inside. Before choosing, subjects look inside the tubes more often when they do not know which 1 contains the food (hidden trials) compared to when they do know this information (visible trials). It is argued, however, that nonmetacognitive general food searching strategies can explain this looking behavior. To address this issue, 3 orangutans were tested with a novel tubes task in which they were only required to seek information about tool functionality. The results showed that subjects had the ability to search for tool functionality but no subject looked significantly more in hidden trials compared to visible trials. Subjects were retested with the same condition and given a second condition in which the cost of a wrong choice was increased. In both conditions, 2 subjects looked significantly more inside the hidden trials compared to the visible trials. Subjects were also tested with the traditional tubes task in which food was hidden inside 1 tube. All subjects looked inside the tubes significantly more in the hidden trials compared to the visible trials. However, subjects conducted more excessive looks compared to when looking for tool functionality. I suggest that excessive searches may be caused by food being a strong stimulus and discuss the relevance of this possibility for metacognitive research involving the tubes task. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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).

All quantum observables in a hidden-variable model must commute simultaneously

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

Malley, James D.

Under a standard set of assumptions for a hidden-variable model for quantum events we show that all observables must commute simultaneously. This seems to be an ultimate statement about the inapplicability of the usual hidden-variable model for quantum events. And, despite Bell's complaint that a key condition of von Neumann's was quite unrealistic, we show that these conditions, under which von Neumann produced the first no-go proof, are entirely equivalent to those introduced by Bell and Kochen and Specker. As these conditions are also equivalent to those under which the Bell-Clauster-Horne inequalities are derived, we see that the experimental violationsmore » of the inequalities demonstrate only that quantum observables do not commute.« less

Fairness Considerations When I Know More than You Do: Developmental Comparisons

PubMed Central

Overgaauw, Sandy; Güroğlu, Berna; Crone, Eveline A.

2012-01-01

The Ultimatum Game (UG) is a valuable paradigm to study fairness considerations. Here, we tested developmental differences between altruistic and strategic motivations in fairness considerations using a version of the UG with hidden conditions. Participants were proposers and could divide coins between themselves and an anonymous other. Hidden information conditions involved division of coins where some coins were only visible to the participant (e.g., 8/2 condition where, from the total of 10 coins, 8 coins were visible to both players and 2 coins only visible to the proposer). In total, 22 young adults and 79 children between ages 8 and 13 played multiple one-shot versions of the UG with hidden conditions with anonymous others. Overall analyses confirmed validity of the task and showed that participants of all age groups had strategic intentions. Specific task analyses revealed that adults divided the coins equally in the standard UG conditions, but gave less to the second player in the hidden information conditions. The developmental comparisons revealed an age × condition interaction, such that adults and 10- to 12-year-old children differentiated between standard and hidden conditions more than 8- to 9-year-old children. These findings indicate that young children have a basic understanding of different strategic motives, but that behavior of adults and older children is driven more by strategic intentions. PMID:23162494

Quantum gap and spin-wave excitations in the Kitaev model on a triangular lattice

NASA Astrophysics Data System (ADS)

Avella, Adolfo; Di Ciolo, Andrea; Jackeli, George

2018-05-01

We study the effects of quantum fluctuations on the dynamical generation of a gap and on the evolution of the spin-wave spectra of a frustrated magnet on a triangular lattice with bond-dependent Ising couplings, analog of the Kitaev honeycomb model. The quantum fluctuations lift the subextensive degeneracy of the classical ground-state manifold by a quantum order-by-disorder mechanism. Nearest-neighbor chains remain decoupled and the surviving discrete degeneracy of the ground state is protected by a hidden model symmetry. We show how the four-spin interaction, emergent from the fluctuations, generates a spin gap shifting the nodal lines of the linear spin-wave spectrum to finite energies.

Inflation, symmetry, and B-modes

DOE PAGES

Hertzberg, Mark P.

2015-04-20

Here, we examine the role of using symmetry and effective field theory in inflationary model building. We describe the standard formulation of starting with an approximate shift symmetry for a scalar field, and then introducing corrections systematically in order to maintain control over the inflationary potential. We find that this leads to models in good agreement with recent data. On the other hand, there are attempts in the literature to deviate from this paradigm by envoking other symmetries and corrections. In particular: in a suite of recent papers, several authors have made the claim that standard Einstein gravity with amore » cosmological constant and a massless scalar carries conformal symmetry. They claim this conformal symmetry is hidden when the action is written in the Einstein frame, and so has not been fully appreciated in the literature. They further claim that such a theory carries another hidden symmetry; a global SO(1,1) symmetry. By deforming around the global SO(1,1) symmetry, they are able to produce a range of inflationary models with asymptotically flat potentials, whose flatness is claimed to be protected by these symmetries. These models tend to give rise to B-modes with small amplitude. Here we explain that standard Einstein gravity does not in fact possess conformal symmetry. Instead these authors are merely introducing a redundancy into the description, not an actual conformal symmetry. Furthermore, we explain that the only real (global) symmetry in these models is not at all hidden, but is completely manifest when expressed in the Einstein frame; it is in fact the shift symmetry of a scalar field. When analyzed systematically as an effective field theory, deformations do not generally produce asymptotically flat potentials and small B-modes as suggested in these recent papers. Instead, deforming around the shift symmetry systematically, tends to produce models of inflation with B-modes of appreciable amplitude. Such simple models typically also produce the observed red spectral index, Gaussian fluctuations, etc. In short: simple models of inflation, organized by expanding around a shift symmetry, are in excellent agreement with recent data.« less

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

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.

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...

Hidden magnetism in periodically modulated one dimensional dipolar fermions

NASA Astrophysics Data System (ADS)

Fazzini, S.; Montorsi, A.; Roncaglia, M.; Barbiero, L.

2017-12-01

The experimental realization of time-dependent ultracold lattice systems has paved the way towards the implementation of new Hubbard-like Hamiltonians. We show that in a one-dimensional two-components lattice dipolar Fermi gas the competition between long range repulsion and correlated hopping induced by periodically modulated on-site interaction allows for the formation of hidden magnetic phases, with degenerate protected edge modes. The magnetism, characterized solely by string-like nonlocal order parameters, manifests in the charge and/or in the spin degrees of freedom. Such behavior is enlighten by employing Luttinger liquid theory and numerical methods. The range of parameters for which hidden magnetism is present can be reached by means of the currently available experimental setups and probes.

Modeling Ability Differentiation in the Second-Order Factor Model

ERIC Educational Resources Information Center

Molenaar, Dylan; Dolan, Conor V.; van der Maas, Han L. J.

2011-01-01

In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid-Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals,…

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.

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.

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.

Impact-acoustics inspection of tile-wall bonding integrity via wavelet transform and hidden Markov models

NASA Astrophysics Data System (ADS)

Luk, B. L.; Liu, K. P.; Tong, F.; Man, K. F.

2010-05-01

The impact-acoustics method utilizes different information contained in the acoustic signals generated by tapping a structure with a small metal object. It offers a convenient and cost-efficient way to inspect the tile-wall bonding integrity. However, the existence of the surface irregularities will cause abnormal multiple bounces in the practical inspection implementations. The spectral characteristics from those bounces can easily be confused with the signals obtained from different bonding qualities. As a result, it will deteriorate the classic feature-based classification methods based on frequency domain. Another crucial difficulty posed by the implementation is the additive noise existing in the practical environments that may also cause feature mismatch and false judgment. In order to solve this problem, the work described in this paper aims to develop a robust inspection method that applies model-based strategy, and utilizes the wavelet domain features with hidden Markov modeling. It derives a bonding integrity recognition approach with enhanced immunity to surface roughness as well as the environmental noise. With the help of the specially designed artificial sample slabs, experiments have been carried out with impact acoustic signals contaminated by real environmental noises acquired under practical inspection background. The results are compared with those using classic method to demonstrate the effectiveness of the proposed method.

Enhanced axion-photon coupling in GUT with hidden photon

NASA Astrophysics Data System (ADS)

Daido, Ryuji; Takahashi, Fuminobu; Yokozaki, Norimi

2018-05-01

We show that the axion coupling to photons can be enhanced in simple models with a single Peccei-Quinn field, if the gauge coupling unification is realized by a large kinetic mixing χ = O (0.1) between hypercharge and unbroken hidden U(1)H. The key observation is that the U(1)H gauge coupling should be rather strong to induce such large kinetic mixing, leading to enhanced contributions of hidden matter fields to the electromagnetic anomaly. We find that the axion-photon coupling is enhanced by about a factor of 10-100 with respect to the GUT-axion models with E / N = 8 / 3.

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.

Identification of related gene/protein names based on an HMM of name variations.

PubMed

Yeganova, L; Smith, L; Wilbur, W J

2004-04-01

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a special case of the problem of named entity recognition, while the second is a special case of the problem of automatic term recognition (ATR). We study the second problem, that of gene or protein name variation. Here we describe a system which, given a query gene or protein name, identifies related gene or protein names in a large list. The system is based on a dynamic programming algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a fully trainable hidden Markov model.

Bayesian model selection applied to artificial neural networks used for water resources modeling

NASA Astrophysics Data System (ADS)

Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.

2008-04-01

Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.

Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Bandyopadhyay, Goutami

2007-01-01

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.

New second order Mumford-Shah model based on Γ-convergence approximation for image processing

NASA Astrophysics Data System (ADS)

Duan, Jinming; Lu, Wenqi; Pan, Zhenkuan; Bai, Li

2016-05-01

In this paper, a second order variational model named the Mumford-Shah total generalized variation (MSTGV) is proposed for simultaneously image denoising and segmentation, which combines the original Γ-convergence approximated Mumford-Shah model with the second order total generalized variation (TGV). For image denoising, the proposed MSTGV can eliminate both the staircase artefact associated with the first order total variation and the edge blurring effect associated with the quadratic H1 regularization or the second order bounded Hessian regularization. For image segmentation, the MSTGV can obtain clear and continuous boundaries of objects in the image. To improve computational efficiency, the implementation of the MSTGV does not directly solve its high order nonlinear partial differential equations and instead exploits the efficient split Bregman algorithm. The algorithm benefits from the fast Fourier transform, analytical generalized soft thresholding equation, and Gauss-Seidel iteration. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed model.

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.

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

Selfishness as second-order altruism

PubMed Central

Eldakar, Omar Tonsi; Wilson, David Sloan

2008-01-01

Selfishness is seldom considered a group-beneficial strategy. In the typical evolutionary formulation, altruism benefits the group, selfishness undermines altruism, and the purpose of the model is to identify mechanisms, such as kinship or reciprocity, that enable altruism to evolve. Recent models have explored punishment as an important mechanism favoring the evolution of altruism, but punishment can be costly to the punisher, making it a form of second-order altruism. This model identifies a strategy called “selfish punisher” that involves behaving selfishly in first-order interactions and altruistically in second-order interactions by punishing other selfish individuals. Selfish punishers cause selfishness to be a self-limiting strategy, enabling altruists to coexist in a stable equilibrium. This polymorphism can be regarded as a division of labor, or mutualism, in which the benefits obtained by first-order selfishness help to “pay” for second-order altruism. PMID:18448681

Hidden Markov models for character recognition.

PubMed

Vlontzos, J A; Kung, S Y

1992-01-01

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

Population decoding of motor cortical activity using a generalized linear model with hidden states.

PubMed

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

2010-06-15

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

PubMed Central

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

2010-01-01

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

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.

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.

Nonlinear identification of the total baroreflex arc: higher-order nonlinearity

PubMed Central

Moslehpour, Mohsen; Kawada, Toru; Sunagawa, Kenji; Sugimachi, Masaru

2016-01-01

The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). The nonlinear dynamics of this system were recently characterized. First, Gaussian white noise CSP stimulation was employed in open-loop conditions in normotensive and hypertensive rats with sectioned vagal and aortic depressor nerves. Nonparametric system identification was then applied to measured CSP and AP to establish a second-order nonlinear Uryson model. The aim in this study was to assess the importance of higher-order nonlinear dynamics via development and evaluation of a third-order nonlinear model of the total arc using the same experimental data. Third-order Volterra and Uryson models were developed by employing nonparametric and parametric identification methods. The R2 values between the AP predicted by the best third-order Volterra model and measured AP in response to Gaussian white noise CSP not utilized in developing the model were 0.69 ± 0.03 and 0.70 ± 0.03 for normotensive and hypertensive rats, respectively. The analogous R2 values for the best third-order Uryson model were 0.71 ± 0.03 and 0.73 ± 0.03. These R2 values were not statistically different from the corresponding values for the previously established second-order Uryson model, which were both 0.71 ± 0.03 (P > 0.1). Furthermore, none of the third-order models predicted well-known nonlinear behaviors including thresholding and saturation better than the second-order Uryson model. Additional experiments suggested that the unexplained AP variance was partly due to higher brain center activity. In conclusion, the second-order Uryson model sufficed to represent the sympathetically mediated total arc under the employed experimental conditions. PMID:27629885

Optimal Product Placement.

PubMed

Hsu, Chia-Ling; Matta, Rafael; Popov, Sergey V; Sogo, Takeharu

2017-01-01

We model a market, such as an online software market, in which an intermediary connects sellers and buyers by displaying sellers' products. With two vertically-differentiated products, an intermediary can place either: (1) one product, not necessarily the better one, on the first page, and the other hidden on the second page; or (2) both products on the first page. We show that it can be optimal for the intermediary to obfuscate a product-possibly the better one-since this weakens price competition and allows the sellers to extract a greater surplus from buyers; however, it is not socially optimal. The choice of which one to obfuscate depends on the distribution of search costs.

Cascaded Amplitude Modulations in Sound Texture Perception

PubMed Central

McWalter, Richard; Dau, Torsten

2017-01-01

Sound textures, such as crackling fire or chirping crickets, represent a broad class of sounds defined by their homogeneous temporal structure. It has been suggested that the perception of texture is mediated by time-averaged summary statistics measured from early auditory representations. In this study, we investigated the perception of sound textures that contain rhythmic structure, specifically second-order amplitude modulations that arise from the interaction of different modulation rates, previously described as “beating” in the envelope-frequency domain. We developed an auditory texture model that utilizes a cascade of modulation filterbanks that capture the structure of simple rhythmic patterns. The model was examined in a series of psychophysical listening experiments using synthetic sound textures—stimuli generated using time-averaged statistics measured from real-world textures. In a texture identification task, our results indicated that second-order amplitude modulation sensitivity enhanced recognition. Next, we examined the contribution of the second-order modulation analysis in a preference task, where the proposed auditory texture model was preferred over a range of model deviants that lacked second-order modulation rate sensitivity. Lastly, the discriminability of textures that included second-order amplitude modulations appeared to be perceived using a time-averaging process. Overall, our results demonstrate that the inclusion of second-order modulation analysis generates improvements in the perceived quality of synthetic textures compared to the first-order modulation analysis considered in previous approaches. PMID:28955191

Cascaded Amplitude Modulations in Sound Texture Perception.

PubMed

McWalter, Richard; Dau, Torsten

2017-01-01

Sound textures, such as crackling fire or chirping crickets, represent a broad class of sounds defined by their homogeneous temporal structure. It has been suggested that the perception of texture is mediated by time-averaged summary statistics measured from early auditory representations. In this study, we investigated the perception of sound textures that contain rhythmic structure, specifically second-order amplitude modulations that arise from the interaction of different modulation rates, previously described as "beating" in the envelope-frequency domain. We developed an auditory texture model that utilizes a cascade of modulation filterbanks that capture the structure of simple rhythmic patterns. The model was examined in a series of psychophysical listening experiments using synthetic sound textures-stimuli generated using time-averaged statistics measured from real-world textures. In a texture identification task, our results indicated that second-order amplitude modulation sensitivity enhanced recognition. Next, we examined the contribution of the second-order modulation analysis in a preference task, where the proposed auditory texture model was preferred over a range of model deviants that lacked second-order modulation rate sensitivity. Lastly, the discriminability of textures that included second-order amplitude modulations appeared to be perceived using a time-averaging process. Overall, our results demonstrate that the inclusion of second-order modulation analysis generates improvements in the perceived quality of synthetic textures compared to the first-order modulation analysis considered in previous approaches.

A second-order closure analysis of turbulent diffusion flames. [combustion physics

NASA Technical Reports Server (NTRS)

Varma, A. K.; Fishburne, E. S.; Beddini, R. A.

1977-01-01

A complete second-order closure computer program for the investigation of compressible, turbulent, reacting shear layers was developed. The equations for the means and the second order correlations were derived from the time-averaged Navier-Stokes equations and contain third order and higher order correlations, which have to be modeled in terms of the lower-order correlations to close the system of equations. In addition to fluid mechanical turbulence models and parameters used in previous studies of a variety of incompressible and compressible shear flows, a number of additional scalar correlations were modeled for chemically reacting flows, and a typical eddy model developed for the joint probability density function for all the scalars. The program which is capable of handling multi-species, multistep chemical reactions, was used to calculate nonreacting and reacting flows in a hydrogen-air diffusion flame.

Rare Z boson decays to a hidden sector

DOE PAGES

Blinov, Nikita; Izaguirre, Eder; Shuve, Brian

2018-01-18

We demonstrate that rare decays of the Standard Model Z boson can be used to discover and characterize the nature of new hidden-sector particles. We propose new searches for these particles in soft, high-multiplicity leptonic final states at the Large Hadron Collider. The proposed searches are sensitive to low-mass particles produced in Z decays, and we argue that these striking signatures can shed light on the hidden-sector couplings and mechanism for mass generation.

Rare Z boson decays to a hidden sector

DOE PAGES

Blinov, Nikita; Izaguirre, Eder; Shuve, Brian

2018-01-01

We demonstrate that rare decays of the Standard Model Z boson can be used to discover and characterize the nature of new hidden-sector particles. We propose new searches for these particles in soft, high-multiplicity leptonic final states at the Large Hadron Collider. The proposed searches are sensitive to low-mass particles produced in Z decays, and we argue that these striking signatures can shed light on the hidden-sector couplings and mechanism for mass generation.

Rare Z boson decays to a hidden sector

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

Blinov, Nikita; Izaguirre, Eder; Shuve, Brian

We demonstrate that rare decays of the Standard Model Z boson can be used to discover and characterize the nature of new hidden-sector particles. We propose new searches for these particles in soft, high-multiplicity leptonic final states at the Large Hadron Collider. The proposed searches are sensitive to low-mass particles produced in Z decays, and we argue that these striking signatures can shed light on the hidden-sector couplings and mechanism for mass generation.

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.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

PubMed

Ranganayaki, V; Deepa, S N

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

PubMed Central

Ranganayaki, V.; Deepa, S. N.

2016-01-01

Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973

Measurement Invariance of Second-Order Factor Model of the Multifactor Leadership Questionnaire (MLQ) across K-12 Principal Gender

ERIC Educational Resources Information Center

Xu, Lihua; Wubbena, Zane; Stewart, Trae

2016-01-01

Purpose: The purpose of this paper is to investigate the factor structure and the measurement invariance of the Multifactor Leadership Questionnaire (MLQ) across gender of K-12 school principals (n=6,317) in the USA. Design/methodology/approach: Nine first-order factor models and four second-order factor models were tested using confirmatory…

Navier-Stokes computation of compressible turbulent flows with a second order closure, part 1

NASA Technical Reports Server (NTRS)

Haminh, Hieu; Kollmann, Wolfgang; Vandromme, Dany

1990-01-01

A second order closure turbulence model for compressible flows is developed and implemented in a 2D Reynolds-averaged Navier-Stokes solver. From the beginning where a kappa-epsilon turbulence model was implemented in the bidiagonal implicit method of MACCORMACK (referred to as the MAC3 code) to the final stage of implementing a full second order closure in the efficient line Gauss-Seidel algorithm, numerous work was done, individually and collectively. Besides the collaboration itself, the final product of this work is a second order closure derived from the Launder, Reece, and Rodi model to account for near wall effects, which has been called FRAME model, which stands for FRench-AMerican-Effort. During the reporting period, two different problems were worked out. The first was to provide Ames researchers with a reliable compressible boundary layer code including a wide collection of turbulence models for quick testing of new terms, both in two equations and in second order closure (LRR and FRAME). The second topic was to complete the implementation of the FRAME model in the MAC5 code. The work related to these two different contributions is reported. dilatation in presence of stron shocks. This work, which has been conducted during a work at the Center for Turbulence Research with Zeman aimed also to cros-check earlier assumptions by Rubesin and Vandromme.

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

Second-order closure models for supersonic turbulent flows

NASA Technical Reports Server (NTRS)

Speziale, Charles G.; Sarkar, Sutanu

1991-01-01

Recent work by the authors on the development of a second-order closure model for high-speed compressible flows is reviewed. This turbulence closure is based on the solution of modeled transport equations for the Favre-averaged Reynolds stress tensor and the solenoidal part of the turbulent dissipation rate. A new model for the compressible dissipation is used along with traditional gradient transport models for the Reynolds heat flux and mass flux terms. Consistent with simple asymptotic analyses, the deviatoric part of the remaining higher-order correlations in the Reynolds stress transport equation are modeled by a variable density extension of the newest incompressible models. The resulting second-order closure model is tested in a variety of compressible turbulent flows which include the decay of isotropic turbulence, homogeneous shear flow, the supersonic mixing layer, and the supersonic flat-plate turbulent boundary layer. Comparisons between the model predictions and the results of physical and numerical experiments are quite encouraging.

Second-order closure models for supersonic turbulent flows

NASA Technical Reports Server (NTRS)

Speziale, Charles G.; Sarkar, Sutanu

1991-01-01

Recent work on the development of a second-order closure model for high-speed compressible flows is reviewed. This turbulent closure is based on the solution of modeled transport equations for the Favre-averaged Reynolds stress tensor and the solenoidal part of the turbulent dissipation rate. A new model for the compressible dissipation is used along with traditional gradient transport models for the Reynolds heat flux and mass flux terms. Consistent with simple asymptotic analyses, the deviatoric part of the remaining higher-order correlations in the Reynolds stress transport equations are modeled by a variable density extension of the newest incompressible models. The resulting second-order closure model is tested in a variety of compressible turbulent flows which include the decay of isotropic turbulence, homogeneous shear flow, the supersonic mixing layer, and the supersonic flat-plate turbulent boundary layer. Comparisons between the model predictions and the results of physical and numerical experiments are quite encouraging.

Simple Chaotic Flow with Circle and Square Equilibrium

NASA Astrophysics Data System (ADS)

Gotthans, Tomas; Sprott, Julien Clinton; Petrzela, Jiri

Simple systems of third-order autonomous nonlinear differential equations can exhibit chaotic behavior. In this paper, we present a new class of chaotic flow with a square-shaped equilibrium. This unique property has apparently not yet been described. Such a system belongs to a newly introduced category of chaotic systems with hidden attractors that are interesting and important in engineering applications. The mathematical model is accompanied by an electrical circuit implementation, demonstrating structural stability of the strange attractor. The circuit is simulated with PSpice, constructed, and analyzed (measured).

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

Multilayer neural networks for reduced-rank approximation.

PubMed

Diamantaras, K I; Kung, S Y

1994-01-01

This paper is developed in two parts. First, the authors formulate the solution to the general reduced-rank linear approximation problem relaxing the invertibility assumption of the input autocorrelation matrix used by previous authors. The authors' treatment unifies linear regression, Wiener filtering, full rank approximation, auto-association networks, SVD and principal component analysis (PCA) as special cases. The authors' analysis also shows that two-layer linear neural networks with reduced number of hidden units, trained with the least-squares error criterion, produce weights that correspond to the generalized singular value decomposition of the input-teacher cross-correlation matrix and the input data matrix. As a corollary the linear two-layer backpropagation model with reduced hidden layer extracts an arbitrary linear combination of the generalized singular vector components. Second, the authors investigate artificial neural network models for the solution of the related generalized eigenvalue problem. By introducing and utilizing the extended concept of deflation (originally proposed for the standard eigenvalue problem) the authors are able to find that a sequential version of linear BP can extract the exact generalized eigenvector components. The advantage of this approach is that it's easier to update the model structure by adding one more unit or pruning one or more units when the application requires it. An alternative approach for extracting the exact components is to use a set of lateral connections among the hidden units trained in such a way as to enforce orthogonality among the upper- and lower-layer weights. The authors call this the lateral orthogonalization network (LON) and show via theoretical analysis-and verify via simulation-that the network extracts the desired components. The advantage of the LON-based model is that it can be applied in a parallel fashion so that the components are extracted concurrently. Finally, the authors show the application of their results to the solution of the identification problem of systems whose excitation has a non-invertible autocorrelation matrix. Previous identification methods usually rely on the invertibility assumption of the input autocorrelation, therefore they can not be applied to this case.

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

Exploring convolutional neural networks for drug–drug interaction extraction

PubMed Central

Segura-Bedmar, Isabel; Martínez, Paloma

2017-01-01

Abstract Drug–drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which usually require considerable time and effort. One possible alternative to these approaches includes deep learning. This technique aims to automatically learn the best feature representation from the input data for a given task. The purpose of this paper is to examine whether a convolutional neural network (CNN), which only uses word embeddings as input features, can be applied successfully to classify DDIs from biomedical texts. Proposed herein, is a CNN architecture with only one hidden layer, thus making the model more computationally efficient, and we perform detailed experiments in order to determine the best settings of the model. The goal is to determine the best parameter of this basic CNN that should be considered for future research. The experimental results show that the proposed approach is promising because it attained the second position in the 2013 rankings of the DDI extraction challenge. However, it obtained worse results than previous works using neural networks with more complex architectures. PMID:28605776

Proposal of a service delivery integration index of home care for older persons: application in several European cities

PubMed Central

Henrard, Jean-Claude; Ankri, Joël; Frijters, Dinnus; Carpenter, Iain; Topinkova, Eva; Garms-Homolova, Vjenka; Finne-Soveri, Harriett; Sørbye, Liv Wergeland; Jónsson, Palmi V.; Ljunggren, Gunnar; Schroll, Marianne; Wagner, Cordula; Bernabei, Roberto

2006-01-01

Abstract Purpose To propose an integration index of home care delivery to older persons, to study its validity and to apply it to home care services of European cities. Theory Home care delivery integration was based on two dimensions referring to process-centred integration and organisational structure approach. Method Items considered as part of both dimensions according to an expert consensus (face validity) were extracted from a standardised questionnaire used in “Aged in Home care” (AdHoc) study to capture basic characteristics of home care services. Their summation leads to a services' delivery integration index. This index was applied to AdHoc services. A factor analysis was computed in order to empirically test the validity of the theoretical constructs. The plot of the settings was performed. Results Application of the index ranks home care services in four groups according to their score. Factor analysis identifies a first factor which opposes working arrangement within service to organisational structure bringing together provisions for social care. A second factor corresponds to basic nursing care and therapies. Internal consistency for those three domains ranges from 0.78 to 0.93. When plotting the different settings different models of service delivery appear. Conclusion The proposed index shows that behind a total score several models of care delivery are hidden. Comparison of service delivery integration should take into account this heterogeneity. PMID:17006549

Magnetic and superconducting competition within the Hubbard dimer. Exact solution

NASA Astrophysics Data System (ADS)

Matlak, M.; Somska, T.; Grabiec, B.

2005-02-01

We express the Hubbard dimer Hamiltonian in the second quantization with theuse of the Hubbard and spin operators. We consider the case of positive and negative U. We decompose the resulting Hamiltonian into several parts collecting all the terms belonging to the same energy level. Such a decomposition visualizes explicitely all intrinsic interactions competing together and deeply hidden in the original form of the dimer Hamiltonian. Among them are competitive ferromagnetic and antiferromagnetic interactions. There are also hopping terms present which describe Cooper pairs hopping between sites 1 and 2 with positive and negative coupling constants (similar as in Kulik-Pedan, Penson-Kolb models). We show that the competition between intrinsic interactions strongly depends on the model parametrs and the averaged occupation number of electrons n [0, 4] resulting in different regimes of the model (as e.g. t-J model regime, etc.).

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

PubMed

Steele, James S; Bush, Keith; Stowe, Zachary N; James, George A; Smitherman, Sonet; Kilts, Clint D; Cisler, Josh

2018-01-01

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior.

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates

PubMed Central

Bush, Keith; Stowe, Zachary N.; James, George A.; Smitherman, Sonet; Kilts, Clint D.; Cisler, Josh

2018-01-01

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior. PMID:29489856

Galactic center γ-ray excess in hidden sector DM models with dark gauge symmetries: local Z{sub 3} symmetry as an example

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

Ko, P.; Tang, Yong

We show that hidden sector dark matter (DM) models with local dark gauge symmetries make a natural playground for the possible γ-ray excess from the galactic center (GC). We first discuss in detail the GC γ-ray excess in a scalar dark matter (DM) model with local Z{sub 3} symmetry which was recently proposed by the present authors. Within this model, scalar DM with mass 30–70 GeV is allowed due to the newly-opened (semi-)annihilation channels of a DM pair into dark Higgs ϕ and/or dark photon Z′ pair, and the γ-ray spectrum from the GC can be fit within this model.more » Then we argue that the GC gamma ray excess can be easily accommodated within hidden sector dark matter models where DM is stabilized by local gauge symmetries, due to the presence of dark Higgs (and also dark photon for Abelian dark gauge symmetry)« less

Physical analysis and second-order modelling of an unsteady turbulent flow - The oscillating boundary layer on a flat plate

NASA Technical Reports Server (NTRS)

Ha Minh, H.; Viegas, J. R.; Rubesin, M. W.; Spalart, P.; Vandromme, D. D.

1989-01-01

The turbulent boundary layer under a freestream whose velocity varies sinusoidally in time around a zero mean is computed using two second order turbulence closure models. The time or phase dependent behavior of the Reynolds stresses are analyzed and results are compared to those of a previous SPALART-BALDWIN direct simulation. Comparisons show that the second order modeling is quite satisfactory for almost all phase angles, except in the relaminarization period where the computations lead to a relatively high wall shear stress.

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.

Application of Neutron Tomography in Culture Heritage research.

PubMed

Mongy, T

2014-02-01

Neutron Tomography (NT) investigation of Culture Heritages (CH) is an efficient tool for understanding the culture of ancient civilizations. Neutron imaging (NI) is a-state-of-the-art non-destructive tool in the area of CH and plays an important role in the modern archeology. The NI technology can be widely utilized in the field of elemental analysis. At Egypt Second Research Reactor (ETRR-2), a collimated Neutron Radiography (NR) beam is employed for neutron imaging purposes. A digital CCD camera is utilized for recording the beam attenuation in the sample. This helps for the detection of hidden objects and characterization of material properties. Research activity can be extended to use computer software for quantitative neutron measurement. Development of image processing algorithms can be used to obtain high quality images. In this work, full description of ETRR-2 was introduced with up to date neutron imaging system as well. Tomographic investigation of a clay forged artifact represents CH object was studied by neutron imaging methods in order to obtain some hidden information and highlight some attractive quantitative measurements. Computer software was used for imaging processing and enhancement. Also the Astra Image 3.0 Pro software was employed for high precise measurements and imaging enhancement using advanced algorithms. This work increased the effective utilization of the ETRR-2 Neutron Radiography/Tomography (NR/T) technique in Culture Heritages activities. © 2013 Elsevier Ltd. All rights reserved.

Gauge mediation scenario with hidden sector renormalization in MSSM

NASA Astrophysics Data System (ADS)

Arai, Masato; Kawai, Shinsuke; Okada, Nobuchika

2010-02-01

We study the hidden sector effects on the mass renormalization of a simplest gauge-mediated supersymmetry breaking scenario. We point out that possible hidden sector contributions render the soft scalar masses smaller, resulting in drastically different sparticle mass spectrum at low energy. In particular, in the 5+5¯ minimal gauge-mediated supersymmetry breaking with high messenger scale (that is favored by the gravitino cold dark matter scenario), we show that a stau can be the next lightest superparticle for moderate values of hidden sector self-coupling. This provides a very simple theoretical model of long-lived charged next lightest superparticles, which imply distinctive signals in ongoing and upcoming collider experiments.

Mapping Sub-Second Structure in Mouse Behavior

PubMed Central

Wiltschko, Alexander B.; Johnson, Matthew J.; Iurilli, Giuliano; Peterson, Ralph E.; Katon, Jesse M.; Pashkovski, Stan L.; Abraira, Victoria E.; Adams, Ryan P.; Datta, Sandeep Robert

2015-01-01

Summary Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that three-dimensional (3D) mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously-hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior. PMID:26687221

Cognitive control over learning: Creating, clustering and generalizing task-set structure

PubMed Central

Collins, Anne G.E.; Frank, Michael J.

2013-01-01

Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID:23356780

Microscopic cascading of second-order molecular nonlinearity: New design principles for enhancing third-order nonlinearity.

PubMed

Baev, Alexander; Autschbach, Jochen; Boyd, Robert W; Prasad, Paras N

2010-04-12

Herein, we develop a phenomenological model for microscopic cascading and substantiate it with ab initio calculations. It is shown that the concept of local microscopic cascading of a second-order nonlinearity can lead to a third-order nonlinearity, without introducing any new loss mechanisms that could limit the usefulness of our approach. This approach provides a new molecular design protocol, in which the current great successes achieved in producing molecules with extremely large second-order nonlinearity can be used in a supra molecular organization in a preferred orientation to generate very large third-order response magnitudes. The results of density functional calculations for a well-known second-order molecule, (para)nitroaniline, show that a head-to-tail dimer configuration exhibits enhanced third-order nonlinearity, in agreement with the phenomenological model which suggests that such an arrangement will produce cascading due to local field effects.

Dopamine reward prediction errors reflect hidden state inference across time

PubMed Central

Starkweather, Clara Kwon; Babayan, Benedicte M.; Uchida, Naoshige; Gershman, Samuel J.

2017-01-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a ‘belief state’). In this work, we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling exhibited a striking difference between two tasks that differed only with respect to whether reward was delivered deterministically. Our results favor an associative learning rule that combines cached values with hidden state inference. PMID:28263301

Dopamine reward prediction errors reflect hidden-state inference across time.

PubMed

Starkweather, Clara Kwon; Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2017-04-01

Midbrain dopamine neurons signal reward prediction error (RPE), or actual minus expected reward. The temporal difference (TD) learning model has been a cornerstone in understanding how dopamine RPEs could drive associative learning. Classically, TD learning imparts value to features that serially track elapsed time relative to observable stimuli. In the real world, however, sensory stimuli provide ambiguous information about the hidden state of the environment, leading to the proposal that TD learning might instead compute a value signal based on an inferred distribution of hidden states (a 'belief state'). Here we asked whether dopaminergic signaling supports a TD learning framework that operates over hidden states. We found that dopamine signaling showed a notable difference between two tasks that differed only with respect to whether reward was delivered in a deterministic manner. Our results favor an associative learning rule that combines cached values with hidden-state inference.

A minimal model of neutrino flavor

NASA Astrophysics Data System (ADS)

Luhn, Christoph; Parattu, Krishna Mohan; Wingerter, Akın

2012-12-01

Models of neutrino mass which attempt to describe the observed lepton mixing pattern are typically based on discrete family symmetries with a non-Abelian and one or more Abelian factors. The latter so-called shaping symmetries are imposed in order to yield a realistic phenomenology by forbidding unwanted operators. Here we propose a supersymmetric model of neutrino flavor which is based on the group T 7 and does not require extra {Z} N or U(1) factors in the Yukawa sector, which makes it the smallest realistic family symmetry that has been considered so far. At leading order, the model predicts tribimaximal mixing which arises completely accidentally from a combination of the T 7 Clebsch-Gordan coefficients and suitable flavon alignments. Next-to-leading order (NLO) operators break the simple tribimaximal structure and render the model compatible with the recent results of the Daya Bay and Reno collaborations which have measured a reactor angle of around 9°. Problematic NLO deviations of the other two mixing angles can be controlled in an ultraviolet completion of the model. The vacuum alignment mechanism that we use necessitates the introduction of a hidden flavon sector that transforms under a {Z} 6 symmetry, thereby spoiling the minimality of our model whose flavor symmetry is then T 7 × {Z} 6.

Hidden asymmetry and forward-backward correlations

NASA Astrophysics Data System (ADS)

Bialas, A.; Zalewski, K.

2010-09-01

A model-independent method of studying the forward-backward correlations in symmetric high-energy processes is developed. The method allows a systematic study of the properties of various particle sources and allows one to uncover asymmetric structures hidden in symmetric hadron-hadron and nucleus-nucleus inelastic reactions.

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.

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.

Entanglement generation and manipulation in the Hong-Ou-Mandel experiment: a hidden scenario beyond two-photon interference

NASA Astrophysics Data System (ADS)

Yang, Li-Kai; Cai, Han; Peng, Tao; Wang, Da-Wei

2018-06-01

The Hong‑Ou‑Mandel (HOM) effect was long believed to be a two-photon interference phenomenon. It describes the fact that two indistinguishable photons mixed at a beam splitter will bunch together to one of the two output modes. Considering the two single-photon emitters such as trapped ions, we explore a hidden scenario of the HOM effect, where entanglement can be generated between the two ions when a single photon is detected by one of the detectors. A second photon emitted by the entangled photon sources will be subsequently detected by the same detector. However, we can also control the fate of the second photon by manipulating the entangled state. Instead of two-photon interference, the phase of the entangled state is responsible for the photon’s path in our proposal. Toward a feasible experimental realization, we conduct a quantum jump simulation on the system to show its robustness against experimental errors.

Analysing the hidden curriculum: use of a cultural web

PubMed Central

Mossop, Liz; Dennick, Reg; Hammond, Richard; Robbé, Iain

2013-01-01

CONTEXT Major influences on learning about medical professionalism come from the hidden curriculum. These influences can contribute positively or negatively towards the professional enculturation of clinical students. The fact that there is no validated method for identifying the components of the hidden curriculum poses problems for educators considering professionalism. The aim of this study was to analyse whether a cultural web, adapted from a business context, might assist in the identification of elements of the hidden curriculum at a UK veterinary school. METHODS A qualitative approach was used. Seven focus groups consisting of three staff groups and four student groups were organised. Questioning was framed using the cultural web, which is a model used by business owners to assess their environment and consider how it affects their employees and customers. The focus group discussions were recorded, transcribed and analysed thematically using a combination of a priori and emergent themes. RESULTS The cultural web identified elements of the hidden curriculum for both students and staff. These included: core assumptions; routines; rituals; control systems; organisational factors; power structures, and symbols. Discussions occurred about how and where these issues may affect students’ professional identity development. CONCLUSIONS The cultural web framework functioned well to help participants identify elements of the hidden curriculum. These aspects aligned broadly with previously described factors such as role models and institutional slang. The influence of these issues on a student’s development of a professional identity requires discussion amongst faculty staff, and could be used to develop learning opportunities for students. The framework is promising for the analysis of the hidden curriculum and could be developed as an instrument for implementation in other clinical teaching environments. PMID:23323652

The relationship between eddy-transport and second-order closure models for stratified media and for vortices

NASA Technical Reports Server (NTRS)

Donaldson, C. D.

1973-01-01

The question is considered of how complex a model should be used for the calculation of turbulent shear flows. At the present time there are models varying in complexity from very simple eddy-transport models to models in which all the equations for the nonzero second-order correlations are solved simultaneously with the equations for the mean variables. A discussion is presented of the relationship between these two models of turbulent shear flow. Two types of motion are discussed: first, turbulent shear flow in a stratified medium and, second, the motion in a turbulent line vortex. These two cases are instructive because in the first example eddy-transport methods have proven reasonably effective, whereas in the second, they have led to erroneous conclusions. It is not generally appreciated that the simplest form of eddy-transport theory can be derived from second-order closure models of turbulent flow by a suitably limiting process. This limiting process and the suitability of eddy-transport modeling for stratified media and line vortices are discussed.

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

PubMed Central

Robertson, Colin; Sawford, Kate; Gunawardana, Walimunige S. N.; Nelson, Trisalyn A.; Nathoo, Farouk; Stephen, Craig

2011-01-01

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines. PMID:21949763

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.

(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond

NASA Technical Reports Server (NTRS)

Baldi, Pierre

1994-01-01

We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.

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.

Multi-scale Eulerian model within the new National Environmental Modeling System

NASA Astrophysics Data System (ADS)

Janjic, Zavisa; Janjic, Tijana; Vasic, Ratko

2010-05-01

The unified Non-hydrostatic Multi-scale Model on the Arakawa B grid (NMMB) is being developed at NCEP within the National Environmental Modeling System (NEMS). The finite-volume horizontal differencing employed in the model preserves important properties of differential operators and conserves a variety of basic and derived dynamical and quadratic quantities. Among these, conservation of energy and enstrophy improves the accuracy of nonlinear dynamics of the model. Within further model development, advection schemes of fourth order of formal accuracy have been developed. It is argued that higher order advection schemes should not be used in the thermodynamic equation in order to preserve consistency with the second order scheme used for computation of the pressure gradient force. Thus, the fourth order scheme is applied only to momentum advection. Three sophisticated second order schemes were considered for upgrade. Two of them, proposed in Janjic(1984), conserve energy and enstrophy, but with enstrophy calculated differently. One of them conserves enstrophy as computed by the most accurate second order Laplacian operating on stream function. The other scheme conserves enstrophy as computed from the B grid velocity. The third scheme (Arakawa 1972) is arithmetic mean of the former two. It does not conserve enstrophy strictly, but it conserves other quadratic quantities that control the nonlinear energy cascade. Linearization of all three schemes leads to the same second order linear advection scheme. The second order term of the truncation error of the linear advection scheme has a special form so that it can be eliminated by simply preconditioning the advected quantity. Tests with linear advection of a cone confirm the advantage of the fourth order scheme. However, if a localized, large amplitude and high wave-number pattern is present in initial conditions, the clear advantage of the fourth order scheme disappears. In real data runs, problems with noisy data may appear due to mountains. Thus, accuracy and formal accuracy may not be synonymous. The nonlinear fourth order schemes are quadratic conservative and reduce to the Arakawa Jacobian in case of non-divergent flow. In case of general flow the conservation properties of the new momentum advection schemes impose stricter constraint on the nonlinear cascade than the original second order schemes. However, for non-divergent flow, the conservation properties of the fourth order schemes cannot be proven in the same way as those of the original second order schemes. Therefore, nonlinear tests were carried out in order to check how well the fourth order schemes control the nonlinear energy cascade. In the tests nonlinear shallow water equations are solved in a rotating rectangular domain (Janjic, 1984). The domain is covered with only 17 x 17 grid points. A diagnostic quantity is used to monitor qualitative changes in the spectrum over 116 days of simulated time. All schemes maintained meaningful solutions throughout the test. Among the second order schemes, the best result was obtained with the scheme that conserved enstrophy as computed by the second order Laplacian of the stream function. It was closely followed by the Arakawa (1972) scheme, while the remaining scheme was distant third. The fourth order schemes ranked in the same order, and were competitive throughout the experiments with their second order counterparts in preventing accumulation of energy at small scales. Finally, the impact was examined of the fourth order momentum advection on global medium range forecasts. The 500 mb anomaly correlation coefficient is used as a measure of success of the forecasts. Arakawa, A., 1972: Design of the UCLA general circulation model. Tech. Report No. 7, Department of Meteorology, University of California, Los Angeles, 116 pp. Janjic, Z. I., 1984: Non-linear advection schemes and energy cascade on semi-staggered grids. Monthly Weather Review, 112, 1234-1245.

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.

Multi-Group Covariance and Mean Structure Modeling of the Relationship between the WAIS-III Common Factors and Sex and Educational Attainment in Spain

ERIC Educational Resources Information Center

Dolan, Conor V.; Colom, Roberto; Abad, Francisco J.; Wicherts, Jelte M.; Hessen, David J.; van de Sluis, Sophie

2006-01-01

We investigated sex effects and the effects of educational attainment (EA) on the covariance structure of the WAIS-III in a subsample of the Spanish standardization data. We fitted both first order common factor models and second order common factor models. The latter include general intelligence ("g") as a second order common factor.…

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Deng, Bin; Wang, Jiang; Li, Huiyan; Liu, Chen; Fietkiewicz, Chris; Loparo, Kenneth A.

2017-01-01

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Optimization of an intracavity Q-switched solid-state second order Raman laser

NASA Astrophysics Data System (ADS)

Chen, Zhiqiong; Fu, Xihong; Peng, Hangyu; Zhang, Jun; Qin, Li; Ning, Yongqiang

2017-01-01

In this paper, the model of an intracavity Q-switched second order Raman laser is established, the characteristics of the output 2nd Stokes are simulated. The dynamic balance mechanism among intracavity conversion rates of stimulated emission, first order Raman and second order Raman is obtained. Finally, optimization solutions for increasing output 2nd Stokes pulse energy are proposed.

Modeling of near-wall turbulence

NASA Technical Reports Server (NTRS)

Shih, T. H.; Mansour, N. N.

1990-01-01

An improved k-epsilon model and a second order closure model is presented for low Reynolds number turbulence near a wall. For the k-epsilon model, a modified form of the eddy viscosity having correct asymptotic near wall behavior is suggested, and a model for the pressure diffusion term in the turbulent kinetic energy equation is proposed. For the second order closure model, the existing models are modified for the Reynolds stress equations to have proper near wall behavior. A dissipation rate equation for the turbulent kinetic energy is also reformulated. The proposed models satisfy realizability and will not produce unphysical behavior. Fully developed channel flows are used for model testing. The calculations are compared with direct numerical simulations. It is shown that the present models, both the k-epsilon model and the second order closure model, perform well in predicting the behavior of the near wall turbulence. Significant improvements over previous models are obtained.

Sex Work Research: Methodological and Ethical Challenges

ERIC Educational Resources Information Center

Shaver, Frances M.

2005-01-01

The challenges involved in the design of ethical, nonexploitative research projects with sex workers or any other marginalized population are significant. First, the size and boundaries of the population are unknown, making it extremely difficult to get a representative sample. Second, because membership in hidden populations often involves…

Body size affects the evolution of hidden colour signals in moths.

PubMed

Kang, Changku; Zahiri, Reza; Sherratt, Thomas N

2017-08-30

Many cryptic prey have also evolved hidden contrasting colour signals which are displayed to would-be predators. Given that these hidden contrasting signals may confer additional survival benefits to the prey by startling/intimidating predators, it is unclear why they have evolved in some species, but not in others. Here, we have conducted a comparative phylogenetic analysis of the evolution of colour traits in the family Erebidae (Lepidoptera), and found that the hidden contrasting colour signals are more likely to be found in larger species. To understand why this relationship occurs, we present a general mathematical model, demonstrating that selection for a secondary defence such as deimatic display will be stronger in large species when (i) the primary defence (crypsis) is likely to fail as its body size increases and/or (ii) the secondary defence is more effective in large prey. To test the model assumptions, we conducted behavioural experiments using a robotic moth which revealed that survivorship advantages were higher against wild birds when the moth has contrasting hindwings and large size. Collectively, our results suggest that the evolutionary association between large size and hidden contrasting signals has been driven by a combination of the need for a back-up defence and its efficacy. © 2017 The Author(s).

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.

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.

A theory of cerebellar cortex and adaptive motor control based on two types of universal function approximation capability.

PubMed

Fujita, Masahiko

2016-03-01

Lesions of the cerebellum result in large errors in movements. The cerebellum adaptively controls the strength and timing of motor command signals depending on the internal and external environments of movements. The present theory describes how the cerebellar cortex can control signals for accurate and timed movements. A model network of the cerebellar Golgi and granule cells is shown to be equivalent to a multiple-input (from mossy fibers) hierarchical neural network with a single hidden layer of threshold units (granule cells) that receive a common recurrent inhibition (from a Golgi cell). The weighted sum of the hidden unit signals (Purkinje cell output) is theoretically analyzed regarding the capability of the network to perform two types of universal function approximation. The hidden units begin firing as the excitatory inputs exceed the recurrent inhibition. This simple threshold feature leads to the first approximation theory, and the network final output can be any continuous function of the multiple inputs. When the input is constant, this output becomes stationary. However, when the recurrent unit activity is triggered to decrease or the recurrent inhibition is triggered to increase through a certain mechanism (metabotropic modulation or extrasynaptic spillover), the network can generate any continuous signals for a prolonged period of change in the activity of recurrent signals, as the second approximation theory shows. By incorporating the cerebellar capability of two such types of approximations to a motor system, in which learning proceeds through repeated movement trials with accompanying corrections, accurate and timed responses for reaching the target can be adaptively acquired. Simple models of motor control can solve the motor error vs. sensory error problem, as well as the structural aspects of credit (or error) assignment problem. Two physiological experiments are proposed for examining the delay and trace conditioning of eyelid responses, as well as saccade adaptation, to investigate this novel idea of cerebellar processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Sparse distributed memory and related models

NASA Technical Reports Server (NTRS)

Kanerva, Pentti

1992-01-01

Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.

A comparison of second order derivative based models for time domain reflectometry wave form analysis

USDA-ARS?s Scientific Manuscript database

Adaptive waveform interpretation with Gaussian filtering (AWIGF) and second order bounded mean oscillation operator Z square 2(u,t,r) are TDR analysis methods based on second order differentiation. AWIGF was originally designed for relatively long probe (greater than 150 mm) TDR waveforms, while Z s...

Prediction of Narrow N* and {Lambda}* Resonances with Hidden Charm above 4 GeV

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

Wu Jiajun; Departamento de Fisica Teorica and IFIC, Centro Mixto Universidad de Valencia-CSIC, Institutos de Investigacion de Paterna, Apartado 22085, 46071 Valencia; Molina, R.

2010-12-03

The interaction between various charmed mesons and charmed baryons is studied within the framework of the coupled-channel unitary approach with the local hidden gauge formalism. Several meson-baryon dynamically generated narrow N{sup *} and {Lambda}{sup *} resonances with hidden charm are predicted with mass above 4 GeV and width smaller than 100 MeV. The predicted new resonances definitely cannot be accommodated by quark models with three constituent quarks and can be looked for in the forthcoming PANDA/FAIR experiments.

Desktop computer graphics for RMS/payload handling flight design

NASA Technical Reports Server (NTRS)

Homan, D. J.

1984-01-01

A computer program, the Multi-Adaptive Drawings, Renderings and Similitudes (MADRAS) program, is discussed. The modeling program, written for a desktop computer system (the Hewlett-Packard 9845/C), is written in BASIC and uses modular construction of objects while generating both wire-frame and hidden-line drawings from any viewpoint. The dimensions and placement of objects are user definable. Once the hidden-line calculations are made for a particular viewpoint, the viewpoint may be rotated in pan, tilt, and roll without further hidden-line calculations. The use and results of this program are discussed.

Gravitational lensing of photons coupled to massive particles

NASA Astrophysics Data System (ADS)

Glicenstein, J.-F.

2018-04-01

The gravitational deflection of massless and massive particles, both with and without spin, has been extensively studied. This paper discusses the lensing of a particle which oscillates between two interaction eigenstates. The deflection angle, lens equation and time delay between images are derived in a model of photon to hidden-photon oscillations. In the case of coherent oscillations, the coupled photon behaves as a massive particle with a mass equal to the product of the coupling constant and hidden-photon mass. The conditions for observing coherent photon-hidden photon lensing are discussed.

Matrix Determination of Reflectance of Hidden Object via Indirect Photography

DTIC Science & Technology

2012-03-01

the hidden object. This thesis provides an alternative method of processing the camera images by modeling the system as a set of transport and...Distribution Function ( BRDF ). Figure 1. Indirect photography with camera field of view dictated by point of illumination. 3 1.3 Research Focus In an...would need to be modeled using radiometric principles. A large amount of the improvement in this process was due to the use of a blind

An information hidden model holding cover distributions

NASA Astrophysics Data System (ADS)

Fu, Min; Cai, Chao; Dai, Zuxu

2018-03-01

The goal of steganography is to embed secret data into a cover so no one apart from the sender and intended recipients can find the secret data. Usually, the way the cover changing was decided by a hidden function. There were no existing model could be used to find an optimal function which can greatly reduce the distortion the cover suffered. This paper considers the cover carrying secret message as a random Markov chain, taking the advantages of a deterministic relation between initial distributions and transferring matrix of the Markov chain, and takes the transferring matrix as a constriction to decrease statistical distortion the cover suffered in the process of information hiding. Furthermore, a hidden function is designed and the transferring matrix is also presented to be a matrix from the original cover to the stego cover. Experiment results show that the new model preserves a consistent statistical characterizations of original and stego cover.

Statistical Inference in Hidden Markov Models Using k-Segment Constraints

PubMed Central

Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher

2016-01-01

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward–backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. PMID:27226674

Testing higher-order Lagrangian perturbation theory against numerical simulations. 2: Hierarchical models

NASA Technical Reports Server (NTRS)

Melott, A. L.; Buchert, T.; Weib, A. G.

1995-01-01

We present results showing an improvement of the accuracy of perturbation theory as applied to cosmological structure formation for a useful range of scales. The Lagrangian theory of gravitational instability of Friedmann-Lemaitre cosmogonies is compared with numerical simulations. We study the dynamics of hierarchical models as a second step. In the first step we analyzed the performance of the Lagrangian schemes for pancake models, the difference being that in the latter models the initial power spectrum is truncated. This work probed the quasi-linear and weakly non-linear regimes. We here explore whether the results found for pancake models carry over to hierarchical models which are evolved deeply into the non-linear regime. We smooth the initial data by using a variety of filter types and filter scales in order to determine the optimal performance of the analytical models, as has been done for the 'Zel'dovich-approximation' - hereafter TZA - in previous work. We find that for spectra with negative power-index the second-order scheme performs considerably better than TZA in terms of statistics which probe the dynamics, and slightly better in terms of low-order statistics like the power-spectrum. However, in contrast to the results found for pancake models, where the higher-order schemes get worse than TZA at late non-linear stages and on small scales, we here find that the second-order model is as robust as TZA, retaining the improvement at later stages and on smaller scales. In view of these results we expect that the second-order truncated Lagrangian model is especially useful for the modelling of standard dark matter models such as Hot-, Cold-, and Mixed-Dark-Matter.

Maxwell's second- and third-order equations of transfer for non-Maxwellian gases

NASA Technical Reports Server (NTRS)

Baganoff, D.

1992-01-01

Condensed algebraic forms for Maxwell's second- and third-order equations of transfer are developed for the case of molecules described by either elastic hard spheres, inverse-power potentials, or by Bird's variable hard-sphere model. These hardly reduced, yet exact, equations provide a new point of origin, when using the moment method, in seeking approximate solutions in the kinetic theory of gases for molecular models that are physically more realistic than that provided by the Maxwell model. An important by-product of the analysis when using these second- and third-order relations is that a clear mathematical connection develops between Bird's variable hard-sphere model and that for the inverse-power potential.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization.

PubMed

Stifter, Cynthia A; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization

PubMed Central

Stifter, Cynthia A.; Rovine, Michael

2016-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed. PMID:27284272

Hidden gauged U (1 ) model: Unifying scotogenic neutrino and flavor dark matter

NASA Astrophysics Data System (ADS)

Yu, Jiang-Hao

2016-06-01

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

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.

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.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Monitoring volcano activity through Hidden Markov Model

NASA Astrophysics Data System (ADS)

Cassisi, C.; Montalto, P.; Prestifilippo, M.; Aliotta, M.; Cannata, A.; Patanè, D.

2013-12-01

During 2011-2013, Mt. Etna was mainly characterized by cyclic occurrences of lava fountains, totaling to 38 episodes. During this time interval Etna volcano's states (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN), whose automatic recognition is very useful for monitoring purposes, turned out to be strongly related to the trend of RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area. Since RMS time series behavior is considered to be stochastic, we can try to model the system generating its values, assuming to be a Markov process, by using Hidden Markov models (HMMs). HMMs are a powerful tool in modeling any time-varying series. HMMs analysis seeks to recover the sequence of hidden states from the observed emissions. In our framework, observed emissions are characters generated by the SAX (Symbolic Aggregate approXimation) technique, which maps RMS time series values with discrete literal emissions. The experiments show how it is possible to guess volcano states by means of HMMs and SAX.

Hidden Genetic Variation in LCA9-Associated Congenital Blindness Explained by 5'UTR Mutations and Copy-Number Variations of NMNAT1.

PubMed

Coppieters, Frauke; Todeschini, Anne Laure; Fujimaki, Takuro; Baert, Annelot; De Bruyne, Marieke; Van Cauwenbergh, Caroline; Verdin, Hannah; Bauwens, Miriam; Ongenaert, Maté; Kondo, Mineo; Meire, Françoise; Murakami, Akira; Veitia, Reiner A; Leroy, Bart P; De Baere, Elfride

2015-12-01

Leber congenital amaurosis (LCA) is a severe autosomal-recessive retinal dystrophy leading to congenital blindness. A recently identified LCA gene is NMNAT1, located in the LCA9 locus. Although most mutations in blindness genes are coding variations, there is accumulating evidence for hidden noncoding defects or structural variations (SVs). The starting point of this study was an LCA9-associated consanguineous family in which no coding mutations were found in the LCA9 region. Exploring the untranslated regions of NMNAT1 revealed a novel homozygous 5'UTR variant, c.-70A>T. Moreover, an adjacent 5'UTR variant, c.-69C>T, was identified in a second consanguineous family displaying a similar phenotype. Both 5'UTR variants resulted in decreased NMNAT1 mRNA abundance in patients' lymphocytes, and caused decreased luciferase activity in human retinal pigment epithelial RPE-1 cells. Second, we unraveled pseudohomozygosity of a coding NMNAT1 mutation in two unrelated LCA patients by the identification of two distinct heterozygous partial NMNAT1 deletions. Molecular characterization of the breakpoint junctions revealed a complex Alu-rich genomic architecture. Our study uncovered hidden genetic variation in NMNAT1-associated LCA and emphasized a shift from coding to noncoding regulatory mutations and repeat-mediated SVs in the molecular pathogenesis of heterogeneous recessive disorders such as hereditary blindness. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

The Hidden Meaning of Inner Speech.

ERIC Educational Resources Information Center

Pomper, Marlene M.

This paper is concerned with the inner speech process, its relationship to thought and behavior, and its theoretical and educational implications. The paper first defines inner speech as a bridge between thought and written or spoken language and traces its development. Second, it investigates competing theories surrounding the subject with an…

Hidden Voices: L2 Students' Compensatory Writing Strategies

ERIC Educational Resources Information Center

Stockall, Nancy; Villar Cole, Corinna

2016-01-01

This qualitative research study examines how 12 undergraduate second-language learners understood the concept of citations in academic writing. The following questions guided this study: What are the participants' beliefs about citing research? How do students conceive the role and function of citations in their writing assignments? How do they…

Internationalising Practices and Representations of the "'Other" in Second-Level Elite Schools in Ireland

ERIC Educational Resources Information Center

Courtois, Aline

2016-01-01

While Irish elite schools have adopted some internationalising practices, international students are often erased from their "public faces". Based on interviews and analysis of schools' websites, this paper argues that Brooks and Waters' [2014. "The Hidden Internationalism of Elite English Schools." "Sociology,"…

Stability and Hamiltonian formulation of higher derivative theories

NASA Astrophysics Data System (ADS)

Schmidt, Hans-Jürgen

1994-06-01

We analyze the presuppositions leading to instabilities in theories of order higher than second. The type of fourth-order gravity which leads to an inflationary (quasi-de Sitter) period of cosmic evolution by inclusion of one curvature-squared term (i.e., the Starobinsky model) is used as an example. The corresponding Hamiltonian formulation (which is necessary for deducing the Wheeler-DeWitt equation) is found both in the Ostrogradski approach and in another form. As an example, a closed form solution of the Wheeler-DeWitt equation for a spatially flat Friedmann model and L=R2 is found. The method proposed by Simon to bring fourth order gravity to second order can be (if suitably generalized) applied to bring sixth-order gravity to second order.

Keep Your Distance! Using Second-Order Ordinary Differential Equations to Model Traffic Flow

ERIC Educational Resources Information Center

McCartney, Mark

2004-01-01

A simple mathematical model for how vehicles follow each other along a stretch of road is presented. The resulting linear second-order differential equation with constant coefficients is solved and interpreted. The model can be used as an application of solution techniques taught at first-year undergraduate level and as a motivator to encourage…

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

NASA Astrophysics Data System (ADS)

La Cour, Brian R.

2017-07-01

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

Efficiency of perfectly matched layers for seismic wave modeling in second-order viscoelastic equations

NASA Astrophysics Data System (ADS)

Ping, Ping; Zhang, Yu; Xu, Yixian; Chu, Risheng

2016-12-01

In order to improve the perfectly matched layer (PML) efficiency in viscoelastic media, we first propose a split multi-axial PML (M-PML) and an unsplit convolutional PML (C-PML) in the second-order viscoelastic wave equations with the displacement as the only unknown. The advantage of these formulations is that it is easy and efficient to revise the existing codes of the second-order spectral element method (SEM) or finite-element method (FEM) with absorbing boundaries in a uniform equation, as well as more economical than the auxiliary differential equations PML. Three models which are easily suffered from late time instabilities are considered to validate our approaches. Through comparison the M-PML with C-PML efficiency of absorption and stability for long time simulation, it can be concluded that: (1) for an isotropic viscoelastic medium with high Poisson's ratio, the C-PML will be a sufficient choice for long time simulation because of its weak reflections and superior stability; (2) unlike the M-PML with high-order damping profile, the M-PML with second-order damping profile loses its stability in long time simulation for an isotropic viscoelastic medium; (3) in an anisotropic viscoelastic medium, the C-PML suffers from instabilities, while the M-PML with second-order damping profile can be a better choice for its superior stability and more acceptable weak reflections than the M-PML with high-order damping profile. The comparative analysis of the developed methods offers meaningful significance for long time seismic wave modeling in second-order viscoelastic wave equations.

Regime switching model for financial data: Empirical risk analysis

NASA Astrophysics Data System (ADS)

Salhi, Khaled; Deaconu, Madalina; Lejay, Antoine; Champagnat, Nicolas; Navet, Nicolas

2016-11-01

This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001-2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement.

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.

Bayesian Estimation and Inference Using Stochastic Electronics

PubMed Central

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream. PMID:27047326

Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study.

PubMed

Maghsoudi, M; Ghaedi, M; Zinali, A; Ghaedi, A M; Habibi, M H

2015-01-05

In this research, ZnO nanoparticle loaded on activated carbon (ZnO-NPs-AC) was synthesized simply by a low cost and nontoxic procedure. The characterization and identification have been completed by different techniques such as SEM and XRD analysis. A three layer artificial neural network (ANN) model is applicable for accurate prediction of dye removal percentage from aqueous solution by ZnO-NRs-AC following conduction of 270 experimental data. The network was trained using the obtained experimental data at optimum pH with different ZnO-NRs-AC amount (0.005-0.015 g) and 5-40 mg/L of sunset yellow dye over contact time of 0.5-30 min. The ANN model was applied for prediction of the removal percentage of present systems with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 6 neurons. The minimum mean squared error (MSE) of 0.0008 and coefficient of determination (R(2)) of 0.998 were found for prediction and modeling of SY removal. The influence of parameters including adsorbent amount, initial dye concentration, pH and contact time on sunset yellow (SY) removal percentage were investigated and optimal experimental conditions were ascertained. Optimal conditions were set as follows: pH, 2.0; 10 min contact time; an adsorbent dose of 0.015 g. Equilibrium data fitted truly with the Langmuir model with maximum adsorption capacity of 142.85 mg/g for 0.005 g adsorbent. The adsorption of sunset yellow followed the pseudo-second-order rate equation. Copyright © 2014 Elsevier B.V. All rights reserved.

Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study

NASA Astrophysics Data System (ADS)

Maghsoudi, M.; Ghaedi, M.; Zinali, A.; Ghaedi, A. M.; Habibi, M. H.

2015-01-01

In this research, ZnO nanoparticle loaded on activated carbon (ZnO-NPs-AC) was synthesized simply by a low cost and nontoxic procedure. The characterization and identification have been completed by different techniques such as SEM and XRD analysis. A three layer artificial neural network (ANN) model is applicable for accurate prediction of dye removal percentage from aqueous solution by ZnO-NRs-AC following conduction of 270 experimental data. The network was trained using the obtained experimental data at optimum pH with different ZnO-NRs-AC amount (0.005-0.015 g) and 5-40 mg/L of sunset yellow dye over contact time of 0.5-30 min. The ANN model was applied for prediction of the removal percentage of present systems with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) in the hidden layer with 6 neurons. The minimum mean squared error (MSE) of 0.0008 and coefficient of determination (R2) of 0.998 were found for prediction and modeling of SY removal. The influence of parameters including adsorbent amount, initial dye concentration, pH and contact time on sunset yellow (SY) removal percentage were investigated and optimal experimental conditions were ascertained. Optimal conditions were set as follows: pH, 2.0; 10 min contact time; an adsorbent dose of 0.015 g. Equilibrium data fitted truly with the Langmuir model with maximum adsorption capacity of 142.85 mg/g for 0.005 g adsorbent. The adsorption of sunset yellow followed the pseudo-second-order rate equation.

Bayesian Estimation and Inference Using Stochastic Electronics.

PubMed

Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

2016-01-01

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

Equivalent Relaxations of Optimal Power Flow

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

Bose, S; Low, SH; Teeraratkul, T

2015-03-01

Several convex relaxations of the optimal power flow (OPF) problem have recently been developed using both bus injection models and branch flow models. In this paper, we prove relations among three convex relaxations: a semidefinite relaxation that computes a full matrix, a chordal relaxation based on a chordal extension of the network graph, and a second-order cone relaxation that computes the smallest partial matrix. We prove a bijection between the feasible sets of the OPF in the bus injection model and the branch flow model, establishing the equivalence of these two models and their second-order cone relaxations. Our results implymore » that, for radial networks, all these relaxations are equivalent and one should always solve the second-order cone relaxation. For mesh networks, the semidefinite relaxation and the chordal relaxation are equally tight and both are strictly tighter than the second-order cone relaxation. Therefore, for mesh networks, one should either solve the chordal relaxation or the SOCP relaxation, trading off tightness and the required computational effort. Simulations are used to illustrate these results.« less

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.

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.

The Use of Gestalt Interventions in the Treatment of the Resistant Alcohol-Dependent Client.

ERIC Educational Resources Information Center

Ramey, Luellen

1998-01-01

Reviews ethical and practical dilemmas associated with clients who have hidden alcohol dependencies, and proposes an approach rooted in Gestalt counseling theory which confronts these issues and is compatible with a current emerging alcohol-treatment model. Suggests specific activities for addressing client resistance to revealing a hidden alcohol…

Quantile regression reveals hidden bias and uncertainty in habitat models

Treesearch

Brian S. Cade; Barry R. Noon; Curtis H. Flather

2005-01-01

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias...

The hidden consequences of fire suppression

Treesearch

Carol Miller

2012-01-01

Wilderness managers need a way to quantify and monitor the effects of suppressing lightning-caused wildfires, which can alter natural fire regimes, vegetation, and habitat. Using computerized models of fire spread, weather, and fuels, it is now possible to quantify many of the hidden consequences of fire suppression. Case study watersheds in Yosemite and Sequoia-Kings...

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.

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

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.

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

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.

PubMed

Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-Koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

2016-10-01

Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. The proposed model had an accuracy of 94.84% (. 24.42) in order to correct prediction of the ESD disease. Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.

Dynamics and Collapse in a Power System Model with Voltage Variation: The Damping Effect.

PubMed

Ma, Jinpeng; Sun, Yong; Yuan, Xiaoming; Kurths, Jürgen; Zhan, Meng

2016-01-01

Complex nonlinear phenomena are investigated in a basic power system model of the single-machine-infinite-bus (SMIB) with a synchronous generator modeled by a classical third-order differential equation including both angle dynamics and voltage dynamics, the so-called flux decay equation. In contrast, for the second-order differential equation considering the angle dynamics only, it is the classical swing equation. Similarities and differences of the dynamics generated between the third-order model and the second-order one are studied. We mainly find that, for positive damping, these two models show quite similar behavior, namely, stable fixed point, stable limit cycle, and their coexistence for different parameters. However, for negative damping, the second-order system can only collapse, whereas for the third-order model, more complicated behavior may happen, such as stable fixed point, limit cycle, quasi-periodicity, and chaos. Interesting partial collapse phenomena for angle instability only and not for voltage instability are also found here, including collapse from quasi-periodicity and from chaos etc. These findings not only provide a basic physical picture for power system dynamics in the third-order model incorporating voltage dynamics, but also enable us a deeper understanding of the complex dynamical behavior and even leading to a design of oscillation damping in electric power systems.

Calculating Second-Order Effects in MOSFET's

NASA Technical Reports Server (NTRS)

Benumof, Reuben; Zoutendyk, John A.; Coss, James R.

1990-01-01

Collection of mathematical models includes second-order effects in n-channel, enhancement-mode, metal-oxide-semiconductor field-effect transistors (MOSFET's). When dimensions of circuit elements relatively large, effects neglected safely. However, as very-large-scale integration of microelectronic circuits leads to MOSFET's shorter or narrower than 2 micrometer, effects become significant in design and operation. Such computer programs as widely-used "Simulation Program With Integrated Circuit Emphasis, Version 2" (SPICE 2) include many of these effects. In second-order models of n-channel, enhancement-mode MOSFET, first-order gate-depletion region diminished by triangular-cross-section deletions on end and augmented by circular-wedge-cross-section bulges on sides.

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

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.

PubMed

Ong, Lee-Ling S; Xinghua Zhang; Kundukad, Binu; Dauwels, Justin; Doyle, Patrick; Asada, H Harry

2016-08-01

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Using hidden Markov models to align multiple sequences.

PubMed

Mount, David W

2009-07-01

A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.

Arbitrary-order corrections for finite-time drift and diffusion coefficients

NASA Astrophysics Data System (ADS)

Anteneodo, C.; Riera, R.

2009-09-01

We address a standard class of diffusion processes with linear drift and quadratic diffusion coefficients. These contributions to dynamic equations can be directly drawn from data time series. However, real data are constrained to finite sampling rates and therefore it is crucial to establish a suitable mathematical description of the required finite-time corrections. Based on Itô-Taylor expansions, we present the exact corrections to the finite-time drift and diffusion coefficients. These results allow to reconstruct the real hidden coefficients from the empirical estimates. We also derive higher-order finite-time expressions for the third and fourth conditional moments that furnish extra theoretical checks for this class of diffusion models. The analytical predictions are compared with the numerical outcomes of representative artificial time series.

Method and apparatus for obtaining complete speech signals for speech recognition applications

NASA Technical Reports Server (NTRS)

Abrash, Victor (Inventor); Cesari, Federico (Inventor); Franco, Horacio (Inventor); George, Christopher (Inventor); Zheng, Jing (Inventor)

2009-01-01

The present invention relates to a method and apparatus for obtaining complete speech signals for speech recognition applications. In one embodiment, the method continuously records an audio stream comprising a sequence of frames to a circular buffer. When a user command to commence or terminate speech recognition is received, the method obtains a number of frames of the audio stream occurring before or after the user command in order to identify an augmented audio signal for speech recognition processing. In further embodiments, the method analyzes the augmented audio signal in order to locate starting and ending speech endpoints that bound at least a portion of speech to be processed for recognition. At least one of the speech endpoints is located using a Hidden Markov Model.

Non-local Second Order Closure Scheme for Boundary Layer Turbulence and Convection

NASA Astrophysics Data System (ADS)

Meyer, Bettina; Schneider, Tapio

2017-04-01

There has been scientific consensus that the uncertainty in the cloud feedback remains the largest source of uncertainty in the prediction of climate parameters like climate sensitivity. To narrow down this uncertainty, not only a better physical understanding of cloud and boundary layer processes is required, but specifically the representation of boundary layer processes in models has to be improved. General climate models use separate parameterisation schemes to model the different boundary layer processes like small-scale turbulence, shallow and deep convection. Small scale turbulence is usually modelled by local diffusive parameterisation schemes, which truncate the hierarchy of moment equations at first order and use second-order equations only to estimate closure parameters. In contrast, the representation of convection requires higher order statistical moments to capture their more complex structure, such as narrow updrafts in a quasi-steady environment. Truncations of moment equations at second order may lead to more accurate parameterizations. At the same time, they offer an opportunity to take spatially correlated structures (e.g., plumes) into account, which are known to be important for convective dynamics. In this project, we study the potential and limits of local and non-local second order closure schemes. A truncation of the momentum equations at second order represents the same dynamics as a quasi-linear version of the equations of motion. We study the three-dimensional quasi-linear dynamics in dry and moist convection by implementing it in a LES model (PyCLES) and compare it to a fully non-linear LES. In the quasi-linear LES, interactions among turbulent eddies are suppressed but nonlinear eddy—mean flow interactions are retained, as they are in the second order closure. In physical terms, suppressing eddy—eddy interactions amounts to suppressing, e.g., interactions among convective plumes, while retaining interactions between plumes and the environment (e.g., entrainment and detrainment). In a second part, we employ the possibility to include non-local statistical correlations in a second-order closure scheme. Such non-local correlations allow to directly incorporate the spatially coherent structures that occur in the form of convective updrafts penetrating the boundary layer. This allows us to extend the work that has been done using assumed-PDF schemes for parameterising boundary layer turbulence and shallow convection in a non-local sense.

Non-isospectral Hamiltonians, intertwining operators and hidden hermiticity

NASA Astrophysics Data System (ADS)

Bagarello, F.

2011-12-01

We have recently proposed a strategy to produce, starting from a given Hamiltonian h and a certain operator x for which [h,xx]=0 and xx is invertible, a second Hamiltonian h with the same eigenvalues as h and whose eigenvectors are related to those of h by x. Here we extend this procedure to build up a second Hamiltonian, whose eigenvalues are different from those of h, and whose eigenvectors are still related as before. This new procedure is also extended to crypto-hermitian Hamiltonians.

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.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

A fast hidden line algorithm with contour option. M.S. Thesis

NASA Technical Reports Server (NTRS)

Thue, R. E.

1984-01-01

The JonesD algorithm was modified to allow the processing of N-sided elements and implemented in conjunction with a 3-D contour generation algorithm. The total hidden line and contour subsystem is implemented in the MOVIE.BYU Display package, and is compared to the subsystems already existing in the MOVIE.BYU package. The comparison reveals that the modified JonesD hidden line and contour subsystem yields substantial processing time savings, when processing moderate sized models comprised of 1000 elements or less. There are, however, some limitations to the modified JonesD subsystem.

Student portfolios and the hidden curriculum on gender: mapping exclusion.

PubMed

Phillips, Christine B

2009-09-01

The hidden curriculum - the norms, values and practices that are transmitted to students through modelling by preceptors and teachers, and decisions about curricular exclusions and inclusions - can be profoundly important in the socialising of trainee doctors. However, tracking the hidden curriculum as it evolves can be challenging for medical schools. This study aimed to explore the content of student e-portfolios on gender issues, a key perspective often taught through a hidden curriculum. Online posts for a gender and medicine e-portfolio task completed by two cohorts of students in Year 3 of a 4-year medical course (n = 167, 66% female) were analysed using a grounded theory approach. A process of gendered 'othering' was applied to both men and women in the medical school using different pedagogical strategies. Curricular emphases on women's health and lack of support for male students to acquire gynaecological examination skills were seen as explicit ways of excluding males. For female medical students, exclusion tended to be implicit, operating through modelling and aphoristic comments about so-called 'female-friendly' career choices and the negative impact of motherhood on career. E-portfolios can be a useful way of tracking the hidden curriculum as it evolves. Responses to gendered exclusion may be developed more readily for the explicit processes impacting on male students than for the implicit processes impacting on female students, which often reflect structural issues related to training and employment.

Modeling and simulation of continuous wave velocity radar based on third-order DPLL

NASA Astrophysics Data System (ADS)

Di, Yan; Zhu, Chen; Hong, Ma

2015-02-01

Second-order digital phase-locked-loop (DPLL) is widely used in traditional Continuous wave (CW) velocity radar with poor performance in high dynamic conditions. Using the third-order DPLL can improve the performance. Firstly, the echo signal model of CW radar is given. Secondly, theoretical derivations of the tracking performance in different velocity conditions are given. Finally, simulation model of CW radar is established based on Simulink tool. Tracking performance of the two kinds of DPLL in different acceleration and jerk conditions is studied by this model. The results show that third-order PLL has better performance in high dynamic conditions. This model provides a platform for further research of CW radar.

Generalized heat-transport equations: parabolic and hyperbolic models

NASA Astrophysics Data System (ADS)

Rogolino, Patrizia; Kovács, Robert; Ván, Peter; Cimmelli, Vito Antonio

2018-03-01

We derive two different generalized heat-transport equations: the most general one, of the first order in time and second order in space, encompasses some well-known heat equations and describes the hyperbolic regime in the absence of nonlocal effects. Another, less general, of the second order in time and fourth order in space, is able to describe hyperbolic heat conduction also in the presence of nonlocal effects. We investigate the thermodynamic compatibility of both models by applying some generalizations of the classical Liu and Coleman-Noll procedures. In both cases, constitutive equations for the entropy and for the entropy flux are obtained. For the second model, we consider a heat-transport equation which includes nonlocal terms and study the resulting set of balance laws, proving that the corresponding thermal perturbations propagate with finite speed.

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Facilities as teaching tools: A transformative participatory professional development experience

NASA Astrophysics Data System (ADS)

Wilson, Eric A.

Resource consumption continues to increase as the population grows. In order to secure a sustainable future, society must educate the next generation to become "sustainability natives." Schools play a pivotal role in educating a sustainability-literate society. However, a disconnect exists between the hidden curriculum of the built environment and the enacted curriculum. This study employs a transformative participatory professional development model to instruct teachers on how to use their school grounds as teaching tools for the purpose of helping students make explicit choices in energy consumption, materials use, and sustainable living. Incorporating a phenomenological perspective, this study considers the lived experience of two sustainability coordinators. Grounded theory provides an interpretational context for the participants' interactions with each other and the professional development process. Through a year long professional development experience - commencing with an intense, participatory two-day workshop -the participants discussed challenges they faced with integrating facilities into school curriculum and institutionalizing a culture of sustainability. Two major needs were identified in this study. For successful sustainability initiatives, a hybrid model that melds top-down and bottom-up approaches offers the requisite mix of administrative support, ground level buy-in, and excitement vis-a-vis sustainability. Second, related to this hybrid approach, K-12 sustainability coordinators ideally need administrative capabilities with access to decision making, while remaining connected to students in a meaningful way, either directly in the classroom, as a mentor, or through work with student groups and projects.

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Semantic Stability is More Pleasurable in Unstable Episodic Contexts. On the Relevance of Perceptual Challenge in Art Appreciation.

PubMed

Muth, Claudia; Raab, Marius H; Carbon, Claus-Christian

2016-01-01

Research in the field of psychological aesthetics points to the appeal of stimuli which defy easy recognition by being "semantically unstable" but which still allow for creating meaning-in the ongoing process of elaborative perception or as an end product of the entire process. Such effects were reported for hidden images (Muth and Carbon, 2013) as well as Cubist artworks concealing detectable-although fragmented-objects (Muth et al., 2013). To test the stability of the relationship between semantic determinacy and appreciation across different episodic contexts, 30 volunteers evaluated an artistic movie continuously on visual determinacy or liking via the Continuous Evaluation Procedure (CEP, Muth et al., 2015b). The movie consisted of five episodes with emerging Gestalts. In the first between-participants condition, the hidden Gestalts in the movie episodes were of increasing determinacy, in the second condition, the episodes showed decreasing determinacies of hidden Gestalts. In the increasing-determinacy group, visual determinacy was rated higher and showed better predictive quality for liking than in the decreasing-determinacy group. Furthermore, when the movie started with low visual determinacy of hidden Gestalts, unexpectedly strong increases in visual determinacy had a bigger effect on liking than in the condition which allowed for weaker Gestalt recognition after having started with highly determinate Gestalts. The resulting pattern calls for consideration of the episodic context when examining art appreciation.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

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.

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Behavior generation strategy of artificial behavioral system by self-learning paradigm for autonomous robot tasks

NASA Astrophysics Data System (ADS)

Dağlarli, Evren; Temeltaş, Hakan

2008-04-01

In this study, behavior generation and self-learning paradigms are investigated for the real-time applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and it combines them in order to achieve multi goal tasks. The proposed method is composed from three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level. Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered in the behavior based control architecture. The proposed method is tested on a four-wheel driven and four-wheel steered mobile robot with constraints in simulation environment and results are obtained successfully.

An Effective Method for Modeling Two-dimensional Sky Background of LAMOST

NASA Astrophysics Data System (ADS)

Haerken, Hasitieer; Duan, Fuqing; Zhang, Jiannan; Guo, Ping

2017-06-01

Each CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.

What if ? On alternative conceptual models and the problem of their implementation

NASA Astrophysics Data System (ADS)

Neuberg, Jurgen

2015-04-01

Seismic and other monitoring techniques rely on a set of conceptual models on the base of which data sets can be interpreted. In order to do this on an operational level in volcano observatories these models need to be tested and ready for an interpretation in a timely manner. Once established, scientists in charge advising stakeholders and decision makers often stick firmly to these models to avoid confusion by giving alternative versions of interpretations to non-experts. This talk gives an overview of widely accepted conceptual models to interpret seismic and deformation data, and highlights in a few case studies some of the arising problems. Aspects covered include knowledge transfer between research institutions and observatories, data sharing, the problem of up-taking advice, and some hidden problems which turn out to be much more critical in assessing volcanic hazard than the actual data interpretation.

Effect of electrolyte nature on kinetics of remazol yellow G removal by electrocoagulation

NASA Astrophysics Data System (ADS)

Rajabi, M.; Bagheri-Roochi, M.; Asghari, A.

2011-10-01

The present study describes an electrocoagulation process for the removal of remazol yellow G from dye solutions using Iron as the anode and Steel as the cathode. Pseudo-first-order, pseudo-second-order and intraparticle diffusion models were used to analyze the kinetic data obtained at different concentrations in different conditions. The adsorption kinetics was well described by the pseudo-second-order kinetic model.

Quantification of Operational Risk Using A Data Mining

NASA Technical Reports Server (NTRS)

Perera, J. Sebastian

1999-01-01

What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process

Communication cost of simulating Bell correlations.

PubMed

Toner, B F; Bacon, D

2003-10-31

What classical resources are required to simulate quantum correlations? For the simplest and most important case of local projective measurements on an entangled Bell pair state, we show that exact simulation is possible using local hidden variables augmented by just one bit of classical communication. Certain quantum teleportation experiments, which teleport a single qubit, therefore admit a local hidden variables model.

The Development of Comprehensive Search Skills.

ERIC Educational Resources Information Center

Wellman, Henry M.; And Others

1984-01-01

One experiment examined two and one-half, three and one-half and four and one-half year olds' ability to conduct nonredundant comprehensive searches of small lidded trash cans. A second experiment examined three , four, and five year olds' ability to find Easter eggs hidden on a large playground. Results were discussed in relation to developmental…

Can Newton's Third Law Be "Derived" from the Second?

ERIC Educational Resources Information Center

Gangopadhyaya, Asim; Harrington, James

2017-01-01

Newton's laws have engendered much discussion over several centuries. Today, the internet is awash with a plethora of information on this topic. We find many references to Newton's laws, often discussions of various types of misunderstandings and ways to explain them. Here we present an intriguing example that shows an assumption hidden in…

World View: The Second Hidden Dimension.

ERIC Educational Resources Information Center

Skow, Lisa; And Others

Proposing that world view is a dimension of culture which lies below the surface of human behavior at the level of the subconscious, this paper argues that this often ignored dimension of culture profoundly influences human communication. The paper is divided into two sections. First, world view is defined and its importance in explaining how…

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

NASA Astrophysics Data System (ADS)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

Measuring the usefulness of hidden units in Boltzmann machines with mutual information.

PubMed

Berglund, Mathias; Raiko, Tapani; Cho, Kyunghyun

2015-04-01

Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in deep learning, but it is often difficult to measure their performance in general, or measure the importance of individual hidden units in specific. We propose to use mutual information to measure the usefulness of individual hidden units in Boltzmann machines. The measure is fast to compute, and serves as an upper bound for the information the neuron can pass on, enabling detection of a particular kind of poor training results. We confirm experimentally that the proposed measure indicates how much the performance of the model drops when some of the units of an RBM are pruned away. We demonstrate the usefulness of the measure for early detection of poor training in DBMs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Validation of a RANS transition model using a high-order weighted compact nonlinear scheme

NASA Astrophysics Data System (ADS)

Tu, GuoHua; Deng, XiaoGang; Mao, MeiLiang

2013-04-01

A modified transition model is given based on the shear stress transport (SST) turbulence model and an intermittency transport equation. The energy gradient term in the original model is replaced by flow strain rate to saving computational costs. The model employs local variables only, and then it can be conveniently implemented in modern computational fluid dynamics codes. The fifth-order weighted compact nonlinear scheme and the fourth-order staggered scheme are applied to discrete the governing equations for the purpose of minimizing discretization errors, so as to mitigate the confusion between numerical errors and transition model errors. The high-order package is compared with a second-order TVD method on simulating the transitional flow of a flat plate. Numerical results indicate that the high-order package give better grid convergence property than that of the second-order method. Validation of the transition model is performed for transitional flows ranging from low speed to hypersonic speed.

Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

PubMed Central

Yang, Sejung; Lee, Byung-Uk

2015-01-01

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138

A second-order impact model for forest fire regimes.

PubMed

Maggi, Stefano; Rinaldi, Sergio

2006-09-01

We present a very simple "impact" model for the description of forest fires and show that it can mimic the known characteristics of wild fire regimes in savannas, boreal forests, and Mediterranean forests. Moreover, the distribution of burned biomasses in model generated fires resemble those of burned areas in numerous large forests around the world. The model has also the merits of being the first second-order model for forest fires and the first example of the use of impact models in the study of ecosystems.

Reconsidering nutrition science: critical reflection with a cultural lens

PubMed Central

2014-01-01

Background European culture gave birth to modern science as a means to investigate and explain the natural world. The biomedical disciplines that have since emerged, including nutrition, presuppose a web of basic presuppositions, background assumptions and implicit cultural values that are often overlooked and escape peer review. These "hidden subjectivities" are widely taken-for-granted while exerting a powerful hold on the scope, direction and patterns of disciplinary thought. Nutrition science currently has no accepted means of collectively attending to hidden subjectivities embedded within its methods and practice. Here I propose that directing inquiry into these dimensions holds potential to advance our discipline. Methods This critically reflective approach emerged from critical theory and the practice of cross-cultural engagement (CCE). CCE deliberately seeks out and critically engages food and health understandings of non-European cultures. Its protocol includes cognitive frameshifting, a practice of temporarily stepping outside of habitual thought patterns and into a non-biomedical framework of background assumptions. A cultural lens metaphor derives from CCE practice and is forwarded here as a viable means for restoring critically reflective attention to hidden subjectivities while also inviting further CCE practice within the discipline. Results Critical reflection with a cultural lens allows cognitive attachments to materialism, reductionism, mechanistic thought, naïve realism, control over nature and pervasive subject-object dichotomies between mind and matter, scientist and nature, experience and reality, among many others to become more available for critical consideration. Culturally diverse food and health understandings otherwise dismissed as "unscientific" or held in abeyance gain value as alternative assumptive frameworks and cognitive models that can be temporarily inhabited for further intercultural reflection and insight. Conclusion Critical reflection with a cultural lens allows reconsideration of nutrition science in light of its culturally specific origin and foundation. This perspective can advance the discipline in two ways. First, it extends skeptical inquiry into hidden subjectivities that are otherwise implicit and seldom given over to critical consideration. Second, it can broaden scholarly inquiry through deliberate attempts to cross cognitive boundaries and empathically inhabit different cognitive worlds. This developmental practice holds potential to both deepen and broaden disciplinary inquiry. PMID:24886077

Reconsidering nutrition science: critical reflection with a cultural lens.

PubMed

Hassel, Craig A

2014-05-02

European culture gave birth to modern science as a means to investigate and explain the natural world. The biomedical disciplines that have since emerged, including nutrition, presuppose a web of basic presuppositions, background assumptions and implicit cultural values that are often overlooked and escape peer review. These "hidden subjectivities" are widely taken-for-granted while exerting a powerful hold on the scope, direction and patterns of disciplinary thought. Nutrition science currently has no accepted means of collectively attending to hidden subjectivities embedded within its methods and practice. Here I propose that directing inquiry into these dimensions holds potential to advance our discipline. This critically reflective approach emerged from critical theory and the practice of cross-cultural engagement (CCE). CCE deliberately seeks out and critically engages food and health understandings of non-European cultures. Its protocol includes cognitive frameshifting, a practice of temporarily stepping outside of habitual thought patterns and into a non-biomedical framework of background assumptions. A cultural lens metaphor derives from CCE practice and is forwarded here as a viable means for restoring critically reflective attention to hidden subjectivities while also inviting further CCE practice within the discipline. Critical reflection with a cultural lens allows cognitive attachments to materialism, reductionism, mechanistic thought, naïve realism, control over nature and pervasive subject-object dichotomies between mind and matter, scientist and nature, experience and reality, among many others to become more available for critical consideration. Culturally diverse food and health understandings otherwise dismissed as "unscientific" or held in abeyance gain value as alternative assumptive frameworks and cognitive models that can be temporarily inhabited for further intercultural reflection and insight. Critical reflection with a cultural lens allows reconsideration of nutrition science in light of its culturally specific origin and foundation. This perspective can advance the discipline in two ways. First, it extends skeptical inquiry into hidden subjectivities that are otherwise implicit and seldom given over to critical consideration. Second, it can broaden scholarly inquiry through deliberate attempts to cross cognitive boundaries and empathically inhabit different cognitive worlds. This developmental practice holds potential to both deepen and broaden disciplinary inquiry.

Association between perceived social stigma against mental disorders and use of health services for psychological distress symptoms in the older adult population: validity of the STIG scale.

PubMed

Préville, Michel; Mechakra Tahiri, Samia Djemaa; Vasiliadis, Helen-Maria; Quesnel, Louise; Gontijo-Guerra, Samantha; Lamoureux-Lamarche, Catherine; Berbiche, Djamal

2015-01-01

To document the reliability, construct and nomological validity of the perceived Social Stigmatisation (STIG) scale in the older adult population. Cross-sectional survey. Primary medical health services clinics. Probabilistic sample of older adults aged 65 years and over waiting for medical services in the general medical sector (n = 1765). Perceived social stigma against people with a mental health problem was measured using the STIG scale composed of seven indicators. A second-order measurement model of perceived social stigma fitted adequately the observed data. The reliability of the STIG scale was 0.83. According to our results, 39.6% of older adults had a significant level of perceived social stigma against people with a mental health problem. RESULTS showed that the perception of social stigma against mental health problems was not significantly associated with a respondent gender and age. RESULTS also showed that the perception of social stigma against the mental health problems was directly associated with the respondents' need for improved mental health (b = -0.10) and indirectly associated with their use of primary medical health services for psychological distress symptoms (b = -0.07). RESULTS lead us to conclude that social stigma against mental disorders perceived by older adults may limit help-seeking behaviours and warrants greater public health and public policy attention. Also, results lead us to conclude that physicians should pay greater attention to their patients' attitudes against mental disorders in order to identify possible hidden mental health problems.

Linearly first- and second-order, unconditionally energy stable schemes for the phase field crystal model

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

Yang, Xiaofeng, E-mail: xfyang@math.sc.edu; Han, Daozhi, E-mail: djhan@iu.edu

2017-02-01

In this paper, we develop a series of linear, unconditionally energy stable numerical schemes for solving the classical phase field crystal model. The temporal discretizations are based on the first order Euler method, the second order backward differentiation formulas (BDF2) and the second order Crank–Nicolson method, respectively. The schemes lead to linear elliptic equations to be solved at each time step, and the induced linear systems are symmetric positive definite. We prove that all three schemes are unconditionally energy stable rigorously. Various classical numerical experiments in 2D and 3D are performed to validate the accuracy and efficiency of the proposedmore » schemes.« less

Religion, Spirituality, and the Hidden Curriculum: Medical Student and Faculty Reflections.

PubMed

Balboni, Michael J; Bandini, Julia; Mitchell, Christine; Epstein-Peterson, Zachary D; Amobi, Ada; Cahill, Jonathan; Enzinger, Andrea C; Peteet, John; Balboni, Tracy

2015-10-01

Religion and spirituality play an important role in physicians' medical practice, but little research has examined their influence within the socialization of medical trainees and the hidden curriculum. The objective is to explore the role of religion and spirituality as they intersect with aspects of medicine's hidden curriculum. Semiscripted, one-on-one interviews and focus groups (n = 33 respondents) were conducted to assess Harvard Medical School student and faculty experiences of religion/spirituality and the professionalization process during medical training. Using grounded theory, theme extraction was performed with interdisciplinary input (medicine, sociology, and theology), yielding a high inter-rater reliability score (kappa = 0.75). Three domains emerged where religion and spirituality appear as a factor in medical training. First, religion/spirituality may present unique challenges and benefits in relation to the hidden curriculum. Religious/spiritual respondents more often reported to struggle with issues of personal identity, increased self-doubt, and perceived medical knowledge inadequacy. However, religious/spiritual participants less often described relationship conflicts within the medical team, work-life imbalance, and emotional stress arising from patient suffering. Second, religion/spirituality may influence coping strategies during encounters with patient suffering. Religious/spiritual trainees described using prayer, faith, and compassion as means for coping whereas nonreligious/nonspiritual trainees discussed compartmentalization and emotional repression. Third, levels of religion/spirituality appear to fluctuate in relation to medical training, with many trainees experiencing an increase in religiousness/spirituality during training. Religion/spirituality has a largely unstudied but possibly influential role in medical student socialization. Future study is needed to characterize its function within the hidden curriculum. Copyright © 2015 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Religion, Spirituality, and the Hidden Curriculum: Medical Student and Faculty Reflections

PubMed Central

Balboni, Michael J.; Bandini, Julia; Mitchell, Christine; Epstein-Peterson, Zachary D.; Amobi, Ada; Cahill, Jonathan; Enzinger, Andrea C.; Peteet, John; Balboni, Tracy

2017-01-01

Context Religion and spirituality play an important role in physicians’ medical practice, but little research has examined their influence within the socialization of medical trainees and the hidden curriculum. Objectives The objective is to explore the role of religion and spirituality as they intersect with aspects of medicine’s hidden curriculum. Methods Semiscripted, one-on-one interviews and focus groups (n = 33 respondents) were conducted to assess Harvard Medical School student and faculty experiences of religion/spirituality and the professionalization process during medical training. Using grounded theory, theme extraction was performed with interdisciplinary input (medicine, sociology, and theology), yielding a high inter-rater reliability score (kappa = 0.75). Results Three domains emerged where religion and spirituality appear as a factor in medical training. First, religion/spirituality may present unique challenges and benefits in relation to the hidden curriculum. Religious/spiritual respondents more often reported to struggle with issues of personal identity, increased self-doubt, and perceived medical knowledge inadequacy. However, religious/spiritual participants less often described relationship conflicts within the medical team, work-life imbalance, and emotional stress arising from patient suffering. Second, religion/spirituality may influence coping strategies during encounters with patient suffering. Religious/spiritual trainees described using prayer, faith, and compassion as means for coping whereas nonreligious/nonspiritual trainees discussed compartmentalization and emotional repression. Third, levels of religion/spirituality appear to fluctuate in relation to medical training, with many trainees experiencing an increase in religiousness/spirituality during training. Conclusion Religion/spirituality has a largely unstudied but possibly influential role in medical student socialization. Future study is needed to characterize its function within the hidden curriculum. PMID:26025271

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Uncovering hidden heterogeneity: Geo-statistical models illuminate the fine scale effects of boating infrastructure on sediment characteristics and contaminants.

PubMed

Hedge, L H; Dafforn, K A; Simpson, S L; Johnston, E L

2017-06-30

Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Where-Fi: a dynamic energy-efficient multimedia distribution framework for MANETs

NASA Astrophysics Data System (ADS)

Mohapatra, Shivajit; Carbunar, Bogdan; Pearce, Michael; Chaudhri, Rohit; Vasudevan, Venu

2008-01-01

Next generation mobile ad-hoc applications will revolve around users' need for sharing content/presence information with co-located devices. However, keeping such information fresh requires frequent meta-data exchanges, which could result in significant energy overheads. To address this issue, we propose distributed algorithms for energy efficient dissemination of presence and content usage information between nodes in mobile ad-hoc networks. First, we introduce a content dissemination protocol (called CPMP) for effectively distributing frequent small meta-data updates between co-located devices using multicast. We then develop two distributed algorithms that use the CPMP protocol to achieve "phase locked" wake up cycles for all the participating nodes in the network. The first algorithm is designed for fully-connected networks and then extended in the second to handle hidden terminals. The "phase locked" schedules are then exploited to adaptively transition the network interface to a deep sleep state for energy savings. We have implemented a prototype system (called "Where-Fi") on several Motorola Linux-based cell phone models. Our experimental results show that for all network topologies our algorithms were able to achieve "phase locking" between nodes even in the presence of hidden terminals. Moreover, we achieved battery lifetime extensions of as much as 28% for fully connected networks and about 20% for partially connected networks.

Second order modeling of boundary-free turbulent shear flows

NASA Technical Reports Server (NTRS)

Shih, T.-H.; Chen, Y.-Y.; Lumley, J. L.

1991-01-01

A set of realizable second order models for boundary-free turbulent flows is presented. The constraints on second order models based on the realizability principle are re-examined. The rapid terms in the pressure correlations for both the Reynolds stress and the passive scalar flux equations are constructed to exactly satisfy the joint realizability. All other model terms (return-to-isotropy, third moments, and terms in the dissipation equations) already satisfy realizability. To correct the spreading rate of the axisymmetric jet, an extra term is added to the dissipation equation which accounts for the effect of mean vortex stretching on dissipation. The test flows used in this study are the mixing shear layer, plane jet, axisymmetric jet, and plane wake. The numerical solutions show that the unified model equations predict all these flows reasonably. It is expected that these models would be suitable for more complex and critical flows.

Cryptic mosaicism involving a second chromosome X in patients with Turner syndrome.

PubMed

Araújo, A; Ramos, E S

2008-05-01

The high abortion rate of 45,X embryos indicates that patients with Turner syndrome and 45,X karyotype could be mosaics, in at least one phase of embryo development or cellular lineage, due to the need for the other sex chromosome presence for conceptus to be compatible with life. In cases of structural chromosomal aberrations or hidden mosaicism, conventional cytogenetic techniques can be ineffective and molecular investigation is indicated. Two hundred and fifty patients with Turner syndrome stigmata were studied and 36 who had female genitalia and had been cytogenetically diagnosed as having "pure" 45,X karyotype were selected after 100 metaphases were analyzed in order to exclude mosaicism and the presence of genomic Y-specific sequences (SRY, TSPY, and DAZ) was excluded by PCR. Genomic DNA was extracted from peripheral blood and screened by the human androgen receptor (HUMARA) assay. The HUMARA gene has a polymorphic CAG repeat and, in the presence of a second chromosome with a different HUMARA allele, a second band will be amplified by PCR. Additionally, the CAG repeats contain two methylation-sensitive HpaII enzyme restriction sites, which can be used to verify skewed inactivation. Twenty-five percent (9/36) of the cases showed a cryptic mosaicism involving a second X and approximately 14% (5/36), or 55% (5/9) of the patients with cryptic mosaicism, also presented skewed inactivation. The laboratory identification of the second X chromosome and its inactivation pattern are important for the clinical management (hormone replacement therapy, and inclusion in an oocyte donation program) and prognostic counseling of patients with Turner syndrome.

A unified model for transfer alignment at random misalignment angles based on second-order EKF

NASA Astrophysics Data System (ADS)

Cui, Xiao; Mei, Chunbo; Qin, Yongyuan; Yan, Gongmin; Liu, Zhenbo

2017-04-01

In the transfer alignment process of inertial navigation systems (INSs), the conventional linear error model based on the small misalignment angle assumption cannot be applied to large misalignment situations. Furthermore, the nonlinear model based on the large misalignment angle suffers from redundant computation with nonlinear filters. This paper presents a unified model for transfer alignment suitable for arbitrary misalignment angles. The alignment problem is transformed into an estimation of the relative attitude between the master INS (MINS) and the slave INS (SINS), by decomposing the attitude matrix of the latter. Based on the Rodriguez parameters, a unified alignment model in the inertial frame with the linear state-space equation and a second order nonlinear measurement equation are established, without making any assumptions about the misalignment angles. Furthermore, we employ the Taylor series expansions on the second-order nonlinear measurement equation to implement the second-order extended Kalman filter (EKF2). Monte-Carlo simulations demonstrate that the initial alignment can be fulfilled within 10 s, with higher accuracy and much smaller computational cost compared with the traditional unscented Kalman filter (UKF) at large misalignment angles.

Efficient view based 3-D object retrieval using Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

General gauge mediation in five dimensions

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

McGarrie, Moritz; Russo, Rodolfo

2010-08-01

We use the ''general gauge mediation'' (GGM) formalism to describe a five-dimensional setup with an S{sup 1}/Z{sub 2} orbifold. We first consider a model independent supersymmetry breaking hidden sector on one boundary and generic chiral matter on another. Using the definition of GGM, the effects of the hidden sector are contained in a set of global symmetry current correlator functions and is mediated through the bulk. We find the gaugino, sfermion and hyperscalar mass formulas for minimal and generalized messengers in different regimes of a large, small and intermediate extra dimension. Then we use the five-dimensional GGM formalism to constructmore » a model in which an SU(5) Intriligator, Seiberg and Shih (ISS) model is located on the hidden boundary. We weakly gauge a global symmetry of the ISS model and associate it with the bulk vector superfield. Compared to four-dimensional GGM, there is a natural way to adjust the gaugino versus sfermion mass ratio by a factor (Ml){sup 2}, where M is a characteristic mass scale of the supersymmetry breaking sector and l is the length of the extra dimension.« less

Context-invariant quasi hidden variable (qHV) modelling of all joint von Neumann measurements for an arbitrary Hilbert space

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

Loubenets, Elena R.

We prove the existence for each Hilbert space of the two new quasi hidden variable (qHV) models, statistically noncontextual and context-invariant, reproducing all the von Neumann joint probabilities via non-negative values of real-valued measures and all the quantum product expectations—via the qHV (classical-like) average of the product of the corresponding random variables. In a context-invariant model, a quantum observable X can be represented by a variety of random variables satisfying the functional condition required in quantum foundations but each of these random variables equivalently models X under all joint von Neumann measurements, regardless of their contexts. The proved existence ofmore » this model negates the general opinion that, in terms of random variables, the Hilbert space description of all the joint von Neumann measurements for dimH≥3 can be reproduced only contextually. The existence of a statistically noncontextual qHV model, in particular, implies that every N-partite quantum state admits a local quasi hidden variable model introduced in Loubenets [J. Math. Phys. 53, 022201 (2012)]. The new results of the present paper point also to the generality of the quasi-classical probability model proposed in Loubenets [J. Phys. A: Math. Theor. 45, 185306 (2012)].« 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.

Central Compact Objects in Kes 79 and RCW 103 as `Hidden' Magnetars with Crustal Activity

NASA Astrophysics Data System (ADS)

Popov, S. B.; Kaurov, A. A.; Kaminker, A. D.

2015-05-01

We propose that observations of `hidden' magnetars in central compact objects can be used to probe crustal activity of neutron stars with large internal magnetic fields. Estimates based on calculations by Perna & Pons, Pons & Rea and Kaminker et al. suggest that central compact objects, which are proposed to be `hidden' magnetars, must demonstrate flux variations on the time scale of months-years. However, the most prominent candidate for the `hidden' magnetars - CXO J1852.6+0040 in Kes 79 - shows constant (within error bars) flux. This can be interpreted by lower variable crustal activity than in typical magnetars. Alternatively, CXO J1852.6+0040 can be in a high state of variable activity during the whole period of observations. Then we consider the source 1E161348 - 5055 in RCW103 as another candidate. Employing a simple 2D-modelling we argue that properties of the source can be explained by the crustal activity of the magnetar type. Thus, this object may be supplemented for the three known candidates for the `hidden' magnetars among central compact objects discussed in literature.

Hidden axion dark matter decaying through mixing with QCD axion and the 3.5 keV X-ray line

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

Higaki, Tetsutaro; Kitajima, Naoya; Takahashi, Fuminobu, E-mail: thigaki@post.kek.jp, E-mail: kitajima@tuhep.phys.tohoku.ac.jp, E-mail: fumi@tuhep.phys.tohoku.ac.jp

2014-12-01

Hidden axions may be coupled to the standard model particles through a kinetic or mass mixing with QCD axion. We study a scenario in which a hidden axion constitutes a part of or the whole of dark matter and decays into photons through the mixing, explaining the 3.5 keV X-ray line signal. Interestingly, the required long lifetime of the hidden axion dark matter can be realized for the QCD axion decay constant at an intermediate scale, if the mixing is sufficiently small. In such a two component dark matter scenario, the primordial density perturbations of the hidden axion can bemore » highly non-Gaussian, leading to a possible dispersion in the X-ray line strength from various galaxy clusters and near-by galaxies. We also discuss how the parallel and orthogonal alignment of two axions affects their couplings to gauge fields. In particular, the QCD axion decay constant can be much larger than the actual Peccei-Quinn symmetry breaking.« less

Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations*

PubMed Central

Feehan, Dennis M.; Salganik, Matthew J.

2018-01-01

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies. PMID:29375167

Sacrificial bonds and hidden length in biomaterials -- a kinetic description of strength and toughness in bone

NASA Astrophysics Data System (ADS)

Lieou, Charles K. C.; Elbanna, Ahmed E.; Carlson, Jean M.

2013-03-01

Sacrificial bonds and hidden length in structural molecules account for the greatly increased fracture toughness of biological materials compared to synthetic materials without such structural features, by providing a molecular-scale mechanism of energy dissipation. One example of occurrence of sacrificial bonds and hidden length is in the polymeric glue connection between collagen fibrils in animal bone. In this talk, we propose a simple kinetic model that describes the breakage of sacrificial bonds and the revelation of hidden length, based on Bell's theory. We postulate a master equation governing the rates of bond breakage and formation, at the mean-field level, allowing for the number of bonds and hidden lengths to take up non-integer values between successive, discrete bond-breakage events. This enables us to predict the mechanical behavior of a quasi-one-dimensional ensemble of polymers at different stretching rates. We find that both the rupture peak heights and maximum stretching distance increase with the stretching rate. In addition, our theory naturally permits the possibility of self-healing in such biological structures.

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.

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

Hidden Order as a Source of Interface Superconductivity

NASA Astrophysics Data System (ADS)

Moor, Andreas; Volkov, Anatoly; Efetov, Konstantin

2015-03-01

We propose a new mechanism of the interfacial superconductivity observed in many heterostructures composed of different materials including high-temperature superconductors. Our proposal is based on the use of the Ginzburg-Landau equations applicable to a wide class of systems. The system under consideration is assumed to have, alongside the superconducting order parameter, also another competing order that might be a charge- or spin-density wave. At certain temperatures or doping level the superconducting state is not realized (thus, ``hidden''), while the amplitude of another order parameter corresponds to a minimum of the free energy. We also assume that at an interface or at a defect, the non-superconducting order parameter is suppressed (strongly or weakly), e.g., due to an enhanced impurity scattering. The local superconductivity is shown to emerge at the interface, and the spatial dependence of the corresponding order parameter is described by the Gross-Pitaevskii equation. The quantized values of the temperature and doping levels, at which Δ (x) arises, are determined by the ``energy'' levels of the linearized Gross-Pitaevskii equation, i.e., of the Schrodinger equation. Interestingly, the local superconductivity arises even at a small suppression of the rival order. We appreciate the support from DFG via the Projekt EF 11/8-1; K. B. E. gratefully acknowledges the financial support of the Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST ``MISiS.''

Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis.

PubMed

Burunat, Iballa; Tsatsishvili, Valeri; Brattico, Elvira; Toiviainen, Petri

2017-01-01

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants' (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.e., under a varying number of assumed independent sources, to avoid relying on prior model order assumptions. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory (perception) and motor (action) areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed (a) a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and (b) group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

Multilinear Graph Embedding: Representation and Regularization for Images.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting

2014-02-01

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

Baryogenesis and gravitational waves from runaway bubble collisions

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

Katz, Andrey; Département de Physique Théorique and Center for Astroparticle Physics; Riotto, Antonio

We propose a novel mechanism for production of baryonic asymmetry in the early Universe. The mechanism takes advantage of the strong first order phase transition that produces runaway bubbles in the hidden sector that propagate almost without friction with ultra-relativistic velocities. Collisions of such bubbles can non-thermally produce heavy particles that further decay out-of-equilibrium into the SM and produce the observed baryonic asymmetry. This process can proceed at the very low temperatures, providing a new mechanism of post-sphaleron baryogenesis. In this paper we present a fully calculable model which produces the baryonic asymmetry along these lines as well as evadesmore » all the existing cosmological constraints. We emphasize that the Gravitational Waves signal from the first order phase transition is completely generic and can potentially be detected by the future eLISA interferometer. We also discuss other potential signals, which are more model dependent, and point out the unresolved theoretical questions related to our proposal.« less

Unified origin for baryonic visible matter and antibaryonic dark matter.

PubMed

Davoudiasl, Hooman; Morrissey, David E; Sigurdson, Kris; Tulin, Sean

2010-11-19

We present a novel mechanism for generating both the baryon and dark matter densities of the Universe. A new Dirac fermion X carrying a conserved baryon number charge couples to the standard model quarks as well as a GeV-scale hidden sector. CP-violating decays of X, produced nonthermally in low-temperature reheating, sequester antibaryon number in the hidden sector, thereby leaving a baryon excess in the visible sector. The antibaryonic hidden states are stable dark matter. A spectacular signature of this mechanism is the baryon-destroying inelastic scattering of dark matter that can annihilate baryons at appreciable rates relevant for nucleon decay searches.

Change and Anomaly Detection in Real-Time GPS Data

NASA Astrophysics Data System (ADS)

Granat, R.; Pierce, M.; Gao, X.; Bock, Y.

2008-12-01

The California Real-Time Network (CRTN) is currently generating real-time GPS position data at a rate of 1-2Hz at over 80 locations. The CRTN data presents the possibility of studying dynamical solid earth processes in a way that complements existing seismic networks. To realize this possibility we have developed a prototype system for detecting changes and anomalies in the real-time data. Through this system, we can can correlate changes in multiple stations in order to detect signals with geographical extent. Our approach involves developing a statistical model for each GPS station in the network, and then using those models to segment the time series into a number of discrete states described by the model. We use a hidden Markov model (HMM) to describe the behavior of each station; fitting the model to the data requires neither labeled training examples nor a priori information about the system. As such, HMMs are well suited to this problem domain, in which the data remains largely uncharacterized. There are two main components to our approach. The first is the model fitting algorithm, regularized deterministic annealing expectation- maximization (RDAEM), which provides robust, high-quality results. The second is a web service infrastructure that connects the data to the statistical modeling analysis and allows us to easily present the results of that analysis through a web portal interface. This web service approach facilitates the automatic updating of station models to keep pace with dynamical changes in the data. Our web portal interface is critical to the process of interpreting the data. A Google Maps interface allows users to visually interpret state changes not only on individual stations but across the entire network. Users can drill down from the map interface to inspect detailed results for individual stations, download the time series data, and inspect fitted models. Alternatively, users can use the web portal look at the evolution of changes on the network by moving backwards and forwards in time.

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

A preliminary compressible second-order closure model for high speed flows

NASA Technical Reports Server (NTRS)

Speziale, Charles G.; Sarkar, Sutanu

1989-01-01

A preliminary version of a compressible second-order closure model that was developed in connection with the National Aero-Space Plane Project is presented. The model requires the solution of transport equations for the Favre-averaged Reynolds stress tensor and dissipation rate. Gradient transport hypotheses are used for the Reynolds heat flux, mass flux, and turbulent diffusion terms. Some brief remarks are made about the direction of future research to generalize the model.

Evaluation of the kinetic oxidation of aqueous volatile organic compounds by permanganate.

PubMed

Mahmoodlu, Mojtaba G; Hassanizadeh, S Majid; Hartog, Niels

2014-07-01

The use of permanganate solutions for in-situ chemical oxidation (ISCO) is a well-established groundwater remediation technology, particularly for targeting chlorinated ethenes. The kinetics of oxidation reactions is an important ISCO remediation design aspect that affects the efficiency and oxidant persistence. The overall rate of the ISCO reaction between oxidant and contaminant is typically described using a second-order kinetic model while the second-order rate constant is determined experimentally by means of a pseudo first order approach. However, earlier studies of chlorinated hydrocarbons have yielded a wide range of values for the second-order rate constants. Also, there is limited insight in the kinetics of permanganate reactions with fuel-derived groundwater contaminants such as toluene and ethanol. In this study, batch experiments were carried out to investigate and compare the oxidation kinetics of aqueous trichloroethylene (TCE), ethanol, and toluene in an aqueous potassium permanganate solution. The overall second-order rate constants were determined directly by fitting a second-order model to the data, instead of typically using the pseudo-first-order approach. The second-order reaction rate constants (M(-1) s(-1)) for TCE, toluene, and ethanol were 8.0×10(-1), 2.5×10(-4), and 6.5×10(-4), respectively. Results showed that the inappropriate use of the pseudo-first-order approach in several previous studies produced biased estimates of the second-order rate constants. In our study, this error was expressed as a function of the extent (P/N) in which the reactant concentrations deviated from the stoichiometric ratio of each oxidation reaction. The error associated with the inappropriate use of the pseudo-first-order approach is negatively correlated with the P/N ratio and reached up to 25% of the estimated second-order rate constant in some previous studies of TCE oxidation. Based on our results, a similar relation is valid for the other volatile organic compounds studied. Copyright © 2013 Elsevier B.V. All rights reserved.

Modeling Driver Behavior near Intersections in Hidden Markov Model

PubMed Central

Li, Juan; He, Qinglian; Zhou, Hang; Guan, Yunlin; Dai, Wei

2016-01-01

Intersections are one of the major locations where safety is a big concern to drivers. Inappropriate driver behaviors in response to frequent changes when approaching intersections often lead to intersection-related crashes or collisions. Thus to better understand driver behaviors at intersections, especially in the dilemma zone, a Hidden Markov Model (HMM) is utilized in this study. With the discrete data processing, the observed dynamic data of vehicles are used for the inference of the Hidden Markov Model. The Baum-Welch (B-W) estimation algorithm is applied to calculate the vehicle state transition probability matrix and the observation probability matrix. When combined with the Forward algorithm, the most likely state of the driver can be obtained. Thus the model can be used to measure the stability and risk of driver behavior. It is found that drivers’ behaviors in the dilemma zone are of lower stability and higher risk compared with those in other regions around intersections. In addition to the B-W estimation algorithm, the Viterbi Algorithm is utilized to predict the potential dangers of vehicles. The results can be applied to driving assistance systems to warn drivers to avoid possible accidents. PMID:28009838

Advancements in engineering turbulence modeling

NASA Technical Reports Server (NTRS)

Shih, T.-H.

1991-01-01

Some new developments in two-equation models and second order closure models are presented. Two-equation models (k-epsilon models) have been widely used in computational fluid dynamics (CFD) for engineering problems. Most of low-Reynolds number two-equation models contain some wall-distance damping functions to account for the effect of wall on turbulence. However, this often causes the confusion and difficulties in computing flows with complex geometry and also needs an ad hoc treatment near the separation and reattachment points. A set of modified two-equation models is proposed to remove the aforementioned shortcomings. The calculations using various two-equation models are compared with direct numerical simulations of channel flow and flat boundary layers. Development of a second order closure model is also discussed with emphasis on the modeling of pressure related correlation terms and dissipation rates in the second moment equations. All the existing models poorly predict the normal stresses near the wall and fail to predict the 3-D effect of mean flow on the turbulence (e.g. decrease in the shear stress caused by the cross flow in the boundary layer). The newly developed second order near-wall turbulence model is described and is capable of capturing the near-wall behavior of turbulence as well as the effect of 3-D mean flow on the turbulence.

Hidden magnetism and quantum criticality in the heavy fermion superconductor CeRhIn5.

PubMed

Park, Tuson; Ronning, F; Yuan, H Q; Salamon, M B; Movshovich, R; Sarrao, J L; Thompson, J D

2006-03-02

With only a few exceptions that are well understood, conventional superconductivity does not coexist with long-range magnetic order (for example, ref. 1). Unconventional superconductivity, on the other hand, develops near a phase boundary separating magnetically ordered and magnetically disordered phases. A maximum in the superconducting transition temperature T(c) develops where this boundary extrapolates to zero Kelvin, suggesting that fluctuations associated with this magnetic quantum-critical point are essential for unconventional superconductivity. Invariably, though, unconventional superconductivity masks the magnetic phase boundary when T < T(c), preventing proof of a magnetic quantum-critical point. Here we report specific-heat measurements of the pressure-tuned unconventional superconductor CeRhIn5 in which we find a line of quantum-phase transitions induced inside the superconducting state by an applied magnetic field. This quantum-critical line separates a phase of coexisting antiferromagnetism and superconductivity from a purely unconventional superconducting phase, and terminates at a quantum tetracritical point where the magnetic field completely suppresses superconductivity. The T --> 0 K magnetic field-pressure phase diagram of CeRhIn5 is well described with a theoretical model developed to explain field-induced magnetism in the high-T(c) copper oxides, but in which a clear delineation of quantum-phase boundaries has not been possible. These experiments establish a common relationship among hidden magnetism, quantum criticality and unconventional superconductivity in copper oxides and heavy-electron systems such as CeRhIn5.

Kinematic sensitivity of robot manipulators

NASA Technical Reports Server (NTRS)

Vuskovic, Marko I.

1989-01-01

Kinematic sensitivity vectors and matrices for open-loop, n degrees-of-freedom manipulators are derived. First-order sensitivity vectors are defined as partial derivatives of the manipulator's position and orientation with respect to its geometrical parameters. The four-parameter kinematic model is considered, as well as the five-parameter model in case of nominally parallel joint axes. Sensitivity vectors are expressed in terms of coordinate axes of manipulator frames. Second-order sensitivity vectors, the partial derivatives of first-order sensitivity vectors, are also considered. It is shown that second-order sensitivity vectors can be expressed as vector products of the first-order sensitivity vectors.

Transdisciplinary research in theatrical literature through technological integration and interfacing information

NASA Astrophysics Data System (ADS)

Pop, P. P.; Pop-Vădean, A.; Barz, C.; Latinovic, T.

2016-08-01

This paper aims to address more confident mathematical laws to explain a literary phenomenon. For this we studied the play "O scrisoare pierduta (Lost Letter)" written by Ion Luca Caragiale, in order to establish some connection between the characters but also to show certain aspects hidden by the author under the personality of the characters. We use transdisciplinary research to get from measurements and calculations results who will demonstrate objective of the proposed research. The challenge is to find those favorite characters by the author. Information and communications technology is a tool for research that will integrate this knowledge for modeling and interfacing.

EPR Steering inequalities with Communication Assistance

PubMed Central

Nagy, Sándor; Vértesi, Tamás

2016-01-01

In this paper, we investigate the communication cost of reproducing Einstein-Podolsky-Rosen (EPR) steering correlations arising from bipartite quantum systems. We characterize the set of bipartite quantum states which admits a local hidden state model augmented with c bits of classical communication from an untrusted party (Alice) to a trusted party (Bob). In case of one bit of information (c = 1), we show that this set has a nontrivial intersection with the sets admitting a local hidden state and a local hidden variables model for projective measurements. On the other hand, we find that an infinite amount of classical communication is required from an untrusted Alice to a trusted Bob to simulate the EPR steering correlations produced by a two-qubit maximally entangled state. It is conjectured that a state-of-the-art quantum experiment would be able to falsify two bits of communication this way. PMID:26880376

Superconductivity from fractionalized excitations in URu2Si2

NASA Astrophysics Data System (ADS)

Hsu, Chen-Hsuan; Chakravarty, Sudip

2014-10-01

An unconventional pairing mechanism in the heavy-fermion material URu2Si2 is studied. We propose a mixed singlet-triplet d-density wave to be the hidden-order state in URu2Si2. The exotic order is topologically nontrivial and supports a charge 2e skyrmionic spin texture, which is assumed to fractionalize into merons and antimerons at the deconfined quantum critical point. The interaction between these fractional particles results in a (pseudo)spin-singlet chiral d-wave superconducting state, which breaks time-reversal symmetry. Therefore, it is highly likely to produce a nonzero signal of the polar Kerr effect at the onset of the superconductivity, consistent with recent experiments. In addition, the nodal structures of the possible pairing functions in our model are consistent with the thermodynamic experiments in URu2Si2.

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)

Young Children's Recognition of How and when Knowledge Was Acquired

ERIC Educational Resources Information Center

Tang, Connie M.; Bartsch, Karen

2012-01-01

Two experiments investigated young children's understanding of how and when knowledge was acquired. In Experiment 1, thirty 4- and 5-year-olds were shown or told about various toys hidden in distinctive containers in two sessions a week apart. In the second session, children were asked how and when they learned the containers' contents. They more…

Undesirable Features of the Medical Learning Environment: A Narrative Review of the Literature

ERIC Educational Resources Information Center

Benbassat, Jochanan

2013-01-01

The objective of this narrative review of the literature is to draw attention to four undesirable features of the medical learning environment (MLE). First, students' fears of personal inadequacy and making errors are enhanced rather than alleviated by the hidden curriculum of the clinical teaching setting; second, the MLE projects a denial…

Mandatory Online Training: Transmissive Learning, Issues of Abuse and Hidden Agendas

ERIC Educational Resources Information Center

Barratt-Pugh, Llandis; Bahn, Susanne; Scholz, Alex

2011-01-01

This paper is based on two studies of mandatory online learning which although unrelated and conducted eight years apart nevertheless provide an illuminating comparison of mandatory training delivered online. The first study was carried out in 2002 in a state government department and the second in 2010 with the commercial construction industry,…

Exploring the Hidden Agenda in the Representation of Culture in International and Localised ELT Textbooks

ERIC Educational Resources Information Center

Tajeddin, Zia; Teimournezhad, Shohreh

2015-01-01

The rise of English as an international language (EIL) has challenged the focus on native-speaker culture in second language teaching and learning. Exposing learners to a single culture is no longer considered sufficient as intercultural language teaching and understanding gains momentum. The aim of this study was to investigate the representation…

Effect of Frequency and Idiomaticity on Second Language Reading Comprehension

ERIC Educational Resources Information Center

Martinez, Ron; Murphy, Victoria A.

2011-01-01

A number of studies claim that knowledge of 5,000-8,000 of the most frequent words should provide at least 95% coverage of most unsimplified texts in English, arguably enough to guess or ignore most unknown words while reading (Hirsh & Nation, 1992; Hu & Nation, 2000; Laufer, 1991; Nation, 2006). However, perhaps hidden in that 95% figure…

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.

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.

Structure of the first- and second-neighbor shells of simulated water: Quantitative relation to translational and orientational order

NASA Astrophysics Data System (ADS)

Yan, Zhenyu; Buldyrev, Sergey V.; Kumar, Pradeep; Giovambattista, Nicolas; Debenedetti, Pablo G.; Stanley, H. Eugene

2007-11-01

We perform molecular dynamics simulations of water using the five-site transferable interaction potential (TIP5P) model to quantify structural order in both the first shell (defined by four nearest neighbors) and second shell (defined by twelve next-nearest neighbors) of a central water molecule. We find that the anomalous decrease of orientational order upon compression occurs in both shells, but the anomalous decrease of translational order upon compression occurs mainly in the second shell. The decreases of translational order and orientational order upon compression (called the “structural anomaly”) are thus correlated only in the second shell. Our findings quantitatively confirm the qualitative idea that the thermodynamic, structural, and hence dynamic anomalies of water are related to changes upon compression in the second shell.